Showing 1–50 of 267 results for author: Psaltis, D

Probing jet base emission of M87* with the 2021 Event Horizon Telescope observations

Authors: Saurabh, Hendrik Müller, Sebastiano D. von Fellenberg, Paul Tiede, Michael Janssen, Lindy Blackburn, Avery E. Broderick, Erandi Chavez, Boris Georgiev, Thomas P. Krichbaum, Kotaro Moriyama, Dhanya G. Nair, Iniyan Natarajan, Jongho Park, Andrew Thomas West, Maciek Wielgus, Kazunori Akiyama, Ezequiel Albentosa-Ruíz, Antxon Alberdi, Walter Alef, Juan Carlos Algaba, Richard Anantua, Keiichi Asada, Rebecca Azulay, Uwe Bach , et al. (260 additional authors not shown)

Abstract: We investigate the presence and spatial characteristics of the jet base emission in M87* at 230 GHz, enabled by the enhanced uv coverage in the 2021 Event Horizon Telescope (EHT) observations. The addition of the 12-m Kitt Peak Telescope and NOEMA provides two key intermediate-length baselines to SMT and the IRAM 30-m, giving sensitivity to emission structures at scales of $\sim250~μ$as and… ▽ More We investigate the presence and spatial characteristics of the jet base emission in M87* at 230 GHz, enabled by the enhanced uv coverage in the 2021 Event Horizon Telescope (EHT) observations. The addition of the 12-m Kitt Peak Telescope and NOEMA provides two key intermediate-length baselines to SMT and the IRAM 30-m, giving sensitivity to emission structures at scales of $\sim250~μ$as and $\sim2500~μ$as (0.02 pc and 0.2 pc). Without these baselines, earlier EHT observations lacked the capability to constrain emission on large scales, where a "missing flux" of order $\sim1$ Jy is expected. To probe these scales, we analyzed closure phases, robust against station-based gain errors, and modeled the jet base emission using a simple Gaussian offset from the compact ring emission at separations $>100~μ$as. Our analysis reveals a Gaussian feature centered at ($Δ$RA $\approx320~μ$as, $Δ$Dec $\approx60~μ$as), a projected separation of $\approx5500$ AU, with a flux density of only $\sim60$ mJy, implying that most of the missing flux in previous studies must arise from larger scales. Brighter emission at these scales is ruled out, and the data do not favor more complex models. This component aligns with the inferred direction of the large-scale jet and is consistent with emission from the jet base. While our findings indicate detectable jet base emission at 230 GHz, coverage from only two intermediate baselines limits reconstruction of its morphology. We therefore treat the recovered Gaussian as an upper limit on the jet base flux density. Future EHT observations with expanded intermediate-baseline coverage will be essential to constrain the structure and nature of this component. △ Less

Submitted 1 December, 2025; originally announced December 2025.

Comments: 19 pages, 18 figures. Abstract shortened with respect to the manuscript. Accepted in Astronomy & Astrophysics

High resolution Fluorescence lifetime IMaging Micro-Endoscopy (FLIMME) using a single multimode fiber

Authors: Victoria Fay, Ye Pu, Omer Tzang, Antonio Caravaca, Rafael Piestun, Genrich Tolstonog, Christian Simon, Demetri Psaltis, Christophe Moser

Abstract: Endoscopic optical imaging using a single multimode fiber (MMF) has emerged as a promising approach for highly compact, minimally invasive, and high-resolution imaging. Unlike conventional fiber bundles, MMF-based endomicroscopes exploit the controlled excitation of multiple spatially overlapping modes in a single MMF. of core diameters of tens of micrometers. to deliver and collect light to form… ▽ More Endoscopic optical imaging using a single multimode fiber (MMF) has emerged as a promising approach for highly compact, minimally invasive, and high-resolution imaging. Unlike conventional fiber bundles, MMF-based endomicroscopes exploit the controlled excitation of multiple spatially overlapping modes in a single MMF. of core diameters of tens of micrometers. to deliver and collect light to form images with sub-micrometer resolution. Here, we introduce a fluorescence lifetime imaging microscopy (FLIM) modality to the MMF endomicroscope. We use amplitude modulation of a 405 nm single-mode light source at radio frequency (RF) and lock-in detection of autofluorescence to obtain intensity and lifetime images at sub-micrometer resolution. We experimentally demonstrate the capability of the ultrathin endomicroscope to perform label-free imaging in thick ex vivo murine submandibular gland tissue. With a temporal resolution of 0.03 ns, the FLIM images show distinguished structures of lifetime differences down to 0.5 ns. The combination of sub-micrometer fluorescence intensity and lifetime images in a minimally invasive endomicroscope opens new avenues for label-free cancer detection. △ Less

Submitted 30 September, 2025; originally announced October 2025.

Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies

Authors: Yanan Niu, Demetri Psaltis, Christophe Moser, Luisa Lambertini

Abstract: Accurate solar forecasting underpins effective renewable energy management. We present SolarCAST, a causally informed model predicting future global horizontal irradiance (GHI) at a target site using only historical GHI from site X and nearby stations S - unlike prior work that relies on sky-camera or satellite imagery requiring specialized hardware and heavy preprocessing. To deliver high accurac… ▽ More Accurate solar forecasting underpins effective renewable energy management. We present SolarCAST, a causally informed model predicting future global horizontal irradiance (GHI) at a target site using only historical GHI from site X and nearby stations S - unlike prior work that relies on sky-camera or satellite imagery requiring specialized hardware and heavy preprocessing. To deliver high accuracy with only public sensor data, SolarCAST models three classes of confounding factors behind X-S correlations using scalable neural components: (i) observable synchronous variables (e.g., time of day, station identity), handled via an embedding module; (ii) latent synchronous factors (e.g., regional weather patterns), captured by a spatio-temporal graph neural network; and (iii) time-lagged influences (e.g., cloud movement across stations), modeled with a gated transformer that learns temporal shifts. It outperforms leading time-series and multimodal baselines across diverse geographical conditions, and achieves a 25.9% error reduction over the top commercial forecaster, Solcast. SolarCAST offers a lightweight, practical, and generalizable solution for localized solar forecasting. △ Less

Submitted 18 September, 2025; originally announced September 2025.

Comments: Accepted to CIKM 2025

ACM Class: I.2.6; I.5.4

Journal ref: Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM '25), November 10--14, 2025, Seoul, Republic of Korea

Exploring Expert Specialization through Unsupervised Training in Sparse Mixture of Experts

Authors: Strahinja Nikolic, Ilker Oguz, Demetri Psaltis

Abstract: Understanding the internal organization of neural networks remains a fundamental challenge in deep learning interpretability. We address this challenge by exploring a novel Sparse Mixture of Experts Variational Autoencoder (SMoE-VAE) architecture. We test our model on the QuickDraw dataset, comparing unsupervised expert routing against a supervised baseline guided by ground-truth labels. Surprisin… ▽ More Understanding the internal organization of neural networks remains a fundamental challenge in deep learning interpretability. We address this challenge by exploring a novel Sparse Mixture of Experts Variational Autoencoder (SMoE-VAE) architecture. We test our model on the QuickDraw dataset, comparing unsupervised expert routing against a supervised baseline guided by ground-truth labels. Surprisingly, we find that unsupervised routing consistently achieves superior reconstruction performance. The experts learn to identify meaningful sub-categorical structures that often transcend human-defined class boundaries. Through t-SNE visualizations and reconstruction analysis, we investigate how MoE models uncover fundamental data structures that are more aligned with the model's objective than predefined labels. Furthermore, our study on the impact of dataset size provides insights into the trade-offs between data quantity and expert specialization, offering guidance for designing efficient MoE architectures. △ Less

Submitted 12 September, 2025; originally announced September 2025.

Comments: 14 pages, 7 figures

A Hybrid Algorithm for Drift-Kinetic Particle Dynamics within General Relativistic Magnetohydrodynamics Simulations of Black Holes Accretion Flows

Authors: Tyler Trent, Dimitrios Psaltis, Feryal Özel

Abstract: Astrophysical plasmas in relativistic spacetimes, such as black hole accretion flows, are often weakly collisional and require kinetic modeling to capture non-local transport and particle acceleration. However, the extreme scale separation between microscopic and macroscopic processes limits the feasibility of fully kinetic simulations. A covariant guiding center formalism has recently been derive… ▽ More Astrophysical plasmas in relativistic spacetimes, such as black hole accretion flows, are often weakly collisional and require kinetic modeling to capture non-local transport and particle acceleration. However, the extreme scale separation between microscopic and macroscopic processes limits the feasibility of fully kinetic simulations. A covariant guiding center formalism has recently been derived to address this challenge in curved spacetimes. We present a new hybrid numerical algorithm based on this formalism, which evolves the trajectories of charged particles over macroscopic timescales in GRMHD backgrounds. To address numerical instabilities in the equations of motion, we develop a semi-implicit integrator that ensures stable evolution in strong-field environments. We apply our method to GRMHD simulations of black hole accretion flows, demonstrating its accuracy and efficiency across a range of physical conditions. △ Less

Submitted 15 July, 2025; originally announced July 2025.

Modeling Targets and Optimal Frequencies for Imaging the Shadows of Nearby Supermassive Black Holes

Authors: J. Cole Faggert, Feryal Ozel, Dimitrios Psaltis

Abstract: Horizon-scale imaging of supermassive black holes has opened a new window onto the studies of strong-field gravity and plasma physics in low-luminosity accretion flows. As future efforts aim to image fainter and smaller angular-size targets, primarily through space-based very long baseline interferometry (VLBI), it is important to identify optimal sources and observing strategies for such studies.… ▽ More Horizon-scale imaging of supermassive black holes has opened a new window onto the studies of strong-field gravity and plasma physics in low-luminosity accretion flows. As future efforts aim to image fainter and smaller angular-size targets, primarily through space-based very long baseline interferometry (VLBI), it is important to identify optimal sources and observing strategies for such studies. In this work, we assess the prospects for imaging black hole shadows in a broad population of nearby supermassive black holes by modeling their accretion flows using a covariant semi-analytic model for the flow and general relativistic ray tracing. We explore the influence of black hole and accretion flow parameters on spectra, image morphology, and the critical frequency at which the flows become optically thin. We identify three general classes of sources: those that become transparent at traditional imaging frequencies; those requiring higher frequencies; and those unlikely to be transparent down to the black hole shadow in the submillimeter band. Our results will inform target selection and wavelength optimization for future VLBI arrays, where both resolution and transparency are essential for resolving black hole shadows. △ Less

Submitted 18 June, 2025; originally announced June 2025.

Comments: 21 pages, 17 figures. Submitted to ApJ

Limits to Extracting Neutron-Star Physics Constraints from NICER Pulse Profiles

Authors: Tolga Guver, Dimitrios Psaltis, Feryal Ozel, Tong Zhao

Abstract: Modeling energy-dependent X-ray pulse profiles from rotation-powered millisecond pulsars observed with NICER has emerged as a promising avenue for measuring neutron star radii and probing the equation of state of cold, ultra-dense matter. However, pulse profile models have often required an unwieldy number of parameters to account for complex surface emission geometries, introducing the risk of ov… ▽ More Modeling energy-dependent X-ray pulse profiles from rotation-powered millisecond pulsars observed with NICER has emerged as a promising avenue for measuring neutron star radii and probing the equation of state of cold, ultra-dense matter. However, pulse profile models have often required an unwieldy number of parameters to account for complex surface emission geometries, introducing the risk of overfitting and degeneracies. To explore the number of model parameters that can be inferred uniquely, we perform a quantitative assessment of the information content in X-ray pulse profiles by applying Fourier methods. We determine the number of independent observables that can be reliably extracted from the pulse shapes, as well as from complementary X-ray spectral data obtained with XMM-Newton, for key NICER targets. Our analysis provides a framework for evaluating the match between model complexity and data constraints. It also demonstrates the importance of incorporating in the model the pulsed components of the magnetospheric non-thermal emission, which often contributes significantly to the observed spectra. Our results highlight limitations in previous inferences of neutron-star radii from NICER observations, which have incorporated model complexity not supported by the data. △ Less

Submitted 16 June, 2025; originally announced June 2025.

Comments: Submitted to AAS Journals

Machine Learning Acceleration of Neutron Star Pulse Profile Modeling

Authors: Preston G. Waldrop, Dimitrios Psaltis, Tong Zhao

Abstract: Ray tracing algorithms that compute pulse profiles from rotating neutron stars are essential tools for constraining neutron-star properties with data from missions such as NICER. However, the high computational cost of these simulations presents a significant bottleneck for inference algorithms that require millions of evaluations, such as Markov Chain Monte Carlo methods. In this work, we develop… ▽ More Ray tracing algorithms that compute pulse profiles from rotating neutron stars are essential tools for constraining neutron-star properties with data from missions such as NICER. However, the high computational cost of these simulations presents a significant bottleneck for inference algorithms that require millions of evaluations, such as Markov Chain Monte Carlo methods. In this work, we develop a residual neural network model that accelerates this calculation by predicting the observed flux from the surface of a spinning neutron star as a function of its physical parameters and rotational phase. Leveraging GPU-parallelized evaluation, we demonstrate that our model achieves many orders-of-magnitude speedup compared to traditional ray tracing while maintaining high accuracy. We also show that the trained network can efficiently accommodate complex emission geometries, including non-circular and multiple hot spots, by integrating over localized flux predictions. △ Less

Submitted 12 June, 2025; originally announced June 2025.

Comments: 10 pages, 12 figures, submitted to the Astrophysical Journal

Origin of the ring ellipticity in the black hole images of M87*

Authors: Rohan Dahale, Ilje Cho, Kotaro Moriyama, Kaj Wiik, Paul Tiede, José L. Gómez, Chi-kwan Chan, Roman Gold, Vadim Y. Bernshteyn, Marianna Foschi, Britton Jeter, Hung-Yi Pu, Boris Georgiev, Abhishek V. Joshi, Alejandro Cruz-Osorio, Iniyan Natarajan, Avery E. Broderick, León D. S. Salas, Koushik Chatterjee, Kazunori Akiyama, Ezequiel Albentosa-Ruíz, Antxon Alberdi, Walter Alef, Juan Carlos Algaba, Richard Anantua , et al. (251 additional authors not shown)

Abstract: We investigate the origin of the elliptical ring structure observed in the images of the supermassive black hole M87*, aiming to disentangle contributions from gravitational, astrophysical, and imaging effects. Leveraging the enhanced capabilities of the Event Horizon Telescope (EHT) 2018 array, including improved $(u,v)$-coverage from the Greenland Telescope, we measure the ring's ellipticity usi… ▽ More We investigate the origin of the elliptical ring structure observed in the images of the supermassive black hole M87*, aiming to disentangle contributions from gravitational, astrophysical, and imaging effects. Leveraging the enhanced capabilities of the Event Horizon Telescope (EHT) 2018 array, including improved $(u,v)$-coverage from the Greenland Telescope, we measure the ring's ellipticity using five independent imaging methods, obtaining a consistent average value of $τ= 0.08_{-0.02}^{+0.03}$ with a position angle $ξ= 50.1_{-7.6}^{+6.2}$ degrees. To interpret this measurement, we compare against General Relativistic Magnetohydrodynamic (GRMHD) simulations spanning a wide range of physical parameters including thermal or non-thermal electron distribution function, spins, and ion-to-electron temperature ratios in both low and high-density regions. We find no statistically significant correlation between spin and ellipticity in GRMHD images. Instead, we identify a correlation between ellipticity and the fraction of non-ring emission, particularly in non-thermal models and models with higher jet emission. These results indicate that the ellipticity measured from the \m87 emission structure is consistent with that expected from simulations of turbulent accretion flows around black holes, where it is dominated by astrophysical effects rather than gravitational ones. Future high-resolution imaging, including space very long baseline interferometry and long-term monitoring, will be essential to isolate gravitational signatures from astrophysical effects. △ Less

Submitted 15 May, 2025; originally announced May 2025.

Comments: 18 pages, 13 figures

Journal ref: A&A 699, A279 (2025)

Solar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series

Authors: Yanan Niu, Roy Sarkis, Demetri Psaltis, Mario Paolone, Christophe Moser, Luisa Lambertini

Abstract: Accurate intraday solar irradiance forecasting is crucial for optimizing dispatch planning and electricity trading. For this purpose, we introduce a novel and effective approach that includes three distinguishing components from the literature: 1) the uncommon use of single-frame public camera imagery; 2) solar irradiance time series scaled with a proposed normalization step, which boosts performa… ▽ More Accurate intraday solar irradiance forecasting is crucial for optimizing dispatch planning and electricity trading. For this purpose, we introduce a novel and effective approach that includes three distinguishing components from the literature: 1) the uncommon use of single-frame public camera imagery; 2) solar irradiance time series scaled with a proposed normalization step, which boosts performance; and 3) a lightweight multimodal model, called Solar Multimodal Transformer (SMT), that delivers accurate short-term solar irradiance forecasting by combining images and scaled time series. Benchmarking against Solcast, a leading solar forecasting service provider, our model improved prediction accuracy by 25.95%. Our approach allows for easy adaptation to various camera specifications, offering broad applicability for real-world solar forecasting challenges. △ Less

Submitted 28 February, 2025; originally announced March 2025.

Comments: WACV2025

The putative center in NGC 1052

Authors: Anne-Kathrin Baczko, Matthias Kadler, Eduardo Ros, Christian M. Fromm, Maciek Wielgus, Manel Perucho, Thomas P. Krichbaum, Mislav Baloković, Lindy Blackburn, Chi-kwan Chan, Sara Issaoun, Michael Janssen, Luca Ricci, Kazunori Akiyama, Ezequiel Albentosa-Ruíz, Antxon Alberdi, Walter Alef, Juan Carlos Algaba, Richard Anantua, Keiichi Asada, Rebecca Azulay, Uwe Bach, David Ball, Bidisha Bandyopadhyay, John Barrett , et al. (262 additional authors not shown)

Abstract: Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combine… ▽ More Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 μas from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength. △ Less

Submitted 15 January, 2025; originally announced January 2025.

Comments: 22 pages, 10 figures, published in A&A

Journal ref: A&A, 692, A205 (2024)

Training Hybrid Neural Networks with Multimode Optical Nonlinearities Using Digital Twins

Authors: Ilker Oguz, Louis J. E. Suter, Jih-Liang Hsieh, Mustafa Yildirim, Niyazi Ulas Dinc, Christophe Moser, Demetri Psaltis

Abstract: The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hardware. Incorporating complex physical events in networks as fixed, efficient computation modules can address this demand by decreasing the comple… ▽ More The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hardware. Incorporating complex physical events in networks as fixed, efficient computation modules can address this demand by decreasing the complexity of trainable layers. Here, we utilize ultrashort pulse propagation in multimode fibers, which perform large-scale nonlinear transformations, for this purpose. Training the hybrid architecture is achieved through a neural model that differentiably approximates the optical system. The training algorithm updates the neural simulator and backpropagates the error signal over this proxy to optimize layers preceding the optical one. Our experimental results achieve state-of-the-art image classification accuracies and simulation fidelity. Moreover, the framework demonstrates exceptional resilience to experimental drifts. By integrating low-energy physical systems into neural networks, this approach enables scalable, energy-efficient AI models with significantly reduced computational demands. △ Less

Submitted 14 January, 2025; originally announced January 2025.

Comments: 17 pages, 6 figures

Roadmap on Neuromorphic Photonics

Authors: Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi, H. Ballani, Sylvain Barbay, Stefano Biasi, Peter Bienstman, Simon Bilodeau, Wim Bogaerts, Fabian Böhm, G. Brennan, Sonia Buckley, Xinlun Cai, Marcello Calvanese Strinati, B. Canakci, Benoit Charbonnier, Mario Chemnitz, Yitong Chen, Stanley Cheung, Jeff Chiles, Suyeon Choi, Demetrios N. Christodoulides, Lukas Chrostowski, J. Chu, J. H. Clegg , et al. (125 additional authors not shown)

Abstract: This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementation philosophies reported in the field. It emphasizes the critical role of cross-disciplinary collaboration in this rapidly evolving field. △ Less

Submitted 16 January, 2025; v1 submitted 14 January, 2025; originally announced January 2025.

A multi-frequency study of sub-parsec jets with the Event Horizon Telescope

Authors: Jan Röder, Maciek Wielgus, Andrei P. Lobanov, Thomas P. Krichbaum, Dhanya G. Nair, Sang-Sung Lee, Eduardo Ros, Vincent L. Fish, Lindy Blackburn, Chi-kwan Chan, Sara Issaoun, Michael Janssen, Michael D. Johnson, Sheperd S. Doeleman, Geoffrey C. Bower, Geoffrey B. Crew, Remo P. J. Tilanus, Tuomas Savolainen, C. M. Violette Impellizzeri, Antxon Alberdi, Anne-Kathrin Baczko, José L. Gómez, Ru-Sen Lu, Georgios F. Paraschos, Efthalia Traianou , et al. (265 additional authors not shown)

Abstract: The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We… ▽ More The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-Königl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations. △ Less

Submitted 9 January, 2025; originally announced January 2025.

Journal ref: A&A 695, A233 (2025)

Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data

Authors: Matthew Golden, Kaushik Satapathy, Dimitrios Psaltis

Abstract: The discovery of dynamical models from data represents a crucial step in advancing our understanding of physical systems. Library-based sparse regression has emerged as a powerful method for inferring governing equations directly from spatiotemporal data, but current model-agnostic implementations remain computationally expensive, limiting their applicability to data that lack substantial complexi… ▽ More The discovery of dynamical models from data represents a crucial step in advancing our understanding of physical systems. Library-based sparse regression has emerged as a powerful method for inferring governing equations directly from spatiotemporal data, but current model-agnostic implementations remain computationally expensive, limiting their applicability to data that lack substantial complexity. To overcome these challenges, we introduce a scalable framework that enables efficient discovery of complex dynamical models across a wide range of applications. We demonstrate the capabilities of our approach, by ``discovering'' the equations of magnetohydrodynamics (MHD) from synthetic data generated by high-resolution simulations of turbulent MHD flows with viscous and Ohmic dissipation. Using a library of candidate terms that is $\gtrsim 10$ times larger than those in previous studies, we accurately recover the full set of MHD equations, including the subtle dissipative terms that are critical to the dynamics of the system. Our results establish sparse regression as a practical tool for uncovering fundamental physical laws from complex, high-dimensional data without assumptions on the underlying symmetry or the form of any governing equation. △ Less

Submitted 7 January, 2025; originally announced January 2025.

Demographics of black holes at $<$100 R$_{\rm g}$ scales: accretion flows, jets, and shadows

Authors: Dhanya G. Nair, Neil M. Nagar, Venkatessh Ramakrishnan, Maciek Wielgus, Vicente Arratia, Thomas P. Krichbaum, Xinyue A. Zhang, Angelo Ricarte, Silpa S., Joaquín Hernández-Yévenes, Nicole M. Ford, Bidisha Bandyopadhyay, Mark Gurwell, Roman Burridge, Dominic W. Pesce, Sheperd S. Doeleman, Jae-Young Kim, Daewon Kim, Michael Janssen, Sebastiano D. von Fellenberg, Christian M. Fromm, Deokhyeong Lee, Heino Falcke, Jan Wagner, Geoffrey C. Bower , et al. (65 additional authors not shown)

Abstract: Using the Event Horizon Telescope (EHT), the gravitationally lensed rings around the supermassive black holes (SMBHs) in Messier 87 (M87) and Sagittarius A* (Sgr A*) have now been successfully imaged at a resolution under 10 gravitational radii (R$_{\rm g}$ $ = \rm{GM/c^2}$). To expand studies beyond M87 and Sgr A*, we have constructed the Event Horizon and Environs (ETHER) sample, a comprehensive… ▽ More Using the Event Horizon Telescope (EHT), the gravitationally lensed rings around the supermassive black holes (SMBHs) in Messier 87 (M87) and Sagittarius A* (Sgr A*) have now been successfully imaged at a resolution under 10 gravitational radii (R$_{\rm g}$ $ = \rm{GM/c^2}$). To expand studies beyond M87 and Sgr A*, we have constructed the Event Horizon and Environs (ETHER) sample, a comprehensive database encompassing approximately 3.15 million SMBH mass estimates, $\sim$ 20,000 Very-Long Baseline Interferometry (VLBI) radio flux densities, and $\sim$ 36,000 hard X-ray flux densities. This database is designed to identify and optimize target selection for the EHT and its upgrades on the ground and in space. We have identified a Gold Sample (GS) of nearby low-luminosity Active Galactic Nuclei (AGNs) within it that are ideal for studying jet bases and potentially imaging black hole shadows. We observed 27 of these AGNs using the EHT from 2022 to 2024, providing an opportunity to resolve and image accretion flows and jets at resolutions of $\leq$ 100 R$_{\rm g}$. Only a few SMBHs have sufficiently high enough flux density to be imaged at scales of $\leq$ 50 R$_{\rm g}$ with the present EHT. Among these are M87, Sgr A*, NGC4594 (Sombrero/M104), NGC4261, and NGC4374 (Messier 84/M84). Of these, NGC4261, Sombrero, and M84 have been observed and/or are scheduled for deep imaging with EHT+ALMA from 2023 to 2025. Sombrero, NGC4261, M84, NGC4278, and NGC5232 are clearly detected in our EHT+ALMA observations in 2022, indicating that the 230 GHz flux density from the accretion flows is significantly high. Ongoing imaging of the ETHER GS will enable measurements of black hole mass and spin, help constrain General Relativity, and enrich our understanding of jet launching and accretion inflows across a broad multi-parameter space, including black hole mass, spin, accretion rate, and orientation. △ Less

Submitted 28 December, 2024; originally announced December 2024.

Comments: 9 pages, 6 figures, 1 table, published in Proceedings of the 16th EVN Symposium, Ed. E. Ros, P. Benke, S.A. Dzib, I. Rottmann, & J.A. Zensus, Bonn: Max-Planck-Institut für Radioastronomie, 2024, pages 75-84, https://cloud.mpifr-bonn.mpg.de/index.php/s/BkX2CC2Xjn2aKR4

A Counterintuitive Correlation Between Neutron-Star Radii Inferred from Pulse Modeling and Surface Emission Beaming

Authors: Tong Zhao, Dimitrios Psaltis, Feryal Ozel

Abstract: Thermal X-ray emission from rotation-powered millisecond pulsars, shaped by gravitational lensing and the beaming of the surface radiation, provides critical insights into neutron star properties. This approach has been the focus of observations with the NICER mission. Using a semi-analytic model to calculate pulse profiles, we investigate the effects of adopting incorrect beaming models on the in… ▽ More Thermal X-ray emission from rotation-powered millisecond pulsars, shaped by gravitational lensing and the beaming of the surface radiation, provides critical insights into neutron star properties. This approach has been the focus of observations with the NICER mission. Using a semi-analytic model to calculate pulse profiles, we investigate the effects of adopting incorrect beaming models on the inferred compactness of neutron stars. We demonstrate that assuming a more centrally peaked beaming pattern when fitting data from a more isotropic emitter leads to an underestimation of compactness in the case of two antipodal polar caps. We present a detailed analysis of this counterintuitive result, offering both qualitative insights and quantitative estimates. If the atmospheric heating in the millisecond pulsars observed with NICER is shallow, the inferred radii for these sources could be significantly overestimated, with important implications for neutron star structure and equation-of-state constraints. △ Less

Submitted 10 March, 2025; v1 submitted 16 December, 2024; originally announced December 2024.

Comments: Submitted to the Astrophysical Journal

Journal ref: Astrophys. J. 982, 112 (2025)

Uncovering Correlations and Biases in Parameter Inference from Neutron-Star Pulse Profile Modeling

Authors: Tong Zhao, Dimitrios Psaltis, Feryal Ozel, Elif Beklen

Abstract: Modeling of X-ray pulse profiles from millisecond pulsars offers a promising method of inferring the mass-to-radius ratios of neutron stars. Recent observations with NICER resulted in measurements of radii for three neutron stars using this technique. In this paper, we explore correlations between model parameters and the degree to which individual parameters can be inferred from pulse profiles, u… ▽ More Modeling of X-ray pulse profiles from millisecond pulsars offers a promising method of inferring the mass-to-radius ratios of neutron stars. Recent observations with NICER resulted in measurements of radii for three neutron stars using this technique. In this paper, we explore correlations between model parameters and the degree to which individual parameters can be inferred from pulse profiles, using an analytic model that allows for an efficient and interpretable exploration. We introduce a new set of model parameters that reduce the most prominent correlations and allow for an efficient sampling of posteriors. We then demonstrate that the degree of beaming of radiation emerging from the neutron star surface has a large impact on the uncertainties in the inferred model parameters. Finally, we show that the uncertainties in the model parameters for neutron stars for which the polar cap temperature falls outside of the NICER energy range are significantly degraded. △ Less

Submitted 16 December, 2024; originally announced December 2024.

Comments: Submitted to the Astrophysical Journal

Kernel Methods for Interferometric Imaging

Authors: Dimitrios Psaltis, Feryal Ozel, Yassine Ben Zineb

Abstract: Increasing the angular resolution of an interferometric array requires placing its elements at large separations. This often leads to sparse coverage and introduces challenges to reconstructing images from interferometric data. We introduce a new interferometric imaging algorithm, KRISP, that is based on kernel methods, is statistically robust, and is agnostic to the underlying image. The algorith… ▽ More Increasing the angular resolution of an interferometric array requires placing its elements at large separations. This often leads to sparse coverage and introduces challenges to reconstructing images from interferometric data. We introduce a new interferometric imaging algorithm, KRISP, that is based on kernel methods, is statistically robust, and is agnostic to the underlying image. The algorithm reconstructs the complete Fourier map up to the maximum observed baseline length based entirely on the data without tuning by a user or training on prior images and reproduces images with high fidelity. KRISP works efficiently for many sparse array configurations even in the presence of significant image structure as long as the typical baseline separation is comparable to or less than the correlation length of the Fourier map, which is inversely proportional to the size of the target image. △ Less

Submitted 2 December, 2024; originally announced December 2024.

Comments: Submitted to the Astrophysical Journal

Advancing Black Hole Imaging with Space-Based Interferometry

Authors: Yassine Ben Zineb, Feryal Ozel, Dimitrios Psaltis

Abstract: Horizon-scale imaging with the Event Horizon Telescope (EHT) has provided transformative insights into supermassive black holes but its resolution and scope are limited by ground-based constraints such as the size of the Earth, its relatively slow rotation, and atmospheric delays. Space-based very long baseline interferometry (VLBI) offers the capability for studying a larger and more diverse samp… ▽ More Horizon-scale imaging with the Event Horizon Telescope (EHT) has provided transformative insights into supermassive black holes but its resolution and scope are limited by ground-based constraints such as the size of the Earth, its relatively slow rotation, and atmospheric delays. Space-based very long baseline interferometry (VLBI) offers the capability for studying a larger and more diverse sample of black holes. We identify a number of nearby supermassive black holes as prime candidates for horizon-scale imaging at millimeter wavelengths, and use source characteristics such as angular size, sky distribution, and variability timescales to shape the design of a space-based array. We identify specific metrics that serve as key predictors of image fidelity and scientific potential, providing a quantitative basis for optimizing mission design parameters. Our analysis demonstrates that the optimal configuration requires two space-based elements in high Earth orbits (HEO) that are not coplanar and are apparently counter-rotating. Our results delineate the key requirements for a space-based VLBI mission, enabling detailed studies of black hole shadows, plasma dynamics, and jet formation, advancing black hole astrophysics beyond the current capabilities of the EHT. △ Less

Submitted 2 December, 2024; originally announced December 2024.

Comments: Submitted to the Astrophysical Journal

First Very Long Baseline Interferometry Detections at 870μm

Authors: Alexander W. Raymond, Sheperd S. Doeleman, Keiichi Asada, Lindy Blackburn, Geoffrey C. Bower, Michael Bremer, Dominique Broguiere, Ming-Tang Chen, Geoffrey B. Crew, Sven Dornbusch, Vincent L. Fish, Roberto García, Olivier Gentaz, Ciriaco Goddi, Chih-Chiang Han, Michael H. Hecht, Yau-De Huang, Michael Janssen, Garrett K. Keating, Jun Yi Koay, Thomas P. Krichbaum, Wen-Ping Lo, Satoki Matsushita, Lynn D. Matthews, James M. Moran , et al. (254 additional authors not shown)

Abstract: The first very long baseline interferometry (VLBI) detections at 870$μ$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescop… ▽ More The first very long baseline interferometry (VLBI) detections at 870$μ$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$λ$ corresponding to an angular resolution, or fringe spacing, of 19$μ$as. The Allan deviation of the visibility phase at 870$μ$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$μ$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time. △ Less

Submitted 9 October, 2024; originally announced October 2024.

Comments: Corresponding author: S. Doeleman

Journal ref: The Astronomical Journal, Volume 168, Issue 3, id.130, 19 pp. 2024

Theoretical Foundation of Black Hole Image Reconstruction using PRIMO

Authors: Dimitrios Psaltis, Feryal Ozel, Lia Medeiros, Tod R. Lauer

Abstract: A new image-reconstruction algorithm, PRIMO, applied to the interferometric data of the M87 black hole collected with the Event Horizon Telescope (EHT), resulted in an image that reached the native resolution of the telescope array. PRIMO is based on learning a compact set of image building blocks obtained from a large library of high-fidelity, physics-based simulations of black hole images. It us… ▽ More A new image-reconstruction algorithm, PRIMO, applied to the interferometric data of the M87 black hole collected with the Event Horizon Telescope (EHT), resulted in an image that reached the native resolution of the telescope array. PRIMO is based on learning a compact set of image building blocks obtained from a large library of high-fidelity, physics-based simulations of black hole images. It uses these building blocks to fill the sparse Fourier coverage of the data that results from the small number of telescopes in the array. In this paper, we show that this approach is readily justified. Since the angular extent of the image of the black hole and of its inner accretion flow is finite, the Fourier space domain is heavily smoothed, with a correlation scale that is at most comparable to the sizes of the data gaps in the coverage of Fourier space with the EHT. Consequently, PRIMO or other machine-learning algorithms can faithfully reconstruct the images without the need to generate information that is unconstrained by the data within the resolution of the array. We also address the completeness of the eigenimages and the compactness of the resulting representation. We show that PRIMO provides a compact set of eigenimages that have sufficient complexity to recreate a broad set of images well beyond those in the training set. △ Less

Submitted 19 August, 2024; originally announced August 2024.

Comments: Submitted to the Astrophysical Journal

Optical Diffusion Models for Image Generation

Authors: Ilker Oguz, Niyazi Ulas Dinc, Mustafa Yildirim, Junjie Ke, Innfarn Yoo, Qifei Wang, Feng Yang, Christophe Moser, Demetri Psaltis

Abstract: Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution. This inference procedure generally utilizes a trained neural network numerous times to obtain the final output, creating significant latency and energy consumption on digital electronic hardware such as GPUs. In this study, we demonstrate that the propagation of a light beam… ▽ More Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution. This inference procedure generally utilizes a trained neural network numerous times to obtain the final output, creating significant latency and energy consumption on digital electronic hardware such as GPUs. In this study, we demonstrate that the propagation of a light beam through a semi-transparent medium can be programmed to implement a denoising diffusion model on image samples. This framework projects noisy image patterns through passive diffractive optical layers, which collectively only transmit the predicted noise term in the image. The optical transparent layers, which are trained with an online training approach, backpropagating the error to the analytical model of the system, are passive and kept the same across different steps of denoising. Hence this method enables high-speed image generation with minimal power consumption, benefiting from the bandwidth and energy efficiency of optical information processing. △ Less

Submitted 31 October, 2024; v1 submitted 15 July, 2024; originally announced July 2024.

Comments: 17 pages, 10 figures

The Unexpected Lack of Asymmetry in the Horizon-Scale Image of Sagittarius A*

Authors: J. Cole Faggert, Feryal Özel, Dimitrios Psaltis

Abstract: The ring-like images of the two supermassive black holes captured by the Event Horizon Telescope (EHT) provide powerful probes of the physics of accretion flows at horizon scales. Specifically, the brightness asymmetry in the images carries information about the angular velocity profile of the inner accretion flow and the inclination of the observer, owing to the Doppler boosts photons experience… ▽ More The ring-like images of the two supermassive black holes captured by the Event Horizon Telescope (EHT) provide powerful probes of the physics of accretion flows at horizon scales. Specifically, the brightness asymmetry in the images carries information about the angular velocity profile of the inner accretion flow and the inclination of the observer, owing to the Doppler boosts photons experience at their site of emission. In this paper, we develop a method for quantifying the brightness asymmetry of black-hole images in the Fourier domain, which can be measured directly from interferometric data. We apply this method to current EHT data and find that the image of Sagittarius A* (Sgr A*) has an unusually low degree of asymmetry that is even lower than that inferred for M87. We then use a covariant semi-analytic model to obtain constraints on the inclinations and velocity profiles of the inner accretion flow for Sgr A*. We find that the lack of significant brightness asymmetry forces the observer inclination to uncomfortably small values ($6-10^\circ$), if the plasma velocity follows Keplerian profiles. Alternatively, larger inclination angles can be accommodated if the plasma velocities are significantly sub-Keplerian and the black hole is not spinning rapidly. △ Less

Submitted 20 June, 2024; originally announced June 2024.

Comments: 12 pages, 5 figures. Submitted to ApJ

Training of Physical Neural Networks

Authors: Ali Momeni, Babak Rahmani, Benjamin Scellier, Logan G. Wright, Peter L. McMahon, Clara C. Wanjura, Yuhang Li, Anas Skalli, Natalia G. Berloff, Tatsuhiro Onodera, Ilker Oguz, Francesco Morichetti, Philipp del Hougne, Manuel Le Gallo, Abu Sebastian, Azalia Mirhoseini, Cheng Zhang, Danijela Marković, Daniel Brunner, Christophe Moser, Sylvain Gigan, Florian Marquardt, Aydogan Ozcan, Julie Grollier, Andrea J. Liu , et al. (3 additional authors not shown)

Abstract: Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also… ▽ More Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained - primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. △ Less

Submitted 5 June, 2024; originally announced June 2024.

Comments: 29 pages, 4 figures

Second-harmonic optical diffraction tomography

Authors: Amirhossein Saba, Carlo Gigli, Ye Pu, Demetri Psaltis

Abstract: Optical diffraction tomography (ODT) has emerged as an important label-free tool in biomedicine to measure the three-dimensional (3D) structure of a biological sample. In this paper, we describe ODT using second-harmonic generation (SHG) which is a coherent nonlinear optical process with a strict symmetry selectivity and has several advantages over traditional fluorescence methods. We report the t… ▽ More Optical diffraction tomography (ODT) has emerged as an important label-free tool in biomedicine to measure the three-dimensional (3D) structure of a biological sample. In this paper, we describe ODT using second-harmonic generation (SHG) which is a coherent nonlinear optical process with a strict symmetry selectivity and has several advantages over traditional fluorescence methods. We report the tomographic retrieval of the 3D second-order nonlinear optical susceptibility using two-dimensional holographic measurements of the SHG fields at different illumination angles and polarization states. The method is a generalization of the conventional linear ODT to the nonlinear scenario. We demonstrate the method with a numerically simulated nanoparticle distribution and an experiment with muscle tissue fibers. Our results show that SHG ODT does not only provide an effective contrast mechanism for label-free imaging but also due to the symmetry requirement enables the visualization of properties that are not otherwise accessible. △ Less

Submitted 18 May, 2024; originally announced May 2024.

Broadband Multi-wavelength Properties of M87 during the 2018 EHT Campaign including a Very High Energy Flaring Episode

Authors: J. C. Algaba, M. Balokovic, S. Chandra, W. Y. Cheong, Y. Z. Cui, F. D'Ammando, A. D. Falcone, N. M. Ford, M. Giroletti, C. Goddi, M. A. Gurwell, K. Hada, D. Haggard, S. Jorstad, A. Kaur, T. Kawashima, S. Kerby, J. Y. Kim, M. Kino, E. V. Kravchenko, S. S. Lee, R. S. Lu, S. Markoff, J. Michail, J. Neilsen , et al. (721 additional authors not shown)

Abstract: The nearby elliptical galaxy M87 contains one of the only two supermassive black holes whose emission surrounding the event horizon has been imaged by the Event Horizon Telescope (EHT). In 2018, more than two dozen multi-wavelength (MWL) facilities (from radio to gamma-ray energies) took part in the second M87 EHT campaign. The goal of this extensive MWL campaign was to better understand the physi… ▽ More The nearby elliptical galaxy M87 contains one of the only two supermassive black holes whose emission surrounding the event horizon has been imaged by the Event Horizon Telescope (EHT). In 2018, more than two dozen multi-wavelength (MWL) facilities (from radio to gamma-ray energies) took part in the second M87 EHT campaign. The goal of this extensive MWL campaign was to better understand the physics of the accreting black hole M87*, the relationship between the inflow and inner jets, and the high-energy particle acceleration. Understanding the complex astrophysics is also a necessary first step towards performing further tests of general relativity. The MWL campaign took place in April 2018, overlapping with the EHT M87* observations. We present a new, contemporaneous spectral energy distribution (SED) ranging from radio to very high energy (VHE) gamma-rays, as well as details of the individual observations and light curves. We also conduct phenomenological modelling to investigate the basic source properties. We present the first VHE gamma-ray flare from M87 detected since 2010. The flux above 350 GeV has more than doubled within a period of about 36 hours. We find that the X-ray flux is enhanced by about a factor of two compared to 2017, while the radio and millimetre core fluxes are consistent between 2017 and 2018. We detect evidence for a monotonically increasing jet position angle that corresponds to variations in the bright spot of the EHT image. Our results show the value of continued MWL monitoring together with precision imaging for addressing the origins of high-energy particle acceleration. While we cannot currently pinpoint the precise location where such acceleration takes place, the new VHE gamma-ray flare already presents a challenge to simple one-zone leptonic emission model approaches, and emphasises the need for combined image and spectral modelling. △ Less

Submitted 5 December, 2024; v1 submitted 24 April, 2024; originally announced April 2024.

Comments: 46 pages, 23 figures, accepted by Astronomy & Astrophysics on August. 29, 2024

Journal ref: A&A 692, A140 (2024)

Covariant Guiding Center Equations for Charged Particle Motions in General Relativistic Spacetimes

Authors: Tyler Trent, Karin Roley, Matthew Golden, Dimitrios Psaltis, Feryal Özel

Abstract: Low density plasmas in curved spacetimes, such as those found in accretion flows around black holes, are challenging to model from first principles, owing to the large scale separation between the characteristic scales of the microscopic processes and large mean-free-paths comparable to the system sizes. Kinetic approaches become necessary to capture the relevant physics but lack the dynamic range… ▽ More Low density plasmas in curved spacetimes, such as those found in accretion flows around black holes, are challenging to model from first principles, owing to the large scale separation between the characteristic scales of the microscopic processes and large mean-free-paths comparable to the system sizes. Kinetic approaches become necessary to capture the relevant physics but lack the dynamic range to model the global characteristics of the systems. In this paper, we develop new covariant guiding center equations of motion for charges in general relativistic spacetimes that are computationally tractable. We decompose the particle motion into a fast gyration, which we integrate analytically and a slow drift of the guiding center, which can be solved numerically. We derive covariant conservation laws for the motions of the guiding centers and show, through a number of limiting cases, that the equations contain all known drift mechanisms. Finally, we present the general relativistic expressions for the various drift velocities in Schwarzschild spacetimes. △ Less

Submitted 1 April, 2024; originally announced April 2024.

Comments: Submitted to ApJ

Programming the scalable optical learning operator with spatial-spectral optimization

Authors: Yi Zhou, Jih-Liang Hsieh, Ilker Oguz, Mustafa Yildirim, Niyazi Ulas Dinc, Carlo Gigli, Kenneth K. Y. Wong, Christophe Moser, Demetri Psaltis

Abstract: Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical techniques are considered promising solutions to these problems with higher speed than their electronic counterparts and with reduced energy consump… ▽ More Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical techniques are considered promising solutions to these problems with higher speed than their electronic counterparts and with reduced energy consumption. Here, we use the optical reservoir computing framework we have previously described (Scalable Optical Learning Operator or SOLO) to program the spatial-spectral output of the light after nonlinear propagation in a multimode fiber. The novelty in the current paper is that the system is programmed through an output sampling scheme, similar to that used in hyperspectral imaging in astronomy. Linear and nonlinear computations are performed by light in the multimode fiber and the high dimensional spatial-spectral information at the fiber output is optically programmed before it reaches the camera. We then used a digital computer to classify the programmed output of the multi-mode fiber using a simple, single layer network. When combining front-end programming and the proposed spatial-spectral programming, we were able to achieve 89.9% classification accuracy on the dataset consisting of chest X-ray images from COVID-19 patients. At the same time, we obtained a decrease of 99% in the number of tunable parameters compared to an equivalently performing digital neural network. These results show that the performance of programmed SOLO is comparable with cutting-edge electronic computing platforms, albeit with a much-reduced number of electronic operations. △ Less

Submitted 4 March, 2024; originally announced March 2024.

The Origin of the Slow-to-Alfvén Wave Cascade Power Ratio and its Implications for Particle Heating in Accretion Flows

Authors: Kaushik Satapathy, Dimitrios Psaltis, Feryal Özel

Abstract: The partition of turbulent heating between ions and electrons in radiatively inefficient accretion flows plays a crucial role in determining the observational appearance of accreting black holes. Modeling this partition is, however, a challenging problem because of the large scale separation between the macroscopic scales at which energy is injected by turbulence and the microscopic ones at which… ▽ More The partition of turbulent heating between ions and electrons in radiatively inefficient accretion flows plays a crucial role in determining the observational appearance of accreting black holes. Modeling this partition is, however, a challenging problem because of the large scale separation between the macroscopic scales at which energy is injected by turbulence and the microscopic ones at which it is dissipated into heat. Recent studies of particle heating from collisionless damping of turbulent energy have shown that the partition of energy between ions and electrons is dictated by the ratio of the energy injected into the slow and Alfvén wave cascades as well as the plasma $β$ parameter. In this paper, we study the mechanism of the injection of turbulent energy into slow- and Alfvén- wave cascades in magnetized shear flows. We show that this ratio depends on the particular ($rφ$) components of the Maxwell and Reynolds stress tensors that cause the transport of angular momentum, the shearing rate, and the orientation of the mean magnetic field with respect to the shear. We then use numerical magnetohydrodynamic shearing-box simulations with background conditions relevant to black hole accretion disks to compute the magnitudes of the stress tensors for turbulence driven by the magneto-rotational instability and derive the injection power ratio between slow and Alfvén wave cascades. We use these results to formulate a local subgrid model for the ion-to-electron heating ratio that depends on the macroscopic characteristics of the accretion flow. △ Less

Submitted 29 April, 2024; v1 submitted 21 February, 2024; originally announced February 2024.

Comments: Accepted for publication in ApJ

Ordered magnetic fields around the 3C 84 central black hole

Authors: G. F. Paraschos, J. -Y. Kim, M. Wielgus, J. Röder, T. P. Krichbaum, E. Ros, I. Agudo, I. Myserlis, M. Moscibrodzka, E. Traianou, J. A. Zensus, L. Blackburn, C. -K. Chan, S. Issaoun, M. Janssen, M. D. Johnson, V. L. Fish, K. Akiyama, A. Alberdi, W. Alef, J. C. Algaba, R. Anantua, K. Asada, R. Azulay, U. Bach , et al. (258 additional authors not shown)

Abstract: 3C84 is a nearby radio source with a complex total intensity structure, showing linear polarisation and spectral patterns. A detailed investigation of the central engine region necessitates the use of VLBI above the hitherto available maximum frequency of 86GHz. Using ultrahigh resolution VLBI observations at the highest available frequency of 228GHz, we aim to directly detect compact structures a… ▽ More 3C84 is a nearby radio source with a complex total intensity structure, showing linear polarisation and spectral patterns. A detailed investigation of the central engine region necessitates the use of VLBI above the hitherto available maximum frequency of 86GHz. Using ultrahigh resolution VLBI observations at the highest available frequency of 228GHz, we aim to directly detect compact structures and understand the physical conditions in the compact region of 3C84. We used EHT 228GHz observations and, given the limited (u,v)-coverage, applied geometric model fitting to the data. We also employed quasi-simultaneously observed, multi-frequency VLBI data for the source in order to carry out a comprehensive analysis of the core structure. We report the detection of a highly ordered, strong magnetic field around the central, SMBH of 3C84. The brightness temperature analysis suggests that the system is in equipartition. We determined a turnover frequency of $ν_m=(113\pm4)$GHz, a corresponding synchrotron self-absorbed magnetic field of $B_{SSA}=(2.9\pm1.6)$G, and an equipartition magnetic field of $B_{eq}=(5.2\pm0.6)$G. Three components are resolved with the highest fractional polarisation detected for this object ($m_\textrm{net}=(17.0\pm3.9)$%). The positions of the components are compatible with those seen in low-frequency VLBI observations since 2017-2018. We report a steeply negative slope of the spectrum at 228GHz. We used these findings to test models of jet formation, propagation, and Faraday rotation in 3C84. The findings of our investigation into different flow geometries and black hole spins support an advection-dominated accretion flow in a magnetically arrested state around a rapidly rotating supermassive black hole as a model of the jet-launching system in the core of 3C84. However, systematic uncertainties due to the limited (u,v)-coverage, however, cannot be ignored. △ Less

Submitted 1 February, 2024; originally announced February 2024.

Comments: 15 pages, 6 figures, published in A&A

Journal ref: Issue: A&A Volume 682, February 2024; Article number: L3; Number of pages: 15

Multicasting Optical Reconfigurable Switch

Authors: Niyazi Ulas Dinc, Mustafa Yildirim, Ilker Oguz, Christophe Moser, Demetri Psaltis

Abstract: Artificial Intelligence (AI) demands large data flows within datacenters, heavily relying on multicasting data transfers. As AI models scale, the requirement for high-bandwidth and low-latency networking compounds. The common use of electrical packet switching faces limitations due to optical-electrical-optical conversion bottlenecks. Optical switches, while bandwidth-agnostic and low-latency, suf… ▽ More Artificial Intelligence (AI) demands large data flows within datacenters, heavily relying on multicasting data transfers. As AI models scale, the requirement for high-bandwidth and low-latency networking compounds. The common use of electrical packet switching faces limitations due to optical-electrical-optical conversion bottlenecks. Optical switches, while bandwidth-agnostic and low-latency, suffer from having only unicast or non-scalable multicasting capability. This paper introduces an optical switching technique addressing this challenge. Our approach enables arbitrarily programmable simultaneous unicast and multicast connectivity, eliminating the need for optical splitters that hinder scalability due to optical power loss. We use phase modulation in multiple layers, tailored to implement any multicast connectivity map. Phase modulation also enables wavelength selectivity on top of spatial selectivity, resulting in an optical switch that implements space-wavelength routing. We conducted simulations and experiments to validate our approach. Our results affirm the concept's feasibility, effectiveness, and scalability, as a multicasting switch by experimentally demonstrating 16 spatial ports using 2 wavelength channels. Numerically, 64 spatial ports with 4 wavelength channels each were simulated, with approximately constant efficiency (< 3 dB) as ports and wavelength channels scale. △ Less

Submitted 28 February, 2024; v1 submitted 25 January, 2024; originally announced January 2024.

Comments: 17 pages, 4 figures, article

Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor

Authors: Jingtian Hu, Kun Liao, Niyazi Ulas Dinc, Carlo Gigli, Bijie Bai, Tianyi Gan, Xurong Li, Hanlong Chen, Xilin Yang, Yuhang Li, Cagatay Isil, Md Sadman Sakib Rahman, Jingxi Li, Xiaoyong Hu, Mona Jarrahi, Demetri Psaltis, Aydogan Ozcan

Abstract: Phase imaging is widely used in biomedical imaging, sensing, and material characterization, among other fields. However, direct imaging of phase objects with subwavelength resolution remains a challenge. Here, we demonstrate subwavelength imaging of phase and amplitude objects based on all-optical diffractive encoding and decoding. To resolve subwavelength features of an object, the diffractive im… ▽ More Phase imaging is widely used in biomedical imaging, sensing, and material characterization, among other fields. However, direct imaging of phase objects with subwavelength resolution remains a challenge. Here, we demonstrate subwavelength imaging of phase and amplitude objects based on all-optical diffractive encoding and decoding. To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air. The subsequent diffractive decoder layers (in air) are jointly designed with the encoder using deep-learning-based optimization, and communicate with the encoder layer to create magnified images of input objects at its output, revealing subwavelength features that would otherwise be washed away due to diffraction limit. We demonstrate that this all-optical collaboration between a diffractive solid-immersion encoder and the following decoder layers in air can resolve subwavelength phase and amplitude features of input objects in a highly compact design. To experimentally demonstrate its proof-of-concept, we used terahertz radiation and developed a fabrication method for creating monolithic multi-layer diffractive processors. Through these monolithically fabricated diffractive encoder-decoder pairs, we demonstrated phase-to-intensity transformations and all-optically reconstructed subwavelength phase features of input objects by directly transforming them into magnified intensity features at the output. This solid-immersion-based diffractive imager, with its compact and cost-effective design, can find wide-ranging applications in bioimaging, endoscopy, sensing and materials characterization. △ Less

Submitted 16 January, 2024; originally announced January 2024.

Comments: 32 Pages, 9 Figures

Journal ref: eLight (2024)

Volume holograms with linear diffraction efficiency relation by (3+1)D printing

Authors: Niyazi Ulas Dinc, Christophe Moser, Demetri Psaltis

Abstract: We demonstrate the fabrication of volume holograms using 2-photon polymerization with dynamic control of light exposure. We refer to our method as (3+1)D printing. Volume holograms that are recorded by interfering reference and signal beams have a diffraction efficiency relation that is inversely proportional with the square of the number of superimposed holograms. By using (3+1)D printing for fab… ▽ More We demonstrate the fabrication of volume holograms using 2-photon polymerization with dynamic control of light exposure. We refer to our method as (3+1)D printing. Volume holograms that are recorded by interfering reference and signal beams have a diffraction efficiency relation that is inversely proportional with the square of the number of superimposed holograms. By using (3+1)D printing for fabrication, the refractive index of each voxel is created independently and thus by, digitally filtering the undesired interference terms, the diffraction efficiency is now inversely proportional to the number of multiplexed gratings. We experimentally demonstrated this linear dependence by recording M=50 volume gratings. To the best of our knowledge, this is the first experimental demonstration of distributed volume holograms that overcome the 1/M^2 limit. △ Less

Submitted 10 October, 2023; originally announced October 2023.

Comments: 8 pages, 9 figures

A new covariant formalism for kinetic plasma simulations in curved spacetimes

Authors: Tyler Trent, Pierre Christian, Chi-kwan Chan, Dimitrios Psaltis, Feryal Ozel

Abstract: Low density plasmas are characterized by a large scale separation between the gyromotion of particles around local magnetic fields and the macroscopic scales of the system, often making global kinetic simulations computationally intractable. The guiding center formalism has been proposed as a powerful tool to bridge the gap between these scales. Despite its usefulness, the guiding center approach… ▽ More Low density plasmas are characterized by a large scale separation between the gyromotion of particles around local magnetic fields and the macroscopic scales of the system, often making global kinetic simulations computationally intractable. The guiding center formalism has been proposed as a powerful tool to bridge the gap between these scales. Despite its usefulness, the guiding center approach has been formulated successfully only in flat spacetimes, limiting its applicability in astrophysical settings. Here, we present a new covariant formalism that leads to kinetic equations in the guiding center limit that are valid in arbitrary spacetimes. Through a variety of experiments, we demonstrate that our equations capture all known gyro-center drifts while overcoming one severe limitation imposed on numerical algorithms by the fast timescales of the particle gyromotion. This formalism will enable explorations of a variety of global plasma kinetic phenomena in the curved spacetimes around black holes and neutron stars. △ Less

Submitted 13 September, 2023; originally announced September 2023.

Comments: Accepted for publication in ApJL

A search for pulsars around Sgr A* in the first Event Horizon Telescope dataset

Authors: Pablo Torne, Kuo Liu, Ralph P. Eatough, Jompoj Wongphechauxsorn, James M. Cordes, Gregory Desvignes, Mariafelicia De Laurentis, Michael Kramer, Scott M. Ransom, Shami Chatterjee, Robert Wharton, Ramesh Karuppusamy, Lindy Blackburn, Michael Janssen, Chi-kwan Chan, Geoffrey B. Crew, Lynn D. Matthews, Ciriaco Goddi, Helge Rottmann, Jan Wagner, Salvador Sanchez, Ignacio Ruiz, Federico Abbate, Geoffrey C. Bower, Juan J. Salamanca , et al. (261 additional authors not shown)

Abstract: The Event Horizon Telescope (EHT) observed in 2017 the supermassive black hole at the center of the Milky Way, Sagittarius A* (Sgr A*), at a frequency of 228.1 GHz ($λ$=1.3 mm). The fundamental physics tests that even a single pulsar orbiting Sgr A* would enable motivate searching for pulsars in EHT datasets. The high observing frequency means that pulsars - which typically exhibit steep emission… ▽ More The Event Horizon Telescope (EHT) observed in 2017 the supermassive black hole at the center of the Milky Way, Sagittarius A* (Sgr A*), at a frequency of 228.1 GHz ($λ$=1.3 mm). The fundamental physics tests that even a single pulsar orbiting Sgr A* would enable motivate searching for pulsars in EHT datasets. The high observing frequency means that pulsars - which typically exhibit steep emission spectra - are expected to be very faint. However, it also negates pulse scattering, an effect that could hinder pulsar detections in the Galactic Center. Additionally, magnetars or a secondary inverse Compton emission could be stronger at millimeter wavelengths than at lower frequencies. We present a search for pulsars close to Sgr A* using the data from the three most-sensitive stations in the EHT 2017 campaign: the Atacama Large Millimeter/submillimeter Array, the Large Millimeter Telescope and the IRAM 30 m Telescope. We apply three detection methods based on Fourier-domain analysis, the Fast-Folding-Algorithm and single pulse search targeting both pulsars and burst-like transient emission; using the simultaneity of the observations to confirm potential candidates. No new pulsars or significant bursts were found. Being the first pulsar search ever carried out at such high radio frequencies, we detail our analysis methods and give a detailed estimation of the sensitivity of the search. We conclude that the EHT 2017 observations are only sensitive to a small fraction ($\lesssim$2.2%) of the pulsars that may exist close to Sgr A*, motivating further searches for fainter pulsars in the region. △ Less

Submitted 29 August, 2023; originally announced August 2023.

Comments: 33 pages, 7 figures, 6 Tables. Accepted for publication in ApJ

Nonlinear Processing with Linear Optics

Authors: Mustafa Yildirim, Niyazi Ulas Dinc, Ilker Oguz, Demetri Psaltis, Christophe Moser

Abstract: Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical implementation of neural networks aims to harness the advantages of optical bandwidth and the energy efficiency of optical interconnections. In the… ▽ More Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical implementation of neural networks aims to harness the advantages of optical bandwidth and the energy efficiency of optical interconnections. In the absence of low-power optical nonlinearities, the challenge in the implementation of multilayer optical networks lies in realizing multiple optical layers without resorting to electronic components. In this study, we present a novel framework that uses multiple scattering that is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field. Theoretical and experimental investigations show that repeating the data by multiple scattering enables non-linear optical computing at low power continuous wave light. Moreover, we empirically found that scaling of this optical framework follows the power law as in state-of-the-art deep digital networks. △ Less

Submitted 13 February, 2024; v1 submitted 17 July, 2023; originally announced July 2023.

Comments: 15 pages and 5 figures

Forward-Forward Training of an Optical Neural Network

Authors: Ilker Oguz, Junjie Ke, Qifei Wang, Feng Yang, Mustafa Yildirim, Niyazi Ulas Dinc, Jih-Liang Hsieh, Christophe Moser, Demetri Psaltis

Abstract: Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics and spatial light modulators, offer promising avenues for achieving this goal. However, training multiple trainable layers in tandem with these… ▽ More Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations. Optics-based platforms, using technologies such as silicon photonics and spatial light modulators, offer promising avenues for achieving this goal. However, training multiple trainable layers in tandem with these physical systems poses challenges, as they are difficult to fully characterize and describe with differentiable functions, hindering the use of error backpropagation algorithm. The recently introduced Forward-Forward Algorithm (FFA) eliminates the need for perfect characterization of the learning system and shows promise for efficient training with large numbers of programmable parameters. The FFA does not require backpropagating an error signal to update the weights, rather the weights are updated by only sending information in one direction. The local loss function for each set of trainable weights enables low-power analog hardware implementations without resorting to metaheuristic algorithms or reinforcement learning. In this paper, we present an experiment utilizing multimode nonlinear wave propagation in an optical fiber demonstrating the feasibility of the FFA approach using an optical system. The results show that incorporating optical transforms in multilayer NN architectures trained with the FFA, can lead to performance improvements, even with a relatively small number of trainable weights. The proposed method offers a new path to the challenge of training optical NNs and provides insights into leveraging physical transformations for enhancing NN performance. △ Less

Submitted 10 August, 2023; v1 submitted 30 May, 2023; originally announced May 2023.

Global Electron Thermodynamics in Radiatively Inefficient Accretion Flows

Authors: Kaushik Satapathy, Dimitrios Psaltis, Feryal Ozel

Abstract: In the collisionless plasmas of radiatively inefficient accretion flows, heating and acceleration of ions and electrons is not well understood. Recent studies in the gyrokinetic limit revealed the importance of incorporating both the compressive and Alfvenic cascades when calculating the partition of dissipated energy between the plasma species. In this paper, we use a covariant analytic model of… ▽ More In the collisionless plasmas of radiatively inefficient accretion flows, heating and acceleration of ions and electrons is not well understood. Recent studies in the gyrokinetic limit revealed the importance of incorporating both the compressive and Alfvenic cascades when calculating the partition of dissipated energy between the plasma species. In this paper, we use a covariant analytic model of the accretion flow to explore the impact of compressive and Alfvenic heating, Coulomb collisions, compressional heating, and radiative cooling on the radial temperature profiles of ions and electrons. We show that, independent of the partition of heat between the plasma species, even a small fraction of turbulent energy dissipated to the electrons makes their temperature scale with a virial profile and the ion-to-electron temperature ratio smaller than in the case of pure Coulomb heating. In contrast, the presence of compressive cascades makes this ratio larger because compressive turbulent energy is channeled primarily into the ions. We calculate the ion-to-electron temperature in the inner accretion flow for a broad range of plasma properties, mass accretion rates, and black hole spins and show that it ranges between $5 \lesssim T_i/T_e \lesssim 40$. We provide a physically motivated expression for this ratio that can be used to calculate observables from simulations of black hole accretion flows for a wide range of conditions. △ Less

Submitted 10 August, 2023; v1 submitted 20 April, 2023; originally announced April 2023.

Comments: Accepted for publication in ApJ

Black holes in classical general relativity and beyond

Authors: Dimitrios Psaltis

Abstract: The Kerr-Newman metric is the unique vacuum solution of the General Relativistic field equations, in which any singularities or spacetime pathologies are hidden behind horizons. They are believed to describe the spacetimes of massive astrophysical objects with no surfaces, which we call black holes. This spacetime, which is defined entirely by the mass, spin, and charge of the black hole, gives ri… ▽ More The Kerr-Newman metric is the unique vacuum solution of the General Relativistic field equations, in which any singularities or spacetime pathologies are hidden behind horizons. They are believed to describe the spacetimes of massive astrophysical objects with no surfaces, which we call black holes. This spacetime, which is defined entirely by the mass, spin, and charge of the black hole, gives rise to a variety of phenomena in the motion of particles and photons outside the horizons that have no Newtonian counterparts. Moreover, the Kerr-Newman spacetime remains remarkably resilient to many attempts in modifying the underlying theory of gravity. The monitoring of stellar orbits around supermassive black holes, the detection of gravitational waves from the coalescence of stellar-mass black holes, and the observation of black-hole shadows in images with horizon-scale resolution, all of which have become possible during the last decade, are offering valuable tools in testing quantitatively the predictions of this remarkable solution to Einstein's equations. △ Less

Submitted 19 April, 2023; originally announced April 2023.

Comments: This chapter is the pre-print of the version currently in production. Please cite this chapter as the following: D. Psaltis. "Black holes in classical general relativity and beyond" in The Encyclopedia of Cosmology (Set 2): Black Holes, edited by Z. Haiman (World Scientific, New Jersey, 2023)

The Image of the M87 Black Hole Reconstructed with PRIMO

Authors: Lia Medeiros, Dimitrios Psaltis, Tod R. Lauer, Feryal Ozel

Abstract: We present a new reconstruction of the Event Horizon Telescope (EHT) image of the M87 black hole from the 2017 data set. We use PRIMO, a novel dictionary-learning based algorithm that uses high-fidelity simulations of accreting black holes as a training set. By learning the correlations between the different regions of the space of interferometric data, this approach allows us to recover high-fide… ▽ More We present a new reconstruction of the Event Horizon Telescope (EHT) image of the M87 black hole from the 2017 data set. We use PRIMO, a novel dictionary-learning based algorithm that uses high-fidelity simulations of accreting black holes as a training set. By learning the correlations between the different regions of the space of interferometric data, this approach allows us to recover high-fidelity images even in the presence of sparse coverage and reach the nominal resolution of the EHT array. The black hole image comprises a thin bright ring with a diameter of $41.5\pm0.6\,μ$as and a fractional width that is at least a factor of two smaller than previously reported. This improvement has important implications for measuring the mass of the central black hole in M87 based on the EHT images. △ Less

Submitted 12 April, 2023; originally announced April 2023.

Comments: 7 pages, 5 figures

Journal ref: Published in ApJL on April 13th 2023

Comparison of Polarized Radiative Transfer Codes used by the EHT Collaboration

Authors: Ben S. Prather, Jason Dexter, Monika Moscibrodzka, Hung-Yi Pu, Thomas Bronzwaer, Jordy Davelaar, Ziri Younsi, Charles F. Gammie, Roman Gold, George N. Wong, Kazunori Akiyama, Antxon Alberdi, Walter Alef, Juan Carlos Algaba, Richard Anantua, Keiichi Asada, Rebecca Azulay, Uwe Bach, Anne-Kathrin Baczko, David Ball, Mislav Baloković, John Barrett, Michi Bauböck, Bradford A. Benson, Dan Bintley , et al. (248 additional authors not shown)

Abstract: Interpretation of resolved polarized images of black holes by the Event Horizon Telescope (EHT) requires predictions of the polarized emission observable by an Earth-based instrument for a particular model of the black hole accretion system. Such predictions are generated by general relativistic radiative transfer (GRRT) codes, which integrate the equations of polarized radiative transfer in curve… ▽ More Interpretation of resolved polarized images of black holes by the Event Horizon Telescope (EHT) requires predictions of the polarized emission observable by an Earth-based instrument for a particular model of the black hole accretion system. Such predictions are generated by general relativistic radiative transfer (GRRT) codes, which integrate the equations of polarized radiative transfer in curved spacetime. A selection of ray-tracing GRRT codes used within the EHT collaboration is evaluated for accuracy and consistency in producing a selection of test images, demonstrating that the various methods and implementations of radiative transfer calculations are highly consistent. When imaging an analytic accretion model, we find that all codes produce images similar within a pixel-wise normalized mean squared error (NMSE) of 0.012 in the worst case. When imaging a snapshot from a cell-based magnetohydrodynamic simulation, we find all test images to be similar within NMSEs of 0.02, 0.04, 0.04, and 0.12 in Stokes I, Q, U , and V respectively. We additionally find the values of several image metrics relevant to published EHT results to be in agreement to much better precision than measurement uncertainties. △ Less

Submitted 21 March, 2023; originally announced March 2023.

Comments: Accepted for publication in ApJ

Nonlinear Optical Data Transformer for Machine Learning

Authors: Mustafa Yildirim, Ilker Oguz, Fabian Kaufmann, Marc Reig Escale, Rachel Grange, Demetri Psaltis, Christophe Moser

Abstract: Modern machine learning models use an ever-increasing number of parameters to train (175 billion parameters for GPT-3) with large datasets to obtain better performance. Bigger is better has been the norm. Optical computing has been reawakened as a potential solution to large-scale computing through optical accelerators that carry out linear operations while reducing electrical power. However, to a… ▽ More Modern machine learning models use an ever-increasing number of parameters to train (175 billion parameters for GPT-3) with large datasets to obtain better performance. Bigger is better has been the norm. Optical computing has been reawakened as a potential solution to large-scale computing through optical accelerators that carry out linear operations while reducing electrical power. However, to achieve efficient computing with light, creating and controlling nonlinearity optically rather than electronically remains a challenge. This study explores a reservoir computing (RC) approach whereby a 14 mm long few-mode waveguide in LiNbO3 on insulator is used as a complex nonlinear optical processor. A dataset is encoded digitally on the spectrum of a femtosecond pulse which is then launched in the waveguide. The output spectrum depends nonlinearly on the input. We experimentally show that a simple digital linear classifier with 784 parameters using the output spectrum from the waveguide as input increased the classification accuracy of several databases compared to non-transformed data, approximately 10$\%$. In comparison, a deep digital neural network (NN) with 40000 parameters was necessary to achieve the same accuracy. Reducing the number of parameters by a factor of $\sim$50 illustrates that a compact optical RC approach can perform on par with a deep digital NN. △ Less

Submitted 19 August, 2022; originally announced August 2022.

Comments: 13 pages, 3 figures and 1 table

Predicting nonlinear optical scattering with physics-driven neural networks

Authors: Carlo Gigli, Amirhossein Saba, Ahmed Bassam Ayoub, Demetri Psaltis

Abstract: Deep neural networks trained on physical losses are emerging as promising surrogates of nonlinear numerical solvers. These tools can predict solutions of Maxwell's equations and compute gradients of output fields with respect to the material and geometrical properties in millisecond times which makes them attractive for inverse design or inverse scattering applications. Here we develop a tunable v… ▽ More Deep neural networks trained on physical losses are emerging as promising surrogates of nonlinear numerical solvers. These tools can predict solutions of Maxwell's equations and compute gradients of output fields with respect to the material and geometrical properties in millisecond times which makes them attractive for inverse design or inverse scattering applications. Here we develop a tunable version of MaxwellNet, a physics driven neural network able to compute light scattering from inhomogenous media with a size comparable with the incident wavelength in the presence of the optical Kerr effect. The weights of the network are dynamically adjusted to take into account the intensity-dependent refractive index of the material. △ Less

Submitted 21 October, 2022; v1 submitted 11 August, 2022; originally announced August 2022.

Programming Nonlinear Propagation for Efficient Optical Learning Machines

Authors: Ilker Oguz, Jih-Liang Hsieh, Niyazi Ulas Dinc, Uğur Teğin, Mustafa Yildirim, Carlo Gigli, Christophe Moser, Demetri Psaltis

Abstract: The ever-increasing demand for processing data with larger machine learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power computation since light propagation through a non-absorbing medium is a lossless operation. However, to carry out useful and efficient computations with l… ▽ More The ever-increasing demand for processing data with larger machine learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power computation since light propagation through a non-absorbing medium is a lossless operation. However, to carry out useful and efficient computations with light, generating and controlling nonlinearity optically is a necessity that is still elusive. Multimode fibers (MMF) have been shown that they can provide nonlinear effects with microwatts of average power while maintaining parallelism and low loss. In this work, we propose an optical neural network architecture, which performs nonlinear optical computation by controlling the propagation of ultrashort pulses in MMF by wavefront shaping. With a surrogate model, optimal sets of parameters are found to program this optical computer for different tasks with minimal utilization of an electronic computer. We show a remarkable decrease of 97% in the number of model parameters, which leads to an overall 99% digital operation reduction compared to an equivalently performing digital neural network. We further demonstrate that a fully optical implementation can also be performed with competitive accuracies. △ Less

Submitted 9 August, 2022; originally announced August 2022.

Comments: 32 pages, 11 figures

Principal-Component Interferometric Modeling (PRIMO), an Algorithm for EHT Data I: Reconstructing Images from Simulated EHT Observations

Authors: Lia Medeiros, Dimitrios Psaltis, Tod R. Lauer, Feryal Ozel

Abstract: The sparse interferometric coverage of the Event Horizon Telescope (EHT) poses a significant challenge for both reconstruction and model fitting of black-hole images. PRIMO is a new principal components analysis-based algorithm for image reconstruction that uses the results of high-fidelity general relativistic, magnetohydrodynamic simulations of low-luminosity accretion flows as a training set. T… ▽ More The sparse interferometric coverage of the Event Horizon Telescope (EHT) poses a significant challenge for both reconstruction and model fitting of black-hole images. PRIMO is a new principal components analysis-based algorithm for image reconstruction that uses the results of high-fidelity general relativistic, magnetohydrodynamic simulations of low-luminosity accretion flows as a training set. This allows the reconstruction of images that are both consistent with the interferometric data and that live in the space of images that is spanned by the simulations. PRIMO follows Monte Carlo Markov Chains to fit a linear combination of principal components derived from an ensemble of simulated images to interferometric data. We show that PRIMO can efficiently and accurately reconstruct synthetic EHT data sets for several simulated images, even when the simulation parameters are significantly different from those of the image ensemble that was used to generate the principal components. The resulting reconstructions achieve resolution that is consistent with the performance of the array and do not introduce significant biases in image features such as the diameter of the ring of emission. △ Less

Submitted 16 December, 2022; v1 submitted 2 August, 2022; originally announced August 2022.

Comments: Accepted to ApJ on Dec. 12, 2022, 23 pages, 19 figures, replaced with accepted version

Analytic Post-Newtonian Astrometric and Spectroscopic Models of Orbits around Black Holes

Authors: Sóley Ó. Hyman, Dimitrios Psaltis, Feryal Özel

Abstract: Observations of the S-stars, the cluster of young stars in the inner 0.1 pc of the Galactic Center, have been crucial in providing conclusive evidence for a supermassive black hole at the center of our galaxy. Since some of the stars have orbits less than that of a typical human lifetime, it is possible to observe multiple orbits and test the weak-field regime of general relativity. Current calcul… ▽ More Observations of the S-stars, the cluster of young stars in the inner 0.1 pc of the Galactic Center, have been crucial in providing conclusive evidence for a supermassive black hole at the center of our galaxy. Since some of the stars have orbits less than that of a typical human lifetime, it is possible to observe multiple orbits and test the weak-field regime of general relativity. Current calculations of S-star orbits require slow and expensive computations in order to numerically solve geodesic equations for many small time steps. In this paper, we present a computationally efficient, first-order post-Newtonian model for the astrometric and spectroscopic data gathered for the S-stars. We find that future, 30-m class telescopes -- and potentially even current large telescopes with very high spectroscopic resolution -- may be able to detect the Shapiro effect for an S-star in the next decade or so. △ Less

Submitted 26 July, 2023; v1 submitted 2 August, 2022; originally announced August 2022.

Comments: 20 pages, 8 figures, 3 tables. v2: edited to match published ApJ version

Journal ref: ApJ 952 35 (2023)

Physics-informed neural networks for diffraction tomography

Authors: Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis

Abstract: We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster… ▽ More We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical solutions. We evaluate our methodology with numerical and experimental results. Our physics-informed neural networks can be generalized for any forward and inverse scattering problem. △ Less

Submitted 28 July, 2022; originally announced July 2022.

Millimeter light curves of Sagittarius A* observed during the 2017 Event Horizon Telescope campaign

Authors: Maciek Wielgus, Nicola Marchili, Ivan Marti-Vidal, Garrett K. Keating, Venkatessh Ramakrishnan, Paul Tiede, Ed Fomalont, Sara Issaoun, Joey Neilsen, Michael A. Nowak, Lindy Blackburn, Charles F. Gammie, Ciriaco Goddi, Daryl Haggard, Daeyoung Lee, Monika Moscibrodzka, Alexandra J. Tetarenko, Geoffrey C. Bower, Chi-Kwan Chan, Koushik Chatterjee, Paul M. Chesler, Jason Dexter, Sheperd S. Doeleman, Boris Georgiev, Mark Gurwell , et al. (6 additional authors not shown)

Abstract: The Event Horizon Telescope (EHT) observed the compact radio source, Sagittarius A* (Sgr A*), in the Galactic Center on 2017 April 5-11 in the 1.3 millimeter wavelength band. At the same time, interferometric array data from the Atacama Large Millimeter/submillimeter Array and the Submillimeter Array were collected, providing Sgr A* light curves simultaneous with the EHT observations. These data s… ▽ More The Event Horizon Telescope (EHT) observed the compact radio source, Sagittarius A* (Sgr A*), in the Galactic Center on 2017 April 5-11 in the 1.3 millimeter wavelength band. At the same time, interferometric array data from the Atacama Large Millimeter/submillimeter Array and the Submillimeter Array were collected, providing Sgr A* light curves simultaneous with the EHT observations. These data sets, complementing the EHT very-long-baseline interferometry, are characterized by a cadence and signal-to-noise ratio previously unattainable for Sgr A* at millimeter wavelengths, and they allow for the investigation of source variability on timescales as short as a minute. While most of the light curves correspond to a low variability state of Sgr A*, the April 11 observations follow an X-ray flare, and exhibit strongly enhanced variability. All of the light curves are consistent with a red noise process, with a power spectral density (PSD) slope measured to be between -2 and -3 on timescales between 1 min and several hours. Our results indicate a steepening of the PSD slope for timescales shorter than 0.3 h. The spectral energy distribution is flat at 220 GHz and there are no time-lags between the 213 and 229 GHz frequency bands, suggesting low optical depth for the event horizon scale source. We characterize Sgr A*'s variability, highlighting the different behavior observed just after the X-ray flare, and use Gaussian process modeling to extract a decorrelation timescale and a PSD slope. We also investigate the systematic calibration uncertainties by analyzing data from independent data reduction pipelines. △ Less

Submitted 14 July, 2022; originally announced July 2022.

Journal ref: The Astrophysical Journal Letters, 930:L19 (2022)

Single-cell phase-contrast tomograms data encoded by 3D Zernike descriptors

Authors: Pasquale Memmolo, Daniele Pirone, Daniele G. Sirico, Lisa Miccio, Vittorio Bianco, Ahmed B. Ayoub, Demetri Psaltis, Pietro Ferraro

Abstract: Phase-contrast tomographic flow cytometry combines quantitative 3D analysis of unstained single cells and high-throughput. A crucial issue of this method is the storage and management of the huge amount of 3D tomographic data. Here we show an effective quasi lossless compression of tomograms data through 3D Zernike descriptors, unlocking data management tasks and computational pipelines that were… ▽ More Phase-contrast tomographic flow cytometry combines quantitative 3D analysis of unstained single cells and high-throughput. A crucial issue of this method is the storage and management of the huge amount of 3D tomographic data. Here we show an effective quasi lossless compression of tomograms data through 3D Zernike descriptors, unlocking data management tasks and computational pipelines that were unattainable until now. △ Less

Submitted 11 July, 2022; originally announced July 2022.