Showing 1–50 of 471 results for author: Anand, S

CLIP-Inspector: Model-Level Backdoor Detection for Prompt-Tuned CLIP via OOD Trigger Inversion

Authors: Akshit Jindal, Saket Anand, Chetan Arora, Vikram Goyal

Abstract: Organisations with limited data and computational resources increasingly outsource model training to Machine Learning as a Service (MLaaS) providers, who adapt vision-language models (VLMs) such as CLIP to downstream tasks via prompt tuning rather than training from scratch. This semi-honest setting creates a security risk where a malicious provider can follow the prompt-tuning protocol yet implan… ▽ More Organisations with limited data and computational resources increasingly outsource model training to Machine Learning as a Service (MLaaS) providers, who adapt vision-language models (VLMs) such as CLIP to downstream tasks via prompt tuning rather than training from scratch. This semi-honest setting creates a security risk where a malicious provider can follow the prompt-tuning protocol yet implant a backdoor, forcing triggered inputs to be classified into an attacker-chosen class, even for out-of-distribution (OOD) data. Such backdoors leave encoders untouched, making them undetectable to existing methods that focus on encoder corruption. Other data-level methods that sanitize data before training or during inference, also fail to answer the critical question, "Is the delivered model backdoored or not?" To address this model-level verification problem, we introduce CLIP-Inspector (CI), a backdoor detection method designed for prompt-tuned CLIP models. Assuming white-box access to the delivered model and a pool of unlabeled OOD images, CI reconstructs possible triggers for each class to determine if the model exhibits backdoor behaviour or not. Additionally, we demonstrate that using CI's reconstructed trigger for fine-tuning on correctly labeled triggered inputs enables us to re-align the model and reduce backdoor effectiveness. Through extensive experiments across ten datasets and four backdoor attacks, we demonstrate that CI can reconstruct effective triggers in a single epoch using only 1,000 OOD images, achieving a 94% detection accuracy (47/50 models). Compared to adapted trigger-inversion baselines, CI yields a markedly higher AUROC score (0.973 vs 0.495/0.687), thus enabling the vetting and post-hoc repair of prompt-tuned CLIP models to ensure safe deployment. △ Less

Submitted 10 April, 2026; originally announced April 2026.

Comments: 17 pages (8 main + 2 references + 7 supplementary), Accepted to CVPR Findings 2026

Investigating ACS/WFC Amp-to-Amp Sensitivities

Authors: Gagandeep S. Anand, Norman A. Grogin

Abstract: Recently, the ACS team applied an Ubercal framework to assess the photometric repeatability of stars observed across the WFC detector using 15 years of post-SM4 calibration data in the globular cluster 47 Tuc (Ryan et al., 2024). A surprising finding was an apparent 0.05 mag global difference in sensitivity between the WFC1 and WFC2 chips, which had not been seen in prior tests of sensitivity vari… ▽ More Recently, the ACS team applied an Ubercal framework to assess the photometric repeatability of stars observed across the WFC detector using 15 years of post-SM4 calibration data in the globular cluster 47 Tuc (Ryan et al., 2024). A surprising finding was an apparent 0.05 mag global difference in sensitivity between the WFC1 and WFC2 chips, which had not been seen in prior tests of sensitivity variations around the field-of-view. Given the many degenerate variables within the Ubercal framework such as CTE losses, time-dependent sensitivity, and flat-field corrections, we obtained new calibration data to perform a straightforward test of the reported $\sim$5$\%$ flux offset between detectors. We observed three white dwarf standards with three filters at four positions on the detector (each on a different amplifier), but with the same number of x and y pixel transfers to mitigate differential CTE-related effects. For the F606W and F814W filters, the agreements are good to 0.4$\%$ on average, and always 1$\%$ or better in individual cases. The consistency of these two filters over all three stars and the four dither positions provides very strong evidence against the large global sensitivity offset between WFC1 and WFC2 as seen in the Ubercal work. Larger variations seen in the bluer F435W filter are likely a result of a sensitivity of the flat field in that filter to underlying spectral type, warranting a future solution. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: ACS ISR 2026-03 (STScI), 10 pages, 4 figures

Adversarial Robustness of Deep State Space Models for Forecasting

Authors: Sribalaji C. Anand, George J. Pappas

Abstract: State-space model (SSM) for time-series forecasting have demonstrated strong empirical performance on benchmark datasets, yet their robustness under adversarial perturbations is poorly understood. We address this gap through a control-theoretic lens, focusing on the recently proposed Spacetime SSM forecaster. We first establish that the decoder-only Spacetime architecture can represent the optimal… ▽ More State-space model (SSM) for time-series forecasting have demonstrated strong empirical performance on benchmark datasets, yet their robustness under adversarial perturbations is poorly understood. We address this gap through a control-theoretic lens, focusing on the recently proposed Spacetime SSM forecaster. We first establish that the decoder-only Spacetime architecture can represent the optimal Kalman predictor when the underlying data-generating process is autoregressive - a property no other SSM possesses. Building on this, we formulate robust forecaster design as a Stackelberg game against worst-case stealthy adversaries constrained by a detection budget, and solve it via adversarial training. We derive closed-form bounds on adversarial forecasting error that expose how open-loop instability, closed-loop instability, and decoder state dimension each amplify vulnerability - offering actionable principles towards robust forecaster design. Finally, we show that even adversaries with no access to the forecaster can nonetheless construct effective attacks by exploiting the model's locally linear input-output behavior, bypassing gradient computations entirely. Experiments on the Monash benchmark datasets highlight that model-free attacks, without any gradient computation, can cause at least 33% more error than projected gradient descent with a small step size. △ Less

Submitted 3 April, 2026; originally announced April 2026.

Comments: 8 pages, 5 figures, conference submission

Insights from GRBs for optical follow-up of gravitational wave counterparts

Authors: Kruthi Krishna, Andrew Levan, Samaya Nissanke, Morgan Fraser, Tomas Ahumada, Shreya Anand, Igor Andreoni, Andreja Gomboc, Mansi Kasliwal, Andrea Melandri, Silvia Piranomonte, Patricia Schmidt

Abstract: Identifying the electromagnetic counterparts to gravitational wave sources is vital to enabling the myriad of investigations possible with multimessenger astronomy. However, locating faint, fast-varying transients within large localisations remains challenging given the uncertainty in their detailed properties. In this work, we investigate how the nearby merger-induced GRBs would be localised by t… ▽ More Identifying the electromagnetic counterparts to gravitational wave sources is vital to enabling the myriad of investigations possible with multimessenger astronomy. However, locating faint, fast-varying transients within large localisations remains challenging given the uncertainty in their detailed properties. In this work, we investigate how the nearby merger-induced GRBs would be localised by the LIGO-Virgo-KAGRA detector network during the fifth gravitational wave observing run (O5) and assess whether their optical counterparts could be detected using gravitational wave localisations alone, without additional localisation from gamma-ray instruments. Counterpart detectability is evaluated using the observed optical afterglow lightcurves of these GRBs and the distance-scaled lightcurve of the kilonova AT2017gfo as a fiducial template. We find that such events can be localised to comparatively small regions of the sky, often only a few to tens of square degrees. As a result, counterparts are detectable by at least one of the available optical telescopes during O5. However, detectability depends strongly on observational depth, as the counterparts are fainter than $22$ mag within a day. Facilities capable of reaching depths of $\gtrsim23$ mag therefore play a key role in recovering these faint counterparts. These results indicate that for such events during O5, the primary challenge for multimessenger discovery will be in achieving sufficient observational depth and reliably identifying the true counterpart among unrelated transients rather than gravitational wave localisation itself. △ Less

Submitted 1 April, 2026; originally announced April 2026.

Comments: 15 pages, 6 figures, 4 tables

Nebular Phase Evolution of SN 2023ixf (I): From Circumstellar Infrared Echo to the onset of in-situ Dust Formation in a Type II Supernova

Authors: Avinash Singh, S. Goto, A. Sarangi, J. Johansson, C. Fransson, S. Barmentloo, J. Sollerman, R. S. Teja, K. Maeda, T. Hamada, N. Sarin, M. Yamanaka, T. Nakaoka, K. S. Kawabata, S. Schulze, A. Jerkstrand, S. Rose, D. K. Sahu, A. Gangopadhyay, G. C. Anupama, T. Ahumada, S. Anand, A. Bochenek, S. J. Brennan, X. Chen , et al. (37 additional authors not shown)

Abstract: We present optical and near-infrared (NIR) photometric and spectroscopic observations of the Type II supernova SN 2023ixf spanning 150 to 750 days, combined with published early-time optical and infrared photometry, and JWST NIRSpec and MIRI spectroscopy, to disentangle circumstellar echo emission from newly formed internal dust. The combined dataset reveals an early infrared excess by 1.8 days, a… ▽ More We present optical and near-infrared (NIR) photometric and spectroscopic observations of the Type II supernova SN 2023ixf spanning 150 to 750 days, combined with published early-time optical and infrared photometry, and JWST NIRSpec and MIRI spectroscopy, to disentangle circumstellar echo emission from newly formed internal dust. The combined dataset reveals an early infrared excess by 1.8 days, a broad secondary NIR rebrightening over about 89 to 175 days, progressive attenuation of the red wing of H-alpha from about 132 days, and CO emission detected by about 217 days. We identify the onset of H-alpha asymmetry as the first direct signature for internal dust formation, and modeling of the H-alpha profile over 140 to 418 days yields an internal silicate-equivalent dust mass of about 1.5e-6 to 6e-5 solar masses. By contrast, the early infrared evolution is best interpreted as echo-dominated: the 1.8 to 33.6 day excess is consistent with a radiative-flash infrared echo from pre-existing circumstellar dust, while the 89 to 175 day rebrightening is more naturally explained by a more extended echo arising from structured wind material. JWST spectral energy distribution modeling further reveals a multi-component infrared continuum in which a cold graphite component traces lingering echo emission, while a colder silicate-bearing component grows to about 2e-3 solar masses, providing the strongest late-time spectral energy distribution evidence that internal CDS/ejecta dust becomes substantial. SN 2023ixf therefore provides one of the clearest time-resolved case studies of dust signatures in a Type II supernova, linking early circumstellar reprocessing with increasingly important in situ dust formation. △ Less

Submitted 14 March, 2026; originally announced March 2026.

Comments: 63 Pages, 26 Figures, 7 Tables, Submitted to AAS Journals, Comments are welcome!

The TRGB-SBF Project. IV. A Color Calibration of the TRGB in the JWST F090W+F150W Filters

Authors: Maksim I. Chazov, Dmitry I. Makarov, R. Brent Tully, Gagandeep S. Anand, Lidia N. Makarova, Yotam Cohen, John P. Blakeslee, Michele Cantiello, Joseph B. Jensen, Gabriella Raimondo

Abstract: Observations with JWST in the F090W band provide a powerful tool for determining galaxy distances based on tip of the red giant branch (TRGB) measurements. It is a great convenience that the TRGB lies at an almost constant absolute magnitude level at low metallicities. However, the TRGB becomes fainter at high metallicities in the F090W filter. Details of this break in slope are critical for preci… ▽ More Observations with JWST in the F090W band provide a powerful tool for determining galaxy distances based on tip of the red giant branch (TRGB) measurements. It is a great convenience that the TRGB lies at an almost constant absolute magnitude level at low metallicities. However, the TRGB becomes fainter at high metallicities in the F090W filter. Details of this break in slope are critical for precision applications in the acquisition of distances. With an absolute scaling set by the maser distance to NGC 4258 (but excluding the uncertainty in that distance), the value $M^\mathrm{TRGB}_\mathrm{F090W} = -4.40 \pm 0.03$ mag (traditional Vega) is found for $(\mathrm{F090W}-\mathrm{F150W})_0<1.65$ mag. The theoretical RGB isochrone that reaches the color 1.65 at the RGB tip corresponds to metallicity $[M/H] = -0.57$ for a 10 Gyr population. The calibration is used to derive distances for 16 galaxies relative to the megamaser host NGC 4258. Revised distances are on average slightly closer than literature values derived from the same data. △ Less

Submitted 11 March, 2026; originally announced March 2026.

Comments: 15 pages, 14 figures, accepted to ApJ

Non-linear visco-elasto-plastic rheology of a viscous vertex model

Authors: Shalabh Kumar Anand, Matthias Merkel

Abstract: Morphogenesis involves complex shape changes of biological tissues. Yet, tissue shape changes depend on tissue rheology, which in turn arises from the interplay of large numbers of cells. Here, we link cell- and tissue-scale mechanics by constructing mean-field rheological relations for the vertex model. In contrast to past work in the field, we study a vertex model with an explicit viscous fricti… ▽ More Morphogenesis involves complex shape changes of biological tissues. Yet, tissue shape changes depend on tissue rheology, which in turn arises from the interplay of large numbers of cells. Here, we link cell- and tissue-scale mechanics by constructing mean-field rheological relations for the vertex model. In contrast to past work in the field, we study a vertex model with an explicit viscous friction. We also include two different cellular mechanisms creating active, anisotropic stresses. Our mean-field model accounts for cell shape and the non-linear elastic and visco-plastic regimes. We validate our results by predicting the response to large-amplitude oscillatory shear. There are several vertex model variants, and comparing to results from the literature, we show that their rheology depends on a number of model details. Our approach should be sufficiently general to construct non-linear mean-field constitutive relations for any cell-based tissue model. △ Less

Submitted 26 February, 2026; originally announced February 2026.

Comments: 12 pages, 9 figures

A Path to an All-Sky Survey with Roman

Authors: Jiwon Jesse Han, Anirudh Chiti, Kai-Feng Chen, Keith Bechtol, Andrea Bellini, Robert Benjamin, Adam Bolton, Ana Bonaca, Alex Broughton, Esra Bulbul, Susan Clark, Charlie Conroy, Suchetha Cooray, John Franklin Crenshaw, Tansu Daylan, Arjun Dey, Alex Drlica-Wagner, Tim Eifler, Kareem El-Badry, Richard M. Feder, Peter Ferguson, Shenming Fu, Sebastian Gomez, Ryan Hickox, Christopher Hirata , et al. (71 additional authors not shown)

Abstract: A deep, space-based, all-sky near-infrared survey carried out with the Nancy Grace Roman Space Telescope would constitute a foundational astronomical infrastructure for decades to come. In this white paper, we present a concrete and feasible path to imaging the entire sky at $\sim0.1''$ resolution, beginning with high-impact fields in Cycle 1 and scaling to ultra-wide coverage within the nominal m… ▽ More A deep, space-based, all-sky near-infrared survey carried out with the Nancy Grace Roman Space Telescope would constitute a foundational astronomical infrastructure for decades to come. In this white paper, we present a concrete and feasible path to imaging the entire sky at $\sim0.1''$ resolution, beginning with high-impact fields in Cycle 1 and scaling to ultra-wide coverage within the nominal mission. This first-epoch survey will reach $\mathrm{H}\sim25.5$ AB mag (5$σ$) and maximize synergies with contemporaneous observatories, while preserving substantial time for other ambitious Roman programs. We outline representative scheduling scenarios and an example Cycle 1 program that triples early Roman-LSST overlap and delivers high-value community data products such as LSST forced photometry, joint \textit{Gaia}-Roman astrometry, and catalogs of Galactic substructure, stong lenses, and other rare systems. The Cycle 1 program will lay the foundation for an eventual all-sky survey, while also delivering high-impact early science. We invite broad community participation in shaping and carrying out both the initial program and the long-term vision of an all-sky Roman survey. △ Less

Submitted 24 February, 2026; originally announced February 2026.

Comments: Comments are welcome and encouraged

Interpolation-Driven Machine Learning Approaches for Plume Shine Dose Estimation: A Comparison of XGBoost, Random Forest, and TabNet

Authors: Biswajit Sadhu, Kalpak Gupte, Trijit Sadhu, S. Anand

Abstract: Despite the success of machine learning (ML) in surrogate modeling, its use in radiation dose assessment is limited by safety-critical constraints, scarce training-ready data, and challenges in selecting suitable architectures for physics-dominated systems. Within this context, rapid and accurate plume shine dose estimation serves as a practical test case, as it is critical for nuclear facility sa… ▽ More Despite the success of machine learning (ML) in surrogate modeling, its use in radiation dose assessment is limited by safety-critical constraints, scarce training-ready data, and challenges in selecting suitable architectures for physics-dominated systems. Within this context, rapid and accurate plume shine dose estimation serves as a practical test case, as it is critical for nuclear facility safety assessment and radiological emergency response, while conventional photon-transport-based calculations remain computationally expensive. In this work, an interpolation-assisted ML framework was developed using discrete dose datasets generated with the pyDOSEIA suite for 17 gamma-emitting radionuclides across varying downwind distances, release heights, and atmospheric stability categories. The datasets were augmented using shape-preserving interpolation to construct dense, high-resolution training data. Two tree-based ML models (Random Forest and XGBoost) and one deep learning (DL) model (TabNet) were evaluated to examine predictive performance and sensitivity to dataset resolution. All models showed higher prediction accuracy with the interpolated high-resolution dataset than with the discrete data; however, XGBoost consistently achieved the highest accuracy. Interpretability analysis using permutation importance (tree-based models) and attention-based feature attribution (TabNet) revealed that performance differences stem from how the models utilize input features. Tree-based models focus mainly on dominant geometry-dispersion features (release height, stability category, and downwind distance), treating radionuclide identity as a secondary input, whereas TabNet distributes attention more broadly across multiple variables. For practical deployment, a web-based GUI was developed for interactive scenario evaluation and transparent comparison with photon-transport reference calculations. △ Less

Submitted 23 February, 2026; originally announced February 2026.

Comments: 28 pages, 11 figures, 3 tables

Robustness of Kardar-Parisi-Zhang-like transport in long-range interacting quantum spin chains

Authors: Sajant Anand, Jack Kemp, Julia Wei, Christopher David White, Michael P. Zaletel, Norman Y. Yao

Abstract: Isotropic integrable spin chains such as the Heisenberg model feature superdiffusive spin transport belonging to an as-yet-unidentified dynamical universality class closely related to that of Kardar, Parisi, and Zhang (KPZ). To determine whether these results extend to more generic one-dimensional models, particularly those realizable in quantum simulators, we investigate spin and energy transport… ▽ More Isotropic integrable spin chains such as the Heisenberg model feature superdiffusive spin transport belonging to an as-yet-unidentified dynamical universality class closely related to that of Kardar, Parisi, and Zhang (KPZ). To determine whether these results extend to more generic one-dimensional models, particularly those realizable in quantum simulators, we investigate spin and energy transport in non-integrable, long-range Heisenberg models using state-of-the-art tensor network methods. Despite the lack of integrability and the asymptotic expectation of diffusion, for power-law models (with exponent $2 < α< \infty$) we observe long-lived $z=3/2$ superdiffusive spin transport and two-point correlators consistent with KPZ scaling functions, up to times $t \sim 10^3/J$. We conjecture that this KPZ-like transport is due to the proximity of such power-law-interacting models to the integrable family of Inozemtsev models, which we show to also exhibit KPZ-like spin transport across all interaction ranges. Finally, we consider anisotropic spin models naturally realized in Rydberg atom arrays and ultracold polar molecules, demonstrating that a wide range of long-lived, non-diffusive transport can be observed in experimental settings. △ Less

Submitted 9 April, 2026; v1 submitted 17 February, 2026; originally announced February 2026.

Comments: 5 pages, 4 figures + 20 pages, 12 figures

Augmenting Parameter-Efficient Pre-trained Language Models with Large Language Models

Authors: Saurabh Anand, Shubham Malaviya, Manish Shukla, Sachin Lodha

Abstract: Training AI models in cybersecurity with help of vast datasets offers significant opportunities to mimic real-world behaviors effectively. However, challenges like data drift and scarcity of labelled data lead to frequent updates of models and the risk of overfitting. To address these challenges, we used parameter-efficient fine-tuning techniques for pre-trained language models wherein we combine… ▽ More Training AI models in cybersecurity with help of vast datasets offers significant opportunities to mimic real-world behaviors effectively. However, challenges like data drift and scarcity of labelled data lead to frequent updates of models and the risk of overfitting. To address these challenges, we used parameter-efficient fine-tuning techniques for pre-trained language models wherein we combine compacters with various layer freezing strategies. To enhance the capabilities of these pre-trained language models, in this work we introduce two strategies that use large language models. In the first strategy, we utilize large language models as data-labelling tools wherein they generate labels for unlabeled data. In the second strategy, large language modes are utilized as fallback mechanisms for predictions having low confidence scores. We perform comprehensive experimental analysis on the proposed strategies on different downstream tasks specific to cybersecurity domain. We empirically demonstrate that by combining parameter-efficient pre-trained models with large language models, we can improve the reliability and robustness of models, making them more suitable for real-world cybersecurity applications. △ Less

Submitted 19 January, 2026; originally announced February 2026.

Comments: 22 pages, 9 figures, 11 tables, short paper was accepted in ACM SAC 2024

Spatial self-organization driven by temporal noise

Authors: Satyam Anand, Guanming Zhang, Stefano Martiniani

Abstract: The counterintuitive emergence of order from noise is a central phenomenon in science, ranging from pattern formation and synchronization to order-by-disorder in frustrated systems. While large-scale spatial self-organization induced by local spatial noise is well studied, whether temporal noise can also drive such organization remains an open question. Here, by studying interacting particle syste… ▽ More The counterintuitive emergence of order from noise is a central phenomenon in science, ranging from pattern formation and synchronization to order-by-disorder in frustrated systems. While large-scale spatial self-organization induced by local spatial noise is well studied, whether temporal noise can also drive such organization remains an open question. Here, by studying interacting particle systems, we show that temporally correlated noise can lead to a self-organized state with suppressed long-range density fluctuations, or hyperuniformity. Further, we develop a fluctuating hydrodynamic theory that quantitatively explains the origin of this phenomenon. Finally, by casting the dynamics as a stochastic optimization problem, we show that temporal correlations lead to better solutions, akin to perturbed gradient descent in neural networks -- where noise is injected during training to escape poor minima. This reveals a striking correspondence between perturbed gradient descent dynamics on the energy landscapes of particle systems and the loss landscapes of neural networks. Our study establishes temporal correlations as a novel mechanism for noise-driven self-organization, with broad implications for self-assembling materials, biological systems, and optimization algorithms that leverage temporal noise for applications like differentially private learning. △ Less

Submitted 30 January, 2026; originally announced January 2026.

Comments: 25 pages, 8 figures

Quantum Cellular Automata on a Dual-Species Rydberg Processor

Authors: Ryan White, Vikram Ramesh, Alexander Impertro, Shraddha Anand, Francesco Cesa, Giuliano Giudici, Thomas Iadecola, Hannes Pichler, Hannes Bernien

Abstract: As quantum devices scale to larger and larger sizes, a significant challenge emerges in scaling their coherent controls accordingly. Quantum cellular automata (QCAs) constitute a promising framework that bypasses this control problem: universal dynamics can be achieved using only a static qubit array and global control operations. Despite an extensive history of theoretical explorations and propos… ▽ More As quantum devices scale to larger and larger sizes, a significant challenge emerges in scaling their coherent controls accordingly. Quantum cellular automata (QCAs) constitute a promising framework that bypasses this control problem: universal dynamics can be achieved using only a static qubit array and global control operations. Despite an extensive history of theoretical explorations and proposals, QCAs have not been experimentally explored in the context of highly-scalable globally-controlled systems. Here we realize QCAs on a dual-species Rydberg array of rubidium and cesium atoms, leveraging independent global control of each species to perform multiple quantum protocols. Seeding an automaton with different initial states, we explore many-body dynamics of quasiparticles and grow GHZ states across both species, highlighting the flexibility of our approach. We further develop a second automaton using a novel mediated entangling gate, enabling generation of 96.7(1.7)%-fidelity Bell states, 17-qubit cluster states, and high-connectivity graph states. Our results demonstrate that simple global controls enable access to a rich variety of applications through the QCA framework. The versatility and scalability of QCAs present compelling opportunities for the development of quantum information systems, as well as new perspectives on quantum many-body dynamics. △ Less

Submitted 23 February, 2026; v1 submitted 22 January, 2026; originally announced January 2026.

Comments: 17 pages, 11 figures

The Progenitor of the Type II-Plateau SN 2025pht in NGC 1637: The Dustiest, Most Luminous Red Supergiant So Far?

Authors: Schuyler D. Van Dyk, Tamas Szalai, Gagandeep S. Anand, Thomas G. Brink, Noah Zimmer, Dan Milisavljevic, Ori D. Fox, Jacob E. Jencson, WeiKang Zheng, Alexei V. Filippenko

Abstract: We provide a characterization of the red supergiant (RSG) progenitor candidate for the nearby Type II-plateau supernova (SN) 2025pht in NGC 1637. The star was first detectable in 2001 by the Hubble Space Telescope (HST) and then again in a dozen bands by the James Webb Space Telescope (JWST) in 2024. This "quasi-snapshot" of the star's nature almost immediately prior to explosion is unprecedented.… ▽ More We provide a characterization of the red supergiant (RSG) progenitor candidate for the nearby Type II-plateau supernova (SN) 2025pht in NGC 1637. The star was first detectable in 2001 by the Hubble Space Telescope (HST) and then again in a dozen bands by the James Webb Space Telescope (JWST) in 2024. This "quasi-snapshot" of the star's nature almost immediately prior to explosion is unprecedented. The RSG varied in brightness, and we posit that it could have been a pulsating variable, possibly with a long period of ~660 days. The largest uncertainty is the host-galaxy distance, which we establish to be 10.73+/-1.76 Mpc. The star was also heavily extinguished by interstellar dust internal to the host, with visual extinction A_V(host)~1.7 mag (total A_V(tot)~1.8 mag). Dust radiative-transfer modeling reveals the star's circumstellar medium to be quite dusty and silicate-rich, yielding a bolometric luminosity log(L_bol/L_Sun)=5.08+/-0.16 and a cool effective temperature T_eff=2100--2500 K. The available HST optical data had no bearing on the shape of the candidate's observed spectral energy distribution -- for the first time, without the archival JWST observations we would not have been able to detect and characterize the candidate at all. The SN 2025pht progenitor candidate, although quite similar to that of SN 2023ixf, may be the most luminous candidate identified to date. △ Less

Submitted 13 January, 2026; originally announced January 2026.

Comments: 39 pages, 20 figures, submitted to AAS Journals

Afri-MCQA: Multimodal Cultural Question Answering for African Languages

Authors: Atnafu Lambebo Tonja, Srija Anand, Emilio Villa-Cueva, Israel Abebe Azime, Jesujoba Oluwadara Alabi, Muhidin A. Mohamed, Debela Desalegn Yadeta, Negasi Haile Abadi, Abigail Oppong, Nnaemeka Casmir Obiefuna, Idris Abdulmumin, Naome A Etori, Eric Peter Wairagala, Kanda Patrick Tshinu, Imanigirimbabazi Emmanuel, Gabofetswe Malema, Alham Fikri Aji, David Ifeoluwa Adelani, Thamar Solorio

Abstract: Africa is home to over one-third of the world's languages, yet remains underrepresented in AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark covering 7.5k Q&A pairs across 15 African languages from 12 countries. The benchmark offers parallel English-African language Q&A pairs across text and speech modalities and was entirely created by native speake… ▽ More Africa is home to over one-third of the world's languages, yet remains underrepresented in AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark covering 7.5k Q&A pairs across 15 African languages from 12 countries. The benchmark offers parallel English-African language Q&A pairs across text and speech modalities and was entirely created by native speakers. Benchmarking large language models (LLMs) on Afri-MCQA shows that open-weight models perform poorly across evaluated cultures, with near-zero accuracy on open-ended VQA when queried in native language or speech. To evaluate linguistic competence, we include control experiments meant to assess this specific aspect separate from cultural knowledge, and we observe significant performance gaps between native languages and English for both text and speech. These findings underscore the need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. To support more inclusive multimodal AI development in African languages, we release our Afri-MCQA under academic license or CC BY-NC 4.0 on HuggingFace (https://huggingface.co/datasets/Atnafu/Afri-MCQA) △ Less

Submitted 14 January, 2026; v1 submitted 9 January, 2026; originally announced January 2026.

Evolved Supergiants in PHANGS I: Red Supergiants in 19 Galaxies between 5-20 Mpc with HST and JWST

Authors: Sumit K. Sarbadhicary, David Thilker, Adam K. Leroy, Janice C. Lee, Amirnezam Amiri, Gagandeep S. Anand, Ashley. T. Barnes, Médéric Boquien, Daniel A. Dale, Simthembile Dlamini, Simon C. O. Glover, Ralf S. Klessen, Kirsten L. Larson, Daniel Maschmann, Hsi-An Pan, Jiayi Sun, Leonardo Úbeda, Thomas G. Williams, Aida Wofford, PHANGS Collaboration

Abstract: Red supergiants (RSGs) are important for our understanding of supernova progenitors, stellar populations, stellar evolution, mass loss and dust production. Extragalactic surveys of RSGs have a long history in the Local Group, but few studies exist beyond that due to the limited resolution and sensitivity of ground-based and previous space-based infrared observatories. Here we demonstrate the combi… ▽ More Red supergiants (RSGs) are important for our understanding of supernova progenitors, stellar populations, stellar evolution, mass loss and dust production. Extragalactic surveys of RSGs have a long history in the Local Group, but few studies exist beyond that due to the limited resolution and sensitivity of ground-based and previous space-based infrared observatories. Here we demonstrate the combined power of HST and JWST to push systematic searches of RSGs out to $\sim$20 Mpc. We introduce a catalog of 97057 RSGs -- the largest single-survey release of RSGs -- with masses $\gtrsim$10 M$_{\odot}$ in 19 galaxies from the PHANGS HST+JWST Treasury program. We use HST F814W and JWST F200W photometry to select stars as RSGs based on predicted colors and magnitudes from PARSEC isochrones. The spatial distribution of our recovered RSGs follow the familiar pattern of mostly being concentrated in active star-forming regions such as spiral arms and central starburst rings. The RSG number density on kpc-scales is strongly correlated ($r_s$$\sim$0.82) with local star-formation rate density ($Σ_{SFR}$) traced by extinction-corrected far-ultraviolet (FUV) from GALEX+WISE, and weakly correlated ($r_s$$\sim$0.57) with the total stellar mass density ($Σ_*$), traced by near-infrared emission from WISE+Spitzer. The number of RSGs per mass of stellar populations with ages 6-30 Myr (the likely age range of RSGs $>$10 M$_{\odot}$) is $\sim$1 per 10$^{3.77\pm0.27}$ M$_{\odot}$, assuming constant star-formation rates from FUV+W4. Our sample will be a useful resource for tracking progenitors and feedback sites of future supernovae in PHANGS, age-dating stellar populations, and more. △ Less

Submitted 31 December, 2025; originally announced January 2026.

Comments: 17 pages, 7 figures, 2 tables, submitted to ApJ. RSG catalog will be available with journal publication

The hydrogen-free circumstellar interaction in the Type Ib supernova 2021efd: A clue to the mechanism of the helium-layer stripping

Authors: N. Pyykkinen, T. Nagao, H. Kuncarayakti, M. D. Stritzinger, T. Kangas, K. Maeda, P. Chen, J. Sollerman, C. Burns, S. Bose, G. Folatelli, L. Ferrari, N. Morrell, A. Reguitti, I. Salmaso, S. Mattila, A. Gal-Yam, C. Fremling, S. Anand, M. Kasliwal, C. P. Gutiérrez, L. Galbany, W. Hoogendam, S. Schulze, C. Ashall , et al. (5 additional authors not shown)

Abstract: Stripped-envelope supernovae (SESNe), including Type IIb, Ib, and Ic supernovae (SNe), originate from the explosions of massive stars whose outer envelopes have been largely removed during their lifetimes. The main stripping mechanism for the hydrogen (H) envelope in the progenitors of SESNe is often considered to be interaction with a binary companion, while that for the helium (He) layer is uncl… ▽ More Stripped-envelope supernovae (SESNe), including Type IIb, Ib, and Ic supernovae (SNe), originate from the explosions of massive stars whose outer envelopes have been largely removed during their lifetimes. The main stripping mechanism for the hydrogen (H) envelope in the progenitors of SESNe is often considered to be interaction with a binary companion, while that for the helium (He) layer is unclear. We conducted photometric and spectroscopic observations of the Type Ib SN 2021efd, which shows signs of interaction with H-free circumstellar material (CSM). Around 30 days after the r-band light curve (LC) peak, until at least ~ 770 days, its LCs display excessive luminosity compared to regular SESNe and at least three distinct peaks. The light curve evolution is similar to that of SN 2019tsf, whose previously unpublished spectrum at 400 days is also presented here. The nebular spectrum of SN 2021efd shows narrow emission lines (~ 1000 km/s) in various species. Our observations suggest that SN 2021efd is a Type Ib SN interacting with CSM with the following parameters: The estimated CSM mass, composition, and distribution are at least a few times 0.1 M_sun, H-free, and clumpy, respectively. Based on the estimated ejecta properties, we conclude that this event is a transitional SN whose progenitor was experiencing He-layer stripping at the epoch of the explosion, and was on the way to becoming a carbon-oxygen star from a He star. The estimated CSM properties suggest that the progenitor had some episodic mass ejections with a rate of ~ 0.001-0.01 M_sun/yr for the last decade and slightly smaller before this final phase at least from ~ 200 years before the explosion, for the assumed CSM velocity of 100 km/s. For the case of ~ 1000 km/s, the necessary mass-loss rate would be increased by a factor of ten, and the timescales decreased by a factor of ten. △ Less

Submitted 12 December, 2025; originally announced December 2025.

Comments: 16 pages, 16 figures

Journal ref: A&A 706, A183 (2026)

CORL: Reinforcement Learning of MILP Policies Solved via Branch and Bound

Authors: Akhil S Anand, Elias Aarekol, Martin Mziray Dalseg, Magnus Stalhane, Sebastien Gros

Abstract: Combinatorial sequential decision making problems are typically modeled as mixed integer linear programs (MILPs) and solved via branch and bound (B&B) algorithms. The inherent difficulty of modeling MILPs that accurately represent stochastic real world problems leads to suboptimal performance in the real world. Recently, machine learning methods have been applied to build MILP models for decision… ▽ More Combinatorial sequential decision making problems are typically modeled as mixed integer linear programs (MILPs) and solved via branch and bound (B&B) algorithms. The inherent difficulty of modeling MILPs that accurately represent stochastic real world problems leads to suboptimal performance in the real world. Recently, machine learning methods have been applied to build MILP models for decision quality rather than how accurately they model the real world problem. However, these approaches typically rely on supervised learning, assume access to true optimal decisions, and use surrogates for the MILP gradients. In this work, we introduce a proof of concept CORL framework that end to end fine tunes an MILP scheme using reinforcement learning (RL) on real world data to maximize its operational performance. We enable this by casting an MILP solved by B&B as a differentiable stochastic policy compatible with RL. We validate the CORL method in a simple illustrative combinatorial sequential decision making example. △ Less

Submitted 11 December, 2025; originally announced December 2025.

EP250827b/SN 2025wkm: An X-ray Flash-Supernova Powered by a Central Engine and Circumstellar Interaction

Authors: Gokul P. Srinivasaragavan, Dongyue Li, Xander J. Hall, Ore Gottlieb, Genevieve Schroeder, Heyang Liu, Brendan O'Connor, Chichuan Jin, Mansi Kasliwal, Tomás Ahumada, Qinyu Wu, Christopher L. Fryer, Annabelle E. Niblett, Dong Xu, Maria Edvige Ravasio, Grace Daja, Wenxiong Li, Shreya Anand, Anna Y. Q. Ho, Hui Sun, Daniel A. Perley, Lin Yan, Eric Burns, S. Bradley Cenko, Jesper Sollerman , et al. (69 additional authors not shown)

Abstract: We present the discovery of EP250827b/SN 2025wkm, an X-ray Flash (XRF) discovered by the Einstein Probe (EP), accompanied by a broad-line Type Ic supernova (SN Ic-BL) at $z = 0.1194$. EP250827b possesses a prompt X-ray luminosity of $\sim 10^{45} \, \rm{erg \, s^{-1}}$, lasts over 1000 seconds, and has a peak energy $E_{\rm{p}} < 1.5$ keV at 90% confidence. SN 2025wkm possesses a double-peaked lig… ▽ More We present the discovery of EP250827b/SN 2025wkm, an X-ray Flash (XRF) discovered by the Einstein Probe (EP), accompanied by a broad-line Type Ic supernova (SN Ic-BL) at $z = 0.1194$. EP250827b possesses a prompt X-ray luminosity of $\sim 10^{45} \, \rm{erg \, s^{-1}}$, lasts over 1000 seconds, and has a peak energy $E_{\rm{p}} < 1.5$ keV at 90% confidence. SN 2025wkm possesses a double-peaked light curve (LC), though its bolometric luminosity plateaus after its initial peak for $\sim 20$ days, giving evidence that a central engine is injecting additional energy into the explosion. Its spectrum transitions from a blue to red continuum with clear blueshifted Fe II and Si II broad absorption features, allowing for a SN Ic-BL classification. We do not detect any transient radio emission and rule out the existence of an on-axis, energetic jet $\gtrsim 10^{50}~$erg. In the model we invoke, the collapse gives rise to a long-lived magnetar, potentially surrounded by an accretion disk. Magnetically-driven winds from the magnetar and the disk mix together, and break out with a velocity $\sim 0.35c$ from an extended circumstellar medium with radius $\sim 10^{13}$ cm, generating X-ray breakout emission through free-free processes. The disk outflows and magnetar winds power blackbody emission as they cool, producing the first peak in the SN LC. The spin-down luminosity of the magnetar in combination with the radioactive decay of $^{56}$Ni produces the late-time SN LC. We end by discussing the landscape of XRF-SNe within the context of EP's recent discoveries. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Comments: 43 pages, 20 Figures, Submitted to ApJ Letters

A search for successful and choked jets in nearby broad-lined Type Ic supernovae

Authors: Tanner O'Dwyer, Alessandra Corsi, Sheng Yang, Shreya Anand, S. Bradley Cenko, Gokul P. Srinivasaragavan, Anna Y. Q. Ho, Jesper Sollerman, Bei Zhou, Arvind Balasubramanian, Po-Wen Chang, Marc Kamionkowski, Daniel Perley, Russ R. Laher, Kohta Murase, Frank J. Masci, Mansi M. Kasliwal, Josiah N. Purdum, Matthew J. Graham

Abstract: The observational link between long gamma-ray bursts (GRBs) and broad-lined stripped-envelope core-collapse supernovae (SNe Ic-BL) is well established. Significant progress has been made in constraining what fraction of SNe Ic-BL may power high- or low-luminosity GRBs when viewed at small off-axis angles. However, the GRB-SN connection still lacks a complete understanding in the broader context of… ▽ More The observational link between long gamma-ray bursts (GRBs) and broad-lined stripped-envelope core-collapse supernovae (SNe Ic-BL) is well established. Significant progress has been made in constraining what fraction of SNe Ic-BL may power high- or low-luminosity GRBs when viewed at small off-axis angles. However, the GRB-SN connection still lacks a complete understanding in the broader context of massive-star evolution and explosion physics. Models predict a continuum of outcomes for the fastest ejecta, from choked to ultra-relativistic jets, and observations from radio to X-rays are key to probing these scenarios across a range of viewing angles and velocities. Here, we present results from a coordinated radio-to-X-ray campaign targeting nearby (z<=0.1) SNe Ic-BL designed to explore this diversity. With eight new radio-monitored events and updated data for one previously observed SN, we further tighten constraints on the fraction of SNe Ic-BL as relativistic as SN 1998bw/GRB 980425. We identify SN 2024rjw as a new radio-loud event likely powered by strong interaction with circumstellar material (CSM), and add evidence supporting a similar interpretation for SN 2020jqm. We also establish new limits on the properties of radio-emitting ejecta with velocities consistent with cocoons from choked jets, highlighting SN 2022xxf as a promising cocoon-dominated candidate. These results refine our understanding of the continuum linking ordinary SNe Ic-BL, engine-driven explosions, and GRBs, and contribute to building a sample that will inform future multi-messenger searches for electromagnetic counterparts to high-energy neutrinos. △ Less

Submitted 14 April, 2026; v1 submitted 9 December, 2025; originally announced December 2025.

Comments: 23 Pages, and 15 Figures. Accepted for publication in ApJ. This version reflects changes addressing the referee's comments

On Frequency-Weighted Extended Balanced Truncation

Authors: Sribalaji C. Anand, Henrik Sandberg

Abstract: This paper addresses the problem of frequency-weighted extended balanced truncation for discrete and continuous-time linear time-invariant plants. We show that the frequency-weighted discrete-time plant admits block-diagonal solutions to both the Lyapunov inequality and its extended form. A recursive algorithm for extended balanced truncation is proposed, together with corresponding a-priori error… ▽ More This paper addresses the problem of frequency-weighted extended balanced truncation for discrete and continuous-time linear time-invariant plants. We show that the frequency-weighted discrete-time plant admits block-diagonal solutions to both the Lyapunov inequality and its extended form. A recursive algorithm for extended balanced truncation is proposed, together with corresponding a-priori error bounds. Theoretical results are extended to continuous-time systems and validated through numerical examples. △ Less

Submitted 1 December, 2025; originally announced December 2025.

Comments: 8 pages, conference submission

Deep Learning for Short-Term Precipitation Prediction in Four Major Indian Cities: A ConvLSTM Approach with Explainable AI

Authors: Tanmay Ghosh, Shaurabh Anand, Rakesh Gomaji Nannewar, Nithin Nagaraj

Abstract: Deep learning models for precipitation forecasting often function as black boxes, limiting their adoption in real-world weather prediction. To enhance transparency while maintaining accuracy, we developed an interpretable deep learning framework for short-term precipitation prediction in four major Indian cities: Bengaluru, Mumbai, Delhi, and Kolkata, spanning diverse climate zones. We implemented… ▽ More Deep learning models for precipitation forecasting often function as black boxes, limiting their adoption in real-world weather prediction. To enhance transparency while maintaining accuracy, we developed an interpretable deep learning framework for short-term precipitation prediction in four major Indian cities: Bengaluru, Mumbai, Delhi, and Kolkata, spanning diverse climate zones. We implemented a hybrid Time-Distributed CNN-ConvLSTM (Convolutional Neural Network-Long Short-Term Memory) architecture, trained on multi-decadal ERA5 reanalysis data. The architecture was optimized for each city with a different number of convolutional filters: Bengaluru (32), Mumbai and Delhi (64), and Kolkata (128). The models achieved root mean square error (RMSE) values of 0.21 mm/day (Bengaluru), 0.52 mm/day (Mumbai), 0.48 mm/day (Delhi), and 1.80 mm/day (Kolkata). Through interpretability analysis using permutation importance, Gradient-weighted Class Activation Mapping (Grad-CAM), temporal occlusion, and counterfactual perturbation, we identified distinct patterns in the model's behavior. The model relied on city-specific variables, with prediction horizons ranging from one day for Bengaluru to five days for Kolkata. This study demonstrates how explainable AI (xAI) can provide accurate forecasts and transparent insights into precipitation patterns in diverse urban environments. △ Less

Submitted 14 November, 2025; originally announced November 2025.

Active Learning for Animal Re-Identification with Ambiguity-Aware Sampling

Authors: Depanshu Sani, Mehar Khurana, Saket Anand

Abstract: Animal Re-ID has recently gained substantial attention in the AI research community due to its high impact on biodiversity monitoring and unique research challenges arising from environmental factors. The subtle distinguishing patterns, handling new species and the inherent open-set nature make the problem even harder. To address these complexities, foundation models trained on labeled, large-scal… ▽ More Animal Re-ID has recently gained substantial attention in the AI research community due to its high impact on biodiversity monitoring and unique research challenges arising from environmental factors. The subtle distinguishing patterns, handling new species and the inherent open-set nature make the problem even harder. To address these complexities, foundation models trained on labeled, large-scale and multi-species animal Re-ID datasets have recently been introduced to enable zero-shot Re-ID. However, our benchmarking reveals significant gaps in their zero-shot Re-ID performance for both known and unknown species. While this highlights the need for collecting labeled data in new domains, exhaustive annotation for Re-ID is laborious and requires domain expertise. Our analyses show that existing unsupervised (USL) and AL Re-ID methods underperform for animal Re-ID. To address these limitations, we introduce a novel AL Re-ID framework that leverages complementary clustering methods to uncover and target structurally ambiguous regions in the embedding space for mining pairs of samples that are both informative and broadly representative. Oracle feedback on these pairs, in the form of must-link and cannot-link constraints, facilitates a simple annotation interface, which naturally integrates with existing USL methods through our proposed constrained clustering refinement algorithm. Through extensive experiments, we demonstrate that, by utilizing only 0.033% of all annotations, our approach consistently outperforms existing foundational, USL and AL baselines. Specifically, we report an average improvement of 10.49%, 11.19% and 3.99% (mAP) on 13 wildlife datasets over foundational, USL and AL methods, respectively, while attaining state-of-the-art performance on each dataset. Furthermore, we also show an improvement of 11.09%, 8.2% and 2.06% for unknown individuals in an open-world setting. △ Less

Submitted 11 November, 2025; v1 submitted 9 November, 2025; originally announced November 2025.

Comments: In Proceedings of AAAI Conference on Artificial Intelligence 2026

ZTF25abjmnps (AT2025ulz) and S250818k: A Candidate Superkilonova from a Sub-threshold Sub-Solar Gravitational Wave Trigger

Authors: Mansi M. Kasliwal, Tomas Ahumada, Robert Stein, Viraj Karambelkar, Xander J. Hall, Avinash Singh, Christoffer Fremling, Brian D. Metzger, Mattia Bulla, Vishwajeet Swain, Sarah Antier, Marion Pillas, Malte Busmann, James Freeburn, Sergey Karpov, Aleksandra Bochenek, Brendan O'Connor, Daniel A. Perley, Dalya Akl, Shreya Anand, Andrew Toivonen, Sam Rose, Theophile Jegou du Laz, Chang Liu, Kaustav Das , et al. (39 additional authors not shown)

Abstract: On August 18, 2025, the LIGO-Virgo-KAGRA collaboration reported gravitational waves from a sub-threshold binary neutron star merger. If astrophysical, this event would have a surprisingly low chirp mass, suggesting that at least one neutron star was below a solar mass. The Zwicky Transient Facility mapped the coarse localization and discovered a transient, ZTF25abjmnps (AT2025ulz), that was spatia… ▽ More On August 18, 2025, the LIGO-Virgo-KAGRA collaboration reported gravitational waves from a sub-threshold binary neutron star merger. If astrophysical, this event would have a surprisingly low chirp mass, suggesting that at least one neutron star was below a solar mass. The Zwicky Transient Facility mapped the coarse localization and discovered a transient, ZTF25abjmnps (AT2025ulz), that was spatially and temporally coincident with the gravitational wave trigger. The first week of follow-up suggested properties reminiscent of a GW170817-like kilonova. Subsequent follow-up suggests properties most similar to a young, stripped-envelope, Type IIb supernova. Although we cannot statistically rule out chance coincidence, we undertake due diligence analysis to explore the possible association between ZTF25abjmnps and S250818k. Theoretical models have been proposed wherein sub-solar neutron star(s) may form (and subsequently merge) via accretion disk fragmentation or core fission inside a core-collapse supernova i.e. a ``superkilonova". Here, we qualitatively discuss our multi-wavelength dataset in the context of the superkilonova picture. Future higher significance gravitational wave detections of sub-solar neutron star mergers with extensive electromagnetic follow-up would conclusively resolve this tantalizing multi-messenger association. △ Less

Submitted 13 November, 2025; v1 submitted 27 October, 2025; originally announced October 2025.

Comments: Accepted by ApJL on 11/4/25

Illuminating the Diffuse Radio Emission in Low-Mass Cluster: Abell 13

Authors: Nasmi S Anand, Swarna Chatterjee, Ramij Raja, Majidul Rahaman, Abhirup Datta

Abstract: Recent advances in high-sensitivity radio observations have uncovered a population of faint, ultra-steep-spectrum sources in galaxy clusters, commonly known as radio phoenixes. However, their observational classification remains poorly constrained due to the limited number of confirmed detections. This study presents a detailed multi-frequency, high-sensitivity, and high-resolution analysis of dif… ▽ More Recent advances in high-sensitivity radio observations have uncovered a population of faint, ultra-steep-spectrum sources in galaxy clusters, commonly known as radio phoenixes. However, their observational classification remains poorly constrained due to the limited number of confirmed detections. This study presents a detailed multi-frequency, high-sensitivity, and high-resolution analysis of diffuse radio emission in the merging galaxy cluster Abell 13. Using GMRT (147.5 MHz), uGMRT (400 MHz), ASKAP-low (887.5 MHz), and MGCLS (1284 MHz) images, we detect complex, filamentary diffuse emission with a largest linear extent of 521 kpc. This emission originates from the cluster center and extends westward, confined within the X-ray-emitting intra-cluster medium (ICM). Chandra X-ray data confirm that Abell 13 is undergoing a merger, and the radio morphology reflects signatures of this ongoing dynamical activity. We observed filamentary structures extending towards east-northeast and southwest directions. The spectral index across the emission appears irregular and lacks a coherent spatial gradient. The integrated spectrum reveals a steep spectral index of -1.85 +/- 0.05 and a spectral curvature of -0.93 +/- 0.21. These spectral properties, along with the observed morphology and brightness distribution, are consistent with a re-energization of a fossil radio plasma driven by adiabatic compression, supporting the classification of the emission as a radio phoenix. △ Less

Submitted 24 October, 2025; originally announced October 2025.

Comments: 16 pages, 8 figures, Accepted for Publication in ApJ

Boundary vertices of Strongly Connected Digraphs with respect to `Sum Metric'

Authors: Bijo S. Anand, Manoj Changat, Prasanth G. Narasimha-Shenoi, Mary Shalet Thottungal Joseph, Mithra R, Prakash G. Narasimha-Shenoi

Abstract: Suppose $D = (V, E)$ is a strongly connected digraph and $u, v \in V (D)$. Among the many metrics in graphs, the sum metric warrants further exploration. The sum distance $sd(u, v)$ defined as $sd(u, v) =\overrightarrow{d}(u, v)+\overrightarrow{d}(v, u)$ is a metric where $\overrightarrow{d}(u, v)$ denotes the length of the shortest directed $u - v$ path in $D$. The four main boundary vertices in… ▽ More Suppose $D = (V, E)$ is a strongly connected digraph and $u, v \in V (D)$. Among the many metrics in graphs, the sum metric warrants further exploration. The sum distance $sd(u, v)$ defined as $sd(u, v) =\overrightarrow{d}(u, v)+\overrightarrow{d}(v, u)$ is a metric where $\overrightarrow{d}(u, v)$ denotes the length of the shortest directed $u - v$ path in $D$. The four main boundary vertices in the digraphs are ``boundary vertices, contour vertices, eccentric vertices'', and ``peripheral vertices'' and their relationships have been studied. Also, an attempt is made to study the boundary-type sets of corona product of (di)graphs. The center of the corona product of two strongly connected digraphs is established. All the boundary-type sets and the center of the corona product are established in terms of factor digraphs. △ Less

Submitted 23 October, 2025; originally announced October 2025.

Comments: 15 pages

Quantifying Security for Networked Control Systems: A Review

Authors: Sribalaji C. Anand, Anh Tung Nguyen, André M. H. Teixeira, Henrik Sandberg, Karl H. Johansson

Abstract: Networked Control Systems (NCSs) are integral in critical infrastructures such as power grids, transportation networks, and production systems. Ensuring the resilient operation of these large-scale NCSs against cyber-attacks is crucial for societal well-being. Over the past two decades, extensive research has been focused on developing metrics to quantify the vulnerabilities of NCSs against attack… ▽ More Networked Control Systems (NCSs) are integral in critical infrastructures such as power grids, transportation networks, and production systems. Ensuring the resilient operation of these large-scale NCSs against cyber-attacks is crucial for societal well-being. Over the past two decades, extensive research has been focused on developing metrics to quantify the vulnerabilities of NCSs against attacks. Once the vulnerabilities are quantified, mitigation strategies can be employed to enhance system resilience. This article provides a comprehensive overview of methods developed for assessing NCS vulnerabilities and the corresponding mitigation strategies. Furthermore, we emphasize the importance of probabilistic risk metrics to model vulnerabilities under adversaries with imperfect process knowledge. The article concludes by outlining promising directions for future research. △ Less

Submitted 21 October, 2025; originally announced October 2025.

Comments: Journal submission

Closing the Sim2Real Performance Gap in RL

Authors: Akhil S Anand, Shambhuraj Sawant, Jasper Hoffmann, Dirk Reinhardt, Sebastien Gros

Abstract: Sim2Real aims at training policies in high-fidelity simulation environments and effectively transferring them to the real world. Despite the developments of accurate simulators and Sim2Real RL approaches, the policies trained purely in simulation often suffer significant performance drops when deployed in real environments. This drop is referred to as the Sim2Real performance gap. Current Sim2Real… ▽ More Sim2Real aims at training policies in high-fidelity simulation environments and effectively transferring them to the real world. Despite the developments of accurate simulators and Sim2Real RL approaches, the policies trained purely in simulation often suffer significant performance drops when deployed in real environments. This drop is referred to as the Sim2Real performance gap. Current Sim2Real RL methods optimize the simulator accuracy and variability as proxies for real-world performance. However, these metrics do not necessarily correlate with the real-world performance of the policy as established theoretically and empirically in the literature. We propose a novel framework to address this issue by directly adapting the simulator parameters based on real-world performance. We frame this problem as a bi-level RL framework: the inner-level RL trains a policy purely in simulation, and the outer-level RL adapts the simulation model and in-sim reward parameters to maximize real-world performance of the in-sim policy. We derive and validate in simple examples the mathematical tools needed to develop bi-level RL algorithms that close the Sim2Real performance gap. △ Less

Submitted 20 October, 2025; originally announced October 2025.

Virtual Social Immersive Multi-Sensory E-Commerce

Authors: Alpana Dubey, Suma Mani Kuriakose, Sumukha Anand, Nitish Bhardwaj, Shubhashis Sengupta

Abstract: In this paper, we present a virtual immersive multi sensorial experience, Aromaverse. Aromaverse is an immersive 3D multiplayer environment augmented with olfactive experience where users can experience and customize perfumes. Being multi player, users can join the same space and enjoy a social buying experience. The olfactive experience embodied in the perfume allows users to experience their fra… ▽ More In this paper, we present a virtual immersive multi sensorial experience, Aromaverse. Aromaverse is an immersive 3D multiplayer environment augmented with olfactive experience where users can experience and customize perfumes. Being multi player, users can join the same space and enjoy a social buying experience. The olfactive experience embodied in the perfume allows users to experience their fragrances. This further enhances the user perception of perfumes in a virtual setting. Aromaverse also provides the ability to customize the perfumes by changing their top, mid, and base notes. The customized fragrances can be shared with other users, enabling a shared olfactive experience. To understand users' buying experience in such an environment, we conducted a set of experiments in which participants were requested to explore the space, experience the perfumes, customize them and buy them. They were asked to perform the same activities alone and in the presence of their friends. Various factors including the benefits and limitations of such an experience were captured by the questionnaires. Our results show that the presence of a companion enhances the shopping experience by improving the level of imagination of the product and helping in making purchase decisions. Our findings suggest that multi sensorial XR experiences offer great opportunities to retail firms to improve customer engagement and provide more realistic online experience of products that require other sensory modalities △ Less

Submitted 9 September, 2025; originally announced October 2025.

Comments: This paper was accepted as demo paper at 23rd IEEE International Symposium on Mixed and Augmented Reality (ISMAR). However, it was withdrawn due to Visa issues

ACM Class: H.5

Reliable Active Learning from Unreliable Labels via Neural Collapse Geometry

Authors: Atharv Goel, Sharat Agarwal, Saket Anand, Chetan Arora

Abstract: Active Learning (AL) promises to reduce annotation cost by prioritizing informative samples, yet its reliability is undermined when labels are noisy or when the data distribution shifts. In practice, annotators make mistakes, rare categories are ambiguous, and conventional AL heuristics (uncertainty, diversity) often amplify such errors by repeatedly selecting mislabeled or redundant samples. We p… ▽ More Active Learning (AL) promises to reduce annotation cost by prioritizing informative samples, yet its reliability is undermined when labels are noisy or when the data distribution shifts. In practice, annotators make mistakes, rare categories are ambiguous, and conventional AL heuristics (uncertainty, diversity) often amplify such errors by repeatedly selecting mislabeled or redundant samples. We propose Reliable Active Learning via Neural Collapse Geometry (NCAL-R), a framework that leverages the emergent geometric regularities of deep networks to counteract unreliable supervision. Our method introduces two complementary signals: (i) a Class-Mean Alignment Perturbation score, which quantifies how candidate samples structurally stabilize or distort inter-class geometry, and (ii) a Feature Fluctuation score, which captures temporal instability of representations across training checkpoints. By combining these signals, NCAL-R prioritizes samples that both preserve class separation and highlight ambiguous regions, mitigating the effect of noisy or redundant labels. Experiments on ImageNet-100 and CIFAR100 show that NCAL-R consistently outperforms standard AL baselines, achieving higher accuracy with fewer labels, improved robustness under synthetic label noise, and stronger generalization to out-of-distribution data. These results suggest that incorporating geometric reliability criteria into acquisition decisions can make Active Learning less brittle to annotation errors and distribution shifts, a key step toward trustworthy deployment in real-world labeling pipelines. Our code is available at https://github.com/Vision-IIITD/NCAL. △ Less

Submitted 10 October, 2025; originally announced October 2025.

Comments: Accepted to NeurIPS 2025 Workshop on Reliable ML from Unreliable Data

The Hidden Life of Stars: Embedded Beginnings to AGB Endings in the PHANGS-JWST Sample. I. Catalog of Mid-IR Sources

Authors: Hamid Hassani, Erik Rosolowsky, Adam K. Leroy, Karin Sandstrom, Médéric Boquien, David A. Thilker, Bradley C. Whitmore, Gagandeep S. Anand, Ashley T. Barnes, Yixian Cao, Ryan Chown, Enrico Congiu, Daniel A. Dale, Oleg V. Egorov, Ivan Gerasimov, Kathryn Grasha, Remy Indebetouw, Janice C. Lee, Fu-Heng Liang, Daniel Maschmann, Sharon E. Meidt, Elias K. Oakes, Ismael Pessa, Jérôme Pety, Miguel Querejeta , et al. (6 additional authors not shown)

Abstract: We present a multiwavelength catalog of mid-infrared-selected compact sources in 19 nearby galaxies, combining JWST NIRCam/MIRI, HST UV-optical broadband, H$α$ narrow-band, and ALMA CO observations. We detect 24,945 compact sources at 21 $μ$m and 55,581 at 10 $μ$m. Artificial star tests show 50% completeness limits of $\sim$5 $μ$Jy for the 10 $μ$m catalog, and $\sim$24 $μ$Jy for the 21 $μ$m catalo… ▽ More We present a multiwavelength catalog of mid-infrared-selected compact sources in 19 nearby galaxies, combining JWST NIRCam/MIRI, HST UV-optical broadband, H$α$ narrow-band, and ALMA CO observations. We detect 24,945 compact sources at 21 $μ$m and 55,581 at 10 $μ$m. Artificial star tests show 50% completeness limits of $\sim$5 $μ$Jy for the 10 $μ$m catalog, and $\sim$24 $μ$Jy for the 21 $μ$m catalog. We find that 21 $μ$m compact sources contribute $\sim$20% of the total galaxy emission in that band, but only contribute $5%$ at 10 $μ$m. We classify sources using stellar evolution and population synthesis models combined with empirical classifications derived from the literature. Our classifications include H$α$-bright and dust-embedded optically faint clusters, red supergiants (RSGs), oxygen-rich and carbon-rich AGB stars, and a range of rarer stellar types. In sampling a broad range of star forming environments with a uniform, well-characterized selection, this catalog enables enables analyses of infrared-bright stellar populations. We find that H$α$-faint sources account for only 10% of dusty (likely young) clusters, implying that the infrared-bright, optically-faint phase of cluster evolution is short compared to the H$α$-bright stage. The luminosity functions of 10 and 21 $μ$m sources follow power-law distributions, with the 21 $μ$m slope ($-1.7 \pm 0.1$) similar to that of giant molecular cloud mass functions and ultraviolet bright star-forming complexes, while the 10 $μ$m slope ($-2.0 \pm 0.1$) is closer to that of young stellar clusters. △ Less

Submitted 19 March, 2026; v1 submitted 19 September, 2025; originally announced September 2025.

Comments: 47 pages, 35 figures; accepted for publication in ApJS. The codes and software used in this work are available at https://github.com/hamidnpc/Neloura

Authorship-contribution normalized Sh-index and citations are better research output indicators

Authors: Vishvesh Karthik, Indupalli Sishir Anand, Utkarsha Mahanta, Gaurav Sharma

Abstract: Bibliometric measures, such as total citations and h-index, have become a cornerstone for evaluating academic performance; however, these traditional metrics, being non-weighted, inadequately capture the nuances of individual contributions. To address this constraint, we developed GScholarLens, an open-access browser extension that integrates seamlessly with Google Scholar to enable detailed bibli… ▽ More Bibliometric measures, such as total citations and h-index, have become a cornerstone for evaluating academic performance; however, these traditional metrics, being non-weighted, inadequately capture the nuances of individual contributions. To address this constraint, we developed GScholarLens, an open-access browser extension that integrates seamlessly with Google Scholar to enable detailed bibliometric analysis. GScholarLens categorizes publications by authorship roles, adjusts citation weightings accordingly, and introduces Scholar h-index, Sh-index, an authorship-contribution normalized h-index. This tool proportionally weights citations based on authorship position using heuristic percentages, i.e., corresponding 100 percent, first 90 percent, second 50 percent, co-authors in publications with less than six authors 25 percent, and co-authors with more than six authors 10 percent. Currently, there is no empirical data available for author-contribution weights, however, this proof-of-concept framework can easily adapt more precise author-contribution weightage data decided by authors at the time of manuscript submission along with CRediT, which journals and publishers can mandate. Furthermore, this tool incorporates retraction detection by mapping data from retraction databases into the Google Scholar interface. By aligning bibliometric evaluation more closely with actual scholarly contribution, GScholarLens presents a better open-access framework for academic recognition, particularly within interdisciplinary and highly collaborative research environments. This tool is freely accessible at https://project.iith.ac.in/sharmaglab/gscholarlens/. △ Less

Submitted 4 September, 2025; originally announced September 2025.

Comments: 7 pages, 1 Figure

GRB 250704B: An Off-axis Short GRB with a Long-Lived Afterglow Plateau

Authors: Vishwajeet Swain, Tomás Ahumada, Sameer K. Patil, Yogesh Wagh, Varun Bhalerao, Ehud Nakar, Mansi Kasliwal, Xander J. Hall, Malte Busmann, Shreya Anand, Viraj Karambelkar, Igor Andreoni, G. C. Anupama, Anuraag Arya, Arvind Balasubramanian, Sudhanshu Barway, Jonathan Carney, Michael Coughlin, Deepak Eappachen, James Freeburn, Daniel Gruen, Tanishk Mohan, Brendan O'Connor, Antonella Palmese, Utkarsh Pathak , et al. (5 additional authors not shown)

Abstract: We present a detailed multi-wavelength afterglow study of the short GRB 250704B, extensively monitored in optical and near-infrared bands. Its afterglow displays an unusually long-duration plateau followed by an achromatic break and a steep decline, deviating from canonical GRB afterglows. While long plateaus are often explained by central engine activity, we find that for GRB 250704B, an energy i… ▽ More We present a detailed multi-wavelength afterglow study of the short GRB 250704B, extensively monitored in optical and near-infrared bands. Its afterglow displays an unusually long-duration plateau followed by an achromatic break and a steep decline, deviating from canonical GRB afterglows. While long plateaus are often explained by central engine activity, we find that for GRB 250704B, an energy injection model requires unreasonable parameters. The afterglow is better explained by an off-axis power-law structured jet with a narrow core ($θ_c \approx 0.7^{\circ}$) viewed at a modest angle ($θ_v \approx 1.9^{\circ}$). A comparison with GRB 170817A shows that both events are consistent with the off-axis structured jet scenario, where the shape of the light curve is governed primarily by the geometry of the jet and the viewing angle rather than the energetics, microphysical parameters, or external density. Our results underscore the importance of incorporating the jet structure in GRB modeling. △ Less

Submitted 8 September, 2025; v1 submitted 2 September, 2025; originally announced September 2025.

Comments: submitted

The Perfect Host: JWST Cepheid Observations in a Background-Free SN Ia Host Confirm No Bias in Hubble-Constant Measurements

Authors: Adam G. Riess, Siyang Li, Gagandeep S. Anand, Wenlong Yuan, Louise Breuval, Stefano Casertano, Lucas M. Macri, Dan Scolnic, Yukei S. Murakami, Alexei V. Filippenko, Thomas G. Brink

Abstract: Cycle 1 JWST observations of Cepheids in SN Ia hosts resolved their red-giant-dominated NIR backgrounds, sharply reducing crowding and showing that photometric bias in lower-resolution HST data does not account for the Hubble tension. We present Cycle 2 JWST observations of >100 Cepheids in NGC 3447, a unique system that pushes this test to the limit by transitioning from low to no background cont… ▽ More Cycle 1 JWST observations of Cepheids in SN Ia hosts resolved their red-giant-dominated NIR backgrounds, sharply reducing crowding and showing that photometric bias in lower-resolution HST data does not account for the Hubble tension. We present Cycle 2 JWST observations of >100 Cepheids in NGC 3447, a unique system that pushes this test to the limit by transitioning from low to no background contamination. NGC 3447, an SN Ia host at D~25 Mpc, is an interacting pair comprising (i) a spiral with mixed stellar populations, typical of H0 calibrators, and (ii) a young, star-forming companion (NGC 3447A) devoid of old stars and hence stellar crowdinga rare "perfect host" for testing photometric bias. We detect ~60 long-period Cepheids in each, enabling a "three-way comparison" across HST, JWST, and background-free conditions. We find no component-to-component offset (sigma<0.03 mag; a calibration independent test), and a 50% reduction in scatter to ~0.12 mag in the background-free case, the tightest seen for any SN Ia host. Across Cycles 1-2 we also measure Cepheids in all SH0ES hosts observed by JWST (19 hosts of 24 SNe Ia; >50% of the sample) and find no evidence of bias relative to HST photometry, including for the most crowded, distant hosts. These observations constitute the most rigorous test yet of Cepheid distances and provide strong evidence for their reliability. Combining JWST Cepheid measurements in 19 hosts (24 SNe Ia) with HST data (37 hosts, 42 SNe Ia) yields H0 = 73.49 +/- 0.93 km/s/Mpc. Including 35 TRGB-based calibrations (from HST and JWST) totals 55 SNe Ia and gives H0 = 73.18 +/- 0.88 km/s/Mpc, ~6 sigma above the LambdaCDM+CMB expectation. △ Less

Submitted 1 September, 2025; originally announced September 2025.

Comments: ApJ, submitted, comments welcome

The First RELHIC? Cloud-9 is a Starless Gas Cloud

Authors: Gagandeep S. Anand, Alejandro Benítez-Llambay, Rachael Beaton, Andrew J. Fox, Julio F. Navarro, Elena D'Onghia

Abstract: Five-hundred-meter Aperture Spherical Telescope (FAST) observations have recently identified a compact HI cloud (hereafter Cloud-9) in the vicinity of the spiral galaxy M94. This identification has been confirmed independently by Very Large Array (VLA) and Green Bank Telescope (GBT) observations. Cloud-9 has the same recession velocity as M94, and is therefore at a similar distance ($\sim$4.4 Mpc)… ▽ More Five-hundred-meter Aperture Spherical Telescope (FAST) observations have recently identified a compact HI cloud (hereafter Cloud-9) in the vicinity of the spiral galaxy M94. This identification has been confirmed independently by Very Large Array (VLA) and Green Bank Telescope (GBT) observations. Cloud-9 has the same recession velocity as M94, and is therefore at a similar distance ($\sim$4.4 Mpc). It is compact ($\sim$1$'$ radius, or $\sim$1.4 kpc), dynamically cold ($W_{50}=12$ km/s), non-rotating, and fairly massive, with an HI mass of $\sim 10^{6}$ $M_{\odot}$. Here we present deep Hubble Space Telescope/Advanced Camera for Surveys (HST/ACS) imaging designed to search for a luminous stellar counterpart. We visually rule out the presence of any dwarf galaxy with stellar mass exceeding 10$^{3.5}$$M_{\odot}$. A more robust color-magnitude diagram-based analysis conservatively rules out a 10$^{4}$$M_{\odot}$ stellar counterpart with $99.5^{+0.5}_{-8.2}$$\%$ confidence. The non-detection of a luminous component reinforces the interpretation that this system is a Reionization-Limited HI Cloud (RELHIC); i.e., a starless dark matter halo filled with hydrostatic gas in thermal equilibrium with the cosmic ultraviolet background. Our results make Cloud-9 the leading RELHIC candidate of any known compact HI cloud. This provides strong support for a cornerstone prediction of the $Λ$CDM model, namely the existence of gas-filled starless dark matter halos on sub-galactic mass scales, and constrains the present-day threshold halo mass for galaxy formation. △ Less

Submitted 21 October, 2025; v1 submitted 27 August, 2025; originally announced August 2025.

Comments: 9 pages, 5 figures. in press at ApJL

The infrared jet of M87 observed with JWST

Authors: Jan Röder, Maciek Wielgus, Joseph B. Jensen, Gagandeep S. Anand, R. Brent Tully

Abstract: We present the first JWST+NIRCam images of the giant elliptical active galaxy M87 and its jet at 0.90, 1.50, 2.77 and 3.56 $μ$m. We analysed the large-scale jet structure, identifying prominent components, and determined the near-infrared spectral index. The data were calibrated using the standard JWST pipeline. We subtracted a constant background level and a smooth model of the galaxy surface bri… ▽ More We present the first JWST+NIRCam images of the giant elliptical active galaxy M87 and its jet at 0.90, 1.50, 2.77 and 3.56 $μ$m. We analysed the large-scale jet structure, identifying prominent components, and determined the near-infrared spectral index. The data were calibrated using the standard JWST pipeline. We subtracted a constant background level and a smooth model of the galaxy surface brightness to isolate the jet. The total image fluxes measured in the NIRCam filters follow the infrared bump pattern seen near 1.6 $μ$m in the spectral energy distribution of M87, caused by the surrounding stellar population in the galaxy. The residual jet images broadly agree with the radio to optical synchrotron power law $S_λ\proptoλ^α$ with $α=$0.7-1.0. We identified the most upstream knot L at a distance of (320$\pm$50) mas from the core. The component HST-1, at (950$\pm$50) mas from the core, is transversely resolved, and both the individual images and the spectral index map clearly indicate its double-component substructure with two elements of similar size and flux density, with centroids separated by (150$\pm$20) mas and with a significantly larger spectral index $α$ observed in the downstream component ($α_{\rm do}=0.3$) than in the upstream one ($α_{\rm up}= -0.15$). We also observe the counter-jet component located about 24 arcsec away from the nucleus. △ Less

Submitted 8 September, 2025; v1 submitted 24 July, 2025; originally announced July 2025.

Journal ref: A&A 701, L12 (2025)

Beyond fragmented dopant dynamics in quantum spin lattices: Robust localization and non-Gaussian diffusion

Authors: Mingru Yang, Sajant Anand, Kristian Knakkergaard Nielsen

Abstract: The motion of dopants in magnetic spin lattices has received tremendous attention for at least four decades due to its connection to high-temperature superconductivity. Despite these efforts, we lack a complete understanding of their behavior, especially out of the equilibrium and at nonzero temperatures. In this paper, we take a significant step towards a much deeper understanding based on state-… ▽ More The motion of dopants in magnetic spin lattices has received tremendous attention for at least four decades due to its connection to high-temperature superconductivity. Despite these efforts, we lack a complete understanding of their behavior, especially out of the equilibrium and at nonzero temperatures. In this paper, we take a significant step towards a much deeper understanding based on state-of-the-art matrix-product-state calculations. In particular, we investigate the non-equilibrium dynamics of a dopant in two-leg $t$--$J$ ladders with antiferromagnetic XXZ spin interactions. In the Ising limit, we find that the dopant is localized for all investigated nonzero temperatures due to an emergent disordered potential, with a localization length controlled by the underlying correlation length of the spin lattice, which increases exponentially with decreasing temperature. The dopant, hereby, only delocalizes asymptotically in the zero temperature limit. This greatly generalizes the localization effect discovered recently in Hilbert space fragmented models. In the presence of spin-exchange processes at rate $α$, the dopant diffuses with a diffusion coefficient, $D_h$, depending non-monotonically on $α$. It initially increases linearly as $D_h \propto α$ for $α\ll 1$ before dropping off as $α^{-1}$ for $α> 1$. Moreover, we show that the underlying spin dynamics at infinite temperature behaves qualitatively the same, albeit with important quantitative differences. We substantiate these findings by showing that the dynamics features self-similar scaling behavior, which strongly deviates from the Gaussian behavior of regular diffusion, especially for weak spin exchange. Finally, we show that the diffusion coefficient $D_h$ follows an Arrhenius relation at high temperatures, whereby it is exponentially suppressed upon cooling. △ Less

Submitted 21 October, 2025; v1 submitted 21 July, 2025; originally announced July 2025.

Comments: 22 pages, 14 figures

Journal ref: Phys. Rev. B 112, 165129 (2025)

Converging on the Cepheid Metallicity Dependence: Implications of Non-Standard Gaia Parallax Recalibration on Distance Measures

Authors: Louise Breuval, Gagandeep S. Anand, Richard I. Anderson, Rachael Beaton, Anupam Bhardwaj, Stefano Casertano, Gisella Clementini, Mauricio Cruz Reyes, Giulia De Somma, Martin A. T. Groenewegen, Caroline D. Huang, Pierre Kervella, Saniya Khan, Lucas M. Macri, Marcella Marconi, Javier H. Minniti, Adam G. Riess, Vincenzo Ripepi, Martino Romaniello, Daniel Scolnic, Erasmo Trentin, Piotr Wielgorski, Wenlong Yuan

Abstract: By comparing Cepheid brightnesses with geometric distance measures including Gaia EDR3 parallaxes, most recent analyses conclude metal-rich Cepheids are brighter, quantified as $γ\sim -0.2$ mag/dex. While the value of $γ$ has little impact on the determination of the Hubble constant in contemporary distance ladders (due to the similarity of metallicity across these ladders), $γ$ plays a role in ga… ▽ More By comparing Cepheid brightnesses with geometric distance measures including Gaia EDR3 parallaxes, most recent analyses conclude metal-rich Cepheids are brighter, quantified as $γ\sim -0.2$ mag/dex. While the value of $γ$ has little impact on the determination of the Hubble constant in contemporary distance ladders (due to the similarity of metallicity across these ladders), $γ$ plays a role in gauging the distances to metal-poor dwarf galaxies like the Magellanic Clouds and is of considerable interest in testing stellar models. Recently, Madore & Freedman (2025, hereafter MF25) recalibrated Gaia EDR3 parallaxes by adding to them a magnitude offset to match certain historic Cepheid parallaxes which otherwise differ by $\sim1.6σ$. A calibration which adjusts Gaia parallaxes by applying a magnitude offset (i.e., a multiplicative correction in parallax) differs significantly from the Gaia Team's calibration (Lindegren et al. 2021), which is additive in parallax space - especially at distances much closer than 1 kpc or beyond 10 kpc, outside the $\sim$2-3 kpc range on which the MF25 calibration was based. The MF25 approach reduces $γ$ to zero. If extrapolated, it places nearby cluster distances like the Pleiades too close compared to independent measurements, while leaving distant quasars with negative parallaxes. We conclude that the MF25 proposal for Gaia calibration and $γ\sim 0$ produces farther-reaching consequences, many of which are strongly disfavored by the data. △ Less

Submitted 20 November, 2025; v1 submitted 21 July, 2025; originally announced July 2025.

Comments: Accepted and published in ApJ

Journal ref: Breuval et al 2025, ApJ 994 111

NSF-DOE Vera C. Rubin Observatory Observations of Interstellar Comet 3I/ATLAS (C/2025 N1)

Authors: Colin Orion Chandler, Pedro H. Bernardinelli, Mario Jurić, Devanshi Singh, Henry H. Hsieh, Ian Sullivan, R. Lynne Jones, Jacob A. Kurlander, Dmitrii Vavilov, Siegfried Eggl, Matthew Holman, Federica Spoto, Megan E. Schwamb, Lauren A. MacArthur, Rahil Makadia, Marco Micheli, Aren Heinze, Eric J. Christensen, Wilson Beebe, Aaron Roodman, Kian-Tat Lim, Tim Jenness, James Bosch, Brianna M. Smart, Eric Bellm , et al. (283 additional authors not shown)

Abstract: We report on the observation and measurement of astrometry, photometry, morphology, and activityof the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS) with the NSF-DOE Vera C. Rubin Observatory. Comet 3I/ATLAS, the third known interstellar object, was discovered on UT 2025 July 1. Rubin Observatory had coincidentally collected images of the object's region of the sky during routine… ▽ More We report on the observation and measurement of astrometry, photometry, morphology, and activityof the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS) with the NSF-DOE Vera C. Rubin Observatory. Comet 3I/ATLAS, the third known interstellar object, was discovered on UT 2025 July 1. Rubin Observatory had coincidentally collected images of the object's region of the sky during routine commissioning. Facilitated by Rubin's high resolution and large aperture, we successfully recovered object detections from Rubin observations spanning UT 2025 June 21 (10 days before discovery, when 3I/ATLAS was 4.5 au from the Sun) through the date of discovery, and we acquired additional images through UT 2025 July 20 as part of commissioning. We measure on-sky locations of 3I/ATLAS in Rubin ugrizy bands, with a typical precision of about 70 mas, and briefly describe the reason this is coarser than our measured static source astrometric precision of about 3 mas in Rubin images. We measure grizy magnitudes of 3I/ATLAS photometry at about 0.01 mag precision, detecting no short-term photometric variability above 0.01 mag. We derive an estimated near-nucleus dust-to-nucleus scattering cross-section ratio of eta >= 13 on UT 2025 July 2 based on Rubin photometry and an upper limit nucleus size computed from Hubble Space Telescope observations. We find Rubin colors of g - r = (0.657 +/- 0.013) mag, r - i = (0.235 +/- 0.018) mag, i - z = (0.147 +/- 0.042) mag, z - y = (0.047 +/- 0.052) mag. These data represent the earliest observations of this object by a large (>=8-meter class) telescope and illustrate the type of measurements (and discoveries) Rubin's Legacy Survey of Space and Time (LSST) will begin to provide after it begins in early 2026. △ Less

Submitted 7 April, 2026; v1 submitted 17 July, 2025; originally announced July 2025.

Comments: Collaboration between the NSF-DOE Vera C. Rubin Observatory and the LSST Solar System Science Collaboration (SSSC)

The Spectacle of Fidelity: Blind Resistance and the Wizardry of Prototyping

Authors: Hrittika Bhowmick, Shilpaa Anand

Abstract: Prototyping is widely regarded in Human-Computer Interaction as an iterative process through which ideas are tested and refined, often via visual mockups, screen flows, and coded simulations. This position paper critiques the visual-centric norms embedded in prototyping culture by drawing from the lived experiences of blind scholars and insights from cultural disability studies. It discusses how d… ▽ More Prototyping is widely regarded in Human-Computer Interaction as an iterative process through which ideas are tested and refined, often via visual mockups, screen flows, and coded simulations. This position paper critiques the visual-centric norms embedded in prototyping culture by drawing from the lived experiences of blind scholars and insights from cultural disability studies. It discusses how dominant methods of prototyping rely on an unexamined fidelity to sight, privileging what can be rendered visibly coherent while marginalizing other modes of knowing and making. By repositioning prototyping as a situated, embodied, and relational practice, this paper challenges HCI to rethink what kinds of design participation are legitimized and which are excluded when prototyping is reduced to screen-based simulations. △ Less

Submitted 13 July, 2025; originally announced July 2025.

Comments: 3 pages. Submitted for Access InContext Workshop at CHI'25, April 26, 2025, Yokohama, Japan

ACM Class: H.5.2; K.4.2; D.2.2

Learning To Communicate Over An Unknown Shared Network

Authors: Shivangi Agarwal, Adi Asija, Sanjit K. Kaul, Arani Bhattacharya, Saket Anand

Abstract: As robots (edge-devices, agents) find uses in an increasing number of settings and edge-cloud resources become pervasive, wireless networks will often be shared by flows of data traffic that result from communication between agents and corresponding edge-cloud. In such settings, agent communicating with the edge-cloud is unaware of state of network resource, which evolves in response to not just a… ▽ More As robots (edge-devices, agents) find uses in an increasing number of settings and edge-cloud resources become pervasive, wireless networks will often be shared by flows of data traffic that result from communication between agents and corresponding edge-cloud. In such settings, agent communicating with the edge-cloud is unaware of state of network resource, which evolves in response to not just agent's own communication at any given time but also to communication by other agents, which stays unknown to the agent. We address challenge of an agent learning a policy that allows it to decide whether or not to communicate with its cloud node, using limited feedback it obtains from its own attempts to communicate, to optimize its utility. The policy generalizes well to any number of other agents sharing the network and must not be trained for any particular network configuration. Our proposed policy is a DRL model Query Net (QNet) that we train using a proposed simulation-to-real framework. Our simulation model has just one parameter and is agnostic to specific configurations of any wireless network. It allows training an agent's policy over a wide range of outcomes that an agent's communication with its edge-cloud node may face when using a shared network, by suitably randomizing the simulation parameter. We propose a learning algorithm that addresses challenges observed in training QNet. We validate our simulation-to-real driven approach through experiments conducted on real wireless networks including WiFi and cellular. We compare QNet with other policies to demonstrate its efficacy. WiFi experiments involved as few as five agents, resulting in barely any contention for the network, to as many as fifty agents, resulting in severe contention. The cellular experiments spanned a broad range of network conditions, with baseline RTT ranging from a low of 0.07 second to a high of 0.83 second. △ Less

Submitted 8 July, 2025; originally announced July 2025.

Comments: 22 pages, 15 figures, 4 tables

A unifying approach to self-organizing systems interacting via conservation laws

Authors: Frank Barrows, Guanming Zhang, Satyam Anand, Zixi Chen, Jonathan Lin, Aman Desai, Stefano Martiniani, Francesco Caravelli

Abstract: We present a unified framework for embedding and analyzing dynamical systems using generalized projection operators rooted in local conservation laws. By representing physical, biological, and engineered systems as graphs with incidence and cycle matrices, we derive dual projection operators that decompose network fluxes and potentials. This formalism aligns with principles of non-equilibrium ther… ▽ More We present a unified framework for embedding and analyzing dynamical systems using generalized projection operators rooted in local conservation laws. By representing physical, biological, and engineered systems as graphs with incidence and cycle matrices, we derive dual projection operators that decompose network fluxes and potentials. This formalism aligns with principles of non-equilibrium thermodynamics and captures a broad class of systems governed by flux-forcing relationships and local constraints. We extend this approach to collective dynamics through the PRojective Embedding of Dynamical Systems (PrEDS), which lifts low-dimensional dynamics into a high-dimensional space, enabling both replication and recovery of the original dynamics. When systems fall within the PrEDS class, their collective behavior can be effectively approximated through projection onto a mean-field space. We demonstrate the versatility of PrEDS across diverse domains, including resistive and memristive circuits, adaptive flow networks (e.g., slime molds), elastic string networks, and particle swarms. Notably, we establish a direct correspondence between PrEDS and swarm dynamics, revealing new insights into optimization and self-organization. Our results offer a general theoretical foundation for analyzing complex networked systems and for designing systems that self-organize through local interactions. △ Less

Submitted 15 July, 2025; v1 submitted 3 July, 2025; originally announced July 2025.

Comments: 14 pages double column + 13 pages supplementary

LIGO/Virgo/KAGRA neutron star merger candidate S250206dm: Zwicky Transient Facility observations

Authors: Tomás Ahumada, Shreya Anand, Mattia Bulla, Vaidehi Gupta, Mansi Kasliwal, Robert Stein, Viraj Karambelkar, Eric C. Bellm, Theophile Jegou du Laz, Michael W. Coughlin, Igor Andreoni, Smaranika Banerjee, Aleksandra Bochenek, K-Ryan Hinds, Lei Hu, Antonella Palmese, Daniel Perley, Natalya Pletskova, Anirudh Salgundi, Avinash Singh, Jesper Sollerman, Vishwajeet Swain, Avery Wold, Varun Bhalerao, S. Bradley Cenko , et al. (27 additional authors not shown)

Abstract: We present the searches conducted with the Zwicky Transient Facility (ZTF) in response to S250206dm, a bona fide event with a false alarm rate of one in 25 years, detected by the International Gravitational Wave Network (IGWN). Although the event is significant, the nature of the compact objects involved remains unclear, with at least one likely neutron star. ZTF covered 68% of the localization re… ▽ More We present the searches conducted with the Zwicky Transient Facility (ZTF) in response to S250206dm, a bona fide event with a false alarm rate of one in 25 years, detected by the International Gravitational Wave Network (IGWN). Although the event is significant, the nature of the compact objects involved remains unclear, with at least one likely neutron star. ZTF covered 68% of the localization region, though we did not identify any likely optical counterpart. We describe the ZTF strategy, potential candidates, and the observations that helped rule out candidates, including sources circulated by other collaborations. Similar to Ahumada et al. 2024, we perform a frequentist analysis, using simsurvey, as well as Bayesian analysis, using nimbus, to quantify the efficiency of our searches. We find that, given the nominal distance to this event of 373$\pm$104 Mpc, our efficiencies are above 10% for KNe brighter than $-17.5$ absolute magnitude. Assuming the optical counterpart known as kilonova (KN) lies within the ZTF footprint, our limits constrain the brightest end of the KN parameter space. Through dedicated radiative transfer simulations of KNe from binary neutron star (BNS) and black hole-neutron star (BHNS) mergers, we exclude parts of the BNS KN parameter space. Up to 35% of the models with high wind ejecta mass ($M_{\rm wind} \approx 0.13$ M$_{\odot}$) are ruled out when viewed face-on ($\cosθ_{\rm obs} = 1.0$). Finally, we present a joint analysis using the combined coverage from ZTF and the Gravitational Wave Multimessenger Dark Energy Camera Survey (GW-MMADS). The joint observations cover 73% of the localization region, and the combined efficiency has a stronger impact on rising and slowly fading models, allowing us to rule out 55% of the high-mass KN models viewed face-on. △ Less

Submitted 30 June, 2025; originally announced July 2025.

Comments: submitted to PASP

Fingerprinting SDKs for Mobile Apps and Where to Find Them: Understanding the Market for Device Fingerprinting

Authors: Michael A. Specter, Mihai Christodorescu, Abbie Farr, Bo Ma, Robin Lassonde, Xiaoyang Xu, Xiang Pan, Fengguo Wei, Saswat Anand, Dave Kleidermacher

Abstract: This paper presents a large-scale analysis of fingerprinting-like behavior in the mobile application ecosystem. We take a market-based approach, focusing on third-party tracking as enabled by applications' common use of third-party SDKs. Our dataset consists of over 228,000 SDKs from popular Maven repositories, 178,000 Android applications collected from the Google Play store, and our static analy… ▽ More This paper presents a large-scale analysis of fingerprinting-like behavior in the mobile application ecosystem. We take a market-based approach, focusing on third-party tracking as enabled by applications' common use of third-party SDKs. Our dataset consists of over 228,000 SDKs from popular Maven repositories, 178,000 Android applications collected from the Google Play store, and our static analysis pipeline detects exfiltration of over 500 individual signals. To the best of our knowledge, this represents the largest-scale analysis of SDK behavior undertaken to date. We find that Ads SDKs (the ostensible focus of industry efforts such as Apple's App Tracking Transparency and Google's Privacy Sandbox) appear to be the source of only 30.56% of the fingerprinting behaviors. A surprising 23.92% originate from SDKs whose purpose was unknown or unclear. Furthermore, Security and Authentication SDKs are linked to only 11.7% of likely fingerprinting instances. These results suggest that addressing fingerprinting solely in specific market-segment contexts like advertising may offer incomplete benefit. Enforcing anti-fingerprinting policies is also complex, as we observe a sparse distribution of signals and APIs used by likely fingerprinting SDKs. For instance, only 2% of exfiltrated APIs are used by more than 75% of SDKs, making it difficult to rely on user permissions to control fingerprinting behavior. △ Less

Submitted 27 June, 2025; originally announced June 2025.

Comments: To appear in ACM CCS 2025. Extended from conference version; has added appendices more inclusive author list

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials

Authors: Peichen Zhong, Bowen Deng, Shashwat Anand, Tara Mishra, Gerbrand Ceder

Abstract: Mn-rich disordered rocksalt (DRX) cathode materials exhibit a phase transformation from a disordered to a partially disordered spinel-like structure ($δ$-phase) during electrochemical cycling. In this computational study, we used charge-informed molecular dynamics with a fine-tuned CHGNet foundation potential to investigate the phase transformation in Li$_{x}$Mn$_{0.8}$Ti$_{0.1}$O$_{1.9}$F… ▽ More Mn-rich disordered rocksalt (DRX) cathode materials exhibit a phase transformation from a disordered to a partially disordered spinel-like structure ($δ$-phase) during electrochemical cycling. In this computational study, we used charge-informed molecular dynamics with a fine-tuned CHGNet foundation potential to investigate the phase transformation in Li$_{x}$Mn$_{0.8}$Ti$_{0.1}$O$_{1.9}$F$_{0.1}$. Our results indicate that transition metal migration occurs and reorders to form the spinel-like ordering in an FCC anion framework. The transformed structure contains a higher concentration of non-transition metal (0-TM) face-sharing channels, which are known to improve Li transport kinetics. Analysis of the Mn valence distribution suggests that the appearance of tetrahedral Mn$^{2+}$ is a consequence of spinel-like ordering, rather than the trigger for cation migration as previously suggested. Calculated equilibrium intercalation voltage profiles demonstrate that the $δ$-phase, unlike the ordered spinel, exhibits solid-solution signatures at low voltage. A higher Li capacity is obtained than in the DRX phase. This study provides atomic insights into solid-state phase transformation and its relation to experimental electrochemistry, highlighting the potential of machine learning interatomic potentials for understanding complex oxide materials. △ Less

Submitted 20 September, 2025; v1 submitted 25 June, 2025; originally announced June 2025.

Thermodynamics and Legendre Duality in Optimal Networks

Authors: Amilcare Porporato, Shashank Kumar Anand, Salvatore Calabrese, Luca Ridolfi, Lamberto Rondoni

Abstract: Optimality principles in nonequilibrium transport networks are linked to a thermodynamic formalism based on generalized transport potentials endowed with Legendre duality and related contact structure. This allows quantifying the distance from non-equilibrium operating points, analogously to thermodynamic availability as well as to shed light on optimality principles in relation to different impos… ▽ More Optimality principles in nonequilibrium transport networks are linked to a thermodynamic formalism based on generalized transport potentials endowed with Legendre duality and related contact structure. This allows quantifying the distance from non-equilibrium operating points, analogously to thermodynamic availability as well as to shed light on optimality principles in relation to different imposed constraints. Extremizations of generalized dissipation and entropy production appear as special cases that require power-law resistances and -- for entropy production -- also isothermal conditions. Changes in stability of multiple operating points are interpreted as phase transitions based on non-equilibrium equations of state, while cost-based optimization of transport properties reveals connections to the generalized dissipation in the case of power law costs and linear resistance law, but now with typically unstable operating points which give rise to branched optimal transport. △ Less

Submitted 9 June, 2025; originally announced June 2025.

Emergent universal long-range structure in random-organizing systems

Authors: Satyam Anand, Guanming Zhang, Stefano Martiniani

Abstract: Self-organization through noisy interactions is ubiquitous across physics, mathematics, and machine learning, yet how long-range structure emerges from local noisy dynamics remains poorly understood. Here, we investigate three paradigmatic random-organizing particle systems drawn from distinct domains: models from soft matter physics (random organization, biased random organization) and machine le… ▽ More Self-organization through noisy interactions is ubiquitous across physics, mathematics, and machine learning, yet how long-range structure emerges from local noisy dynamics remains poorly understood. Here, we investigate three paradigmatic random-organizing particle systems drawn from distinct domains: models from soft matter physics (random organization, biased random organization) and machine learning (stochastic gradient descent), each characterized by distinct sources of noise. We discover universal long-range behavior across all systems, namely the suppression of long-range density fluctuations, governed solely by the noise correlation between particles. Furthermore, we establish a connection between the emergence of long-range order and the tendency of stochastic gradient descent to favor flat minima -- a phenomenon widely observed in machine learning. To rationalize these findings, we develop a fluctuating hydrodynamic theory that quantitatively captures all observations. Our study resolves long-standing questions about the microscopic origin of noise-induced hyperuniformity, uncovers striking parallels between stochastic gradient descent dynamics on particle system energy landscapes and neural network loss landscapes, and should have wide-ranging applications -- from the self-assembly of hyperuniform materials to ecological population dynamics and the design of generalizable learning algorithms. △ Less

Submitted 28 May, 2025; originally announced May 2025.

Comments: 27 pages, 8 figures

Journal ref: Nat Commun 17, 2346 (2026)

Phir Hera Fairy: An English Fairytaler is a Strong Faker of Fluent Speech in Low-Resource Indian Languages

Authors: Praveen Srinivasa Varadhan, Srija Anand, Soma Siddhartha, Mitesh M. Khapra

Abstract: What happens when an English Fairytaler is fine-tuned on Indian languages? We evaluate how the English F5-TTS model adapts to 11 Indian languages, measuring polyglot fluency, voice-cloning, style-cloning, and code-mixing. We compare: (i) training from scratch, (ii) fine-tuning English F5 on Indian data, and (iii) fine-tuning on both Indian and English data to prevent forgetting. Fine-tuning with o… ▽ More What happens when an English Fairytaler is fine-tuned on Indian languages? We evaluate how the English F5-TTS model adapts to 11 Indian languages, measuring polyglot fluency, voice-cloning, style-cloning, and code-mixing. We compare: (i) training from scratch, (ii) fine-tuning English F5 on Indian data, and (iii) fine-tuning on both Indian and English data to prevent forgetting. Fine-tuning with only Indian data proves most effective and the resultant IN-F5 is a near-human polyglot; that enables speakers of one language (e.g., Odia) to fluently speak in another (e.g., Hindi). Our results show English pretraining aids low-resource TTS in reaching human parity. To aid progress in other low-resource languages, we study data-constrained setups and arrive at a compute optimal strategy. Finally, we show IN-F5 can synthesize unseen languages like Bhojpuri and Tulu using a human-in-the-loop approach for zero-resource TTS via synthetic data generation. △ Less

Submitted 27 May, 2025; originally announced May 2025.

Security of Gradient Tracking Algorithms Against Malicious Agents

Authors: Sribalaji C. Anand, Alexander J Gallo, Nicola Bastianello

Abstract: Consensus algorithms are fundamental to multi-agent distributed optimization, and their security under adversarial conditions is an active area of research. While prior works primarily establish conditions for successful global consensus under attack, little is known about system behavior when these conditions are violated. This paper addresses this gap by investigating the robustness of the Wang-… ▽ More Consensus algorithms are fundamental to multi-agent distributed optimization, and their security under adversarial conditions is an active area of research. While prior works primarily establish conditions for successful global consensus under attack, little is known about system behavior when these conditions are violated. This paper addresses this gap by investigating the robustness of the Wang--Elia algorithm, which is a robust to noise version of gradient tracking algorithm, in the presence of malicious agents. We consider a network of agents collaboratively minimizing a global cost function, where a subset of agents may transmit faulty information to disrupt consensus. To quantify resilience, we formulate a security metric as an optimization problem, which is rooted in centralized attack detection literature. We provide a tractable reformulation of the optimization problem, and derive conditions under which the metric becomes unbounded, identifying undetectable attack signals that reveal inherent vulnerabilities. To facilitate design and analysis, we propose a well-posed variant of the metric and propose design methods to enhance network robustness against stealthy adversarial attacks. Numerical examples demonstrate the effectiveness of the proposed framework to enhance the resilience of multi-agent distributed optimization. △ Less

Submitted 25 October, 2025; v1 submitted 20 May, 2025; originally announced May 2025.

Comments: under review

KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation

Authors: Nikita Tatarinov, Vidhyakshaya Kannan, Haricharana Srinivasa, Arnav Raj, Harpreet Singh Anand, Varun Singh, Aditya Luthra, Ravij Lade, Agam Shah, Sudheer Chava

Abstract: We introduce KG-MuLQA (Knowledge-Graph-based Multi-Level Question-Answer Extraction): a framework that (1) extracts QA pairs at multiple complexity levels (2) along three key dimensions -- multi-hop retrieval, set operations, and answer plurality, (3) by leveraging knowledge-graph-based document representations. This approach enables fine-grained assessment of model performance across controlled d… ▽ More We introduce KG-MuLQA (Knowledge-Graph-based Multi-Level Question-Answer Extraction): a framework that (1) extracts QA pairs at multiple complexity levels (2) along three key dimensions -- multi-hop retrieval, set operations, and answer plurality, (3) by leveraging knowledge-graph-based document representations. This approach enables fine-grained assessment of model performance across controlled difficulty levels. Using this framework, we construct a dataset of 20,139 QA pairs based on financial credit agreements and evaluate 16 proprietary and open-weight Large Language Models, observing that even the best-performing models struggle with set-based comparisons and multi-hop reasoning over long contexts. Our analysis reveals systematic failure modes tied to semantic misinterpretation and inability to handle implicit relations. △ Less

Submitted 9 January, 2026; v1 submitted 18 May, 2025; originally announced May 2025.