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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 We presentSLIDE, 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 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 thatSLIDEeffectively 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 DP1DiaObjectcatalog.SLIDEwill 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. 

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 We present ultraviolet to infrared observations of the extraordinary Type IIn supernova 2023zkd (SN 2023zkd). Photometrically, it exhibits persistent and luminous precursor emission spanning ∼4 yr preceding discovery (M_r ≈ −15 mag, 1500 days in the observer frame), followed by a secondary stage of gradual brightening in its final year. Post-discovery, it exhibits two photometric peaks of comparable brightness (M_r ≲ −18.7 mag andM_r ≈ −18.4 mag, respectively) separated by 240 days. Spectroscopically, SN 2023zkd exhibits highly asymmetric and multicomponent Balmer and HeIprofiles that we attribute to ejecta interaction with fast-moving (1000–2000 km s⁻¹) He-rich polar material and slow-moving (∼400 km s⁻¹) equatorially distributed H-rich material. HeIIfeatures also appear during the second light curve peak and evolve rapidly. Shock-driven models fit to the multiband photometry suggest that the event is powered by interaction with ∼5–6M_⊙of CSM, with 2–3M_⊙associated with each light curve peak, expelled during mass-loss episodes ∼3–4 yr and ∼1–2 yr prior to explosion. The observed precursor emission, combined with the extreme mass-loss rates required to power each light curve peak, favors either super-Eddington accretion onto a black hole or multiple long-lived eruptions from a massive star to luminosities that have not been previously observed. We consider multiple progenitor scenarios for SN 2023zkd, and find that the brightening optical precursor and inferred explosion properties are most consistent with a massive (M_ZAMS≥ 30M_⊙) and partially stripped He star undergoing an instability-induced merger with a black hole companion. 

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 Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averagedF₁-score of 0.61 ± 0.02 and a total accuracy of 0.83 ± 0.01. Including redshift information improves these metrics to 0.71 ± 0.02 and 0.88 ± 0.01, respectively. We assign new class probabilities to 3558 ZTF transients that show SN-like characteristics (based on the ALeRCE Broker light-curve and stamp classifiers) but lack spectroscopic classifications. Finally, we compare our predicted SN labels with those generated by the ALeRCE light-curve classifier, finding that the two classifiers agree on photometric labels for 82% ± 2% of light curves with spectroscopic labels and 72% ± 0% of light curves without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in real time via the ANTARES Broker, and is designed for simple adaptation to six-band Rubin light curves in the future. 

Abstract Identifying the sites of r-process nucleosynthesis, a primary mechanism of heavy element production, is a key goal of astrophysics. The discovery of the brightest gamma-ray burst (GRB) to date, GRB 221009A, presented an opportunity to spectroscopically test the idea that r-process elements are produced following the collapse of rapidly rotating massive stars. Here we present James Webb Space Telescope observations of GRB 221009A obtained +168 and +170 rest-frame days after the gamma-ray trigger, and demonstrate that they are well described by a SN 1998bw-like supernova (SN) and power-law afterglow, with no evidence for a component from r-process emission. The SN, with a nickel mass of approximately 0.09 M_⊙, is only slightly fainter than the brightness of SN 1998bw at this phase, which indicates that the SN is not an unusual GRB-SN. This demonstrates that the GRB and SN mechanisms are decoupled and that highly energetic GRBs are not likely to produce significant quantities of r-process material, which leaves open the question of whether explosions of massive stars are key sources of r-process elements. Moreover, the host galaxy of GRB 221009A has a very low metallicity of approximately 0.12 Z_⊙and strong H₂emission at the explosion site, which is consistent with recent star formation, hinting that environmental factors are responsible for its extreme energetics. 

Abstract The nearby type II supernova, SN 2023ixf in M101 exhibits signatures of early time interaction with circumstellar material in the first week postexplosion. This material may be the consequence of prior mass loss suffered by the progenitor, which possibly manifested in the form of a detectable presupernova outburst. We present an analysis of long-baseline preexplosion photometric data in theg,w,r,i,z, andyfilters from Pan-STARRS as part of the Young Supernova Experiment, spanning ∼5000 days. We find no significant detections in the Pan-STARRS preexplosion light curves. We train a multilayer perceptron neural network to classify presupernova outbursts. We find no evidence of eruptive presupernova activity to a limiting absolute magnitude of −7 mag. The limiting magnitudes from the full set ofgwrizy(average absolute magnitude ≈ −8 mag) data are consistent with previous preexplosion studies. We use deep photometry from the literature to constrain the progenitor of SN 2023ixf, finding that these data are consistent with a dusty red supergiant progenitor with luminosity $\log \left(L/L_{\odot }\right)$≈ 5.12 and temperature ≈ 3950 K, corresponding to a mass of 14–20M_⊙. 

Abstract We present optical photometry and spectroscopy of the Type IIn supernova (SN) 2021qqp. Its unusual light curve is marked by a long precursor for ≈300 days, a rapid increase in brightness for ≈60 days, and then a sharp increase of ≈1.6 mag in only a few days to a first peak ofM_r≈ −19.5 mag. The light curve then declines rapidly until it rebrightens to a second distinct peak ofM_r≈ −17.3 mag centered at ≈335 days after the first peak. The spectra are dominated by Balmer lines with a complex morphology, including a narrow component with a width of ≈1300 km s⁻¹(first peak) and ≈2500 km s⁻¹(second peak) that we associate with the circumstellar medium (CSM) and a P Cygni component with an absorption velocity of ≈8500 km s⁻¹(first peak) and ≈5600 km s⁻¹(second peak) that we associate with the SN–CSM interaction shell. Using the luminosity and velocity evolution, we construct a flexible analytical model, finding two significant mass-loss episodes with peak mass loss rates of ≈10 and ≈5M_⊙yr⁻¹about 0.8 and 2 yr before explosion, respectively, with a total CSM mass of ≈2–4M_⊙. We show that the most recent mass-loss episode could explain the precursor for the year preceding the explosion. The SN ejecta mass is constrained to be ≈5–30M_⊙for an explosion energy of ≈(3–10) × 10⁵¹erg. We discuss eruptive massive stars (luminous blue variable, pulsational pair instability) and an extreme stellar merger with a compact object as possible progenitor channels. 

Abstract Periodic variables illuminate the physical processes of stars throughout their lifetime. Wide-field surveys continue to increase our discovery rates of periodic variable stars. Automated approaches are essential to identify interesting periodic variable stars for multiwavelength and spectroscopic follow-up. Here we present a novel unsupervised machine-learning approach to hunt for anomalous periodic variables using phase-folded light curves presented in the Zwicky Transient Facility Catalogue of Periodic Variable Stars by Chen et al. We use a convolutional variational autoencoder to learn a low-dimensional latent representation, and we search for anomalies within this latent dimension via an isolation forest. We identify anomalies with irregular variability. Most of the top anomalies are likely highly variable red giants or asymptotic giant branch stars concentrated in the Milky Way galactic disk; a fraction of the identified anomalies are more consistent with young stellar objects. Detailed spectroscopic follow-up observations are encouraged to reveal the nature of these anomalies.