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JWST Reveals Large Reservoirs of Dust and Ongoing Circumstellar Interaction in SN Ibn/Icn 2023xgo over a Year Post-Explosion
Authors:
Kyle W. Davis,
Kirsty Taggart,
Samaporn Tinyanont,
Ryan J. Foley,
Jeonghee Rho,
Katie Auchettl,
Diego Farias,
Ori D. Fox,
Joel Johansson,
Charles D. Kilpatrick,
Kishore C. Patra,
Craig Pellegrino,
Enrico Ramirez-Ruiz,
David A. Coulter,
Yize Dong,
Alexander T. Gagliano,
T. R. Geballe,
Wynn V. Jacobson-Galán,
Jenna Karcheski,
Ravjit Kaur,
Ryan M. Lau,
Thomas Moore,
Seong Hyun Park,
Armin Rest,
Tamás Szalai
, et al. (1 additional authors not shown)
Abstract:
We present infrared (IR) photometric and spectroscopic observations of SN 2023xgo, a recent and nearby Type Ibn/Icn supernova (SN Ibn/Icn) which shows shock interaction with a He/C-rich and H-poor circumstellar medium (CSM). Although interacting SNe are predicted to produce large amounts of dust, the rarity of SNe Ibn and Icn has resulted in few opportunities to observe these objects in the IR at…
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We present infrared (IR) photometric and spectroscopic observations of SN 2023xgo, a recent and nearby Type Ibn/Icn supernova (SN Ibn/Icn) which shows shock interaction with a He/C-rich and H-poor circumstellar medium (CSM). Although interacting SNe are predicted to produce large amounts of dust, the rarity of SNe Ibn and Icn has resulted in few opportunities to observe these objects in the IR at late times. Here, we report observations of SN 2023xgo from JWST (NIRSpec and MIRI), WISE, and Gemini taken out to +377 days post-explosion. At +377 days, the JWST spectrum is consistent with both emission from cool (~300-600 K) silicate dust with $M \gtrsim 3 \times 10^{-2}$ M$_{\odot}$ at a radius similar to the shock radius ($2.3 \times 10^{16}$ cm), and optically thin carbonaceous dust with $M = 8 \times 10^{-3}$ M$_{\odot}$. We also detect narrow (FWHM = 520+/-130 km s$^{-1}$) He I $λ$2.06 micron emission at +377 days, indicating that the SN shock continues to encounter material shed from the star to this late epoch. The emission line is blueshifted from the rest frame by 340+/-40 km s$^{-1}$. The Gemini and WISE observations at ~70-100 days reveal emission from 6.8$\times$10$^{-5}$ M$_{\odot}$ of hot (~1300 K) dust, which we interpret as a lower limit of the total dust mass at that phase. Molecular gas emission is not detected in any data, though emission line profiles in the optical and NIR taken at ~70 days after explosion show progressively less redshifted emission, attributed to attenuation from dust and suggesting that some dust is rapidly forming interior to the unshocked CSM. The large dust mass and rapid onset of dust formation observed in SN 2023xgo show that the unique physical environments of SNe Ibn/Icn facilitate substantial dust formation both before and after the SN.
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Submitted 29 May, 2026;
originally announced June 2026.
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Evidence for Environmental Stripping in the Coma Cluster
Authors:
Richard T. Pomeroy,
Juan P. Madrid,
Conor R. O'Neill,
Alexander T. Gagliano
Abstract:
The stability and longevity of globular clusters (GCs) make them effective tracers of the dynamical histories of galaxies in cluster environments. We construct a catalog of 23,351 GC candidates in the Coma cluster using imaging from the Hubble Space Telescope Advanced Camera for Surveys. We cross-match galaxy data from the SIMBAD, NED, and SDSS archives to construct a galaxy sample and model their…
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The stability and longevity of globular clusters (GCs) make them effective tracers of the dynamical histories of galaxies in cluster environments. We construct a catalog of 23,351 GC candidates in the Coma cluster using imaging from the Hubble Space Telescope Advanced Camera for Surveys. We cross-match galaxy data from the SIMBAD, NED, and SDSS archives to construct a galaxy sample and model their GC populations using the GC specific frequency. We find several galaxies with significantly smaller GC populations than expected from their luminosities, consistent with either tidal stripping or intrinsically low formation efficiencies. We analyze annular and Voronoi GC radial profiles of the BCGs (NGC 4874 and NGC 4889) and other Coma galaxies. A 2D Voronoi density mapping reveals GC populations with marked deficits compared to our modeled expectations, including galaxies in proximity to the BCGs (e.g., IC 3998, NGC 4875, NGC 4876) and others distributed across Coma (e.g., NGC~4908, NGC~4883, IC~4042). Azimuthal symmetry testing suggests past dynamical interactions may have truncated GC systems in some galaxies, while intrinsic deficits are probable in others (e.g., IC 3973, IC 3976, IC 4040, IC 4045). Our results show that GC deficits exist in several Coma galaxies and that the 2D density structure reveals environmental signatures, with asymmetry statistics consistent with directional stripping. These findings highlight GC populations as powerful probes of environmental processing and the dynamical histories of galaxies in dense cluster environments.
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Submitted 17 April, 2026;
originally announced April 2026.
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Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
Authors:
LSST Dark Energy Science Collaboration,
Eric Aubourg,
Camille Avestruz,
Matthew R. Becker,
Biswajit Biswas,
Rahul Biswas,
Boris Bolliet,
Adam S. Bolton,
Clecio R. Bom,
Raphaël Bonnet-Guerrini,
Alexandre Boucaud,
Jean-Eric Campagne,
Chihway Chang,
Aleksandra Ćiprijanović,
Johann Cohen-Tanugi,
Michael W. Coughlin,
John Franklin Crenshaw,
Juan C. Cuevas-Tello,
Juan de Vicente,
Seth W. Digel,
Steven Dillmann,
Mariano Javier de León Dominguez Romero,
Alex Drlica-Wagner,
Sydney Erickson,
Alexander T. Gagliano
, et al. (41 additional authors not shown)
Abstract:
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful…
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The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.
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Submitted 20 January, 2026;
originally announced January 2026.
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Minuet: A Diffusion Autoencoder for Compact Semantic Compression of Multi-Band Galaxy Images
Authors:
Alexander T. Gagliano,
Yunyi Shen,
V. A. Villar
Abstract:
The Vera C. Rubin Observatory is slated to observe nearly 20 billion galaxies during its decade-long Legacy Survey of Space and Time. The rich imaging data it collects will be an invaluable resource for probing galaxy evolution across cosmic time, characterizing the host galaxies of transient phenomena, and identifying novel populations of anomalous systems. While machine learning models have show…
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The Vera C. Rubin Observatory is slated to observe nearly 20 billion galaxies during its decade-long Legacy Survey of Space and Time. The rich imaging data it collects will be an invaluable resource for probing galaxy evolution across cosmic time, characterizing the host galaxies of transient phenomena, and identifying novel populations of anomalous systems. While machine learning models have shown promise for extracting galaxy features from multi-band astronomical imaging, the large dimensionality of the learned latent space presents a challenge for mechanistic interpretability studies. In this work, we present Minuet, a low-dimensional diffusion autoencoder for multi-band galaxy imaging. Minuet is trained to reconstruct 72x72-pixel $grz$ image cutouts of 6M galaxies within $z<1$ from the Dark Energy Camera Legacy Survey using only five latent dimensions. By using a diffusion model conditioned on the transformer-based autoencoder's output for image reconstruction, we achieve semantically-meaningful latent representations of galaxy images while still allowing for high-fidelity, probabilistic reconstructions. We train a series of binary classifiers on Minuet's latent features to quantify their connection to morphological labels from Galaxy Zoo, and a conditional flow to produce posterior distributions of SED-derived redshifts, stellar masses, and star-formation rates. We further show the value of Minuet for nearest neighbor searches in the learned latent space. Minuet provides strong evidence for the low intrinsic dimensionality of galaxy imaging, and introduces a class of astrophysical models that produce highly compact representations for diverse science goals.
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Submitted 3 December, 2025;
originally announced December 2025.
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Optimizing Kilonova Searches: A Case Study of the Type IIb SN 2025ulz in the Localization Volume of the Low-Significance Gravitational Wave Event S250818k
Authors:
Noah Franz,
Bhagya Subrayan,
Charles D. Kilpatrick,
Griffin Hosseinzadeh,
David J. Sand,
Kate D. Alexander,
Wen-fai Fong,
Collin T. Christy,
Jeniveve Pearson,
Tanmoy Laskar,
Brian Hsu,
Jillian Rastinejad,
Michael J. Lundquist,
Edo Berger,
K. Azalee Bostroem,
Clecio R. Bom,
Phelipe Darc,
Mark Gurwell,
Shelbi Hostler Schimpf,
Garrett K. Keating,
Phillip Noel,
Conor Ransome,
Ramprasad Rao,
Luidhy Santana-Silva,
A. Souza Santos
, et al. (32 additional authors not shown)
Abstract:
Kilonovae, the ultraviolet/optical/infrared counterparts to binary neutron star mergers, are an exceptionally rare class of transients. Optical follow-up campaigns are plagued by contaminating transients, which may mimic kilonovae, but do not receive sufficient observations to measure the full photometric evolution. In this work, we present an analysis of the multi-wavelength dataset of supernova…
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Kilonovae, the ultraviolet/optical/infrared counterparts to binary neutron star mergers, are an exceptionally rare class of transients. Optical follow-up campaigns are plagued by contaminating transients, which may mimic kilonovae, but do not receive sufficient observations to measure the full photometric evolution. In this work, we present an analysis of the multi-wavelength dataset of supernova (SN) 2025ulz, a proposed kilonova candidate following the low-significance detection of gravitational waves originating from the potential binary neutron star merger S250818k. Despite an early rapid decline in brightness, our multi-wavelength observations of SN 2025ulz reveal that it is a type IIb supernova. As part of this analysis, we demonstrate the capabilities of a novel quantitative scoring algorithm to determine the likelihood that a transient candidate is a kilonova, based primarily on its 3D location and light curve evolution. We also apply our scoring algorithm to other transient candidates in the localization volume of S250818k and find that, at all times after the discovery of SN 2025ulz, there are $\geq 4$ candidates with a score comparable to SN 2025ulz, indicating that the kilonova search may have benefited from the additional follow-up of other candidates. During future kilonova searches, this type of scoring algorithm will be useful to rule out contaminating transients in real time, optimizing the use of valuable telescope resources.
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Submitted 25 October, 2025; v1 submitted 19 October, 2025;
originally announced October 2025.
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Hierarchical Simulation-Based Inference of Supernova Power Sources and their Physical Properties
Authors:
Edgar P. Vidal,
Alexander T. Gagliano,
Carolina Cuesta-Lazaro
Abstract:
Time domain surveys such as the Vera C. Rubin Observatory are projected to annually discover millions of astronomical transients. This and complementary programs demand fast, automated methods to constrain the physical properties of the most interesting objects for spectroscopic follow up. Traditional approaches to likelihood-based inference are computationally expensive and ignore the multi-compo…
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Time domain surveys such as the Vera C. Rubin Observatory are projected to annually discover millions of astronomical transients. This and complementary programs demand fast, automated methods to constrain the physical properties of the most interesting objects for spectroscopic follow up. Traditional approaches to likelihood-based inference are computationally expensive and ignore the multi-component energy sources powering astrophysical phenomena. In this work, we present a hierarchical simulation-based inference model for multi-band light curves that 1) identifies the energy sources powering an event of interest, 2) infers the physical properties of each subclass, and 3) separates physical anomalies in the learned embedding space. Our architecture consists of a transformer-based light curve summarizer coupled to a flow-matching regression module and a categorical classifier for the physical components. We train and test our model on $\sim$150k synthetic light curves generated with $\texttt{MOSFiT}$. Our network achieves a 90% classification accuracy at identifying energy sources, yields well-calibrated posteriors for all active components, and detects rare anomalies such as tidal disruption events (TDEs) through the learned latent space. This work demonstrates a scalable joint framework for population studies of known transients and the discovery of novel populations in the era of Rubin.
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Submitted 15 October, 2025;
originally announced October 2025.
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Mixture-of-Expert Variational Autoencoders for Cross-Modality Embedding of Type Ia Supernova Data
Authors:
Yunyi Shen,
Alexander T. Gagliano
Abstract:
Time-domain astrophysics relies on heterogeneous and multi-modal data. Specialized models are often constructed to extract information from a single modality, but this approach ignores the wealth of cross-modality information that may be relevant for the tasks to which the model is applied. In this work, we propose a multi-modal, mixture-of-expert variational autoencoder to learn a joint embedding…
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Time-domain astrophysics relies on heterogeneous and multi-modal data. Specialized models are often constructed to extract information from a single modality, but this approach ignores the wealth of cross-modality information that may be relevant for the tasks to which the model is applied. In this work, we propose a multi-modal, mixture-of-expert variational autoencoder to learn a joint embedding for supernova light curves and spectra. Our method, which is inspired by the Perceiver architecture, natively accommodates variable-length inputs and the irregular temporal sampling inherent to supernova light curves. We train our model on radiative transfer simulations and validate its performance on cross-modality reconstruction of supernova spectra and physical parameters from the simulation. Our model achieves superior performance in cross-modality generation to nearest-neighbor searches in a contrastively-trained latent space, showing its promise for constructing informative latent representations of multi-modal astronomical datasets.
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Submitted 22 July, 2025;
originally announced July 2025.
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A Wide Field Map of Ultra-Compact Dwarfs in the Coma Cluster
Authors:
Richard T. Pomeroy,
Juan P. Madrid,
Conor R. O'Neill,
Alexander T. Gagliano
Abstract:
A dataset of 23,351 globular clusters (GCs) and ultra-compact dwarfs (UCDs) in the Coma cluster of galaxies was built using Hubble Space Telescope Advanced Camera for Surveys data. Based on the standard magnitude cut of $M_V \leq -11$, a total of 523 UCD candidates are found within this dataset of Compact Stellar Systems (CSS). From a color-magnitude diagram (CMD) analysis built using this catalog…
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A dataset of 23,351 globular clusters (GCs) and ultra-compact dwarfs (UCDs) in the Coma cluster of galaxies was built using Hubble Space Telescope Advanced Camera for Surveys data. Based on the standard magnitude cut of $M_V \leq -11$, a total of 523 UCD candidates are found within this dataset of Compact Stellar Systems (CSS). From a color-magnitude diagram (CMD) analysis built using this catalog, we find a clear mass-magnitude relation extending marginally into the UCD parameter space. The luminosity function defined by this dataset, shows an excess of sources at bright magnitudes, suggesting a bimodal formation scenario for UCDs. We estimate the number of UCDs with a different origin than GC to be $N_{UCD} \geq 32 \pm 1$. We derive the total number of CSS within the core (1 Mpc) of Coma to be $N_{CSS} \approx 69,400 \pm 1400$. The radial distribution of UCDs in Coma shows that, like GCs, UCDs agglomerate around three giant ellipticals: NGC 4874, NGC 4889, and IC 4051. We find UCDs are more centrally concentrated around these three ellipticals than GCs. IC 4051 has a satellite population of UCDs similar to NGC 4874 and NGC 4889. We estimate only ~14% of UCDs, inhabit the intracluster space (ICUCD) between galaxies in the region, in comparison to ~24% for GCs (ICGC). We find red (metal-rich) UCDs are more likely located closer to a host galaxy, with blue (metal-poor) UCDs showing a greater dispersion and lower average density in the region.
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Submitted 9 July, 2025; v1 submitted 2 June, 2025;
originally announced June 2025.
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Variational diffusion transformers for conditional sampling of supernovae spectra
Authors:
Yunyi Shen,
Alexander T. Gagliano
Abstract:
Type Ia Supernovae (SNe Ia) have become the most precise distance indicators in astrophysics due to their incredible observational homogeneity. Increasing discovery rates, however, have revealed multiple sub-populations with spectroscopic properties that are both diverse and difficult to interpret using existing physical models. These peculiar events are hard to identify from sparsely sampled obse…
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Type Ia Supernovae (SNe Ia) have become the most precise distance indicators in astrophysics due to their incredible observational homogeneity. Increasing discovery rates, however, have revealed multiple sub-populations with spectroscopic properties that are both diverse and difficult to interpret using existing physical models. These peculiar events are hard to identify from sparsely sampled observations and can introduce systematics in cosmological analyses if not flagged early; they are also of broader importance for building a cohesive understanding of thermonuclear explosions. In this work, we introduce DiTSNe-Ia, a variational diffusion-based generative model conditioned on light curve observations and trained to reproduce the observed spectral diversity of SNe Ia. In experiments with realistic light curves and spectra from radiative transfer simulations, DiTSNe-Ia achieves significantly more accurate reconstructions than the widely used SALT3 templates across a broad range of observation phases (from 10 days before peak light to 30 days after it). DiTSNe-Ia yields a mean squared error of 0.108 across all phases-five times lower than SALT3's 0.508-and an after-peak error of just 0.0191, an order of magnitude smaller than SALT3's 0.305. Additionally, our model produces well-calibrated credible intervals with near-nominal coverage, particularly at post-peak phases. DiTSNe-Ia is a powerful tool for rapidly inferring the spectral properties of SNe Ia and other transient astrophysical phenomena for which a physical description does not yet exist.
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Submitted 5 May, 2025;
originally announced May 2025.
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Maven: A Multimodal Foundation Model for Supernova Science
Authors:
Gemma Zhang,
Thomas Helfer,
Alexander T. Gagliano,
Siddharth Mishra-Sharma,
V. Ashley Villar
Abstract:
A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. A…
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A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5M synthetic supernovae using a constrastive objective. We then fine-tune the model on 4,702 observed supernovae from the Zwicky Transient Facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin Observatory, Maven serves as a Rosetta Stone for leveraging large, unlabeled and multimodal time-domain datasets.
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Submitted 29 August, 2024;
originally announced August 2024.
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SN 2024ggi in NGC 3621: Rising Ionization in a Nearby, CSM-Interacting Type II Supernova
Authors:
W. V. Jacobson-Galán,
K. W. Davis,
C. D. Kilpatrick,
L. Dessart,
R. Margutti,
R. Chornock,
R. J. Foley,
P. Arunachalam,
K. Auchettl,
C. R. Bom,
R. Cartier,
D. A. Coulter,
G. Dimitriadis,
D. Dickinson,
M. R. Drout,
A. T. Gagliano,
C. Gall,
B. Garretson,
L. Izzo,
D. O. Jones,
N. LeBaron,
H. -Y. Miao,
D. Milisavljevic,
Y. -C. Pan,
A. Rest
, et al. (6 additional authors not shown)
Abstract:
We present UV/optical/NIR observations and modeling of supernova (SN) 2024ggi, a type II supernova (SN II) located in NGC 3621 at 7.2 Mpc. Early-time ("flash") spectroscopy of SN 2024ggi within +0.8 days of discovery shows emission lines of H I, He I, C III, and N III with a narrow core and broad, symmetric wings (i.e., IIn-like) arising from the photoionized, optically-thick, unshocked circumstel…
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We present UV/optical/NIR observations and modeling of supernova (SN) 2024ggi, a type II supernova (SN II) located in NGC 3621 at 7.2 Mpc. Early-time ("flash") spectroscopy of SN 2024ggi within +0.8 days of discovery shows emission lines of H I, He I, C III, and N III with a narrow core and broad, symmetric wings (i.e., IIn-like) arising from the photoionized, optically-thick, unshocked circumstellar material (CSM) that surrounded the progenitor star at shock breakout. By the next spectral epoch at +1.5 days, SN 2024ggi showed a rise in ionization as emission lines of He II, C IV, N IV/V and O V became visible. This phenomenon is temporally consistent with a blueward shift in the UV/optical colors, both likely the result of shock breakout in an extended, dense CSM. The IIn-like features in SN 2024ggi persist on a timescale of $t_{\rm IIn} = 3.8 \pm 1.6$ days at which time a reduction in CSM density allows the detection of Doppler broadened features from the fastest SN material. SN 2024ggi has peak UV/optical absolute magnitudes of $M_{\rm w2} = -18.7$ mag and $M_{\rm g} = -18.1$ mag that are consistent with the known population of CSM-interacting SNe II. Comparison of SN 2024ggi with a grid of radiation hydrodynamics and non-local thermodynamic equilibrium (nLTE) radiative-transfer simulations suggests a progenitor mass-loss rate of $\dot{M} = 10^{-2}$M$_{\odot}$ yr$^{-1}$ ($v_w$ = 50 km/s), confined to a distance of $r < 5\times 10^{14}$ cm. Assuming a wind velocity of $v_w$ = 50 km/s, the progenitor star underwent an enhanced mass-loss episode in the last ~3 years before explosion.
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Submitted 25 June, 2024; v1 submitted 29 April, 2024;
originally announced April 2024.
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A wide field map of intracluster globular clusters in Coma
Authors:
Juan P. Madrid,
Conor R. O'Neill,
Alexander T. Gagliano,
Joshua R. Marvil
Abstract:
The large-scale distribution of globular clusters in the central region of the Coma cluster of galaxies is derived through the analysis of Hubble Space Telescope/Advanced Camera for Surveys data. Data from three different HST observing programs are combined in order to obtain a full surface density map of globular clusters in the core of Coma. A total of 22,426 Globular cluster candidates were sel…
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The large-scale distribution of globular clusters in the central region of the Coma cluster of galaxies is derived through the analysis of Hubble Space Telescope/Advanced Camera for Surveys data. Data from three different HST observing programs are combined in order to obtain a full surface density map of globular clusters in the core of Coma. A total of 22,426 Globular cluster candidates were selected through a detailed morphological inspection and the analysis of their magnitude and colors in two wavebands, F475W (Sloan g) and F814W (I). The spatial distribution of globular clusters defines three main overdensities in Coma that can be associated with NGC 4889, NGC 4874, and IC 4051 but have spatial scales five to six times larger than individual galaxies. The highest surface density of globular clusters in Coma is spatially coincidental with NGC 4889. The most extended overdensity of globular clusters is associated with NGC 4874. Intracluster globular clusters also form clear bridges between Coma galaxies. Red globular clusters, which agglomerate around the center of the three main subgroups, reach higher surface densities than blue ones.
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Submitted 28 November, 2018;
originally announced November 2018.