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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.

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 to fit simulated LSST 10-year quasar light curves. The driving variability is reconstructed using a latent stochastic differential equation, a physically motivated, generative deep learning method that can model continuous-time stochastic dynamics. By embedding these physical processes into our network, we achieve a model that is more robust and interpretable. We also use transformers to scale our model to tens of millions of parameters. We demonstrate how our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wavebands, even for out-of-distribution driving signals. Our approach provides a powerful and scalable framework that can be adapted to solve other inverse problems in multivariate time series with irregular sampling., Comment: 31 pages, 19 figures

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., Comment: 37 pages, 17 figures

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., Comment: 20 pages, 10 figures, submitted to ApJ

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., Comment: code: https://github.com/ThomasHelfer/multimodal-supernovae data: https://huggingface.co/datasets/thelfer/multimodal_supernovae

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 $3\sigma$ upper limit of $0.83$ M$_\odot$ of dust formed in SN\,2017gci, with a lowest $1\sigma$ 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., Comment: 11 pages, 5 figures, submitted to ApJ

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 pseudo-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 ${\tt MESA+STELLA}$ 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 ($\gtrsim 17\ M_{\odot}$) 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 $0.07\ M_{\odot}$. 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., Comment: 18 pages, 7 figures, submitted to ApJ. Comments welcome

The Hartmann test is a method used to measure the wavefront error in a focal optical system, wherein a mask with a pattern of small holes is placed at the system's aperture stop. By taking an image at a defocused plane, the differences between the ideal and real positions of the reimaged holes (called the transverse ray aberrations) can be measured, which can then be used to estimate the wavefront error. However, the Hartmann test is usually used with an on-axis field. In this paper, we present a wavefront sensing method which generalizes the classical Hartmann test for off-axis field angles and arbitrary reference wavefronts. Our method involves taking images at two defocused planes, and then using the real reimaged hole positions on both planes to estimate the trajectories of rays from the system's exit pupil, at which the reference wavefront is situated. We then propagate the rays forward from the reference wavefront to one of the two defocused planes, in order to find the ideal reimaged hole positions, from which we can compute transverse ray aberrations. We derive and solve a pair of nonlinear partial differential equations relating transverse ray aberrations to wavefront error, using Zernike decomposition and nonlinear least squares. Our method has been verified on simulated data from the 7-lens f/2.25 red camera system of the GMT-Consortium Large Earth Finder (G-CLEF), a high resolution optical echelle spectrograph which will be a first light instrument for the Giant Magellan Telescope (GMT)., Comment: 18 pages, 23 figures, SPIE Astronomical Telescopes + Instrumentation 2024

We present the most comprehensive catalog to date of Type I Superluminous Supernovae (SLSNe), a class of stripped envelope supernovae (SNe) characterized by exceptionally high luminosities. We have compiled a sample of 262 SLSNe reported through 2022 December 31. We verified the spectroscopic classification of each SLSN and collated an exhaustive data set of UV, optical and IR photometry from both publicly available data and our own FLEET observational follow-up program, totaling over 30,000 photometric detections. Using these data we derive observational parameters such as the peak absolute magnitudes, rise and decline timescales, as well as bolometric luminosities, temperature and photospheric radius evolution for all SLSNe. Additionally, we model all light curves using a hybrid model that includes contributions from both a magnetar central engine and the radioactive decay of $^{56}$Ni. We explore correlations among various physical and observational parameters, and recover the previously found relation between ejecta mass and magnetar spin, as well as the overall progenitor pre-explosion mass distribution with a peak at $\approx 6.5$ M$_\odot$. We find no significant redshift dependence for any parameter, and no evidence for distinct sub-types of SLSNe. We find that $< 3$\% of SLSNe are best fit with a significant contribution from radioactive decay $\gtrsim 50$\%, representing a set of relatively dim and slowly declining SNe. We provide several analytical tools designed to simulate typical SLSN light curves across a broad range of wavelengths and phases, enabling accurate K-corrections, bolometric scaling calculations, and inclusion of SLSNe in survey simulations or future comparison works. The complete catalog, including all of the photometry, models, and derived parameters, is made available as an open-source resource on GitHub., Comment: 59 pages, 22 Figures, Submitted to MNRAS

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., Comment: 16 pages, 11 figures

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. To facilitate these studies, we introduce a convolutional variational autoencoder trained to estimate the redshift, stellar mass, and star-formation rates of galaxies from multi-band imaging data. We train and test our physics-informed CVAE on a spectroscopic sample of $\sim$26,000 galaxies within $z<1$ imaged through the Dark Energy Camera Legacy Survey. We show that our model can infer redshift and stellar mass more accurately than the latest image-based self-supervised learning approaches, and is >100x faster than more computationally-intensive SED-fitting techniques. Using a small sample of Green Pea and Red Spiral galaxies reported in the literature, we further demonstrate how this CVAE can be used to rapidly identify rare galaxy populations and interpret what makes them unique., Comment: Accepted to the "Machine Learning and the Physical Sciences" Workshop at NeurIPS 2023

The nearby type II supernova, SN2023ixf in M101 exhibits signatures of early-time interaction with circumstellar material in the first week post-explosion. This material may be the consequence of prior mass loss suffered by the progenitor which possibly manifested in the form of a detectable pre-supernova outburst. We present an analysis of the long-baseline pre-explosion photometric data in $g$, $w$, $r$, $i$, $z$ and $y$ filters from Pan-STARRS as part of the Young Supernova Experiment, spanning $\sim$5,000 days. We find no significant detections in the Pan-STARRS pre-explosion light curve. We train a multilayer perceptron neural network to classify pre-supernova outbursts. We find no evidence of eruptive pre-supernova activity to a limiting absolute magnitude of $-7$. The limiting magnitudes from the full set of $gwrizy$ (average absolute magnitude $\approx$-8) data are consistent with previous pre-explosion studies. We use deep photometry from the literature to constrain the progenitor of SN2023ixf, finding that these data are consistent with a dusty red supergiant (RSG) progenitor with luminosity $\log\left(L/L_\odot\right)$$\approx$5.12 and temperature $\approx$3950K, corresponding to a mass of 14-20 M$_\odot$, Comment: 19 pages, 8 figures, 1 table

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., Comment: Accepted to Machine Learning and the Physical Sciences Workshop at NeurIPS 2023

Identifying the astrophysical sites of the $r$-process, one of the primary mechanisms by which heavy elements are formed, is a key goal of modern astrophysics. The discovery of the brightest gamma-ray burst of all time, GRB 221009A, at a relatively nearby redshift, presented the first opportunity to spectroscopically test the idea that $r$-process elements are produced following the collapse of rapidly rotating massive stars. Here we present spectroscopic and photometric $\textit{James Webb Space Telescope}$ (JWST) observations of GRB 221009A obtained $+168$ and $+170$ rest-frame days after the initial gamma-ray trigger, and demonstrate they are well-described by a supernova (SN) and power-law afterglow, with no evidence for an additional component from $r$-process emission, and that the SN component strongly resembles the near-infrared spectra of previous SNe, including SN 1998bw. We further find that the SN associated with GRB 221009A is slightly fainter than the expected brightness of SN 1998bw at this phase, concluding that the SN is therefore not an unusual GRB-SN. We infer a nickel mass of $\approx0.09$ M$_{\odot}$, consistent with the lack of an obvious SN detection in the early-time data. We find that the host galaxy of GRB 221009A has a very low metallicity of $\approx0.12$ Z$_{\odot}$ and our resolved host spectrum shows that GRB 221009A occurred in a unique environment in its host characterized by strong H$_2$ emission lines consistent with recent star formation, which may hint at environmental factors being responsible for its extreme energetics., Comment: 32 pages, 14 Figures, Submitted to Nature Astronomy

We present the photometric and spectroscopic evolution of SN 2022oqm, a nearby multi-peaked hydrogen- and helium-weak calcium-rich transient (CaRT). SN 2022oqm was detected 13.1 kpc from its host galaxy, the face-on spiral galaxy NGC 5875. Extensive spectroscopic coverage reveals an early hot (T >= 40,000 K) continuum and carbon features observed $\sim$1~day after discovery, SN Ic-like photospheric-phase spectra, and strong forbidden calcium emission starting 38 days after discovery. SN 2022oqm has a relatively high peak luminosity (MB = -17 mag) for (CaRTs), making it an outlier in the population. We determine that three power sources are necessary to explain the light curve (LC), with each corresponding to a distinct peak. The first peak is powered by an expanding blackbody with a power law luminosity, suggesting shock cooling by circumstellar material (CSM). Subsequent LC evolution is powered by a double radioactive decay model, consistent with two sources of photons diffusing through optically thick ejecta. From the LC, we derive an ejecta mass and 56Ni mass of ~0.6 solar masses and ~0.09 solar masses. Spectroscopic modeling suggests 0.6 solar masses of ejecta, and with well-mixed Fe-peak elements throughout. We discuss several physical origins for SN 2022oqm and find either a surprisingly massive white dwarf progenitor or a peculiar stripped envelope model could explain SN 2022oqm. A stripped envelope explosion inside a dense, hydrogen- and helium-poor CSM, akin to SNe Icn, but with a large 56Ni mass and small CSM mass could explain SN 2022oqm. Alternatively, helium detonation on an unexpectedly massive white dwarf could also explain SN 2022oqm., Comment: 35 pages, 17 figures, 7 tables, Accepted for Publication in ApJ

We present optical, ultraviolet, and infrared data of the type II supernova (SN II) 2020jfo at 14.5 Mpc. This wealth of multiwavelength data allows to compare different metrics commonly used to estimate progenitor masses of SN II for the same object. Using its early light curve, we infer SN 2020jfo had a progenitor radius of $\approx$700 $R_{\odot}$, consistent with red supergiants of initial mass $M_{\rm ZAMS}=$11-13 $M_{\odot}$. The decline in its late-time light curve is best fit by a ${}^{56}$Ni mass of 0.018$\pm$0.007 $M_{\odot}$ consistent with that ejected from SN II-P with $\approx$13 $M_{\odot}$ initial mass stars. Early spectra and photometry do not exhibit signs of interaction with circumstellar matter, implying that SN 2020jfo experienced weak mass loss within the final years prior to explosion. Our spectra at $>$250 days are best fit by models from 12 $M_{\odot}$ initial mass stars. We analyzed integral field unit spectroscopy of the stellar population near SN 2020jfo, finding its massive star population had a zero age main sequence mass of 9.7$\substack{+2.5\\-1.3} M_{\odot}$. We identify a single counterpart in pre-explosion imaging and find it has an initial mass of at most $7.2\substack{+1.2\\-0.6} M_{\odot}$. We conclude that the inconsistency between this mass and indirect mass indicators from SN 2020jfo itself is most likely caused by extinction with $A_{V}=2$-3 mag due to matter around the progenitor star, which lowered its observed optical luminosity. As SN 2020jfo did not exhibit extinction at this level or evidence for interaction with circumstellar matter between 1.6-450 days from explosion, we conclude that this material was likely confined within $\approx$3000 $R_{\odot}$ from the progenitor star., Comment: 31 pages, 12 figures, accepted to MNRAS

Type-I Superluminous Supernovae (SLSNe) are an exotic class of core-collapse SN (CCSN) that can be up to 100 times brighter and more slowly-evolving than normal CCSNe. SLSNe represent the end-stages of the most massive stripped stars, and are thought to be powered by the spin-down energy of a millisecond magnetar. Studying them and measuring their physical parameters can help us to better understand stellar mass-loss, evolution, and explosions. Moreover, thanks to their high luminosities, SLSNe can be seen up to greater distances, allowing us to explore how stellar physics evolves as a function of redshift. The High Latitude Time Domain Survey (HLTDS) will provide us with an exquisite dataset that will discover 100s of SLSNe. Here, we focus on the question of which sets of filters and cadences will allow us to best characterize the physical parameters of these SLSNe. We simulate a set of SLSNe at redshifts ranging from z = 0.1 to z = 5.0, using six different sets of filters, and cadences ranging from 5 to 100 days. We then fit these simulated light curves to attempt to recover the input parameter values for their ejecta mass, ejecta velocity, magnetic field strength, and magnetar spin period. We find that four filters are sufficient to accurately characterize SLSNe at redshifts below $z = 3$, and that cadences faster than 20 days are required to obtain measurements with an uncertainty below 10\%, although a cadence of 70 days is still acceptable under certain conditions. Finally, we find that the nominal survey strategy will not be able to properly characterize the most distant SLSNe at $z = 5$. We find that the addition of 60-day cadence observations for 4 years to the nominal HLTDS survey can greatly improve the prospect of characterizing these most extreme and distant SNe, with only an 8\% increase to the time commitment of the survey., Comment: Submitted as part of the Nancy Grace Roman Space Telescope's Core Community Surveys call for white papers

The LIGO HET Response (LIGHETR) project is an enterprise to follow up optical transients (OT) discovered as gravitational wave merger sources by the LIGO/Virgo collaboration (LVC). Early spectroscopy has the potential to constrain crucial parameters such as the aspect angle. The LIGHETR collaboration also includes the capacity to model the spectroscopic evolution of mergers to facilitate a real-time direct comparison of models with our data. The principal facility is the Hobby-Eberly Telescope. LIGHETR uses the massively-replicated VIRUS array of spectrographs to search for associated OTs and obtain early blue spectra and in a complementary role, the low-resolution LRS-2 spectrograph is used to obtain spectra of viable candidates as well as a densely-sampled series of spectra of true counterparts. Once an OT is identified, the anticipated cadence of spectra would match or considerably exceed anything achieved for GW170817 = AT2017gfo for which there were no spectra in the first 12 hours and thereafter only roughly once daily. We describe special HET-specific software written to facilitate the program and attempts to determine the flux limits to undetected sources. We also describe our campaign to follow up OT candidates during the third observational campaign of the LIGO and Virgo Scientific Collaborations. We obtained VIRUS spectroscopy of candidate galaxy hosts for 5 LVC gravitational wave events and LRS-2 spectra of one candidate for the OT associated with S190901ap. We identified that candidate, ZTF19abvionh = AT2019pip, as a possible Wolf-Rayet star in an otherwise unrecognized nearby dwarf galaxy., Comment: 26 pages, 15 figures

We present pre-explosion optical and infrared (IR) imaging at the site of the type II supernova (SN II) 2023ixf in Messier 101 at 6.9 Mpc. We astrometrically registered a ground-based image of SN 2023ixf to archival Hubble Space Telescope (HST), Spitzer Space Telescope (Spitzer), and ground-based near-IR images. A single point source is detected at a position consistent with the SN at wavelengths ranging from HST $R$-band to Spitzer 4.5 $\mu$m. Fitting to blackbody and red supergiant (RSG) spectral-energy distributions (SEDs), we find that the source is anomalously cool with a significant mid-IR excess. We interpret this SED as reprocessed emission in a 8600 $R_{\odot}$ circumstellar shell of dusty material with a mass $\sim$5$\times10^{-5} M_{\odot}$ surrounding a $\log(L/L_{\odot})=4.74\pm0.07$ and $T_{\rm eff}=3920\substack{+200\\-160}$ K RSG. This luminosity is consistent with RSG models of initial mass 11 $M_{\odot}$, depending on assumptions of rotation and overshooting. In addition, the counterpart was significantly variable in pre-explosion Spitzer 3.6 $\mu$m and 4.5 $\mu$m imaging, exhibiting $\sim$70% variability in both bands correlated across 9 yr and 29 epochs of imaging. The variations appear to have a timescale of 2.8 yr, which is consistent with $\kappa$-mechanism pulsations observed in RSGs, albeit with a much larger amplitude than RSGs such as $\alpha$ Orionis (Betelgeuse)., Comment: 14 pages, 5 figures, submitted to ApJL, comments welcome

We present optical photometry and spectroscopy of the Type IIn supernova (SN) 2021qqp. Its unusual light curve is marked by a long precursor for $\approx300$ days, a rapid increase in brightness for $\approx60$ days, and then a sharp increase of $\approx1.6$ mag in only a few days to a first peak of $M_r \approx -19.5$ mag. The light curve then declines rapidly until it re-brightens to a second distinct peak of $M_r \approx -17.3$ mag centered at $\approx335$ days after the first peak. The spectra are dominated by Balmer lines with a complex morphology, including a narrow component with a width of $\approx 1300$ km s$^{-1}$ (first peak) and $\approx 2500$ km s$^{-1}$ (second peak) that we associate with the circumstellar medium (CSM) and a P Cygni component with an absorption velocity of $\approx 8500$ km s$^{-1}$ (first peak) and $\approx 5600$ 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 $\approx 10$ and $\approx 5\,M_{\odot}$ yr$^{-1}$ about $0.8$ and $2$ yr before explosion, respectively, with a total CSM mass of $\approx 2-4\,M_{\odot}$. 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 $\approx 5-30\,M_{\odot}$ for an explosion energy of $\approx (3-10)\times10^{51}$ erg. We discuss eruptive massive stars (luminous blue variable, pulsational pair instability) and an extreme stellar merger with a compact object as possible progenitor channels., Comment: Updated to match the published article in ApJ, 2024 March 29

A growing number of supernovae (SNe) are now known to exhibit evidence for significant interaction with a dense, pre-existing, circumstellar medium (CSM). SNe Ibn comprise one such class that can be characterised by both rapidly evolving light curves and persistent narrow He I lines. The origin of such a dense CSM in these systems remains a pressing question, specifically concerning the progenitor system and mass-loss mechanism. In this paper, we present multi-wavelength data of the Type Ibn SN 2020nxt, including $HST$/STIS ultraviolet spectra. We fit the data with recently updated CMFGEN models designed to handle configurations for SNe Ibn. The UV coverage yields strong constraints on the energetics and, when combined with the CMFGEN models, offer new insight on potential progenitor systems. We find the most successful model is a $\lesssim4 {\rm M}_\odot$ helium star that lost its $\sim 1\,{\rm M}_\odot$ He-rich envelope in the years preceding core collapse. We also consider viable alternatives, such as a He white dwarf merger. Ultimately, we conclude at least some SNe Ibn do not arise from single, massive ($>30 {\rm M}_\odot$) Wolf-Rayet-like stars., Comment: 17 pages, 13 figures, 1 table, submitted to MNRAS

We present high-cadence ultraviolet through near-infrared observations of the Type Ia supernova (SN Ia) 2023bee in NGC~2708 ($D = 32 \pm 3$ Mpc), finding excess flux in the first days after explosion relative to the expected power-law rise from an expanding fireball. This deviation from typical behavior for SNe Ia is particularly obvious in our 10-minute cadence $TESS$ light curve and $Swift$ UV data. Compared to a few other normal SNe Ia with detected early excess flux, the excess flux in SN 2023bee is redder in the UV and less luminous. We present optical spectra of SN 2023bee, including two spectra during the period where the flux excess is dominant. At this time, the spectra are similar to those of other SNe Ia but with weaker Si II, C II and Ca II absorption lines, perhaps because the excess flux creates a stronger continuum. We compare the data to several theoretical models that have been proposed to explain the early flux excess in SNe Ia. Interaction with either a nearby companion star or close-in circumstellar material is expected to produce a faster evolution than seen in the data. Radioactive material in the outer layers of the ejecta, either from a double detonation explosion or simply an explosion with a $^{56}$Ni clump near the surface, can not fully reproduce the evolution either, likely due to the sensitivity of early UV observable to the treatment of the outer part of ejecta in simulation. We conclude that no current model can adequately explain the full set of observations. We find that a relatively large fraction of nearby, bright SNe Ia with high-cadence observations have some amount of excess flux within a few days of explosion. Considering potential asymmetric emission, the physical cause of this excess flux may be ubiquitous in normal SNe Ia., Comment: 21 pages, 12 figures. Accepted by the astrophysical journal

In this work, we present a simple interpolation methodology for spectroscopic time series, based on conventional interpolation techniques (random forests) implemented in widely-available libraries. We demonstrate that our existing library of simulations is sufficient for training, producing interpolated spectra that respond sensitively to varied ejecta parameter, post-merger time, and viewing angle inputs. We compare our interpolated spectra to the AT2017gfo spectral data, and find parameters similar to our previous inferences using broadband light curves. However, the spectral observations have significant systematic short-wavelength residuals relative to our models, which we cannot explain within our existing framework. Similar to previous studies, we argue that an additional blue component is required. We consider a radioactive heating source as a third component characterized by light, slow-moving, lanthanide-free ejecta with $M_{\rm th} = 0.003~M_\odot$, $v_{\rm th} = 0.05$c, and $\kappa_{\rm th} = 1$ cm$^2$/g. When included as part of our radiative transfer simulations, our choice of third component reprocesses blue photons into lower energies, having the opposite effect and further accentuating the blue-underluminosity disparity in our simulations. As such, we are unable to overcome short-wavelength deficits at later times using an additional radioactive heating component, indicating the need for a more sophisticated modeling treatment., Comment: 11 pages, 7 figures, submitted to Phys. Rev. Research, presented at April APS session F13.00006

Flagship near-future surveys targeting $10^8-10^9$ galaxies across cosmic time will soon reveal the processes of galaxy assembly in unprecedented resolution. This creates an immediate computational challenge on effective analyses of the full data-set. With simulation-based inference (SBI), it is possible to attain complex posterior distributions with the accuracy of traditional methods but with a $>10^4$ increase in speed. However, it comes with a major limitation. Standard SBI requires the simulated data to have identical characteristics to the observed data, which is often violated in astronomical surveys due to inhomogeneous coverage and/or fluctuating sky and telescope conditions. In this work, we present a complete SBI-based methodology, ``SBI$^{++}$,'' for treating out-of-distribution measurement errors and missing data. We show that out-of-distribution errors can be approximated by using standard SBI evaluations and that missing data can be marginalized over using SBI evaluations over nearby data realizations in the training set. In addition to the validation set, we apply SBI$^{++}$ to galaxies identified in extragalactic images acquired by the James Webb Space Telescope, and show that SBI$^{++}$ can infer photometric redshifts at least as accurately as traditional sampling methods and crucially, better than the original SBI algorithm using training data with a wide range of observational errors. SBI$^{++}$ retains the fast inference speed of $\sim$1 sec for objects in the observational training set distribution, and additionally permits parameter inference outside of the trained noise and data at $\sim$1 min per object. This expanded regime has broad implications for future applications to astronomical surveys., Comment: Accepted for publication in ApJL; 13 pages, 5 figures. Code and a Jupyter tutorial are made publicly available at https://github.com/wangbingjie/sbi_pp

Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar's brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling., Comment: 22 pages, 12 figures

Observations by the current generation of gravitational-wave detectors have been pivotal in expanding our understanding of the universe. Although tens of exciting compact binary mergers have been observed, neutron star-black hole (NSBH) mergers remained elusive until they were first confidently detected in 2020. The number of NSBH detections is expected to increase with sensitivity improvements of the current detectors and the proposed construction of new observatories over the next decade. In this work, we explore the NSBH detection and measurement capabilities of these upgraded detectors and new observatories using the following metrics: network detection efficiency and detection rate as a function of redshift, distributions of the signal-to-noise ratios, the measurement accuracy of intrinsic and extrinsic parameters, the accuracy of sky position measurement, and the number of early-warning alerts that can be sent to facilitate the electromagnetic follow-up. Additionally, we evaluate the prospects of performing multi-messenger observations of NSBH systems by reporting the number of expected kilonova detections with the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope. We find that as many as $\mathcal{O}(10)$ kilonovae can be detected by these two telescopes every year, depending on the population of the NSBH systems and the equation of state of neutron stars., Comment: 30 pages, 15 figures, 15 tables

The Vera Rubin Observatory, set to begin observations in mid-2024, will increase our discovery rate of supernovae to well over one million annually. There has been a significant push to develop new methodologies to identify, classify and ultimately understand the millions of supernovae discovered with the Rubin Observatory. Here, we present the first simulation-based inference method using normalizing flows, trained to rapidly infer the parameters of toy supernovae model in multivariate, Rubin-like datastreams. We find that our method is well-calibrated compared to traditional inference methodologies (specifically MCMC), requiring only one-ten-thousandth of the CPU hours during test time., Comment: 5 pages, accepted in the Neurips Machine Learning and the Physical Sciences conference

In November 2019 we began operating FLEET (Finding Luminous and Exotic Extragalactic Transients), a machine learning algorithm designed to photometrically identify Type I superluminous supernovae (SLSNe) in transient alert streams. Using FLEET, we spectroscopically classified 21 of the 50 SLSNe identified worldwide between November 2019 and January 2022. Based on our original algorithm, we anticipated that FLEET would achieve a purity of about 50\% for transients with a probability of being a SLSN, \pslsn$>0.5$; the true on-sky purity we obtained is closer to 80\%. Similarly, we anticipated FLEET could reach a completeness of about 30\%, and we indeed measure an upper limit on the completeness of $\approx 33$\%. Here, we present FLEET 2.0, an updated version of FLEET trained on 4,780 transients (almost 3 times more than in FLEET 1.0). FLEET 2.0 has a similar predicted purity to FLEET 1.0, but outperforms FLEET 1.0 in terms of completeness, which is now closer to $\approx 40$\% for transients with \pslsn$>0.5$. Additionally, we explore possible systematics that might arise from the use of FLEET for target selection. We find that the population of SLSNe recovered by FLEET is mostly indistinguishable from the overall SLSN population, in terms of physical and most observational parameters. We provide FLEET as an open source package on GitHub https://github.com/gmzsebastian/FLEET, Comment: 10 pages, 3 figures, submitted to ApJ

We present an expansion of FLEET, a machine learning algorithm optimized to select transients that are most likely to be tidal disruption events (TDEs). FLEET is based on a random forest algorithm trained on the light curves and host galaxy information of 4,779 spectroscopically classified transients. For transients with a probability of being a TDE, \ptde$>0.5$, we can successfully recover TDEs with a $\approx40$\% completeness and a $\approx30$\% purity when using the first 20 days of photometry, or a similar completeness and $\approx50$\% purity when including 40 days of photometry. We find that the most relevant features for differentiating TDEs from other transients are the normalized host separation, and the light curve $(g-r)$ color during peak. Additionally, we use FLEET to produce a list of the 39 most likely TDE candidates discovered by the Zwicky Transient Facility that remain currently unclassified. We explore the use of FLEET for the Legacy Survey of Space and Time on the Vera C. Rubin Observatory (\textit{Rubin}) and the \textit{Nancy Grace Roman Space Telescope} (\textit{Roman}). We simulate the \textit{Rubin} and \textit{Roman} survey strategies and estimate that $\sim 10^4$ TDEs could be discovered every year by \textit{Rubin}, and $\sim200$ TDEs per year by \textit{Roman}. Finally, we run FLEET on the TDEs in our \textit{Rubin} survey simulation and find that we can recover $\sim 30$\% of those at a redshift $z <0.5$ with \ptde$>0.5$. This translates to $\sim3,000$ TDEs per year that FLEET could uncover from \textit{Rubin}. FLEET is provided as a open source package on GitHub https://github.com/gmzsebastian/FLEET, Comment: 17 pages, 12 figures, submitted to ApJ

With the upcoming Vera C.~Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only $\sim 0.1\%$ of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I superluminous supernovae (SLSNe), we must instead rely on photometric classification. In this vein, here we carry out a pilot study of SLSNe from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS) classified photometrically with our SuperRAENN and Superphot algorithms. We first construct a sub-sample of the photometric sample using a list of simple selection metrics designed to minimize contamination and ensure sufficient data quality for modeling. We then fit the multi-band light curves with a magnetar spin-down model using the Modular Open-Source Fitter for Transients (MOSFiT). Comparing the magnetar engine and ejecta parameter distributions of the photometric sample to those of the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample, we find that these samples are overall consistent, but that the photometric sample extends to slower spins and lower ejecta masses, which correspond to lower luminosity events, as expected for photometric selection. While our PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic sample, our methodology paves the way to an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study., Comment: 13 pages, 6 figures, submitted to ApJ

Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educational pathways have emerged. Despite rapid progress, substantial potential at the intersection of cosmology and machine learning remains untapped. In this white paper, we summarize current and ongoing developments relating to the application of machine learning within cosmology and provide a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities., Comment: Contribution to Snowmass 2021. 32 pages

The workshop Dawn VI: Next Generation Observatories took place online over three days, 5-7 October, 2021. More than 200 physicists and astronomers attended to contribute to, and learn from, a discussion of next-generation ground-based gravitational-wave detectors. The program was centered on the next generation of ground-based gravitational-wave observatories and their synergy with the greater landscape of scientific observatories of the 2030s. Cosmic Explorer (CE), a concept developed with US National Science Foundation support, was a particular focus; Einstein Telescope (ET), the European next generation concept, is an important complement and partner in forming a network. The concluding summary of the meeting expressed the sentiment that the observational science accessible to CE and ET, also in combination with data from other non-GW observatories, will stimulate a very broad community of analysts and yield insights which are exciting given the access to GWs from the entire universe. The need, and desire, for closer collaboration between ET and CE was expressed; a three-detector network is optimal for delivering much of the science. The science opportunities afforded by CE and ET are broad and compelling, impacting a wide range of disciplines in physics and high energy astrophysics. There was a consensus that CE is a concept that can deliver the promised science. A strong endorsement of Cosmic Explorer, as described in the CE Horizon Study, is a primary outcome of DAWN VI., Comment: Proceedings of the Dawn VI workshop

Using the second data release from the Zwicky Transient Facility (ZTF, Bellm et al. 2019), Chen et al. (2020) created a ZTF Catalog of Periodic Variable Stars (ZTF CPVS) of 781, 602 periodic variables stars (PVSs) with 11 class labels. Here, we provide a new classification model of PVSs in the ZTF CPVS using a convolutional variational autoencoder and hierarchical random forest. We cross-match the sky-coordinate of PVSs in the ZTF CPVS with those presented in the SIMBAD catalog. We identify non-stellar objects that are not previously classified, including extragalactic objects such as Quasi-Stellar Objects, Active Galactic Nuclei, supernovae and planetary nebulae. We then create a new labelled training set with 13 classes in two levels. We obtain a reasonable level of completeness (> 90 %) for certain classes of PVSs, although we have poorer completeness in other classes (~ 40 % in some cases). Our new labels for the ZTF CPVS are available via Zenodo Cheung et al. (2021)., Comment: 4 pages, 1 figure

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 multi-wavelength 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 \citet{Chen_2020}. 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., Comment: 26 pages, 17 figures. The full version of Table 4 and Table 5 are available upon request

The periodic pulsations of stars teach us about their underlying physical process. We present a convolutional autoencoder-based pipeline as an automatic approach to search for out-of-distribution anomalous periodic variables within The Zwicky Transient Facility Catalog of Periodic Variable Stars (ZTF CPVS). We use an isolation forest to rank each periodic variable by its anomaly score. Our overall most anomalous events have a unique physical origin: they are mostly highly variable and irregular evolved stars. Multiwavelength data suggest that they are most likely Red Giant or Asymptotic Giant Branch stars concentrated in the Milky Way galactic disk. Furthermore, we show how the learned latent features can be used for the classification of periodic variables through a hierarchical random forest. This novel semi-supervised approach allows astronomers to identify the most anomalous events within a given physical class, significantly increasing the potential for scientific discovery., Comment: 7 pages, 4 figures

The core collapse of rapidly rotating massive ~10 Msun stars ("collapsars"), and resulting formation of hyper-accreting black holes, are a leading model for the central engines of long-duration gamma-ray bursts (GRB) and promising sources of r-process nucleosynthesis. Here, we explore the signatures of collapsars from progenitors with extremely massive helium cores >130 Msun above the pair-instability mass gap. While rapid collapse to a black hole likely precludes a prompt explosion in these systems, we demonstrate that disk outflows can generate a large quantity (up to >50 Msun) of ejecta, comprised of >5-10 Msun in r-process elements and ~0.1-1 Msun of $^{56}$Ni, expanding at velocities ~0.1c. Radioactive heating of the disk-wind ejecta powers an optical/infrared transient, with a characteristic luminosity $\sim 10^{42}$ erg s$^{-1}$ and spectral peak in the near-infrared (due to the high optical/UV opacities of lanthanide elements) similar to kilonovae from neutron star mergers, but with longer durations $\gtrsim$ 1 month. These "super-kilonovae" (superKNe) herald the birth of massive black holes >60 Msun, which, as a result of disk wind mass-loss, can populate the pair-instability mass gap 'from above' and could potentially create the binary components of GW190521. SuperKNe could be discovered via wide-field surveys such as those planned with the Roman Space Telescope or via late-time infrared follow-up observations of extremely energetic GRBs. Gravitational waves of frequency ~0.1-50 Hz from non-axisymmetric instabilities in self-gravitating massive collapsar disks are potentially detectable by proposed third-generation intermediate and high-frequency observatories at distances up to hundreds of Mpc; in contrast to the "chirp" from binary mergers, the collapsar gravitational-wave signal decreases in frequency as the disk radius grows ("sad trombone")., Comment: 36 pages, 21 figures, 2 tables

The discovery of the electromagnetic counterpart to the binary neutron star merger GW170817 has opened the era of gravitational-wave multi-messenger astronomy. Rapid identification of the optical/infrared kilonova enabled a precise localization of the source, which paved the way to deep multi-wavelength follow-up and its myriad of related science results. Fully exploiting this new territory of exploration requires the acquisition of electromagnetic data from samples of neutron star mergers and other gravitational wave sources. After GW170817, the frontier is now to map the diversity of kilonova properties and provide more stringent constraints on the Hubble constant, and enable new tests of fundamental physics. The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) can play a key role in this field in the 2020s, when an improved network of gravitational-wave detectors is expected to reach a sensitivity that will enable the discovery of a high rate of merger events involving neutron stars (about tens per year) out to distances of several hundred Mpc. We design comprehensive target-of-opportunity observing strategies for follow-up of gravitational-wave triggers that will make the Rubin Observatory the premier instrument for discovery and early characterization of neutron star and other compact object mergers, and yet unknown classes of gravitational wave events., Comment: Accepted for publication in ApJS. arXiv admin note: text overlap with arXiv:1812.04051

We present the 30-min cadence Kepler/K2 light curve of the Type Ia supernova (SN Ia) SN 2018agk, covering approximately one week before explosion, the full rise phase and the decline until 40 days after peak. We additionally present ground-based observations in multiple bands within the same time range, including the 1-day cadence DECam observations within the first $\sim$5 days after the first light. The Kepler early light curve is fully consistent with a single power-law rise, without evidence of any bump feature. We compare SN 2018agk with a sample of other SNe~Ia without early excess flux from the literature. We find that SNe Ia without excess flux have slowly-evolving early colors in a narrow range ($g-i\approx -0.20\pm0.20$ mag) within the first $\sim 10$ days. On the other hand, among SNe Ia detected with excess, SN 2017cbv and SN 2018oh tend to be bluer, while iPTF16abc's evolution is similar to normal SNe Ia without excess in $g-i$. We further compare the Kepler light curve of SN 2018agk with companion-interaction models, and rule out the existence of a typical non-degenerate companion undergoing Roche-lobe overflow at viewing angles smaller than $45^{\circ}$., Comment: 20 pages, 14 figures, 5 tables. Published in ApJ

There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory. From the haystack of potential science targets, astronomers must allocate scarce resources to study a selection of needles in real time. Here we present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events using 1% of the PLAsTiCC dataset to train the autoencoder. Our unsupervised method uniquely works with unlabeled, real time, multivariate and aperiodic data. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. We find that our pipeline successfully ranks rarer classes of transients as more anomalous. Using simple cuts in anomaly score and uncertainty, we identify a pure (~95% pure) sample of rare transients (i.e., transients other than Type Ia, Type II and Type Ibc supernovae) including superluminous and pair-instability supernovae. Finally, our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow up studies in the era of the Rubin Observatory., Comment: 13 pages,9 figures, submitted to AAS Journals. Comments welcome!

We present optical photometry and spectroscopy of SN\,2019stc (=ZTF19acbonaa), an unusual Type Ic supernova (SN Ic) at a redshift of $z=0.117$. SN\,2019stc exhibits a broad double-peaked light curve, with the first peak having an absolute magnitude of $M_r=-20.0$ mag, and the second peak, about 80 rest-frame days later, $M_r=-19.2$ mag. The total radiated energy is large, $E_{\rm rad}\approx 2.5\times 10^{50}$ erg. Despite its large luminosity, approaching those of Type I superluminous supernovae (SLSNe), SN\,2019stc exhibits a typical SN Ic spectrum, bridging the gap between SLSNe and SNe Ic. The spectra indicate the presence of Fe-peak elements, but modeling of the first light curve peak with radioactive heating alone leads to an unusually high nickel mass fraction of $f_{\rm Ni}\approx 31\%$ ($M_{\rm Ni}\approx 3.2$ M$_\odot$). Instead, if we model the first peak with a combined magnetar spin-down and radioactive heating model we find a better match with $M_{\rm ej}\approx 4$ M$_\odot$, a magnetar spin period of $P_{\rm spin}\approx 7.2$ ms and magnetic field of $B\approx 10^{14}$ G, and $f_{\rm Ni}\lesssim 0.2$ (consistent with SNe Ic). The prominent second peak cannot be naturally accommodated with radioactive heating or magnetar spin-down, but instead can be explained as circumstellar interaction with $\approx 0.7$ $M_\odot$ of hydrogen-free material located $\approx 400$ AU from the progenitor. Including the remnant mass leads to a CO core mass prior to explosion of $\approx 6.5$ M$_\odot$. The host galaxy has a metallicity of $\approx 0.26$ Z$_\odot$, low for SNe Ic but consistent with SLSNe. Overall, we find that SN\,2019stc is a transition object between normal SNe Ic and SLSNe., Comment: 14 pages, 13 figures, Accepted to ApJ

One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star and black-hole neutron-star mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between gravitational-wave signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. In this work we apply a deep-learning approach to rapidly identify and characterize transient gravitational-wave signals from binary neutron-star mergers in real LIGO data. We show for the first time that artificial neural networks can promptly detect and characterize binary neutron star gravitational-wave signals in real LIGO data, and distinguish them from noise and signals from coalescing black-hole binaries. We illustrate this key result by demonstrating that our deep-learning framework classifies correctly all gravitational-wave events from the Gravitational-Wave Transient Catalog, GWTC-1 [Phys. Rev. X 9 (2019), 031040]. These results emphasize the importance of using realistic gravitational-wave detector data in machine learning approaches, and represent a step towards achieving real-time detection and inference of gravitational waves., Comment: 10 pages, 8 figures, 1 table. Updated grant information. Published in Physics Letters B

Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements. Future telescopes will discover thousands of new supernovae nightly, creating a need to flag astrophysically interesting events rapidly for followup study. Ideally, such an anomaly detection pipeline would be independent of our current knowledge and be sensitive to unexpected phenomena. Here we present an unsupervised method to search for anomalous time series in real time for transient, multivariate, and aperiodic signals. We use a RNN-based variational autoencoder to encode supernova time series and an isolation forest to search for anomalous events in the learned encoded space. We apply this method to a simulated dataset of 12,159 supernovae, successfully discovering anomalous supernovae and objects with catastrophically incorrect redshift measurements. This work is the first anomaly detection pipeline for supernovae which works with online datastreams., Comment: 6 pages, 2 figures, written for non-astronomers, submitted to the NeurIPS workshop Machine Learning and the Physical Sciences. Comments welcome!!

Over the past decade wide-field optical time-domain surveys have increased the discovery rate of transients to the point that $\lesssim 10\%$ are being spectroscopically classified. Despite this, these surveys have enabled the discovery of new and rare types of transients, most notably the class of hydrogen-poor superluminous supernovae (SLSN-I), with about 150 events confirmed to date. Here we present a machine-learning classification algorithm targeted at rapid identification of a pure sample of SLSN-I to enable spectroscopic and multi-wavelength follow-up. This algorithm is part of the FLEET (Finding Luminous and Exotic Extragalactic Transients) observational strategy. It utilizes both light curve and contextual information, but without the need for a redshift, to assign each newly-discovered transient a probability of being a SLSN-I. This classifier can achieve a maximum purity of about 85\% (with 20\% completeness) when observing a selection of SLSN-I candidates. Additionally, we present two alternative classifiers that use either redshifts or complete light curves and can achieve an even higher purity and completeness. At the current discovery rate, the FLEET algorithm can provide about $20$ SLSN-I candidates per year for spectroscopic follow-up with 85\% purity; with the Legacy Survey of Space and Time we anticipate this will rise to more than $\sim 10^3$ events per year., Comment: 17 pages, 12 figures, submitted to ApJ

In this note, we present a detailed self-similar solution to the interaction of a uniformly expanding gas and a stationary ambient medium, with an application to supernovae interacting with preexisting circumstellar media (Type IIn SNe). We implement the generalized solution into the Modular Open Source Fitter for Transients (MOSFiT), an open-source Python package for fitting extragalactic transient light curves., Comment: published in RNAAS; implemented in MOSFiT

The classification of supernovae (SNe) and its impact on our understanding of the explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming wide-field time-domain surveys have increased the transient discovery rate far beyond our capacity to obtain even a single spectrum of each new event. We must therefore rely heavily on photometric classification, connecting SN light curves back to their spectroscopically defined classes. Here we present Superphot, an open-source Python implementation of the machine-learning classification algorithm of Villar et al., and apply it to 2315 previously unclassified transients from the Pan-STARRS1 Medium Deep Survey for which we obtained spectroscopic host-galaxy redshifts. Our classifier achieves an overall accuracy of 82%, with completenesses and purities of >80% for the best classes (SNe Ia and superluminous SNe). For the worst performing SN class (SNe Ibc), the completeness and purity fall to 37% and 21%, respectively. Our classifier provides 1257 newly classified SNe Ia, 521 SNe II, 298 SNe Ibc, 181 SNe IIn, and 58 SLSNe. These are among the largest uniformly observed samples of SNe available in the literature and will enable a wide range of statistical studies of each class., Comment: Updated to match published version in ApJ. Companion paper to Villar et al. (2020). Full machine-readable tables available as ancillary files at right

Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory. Photometric classification can enable real-time identification of interesting events for extended multi-wavelength follow-up, as well as archival population studies. Here we present the complete sample of 5,243 "SN-like" light curves (in griz) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey in terms of cadence, filters and depth, making this a useful training set for the community. Using this dataset, we train a novel semi-supervised machine learning algorithm to photometrically classify 2,315 new SN-like light curves with host galaxy spectroscopic redshifts. Our algorithm consists of a random forest supervised classification step and a novel unsupervised step in which we introduce a recurrent autoencoder neural network (RAENN). Our final pipeline, dubbed SuperRAENN, has an accuracy of 87% across five SN classes (Type Ia, Ibc, II, IIn, SLSN-I). We find the highest accuracy rates for Type Ia SNe and SLSNe and the lowest for Type Ibc SNe. Our complete spectroscopically- and photometrically-classified samples break down into: 62.0% Type Ia (1839 objects), 19.8% Type II (553 objects), 4.8% Type IIn (136 objects), 11.7% Type Ibc (291 objects), and 1.6% Type I SLSNe (54 objects). Finally, we discuss how this algorithm can be modified for online LSST data streams., Comment: Submitted to ApJ; Companion paper to Hosseinzadeh et al.; Tables 1 and 2 available on ashleyvillar.com prior to publication

Despite indications that superluminous supernovae (SLSNe) originate from massive progenitors, the lack of a uniformly analyzed statistical sample has so far prevented a detailed view of the progenitor mass distribution. Here we present and analyze the pre-explosion mass distribution of hydrogen-poor SLSN progenitors as determined from uniformly modelled light curves of 62 events. We construct the distribution by summing the ejecta mass posteriors of each event, using magnetar light curve models presented in our previous works (and using a nominal neutron star remnant mass). The resulting distribution spans $3.6-40$ M$_{\odot}$, with a sharp decline at lower masses, and is best fit by a broken power law described by ${\rm d}N/{\rm dlog}M \propto M^{-0.41 \pm 0.06}$ at $3.6-8.6$ M$_{\odot}$ and $\propto M^{-1.26 \pm 0.06}$ at $8.6-40$ M$_{\odot}$. We find that observational selection effects cannot account for the shape of the distribution. Relative to Type Ib/c SNe, the SLSN mass distribution extends to much larger masses and has a different power-law shape, likely indicating that the formation of a magnetar allows more massive stars to explode as some of the rotational energy accelerates the ejecta. Comparing the SLSN distribution with predictions from single and binary star evolution models, we find that binary models for a metallicity of $Z\lesssim 1/3$ Z$_{\odot}$ are best able to reproduce its broad shape, in agreement with the preference of SLSNe for low metallicity environments. Finally, we uncover a correlation between the pre-explosion mass and the magnetar initial spin period, where SLSNe with low masses have slower spins, a trend broadly consistent with the effects of angular momentum transport evident in models of rapidly-rotating carbon-oxygen stars., Comment: 18 pages, 11 figures, Submitted to ApJ

We present an online application, the SuperNova Interactive Fitter (SNIF), which can be used to interactively fit one-zone, analytical models to optical transient light curves powered by (1) the radioactive decay of 56Ni, (2) the spin-down of newly born magnetars, or (3) the interaction between supernova ejecta and pre-existing circumstellar material. This application utilizes the Open Astronomy Catalogs API to connect the user to thousands of publicly available transient light curves from the literature., Comment: Accepted for publication in RNAAS; feature suggestions welcome!; see http://snif.space

Support for early career astronomers who are just beginning to explore astronomy research is imperative to increase retention of diverse practitioners in the field. Since 2010, Astrobites has played an instrumental role in engaging members of the community -- particularly undergraduate and graduate students -- in research. In this white paper, the Astrobites collaboration outlines our multi-faceted online education platform that both eases the transition into astronomy research and promotes inclusive professional development opportunities. We additionally offer recommendations for how the astronomy community can reduce barriers to entry to astronomy research in the coming decade., Comment: 14 pages, 5 figures (including cover page and list of endorsers); Submitted as an Astro2020 Decadal Survey State of the Profession White Paper. Access the Astrobites in the Classroom lesson plans here: https://tinyurl.com/y6kfszbn