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Abstract Type IIn supernovae (SNe IIn) are a highly heterogeneous subclass of core-collapse supernovae, spectroscopically characterized by signatures of interaction with a dense circumstellar medium (CSM). Here, we systematically model the light curves of 142 archival SNe IIn using the Modular Open Source Fitter for Transients. We find that the observed and inferred properties of SN IIn are diverse, but there are some trends. The typical supernova CSM is dense (∼10⁻¹²g cm⁻³) with highly diverse CSM geometry, with a median CSM mass of ∼1M_⊙. The ejecta are typically massive (≳10M_⊙), suggesting massive progenitor systems. We find positive correlations between the CSM mass and the rise and fall times of SNe IIn. Furthermore, there are positive correlations between the rise time and fall times and ther-band luminosity. We estimate the mass-loss rates of our sample (where spectroscopy is available) and find a high median mass-loss rate of ∼10⁻²M_⊙yr⁻¹, with a range between 10⁻³and 1M_⊙yr⁻¹. These mass-loss rates are most similar to the mass loss from great eruptions of luminous blue variables, consistent with the direct progenitor detections in the literature. We also discuss the role that binary interactions may play, concluding that at least some of our SNe IIn may be from massive binary systems. Finally, we estimate a detection rate of 1.6 × 10⁵yr⁻¹in the upcoming Legacy Survey of Space and Time at the Vera C. Rubin Observatory. 

Abstract X-ray observing facilities, such as the Chandra X-ray Observatory and the eROSITA, have detected over a million astronomical sources associated with high-energy phenomena. The arrival of photons as a function of time follows a Poisson process and can vary by orders-of-magnitude, presenting obstacles for common tasks such as source classification, physical property derivation, and anomaly detection. Previous work has either failed to directly capture the Poisson nature of the data or only focuses on Poisson rate function reconstruction. In this work, we present the Poisson Process AutoDecoder (PPAD), which is a neural field decoder that maps fixed-length latent features to continuous Poisson rate functions across energy band and time via unsupervised learning. PPAD reconstructs the rate function and yields a representation at the same time. We demonstrate the efficacy of PPAD via reconstruction, regression, classification, and anomaly detection experiments using the Chandra Source Catalog. 

Abstract For over 25 yr, 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 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. 

Abstract 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. 

Abstract With the advent of the Vera C. Rubin Observatory, the discovery rate of supernovae (SNe) will surpass the rate of SNe with real time spectroscopic follow-up by 3 orders of magnitude. Accurate photometric classifiers are essential to both select interesting events for follow-up in real time and for archival population-level studies. In this work, we investigate the impact of observable host-galaxy information on the classification of SNe, both with and without additional light-curve and redshift information. We find that host-galaxy information alone can successfully isolate relatively pure (>90%) samples of Type Ia SNe with or without redshift information. With redshift information, we can additionally produce somewhat pure (>70%) samples of Type II SNe and superluminous SNe. Additionally with redshift information, host-galaxy properties do not significantly improve the accuracy of SN classification when paired with complete light curves. In the absence of redshift information, however, galaxy properties significantly increase the accuracy of photometric classification. As a part of this analysis, we present the first formal application of a new objective function, the weighted hierarchical cross entropy, to the problem of SN classification. This objective function more naturally accounts for the hierarchical nature of SN classes and, more broadly, transients. Finally, we present a new set of SN classifications for the Pan-STARRS Medium Deep Survey of SNe that lack spectroscopic redshift, increasing the full photometric sample to >4400 events. 

Abstract Enhanced emission in the months to years preceding explosion has been detected for several core-collapse supernovae (SNe). Though the physical mechanisms driving the emission remain hotly debated, the light curves of detected events show long-lived (≥50 days), plateau-like behavior, suggesting hydrogen recombination may significantly contribute to the total energy budget. The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will provide a decade-long photometric baseline to search for this emission, both in binned pre-explosion observations after an SN is detected and in single-visit observations prior to the SN explosion. In anticipation of these searches, we simulate a range of eruptive precursor models to core-collapse SNe and forecast the discovery rates of these phenomena in LSST data. We find a detection rate of ∼40–130 yr⁻¹for SN IIP/IIL precursors and ∼110 yr⁻¹for SN IIn precursors in single-epoch photometry. Considering the first three years of observations with the effects of rolling and observing triplets included, this number grows to a total of 150–400 in binned photometry, with the highest number recovered when binning in 100 day bins for 2020tlf-like precursors and in 20 day bins for other recombination-driven models from the literature. We quantify the impact of using templates contaminated by residual light (from either long-lived or separate precursor emission) on these detection rates, and explore strategies for estimating baseline flux to mitigate these issues. Spectroscopic follow-up of the eruptions preceding core-collapse SNe and detected with LSST will offer important clues to the underlying drivers of terminal-stage mass loss in massive stars. 

Abstract 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 att∼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 att= 50 ± 10 days (observer frame) and a SN at <1.5× the optical/near-IR 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 an electron energy spectral indexp> 2 andν_m<ν_c. Our light curves 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 SN. 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 off_ν∝ν^2.0±1.0. 

Abstract We introduce a new, open-source, Python-based package,extrabol, for inferring the bolometric light curve evolution of extragalactic thermal transients.extraboluses non-parametric Gaussian Process regression for light curve estimation that requires minimal user interaction.extrabolis available via GitHub. 

Abstract We present and analyze the extensive optical broadband photometry of the Type II SN 2023ixf up to 1 yr after explosion. We find that, when compared to two preexisting 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 ofMESA+STELLAsingle-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 ( $E_{\exp }\approx 7\times 10^{50}$erg) explosion of an initially high-mass red supergiant progenitor (≳16.5M_⊙) that lost a significant amount of mass in its prior evolution, leaving a low-mass hydrogen envelope (≲3M_⊙) at the time of explosion, with a radius ≳950R_⊙and a synthesized⁵⁶Ni mass of ≈0.068M_⊙. 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. 

Astrophysical transient phenomena are traditionally classified spectroscopically in a hierarchical taxonomy; however, this graph structure is currently not utilized in neural net-based photometric classifiers for time-domain astrophysics. Instead, independent classifiers are trained for different tiers of classified data, and events are excluded if they fall outside of these well-defined but flat classification schemes. Here, we introduce a weighted hierarchical cross-entropy objective function for classification of astrophysical transients. Our method allows users to directly build and use physics- or observationally-motivated tree-based taxonomies. Our weighted hierarchical cross-entropy loss directly uses this graph to accurately classify all targets into any node of the tree, re-weighting imbalanced classes. We test our novel loss on a set of variable stars and extragalactic transients from the Zwicky Transient Facility, showing that we can achieve similar performance to fine-tuned classifiers with the advantage of notably more flexibility in downstream classification tasks.