arXiv subjects

V. Ashley Villar

Publications and source records attributed to V. Ashley Villar.

At least 37 records · Page 2Linked to original sources

arXiv2025

Enabling Early Transient Discovery in LSST via Difference Imaging with DECam

We present SLIDE, a pipeline that enables transient discovery in data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), using archival images from the Dark Energy Camera (DECam) as templates for difference imaging. We apply this pipeline to the recently released Data Preview 1 (DP1; the first public release of Rubin commissioning data) and search for transients in the resulting difference images. The image subtraction, photometry extraction, and transient detection are all performed on the Rubin Science Platform. We demonstrate that SLIDE effectively extracts clean photometry by circumventing poor or missing LSST templates. We identified 29 previously unreported transients, 12 of which would not have been detected based on the DP1 DiaObject catalog. SLIDE will be especially useful for transient analysis in the early years of LSST, when template coverage will be largely incomplete or when templates may be contaminated by transients present at the time of acquisition. We present multiband light curves for a sample of known transients, along with new transient candidates identified through our search. Finally, we discuss the prospects of applying this pipeline during the main LSST survey. Our pipeline is broadly applicable and will support studies of all transients with slowly evolving phases.

astro-ph.HE↗

arXiv2025

Identification and photometric classification of extragalactic transients in the Vera C. Rubin Observatory's Data Preview 1

The Vera C. Rubin Observatory will soon survey the southern sky, delivering a depth and sky coverage that is unprecedented in time domain astronomy. As part of commissioning, Data Preview 1 (DP1) has been released. It comprises a LSSTComCam observing campaign between November and December 2024 with multi-band imaging of seven fields, covering roughly 0.4 square degrees each, providing a first glimpse into the data products that will become available once the Legacy Survey of Space and Time begins. In this work, we search three fields for extragalactic transients. We identify eight new likely supernovae, and three known ones from a sample of 369,644 difference image analysis objects. Photometric classification using Superphot+ assigns sub-classes with >95% confidence to only one SN Ia and one SN II in this sample. Our findings are in agreement with supernova detection rate predictions of $15\pm4$ supernovae from simulations using simsurvey. The supernova detection rate in the data is possibly affected by the lack of suitable templates. Nevertheless, this work demonstrates the quality of the data products delivered in DP1 and indicates that the Rubin Observatory's Legacy Survey of Space and Time (LSST) is well placed to fulfill its discovery potential in time domain astronomy.

astro-ph.HE↗

arXiv2025

Spectral Diversity in Type Ibn Supernovae and the Large Host Offset of SN2024acyl

In this paper, we first present observations of SN~2024acyl, a normal Type Ibn supernova with a large projected offset ($\sim$35~kpc) from its host galaxy. The low star-formation rate measured at the explosion site raises the possibility that the progenitor of SN~2024acyl may not have been a massive star. We then examine, more broadly, the spectral diversity of Type Ibn supernovae around 20--35 days after peak brightness and identify two distinct groups: Group I, which shows bluer rest-frame optical color and narrower He~I emission lines; and Group II, which shows redder rest-frame optical color and broader He~I lines. Group~I also tends to show higher peak luminosities. The diversity we identify appears to be closely connected to the diversity observed around peak and to persist into late phases ($>80$ days after peak). Given its redder color and broader He~I lines, we classify SN~2024acyl as belonging to Group II. Based on the current dataset, we find no clear connection between this spectral diversity and either the host environments of Type Ibn SNe or their pre-explosion activity. The observed diversity in Type Ibn SNe likely reflects differences in circumstellar material properties and/or explosion energetics. These differences could result from a range of progenitor properties, such as different helium star mass, orbital period and companion type if they are in binary systems, and may indicate fundamentally diverse progenitors. Whether a continuous distribution exists between the two groups remains to be determined and will require further data to explore.

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arXiv2025

JWST and Keck Observations of the Off-Nuclear TDE AT 2024tvd: A Massive Nuclear Star Cluster and Minor-Merger Origin for its Black Hole

We present JWST/NIRSpec and NIRCam observations of the first optically selected off-nuclear tidal disruption event (TDE), AT 2024tvd, along with Keck/KCWI integral field unit spectroscopy. The spectra show broad H and He emission lines that are characteristic of a TDE. Stellar kinematics show smooth host-galaxy morphology and ordered bulge rotation, with no evidence of disturbances in velocity, dispersion, age or metallicity space. We construct the first quasi-simultaneous spectral-energy distribution (SED) from X-rays to infrared for a TDE and decompose it into three components: the TDE accretion flow, an unresolved nuclear star cluster (NSC), and heated dust emission. The accretion component implies a black hole mass of $\log(M_\bullet/M_\odot) = 5.50\pm 0.04$, an instantaneous super-Eddington accretion rate of $\log (\dot{M}/M_{\odot} yr^{-1}) = -1.22 \pm 0.04$, and an outer disk photosphere radius of $\log(r_{out}/r_{g}) = 3.8 \pm 0.1$. The dust emission is well described by a blackbody with $T_{dust} = 873\pm 15$ K and peak luminosity $\log (L_{dust}/erg$ $s^{-1}) = 40.80\pm 0.01$, consistent with a dust echo near the sublimation radius. The SED is best fit when including additional stellar emission above the galaxy background at the TDE location, corresponding to $\log(M_{\star}/M_\odot) = 7.97^{+0.16}_{-0.26}$, which we interpret as a massive NSC or an ultra-compact dwarf galaxy. These results support a minor-merger origin for the MBH responsible for the TDE over scenarios involving gravitational recoil or dynamical ejection from the nucleus.

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arXiv2025

GRB 250702B: Discovery of a Gamma-Ray Burst from a Black Hole Falling into a Star

Gamma-ray bursts are the most luminous electromagnetic events in the universe. Their prompt gamma-ray emission has typical durations between a fraction of a second and several minutes. A rare subset of these events have durations in excess of a thousand seconds, referred to as ultra-long gamma-ray bursts. Here, we report the discovery of the longest gamma-ray burst ever seen with a ~25,000 s gamma-ray duration, GRB 250702B, and characterize this event using data from four instruments in the InterPlanetary Network and the Monitor of All-sky X-ray Image. We find a hard spectrum, subsecond variability, and high total energy, which are only known to arise from ultrarelativistic jets powered by a rapidly-spinning stellar-mass central engine. These properties and the extreme duration are together incompatible with all confirmed gamma-ray burst progenitors and nearly all models in the literature. This burst is naturally explained with the helium merger model, where a field binary ends when a black hole falls into a stripped star and proceeds to consume and explode it from within. Under this paradigm, GRB 250702B adds to the growing evidence that helium stars expand and that some ultra-long GRBs have similar evolutionary pathways as collapsars, stellar-mass gravitational wave sources, and potentially rare types of supernovae.

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arXiv2025

One Year of SN 2023ixf: Breaking Through the Degenerate Parameter Space in Light-Curve Models with Pulsating Progenitors

We present and analyze the extensive optical broadband photometry of the Type II SN 2023ixf up to one year after explosion. We find that, when compared to two pre-existing model grids, the bolometric light curve is consistent with drastically different combinations of progenitor and explosion properties. This may be an effect of known degeneracies in Type IIP light-curve models. We independently compute a large grid of $\texttt{MESA+STELLA}$ single-star progenitor and light-curve models with various zero-age main-sequence masses, mass-loss efficiencies, and convective efficiencies. Using the observed progenitor variability as an additional constraint, we select stellar models consistent with the pulsation period and explode them according to previously established scaling laws to match plateau properties. Our hydrodynamic modeling indicates that SN 2023ixf is most consistent with a moderate-energy ($E_{\rm exp}\approx7\times10^{50}$ erg) explosion of an initially high-mass red supergiant progenitor ($\gtrsim 16.5\ M_{\odot}$) that lost a significant amount of mass in its prior evolution, leaving a low-mass hydrogen envelope ($\lesssim 3\ M_{\odot}$) at the time of explosion, with a radius $\gtrsim 950\ R_{\odot}$ and a synthesized $^{56}$Ni mass of $\approx0.068\ M_{\odot}$. We posit that previous mass transfer in a binary system may have stripped the envelope of SN 2023ixf's progenitor. The analysis method with pulsation period presented in this work offers a way to break degeneracies in light-curve modeling in the future, particularly with the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time, when a record of progenitor variability will be more common.

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arXiv2025

Radiative Transfer Modeling of Stripped-Envelope Supernovae. I: A Grid for Ejecta Parameter Inference

We present 1,800 multiwavelength Type Ib/c supernovae light curve models obtained by running the radiation transport code Sedona and varying the mass distribution, velocity profile, and abundance ejecta profiles of helium star progenitors. To create a flexible but physically-informed grid, we use autoencoders to construct a representation of ejecta profiles derived from stellar evolution models. We present simulated nearest-neighbor multiband light curves matches to SN 1994I, SN 2007gr, and iPTF13bvn to demonstrate that realistic light curves can be generated in our grid. We show that the ejecta velocity distribution, in particular, strongly influences the light curve, while variation in Ni-56 alone has a limited impact on the bolometric light curve, even in extreme and unphysical mixing schemes; however, mixing can modestly impact color evolution. Finally, we show that the Ni-56 mass, ejecta mass, and both the magnitude and structure of ejecta velocity distribution can be inferred from the multiband light curves, enabling improved inference over widely used semianalytical models.

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arXiv2025

Joint Modeling of Quasar Variability and Accretion Disk Reprocessing using Latent Stochastic Differential Equations

Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series.

astro-ph.GA↗

arXiv2025

Causal Foundation Models: Disentangling Physics from Instrument Properties

Foundation models for structured time series data must contend with a fundamental challenge: observations often conflate the true underlying physical phenomena with systematic distortions introduced by measurement instruments. This entanglement limits model generalization, especially in heterogeneous or multi-instrument settings. We present a causally-motivated foundation model that explicitly disentangles physical and instrumental factors using a dual-encoder architecture trained with structured contrastive learning. Leveraging naturally occurring observational triplets (i.e., where the same target is measured under varying conditions, and distinct targets are measured under shared conditions) our model learns separate latent representations for the underlying physical signal and instrument effects. Evaluated on simulated astronomical time series designed to resemble the complexity of variable stars observed by missions like NASA's Transiting Exoplanet Survey Satellite (TESS), our method significantly outperforms traditional single-latent space foundation models on downstream prediction tasks, particularly in low-data regimes. These results demonstrate that our model supports key capabilities of foundation models, including few-shot generalization and efficient adaptation, and highlight the importance of encoding causal structure into representation learning for structured data.

cs.LG↗

arXiv2025

SPLASH: A Rapid Host-Based Supernova Classifier for Wide-Field Time-Domain Surveys

The upcoming Legacy Survey of Space and Time (LSST) conducted by the Vera C. Rubin Observatory will detect millions of supernovae (SNe) and generate millions of nightly alerts, far outpacing available spectroscopic resources. Rapid, scalable photometric classification methods are therefore essential for identifying young SNe for follow-up and enabling large-scale population studies. We present SPLASH, a host-based classification pipeline that infers supernova classes using only host galaxy photometry. SPLASH first associates SNe with their hosts (yielding a redshift estimate), then infers host galaxy stellar mass and star formation rate using deep learning, and finally classifies SNe using a random forest trained on these inferred properties, along with host-SN angular separation and redshift. SPLASH achieves a binary (Type Ia vs. core-collapse) classification accuracy of $76\%$ and an F1-score of $69\%$, comparable to other state-of-the-art methods. By selecting only the most confident predictions, SPLASH can return highly pure subsets of all major SN types, making it well-suited for targeted follow-up. Its efficient design allows classification of $\sim 500$ SNe per second, making it ideal for next-generation surveys. Moreover, its intermediate inference step enables selection of transients by host environment, providing a tool not only for classification but also for probing the demographics of stellar death.

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arXiv2025

Multidisciplinary Science in the Multimessenger Era

Astrophysical observations of the cosmos allow us to probe extreme physics and answer foundational questions on our universe. Modern astronomy is increasingly operating under a holistic approach, probing the same question with multiple diagnostics including how sources vary over time, how they appear across the electromagnetic spectrum, and through their other signatures, including gravitational waves, neutrinos, cosmic rays, and dust on Earth. Astrophysical observations are now reaching the point where approximate physics models are insufficient. Key sources of interest are explosive transients, whose understanding requires multidisciplinary studies at the intersection of astrophysics, gravity, nuclear science, plasma physics, fluid dynamics and turbulence, computation, particle physics, atomic, molecular, and optical science, condensed matter and materials science, radiation transport, and high energy density physics. This white paper provides an overview of the major scientific advances that lay at the intersection of physics and astronomy and are best probed through time-domain and multimessenger astrophysics, an exploration of how multidisciplinary science can be fostered, and introductory descriptions of the relevant scientific disciplines and key astrophysical sources of interest.

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arXiv2025

Late-time HST and JWST Observations of GRB 221009A: Evidence for a Break in the Light Curve at 50 Days

GRB 221009A is one of the brightest transients ever observed with the highest peak gamma-ray flux for a gamma-ray burst (GRB). A type Ic-BL supernova (SN), SN 2022xiw, was definitively detected in late-time JWST spectroscopy (t = 195 days, observer-frame). However, photometric studies have found SN 2022xiw to be less luminous (10-70%) than the canonical GRB-SN, SN 1998bw. We present late-time Hubble Space Telescope (HST)/WFC3 and JWST/NIRCam imaging of the afterglow and host galaxy of GRB 221009A at t ~ 185, 277, and 345 days post-trigger. Our joint archival ground, HST, and JWST light curve fits show strong support for a break in the light curve decay slope at t = 50 +/- 10 days (observer-frame) and a supernova at $< 1.5 \times$ the optical/NIR flux of SN 1998bw. This break is consistent with an interpretation as a jet break when requiring slow-cooling electrons in a wind medium with the electron energy spectral index, p > 2, and $ν_m < ν_c$. Our light curve and joint HST/JWST spectral energy distribution (SED) also show evidence for the late-time emergence of a bluer component in addition to the fading afterglow and supernova. We find consistency with the interpretations that this source is either a young, massive, low-metallicity star cluster or a scattered light echo of the afterglow with a SED shape of $f_ν \propto ν^{2.0\pm1.0}$.

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arXiv2024

Rubin ToO 2024: Envisioning the Vera C. Rubin Observatory LSST Target of Opportunity program

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

astro-ph.IM↗

arXiv2024

Explaining Non-Merger Gamma-Ray Bursts and Broad-Lined Supernovae with Close Binary Progenitors with Black Hole Central Engine

For over 25 years, the origin of long-duration gamma-ray bursts (lGRBs) has been linked to the collapse of rotating massive stars. However, we have yet to pinpoint the stellar progenitor powering these transients. Moreover, the dominant engine powering the explosions remains open to debate. Observations of both lGRBs, supernovae associated with these GRBs, such as broad-line (BL) stripped-envelope (type Ic) supernovae (hereafter, Ic-BL) supernovae (SNe) and perhaps superluminous SNe, fast blue optical transients, and fast x-ray transients, may provide clues to both engines and progenitors. In this paper, we conduct a detailed study of the tight-binary formation scenario for lGRBs, comparing this scenario to other leading progenitor models. Combining this progenitor scenario with different lGRB engines, we can compare to existing data and make predictions for future observational tests. We find that the combination of the tight-binary progenitor scenario with the black hole accretion disk (BHAD) engine can explain lGRBs, low-luminosity GRBs, ultra-long GRBs, and Ic-BL. We discuss the various progenitor properties required for these different subclasses and note such systems would be future gravitational wave merger sources. We show that the current literature on other progenitor-engine scenarios cannot explain all of these transient classes with a single origin, motivating additional work. We find that the tight-binary progenitor with a magnetar engine is excluded by existing observations. The observations can be used to constrain the properties of stellar evolution, the nature of the GRB and the associated SN engines in lGRBs and Ic-BL. We discuss the future observations needed to constrain our understanding of these rare, but powerful, explosions.

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arXiv2024

PS1-11aop: Probing the Mass Loss History of a Luminous Interacting Supernova Prior to its Final Eruption with Multi-wavelength Observations

Luminous interacting supernovae are a class of stellar explosions whose progenitors underwent vigorous mass loss in the years prior to core-collapse. While the mechanism by which this material is ejected is still debated, obtaining the full density profile of the circumstellar medium (CSM) could reveal more about this process. Here, we present an extensive multi-wavelength study of PS1-11aop, a luminous and slowly declining Type IIn SN discovered by the PanSTARRS Medium Deep Survey. PS1-11aop had a peak r-band magnitude of $-$20.5\,mag, a total radiated energy $>$ 8$\times$10$^{50}$\,erg, and it exploded near the center of a star-forming galaxy with super-solar metallicity. We obtained multiple detections at the location of PS1-11aop in the radio and X-ray bands between 4 and 10\,years post-explosion, and if due to the SN, it is one of the most luminous radio supernovae identified to date. Taken together, the multiwavelength properties of PS1-11aop are consistent with a CSM density profile with multiple zones. The early optical emission is consistent with the supernova blastwave interacting with a dense and confined CSM shell which contains multiple solar masses of material that was likely ejected in the final $<$10-100 years prior to the explosion,($\sim$0.05$-$1.0 M$_{\odot}$yr$^{-1}$ at radii of $\lesssim$10$^{16}$\,cm). The radio observations, on the other hand, are consistent with a sparser environment ($\lesssim$2$\times 10^{-3}$ M$_{\odot}$yr$^{-1}$ at radii of $\sim$0.5-1$\times$10$^{17}$\,cm) -- thus probing the history of the progenitor star prior to its final mass loss episode.

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arXiv2024

Kilonova Light-Curve Interpolation with Neural Networks

Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multi-band inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle.

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arXiv2024

Maven: A Multimodal Foundation Model for Supernova Science

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.

astro-ph.HE↗

arXiv2024

Constraining Dust Formation in the Superluminous Supernova 2017gci with JWST Observations

We present JWST/MIRI observations of the Type I superluminous supernova (SLSN) 2017gci taken over 2000 rest-frame days after the supernova (SN) exploded, which represent the latest phase images taken of any known SLSN. We find that archival \WISE detections of SN\,2017gci taken 70 to 200 days after explosion are most likely explained by an IR dust echo from a $\sim 3 \times 10^{-4}$ M$_\odot$ shell of pre-existing dust, as opposed to freshly-formed dust. New JWST observations reveal IR emission in the field of SN\,2017gci, which we determine is most likely dominated by the host galaxy of the SN, based on the expected flux of the galaxy and the measurable separation between said emission and the location of the SN. Based on models for IR emission of carbonate dust, we place a $3σ$ upper limit of $0.83$ M$_\odot$ of dust formed in SN\,2017gci, with a lowest $1σ$ limit of $0.44$ M$_\odot$. Infrared (IR) detections of other SLSNe have suggested that SLSNe could be among the most efficient dust producers in the universe. Our results suggest that SLSNe do not necessarily form more dust than other types of SNe, but instead might have a more accelerated dust formation process. More IR observations of a larger sample of SLSNe will be required to determine how efficient dust production is in SLSNe.

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