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We present a comprehensive analysis of the photometric and spectroscopic evolution of SN 2021foa, unique among the class of transitional supernovae for repeatedly changing its spectroscopic appearance from hydrogen-to-helium-to-hydrogen dominated (IIn-to-Ibn-to-IIn) within 50 days past peak brightness. The spectra exhibit multiple narrow (≈300–600 km s−1) absorption lines of hydrogen, helium, calcium, and iron together with broad helium emission lines with a full width at half-maximum (FWHM) of ∼6000 km s−1. For a steady, wind mass-loss regime, light-curve modeling results in an ejecta mass of ∼8 M ⊙ and circumstellar material (CSM) mass below 1 M ⊙, and an ejecta velocity consistent with the FWHM of the broad helium lines. We obtain a mass-loss rate of ≈2 M ⊙ yr−1. This mass-loss rate is 3 orders of magnitude larger than derived for normal Type II supernovae. We estimate that the bulk of the CSM of SN 2021foa must have been expelled within half a year, about 12 yr ago. Our analysis suggests that SN 2021foa had a helium-rich ejecta that swept up a dense shell of hydrogen-rich CSM shortly after explosion. At about 60 days past peak brightness, the photosphere recedes through the dense ejecta-CSM region, occulting much of the redshifted emission of the hydrogen and helium lines, which results in an observed blueshift (∼−3000 km s−1). Strong mass-loss activity prior to explosion, such as those seen in SN 2009ip-like objects and SN 2021foa as precursor emission, are the likely origin of a complex, multiple-shell CSM close to the progenitor star. [ABSTRACT FROM AUTHOR]

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-averaged F 1-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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The next generation of wide-field cosmic microwave background (CMB) surveys are uniquely poised to open a new window into time-domain astronomy in the millimeter band. Here, we explore the discovery phase space for extragalactic transients with near-term and future CMB experiments to characterize the expected population. We use existing millimeter-band light curves of known transients (gamma-ray bursts, tidal disruption events, fast blue optical transients (FBOTs), neutron star mergers) and theoretical models, in conjunction with known and estimated volumetric rates. Using Monte Carlo simulations of various CMB survey designs (area, cadence, depth, duration) we estimate the detection rates and the resulting light-curve characteristics. We find that existing and near-term surveys will find tens to hundreds of long-duration gamma-ray bursts (LGRBs), driven primarily by detections of the reverse shock emission, and including off-axis LGRBs. Next-generation experiments (CMB-S4, CMB-HD) will find tens of FBOTs in the nearby universe and will detect a few tidal disruption events. CMB-HD will additionally detect a small number of short gamma-ray bursts, where these will be discovered within the detection volume of next-generation gravitational wave experiments like the Cosmic Explorer. [ABSTRACT FROM AUTHOR]

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 Mr = −20.0 mag, and the second peak, about 80 rest-frame days later, Mr = −19.2 mag. The total radiated energy is large, Erad ≈ 2.5 × 1050 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 fNi ≈ 0.31 (MNi ≈ 3.2 M⊙). Instead, if we model the first peak with a combined magnetar spin-down and radioactive heating model we find a better match with Mej ≈ 4 M⊙, a magnetar spin period of Pspin ≈ 7.2 ms, and magnetic field of B ≈ 1014 G, and fNi ≲ 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 ≈0.7 M⊙ of hydrogen-free material located ≈400 au from the progenitor. Accounting for the ejecta mass, circumstellar shell mass, and remnant neutron star mass, we infer a CO core mass prior to explosion of ≈6.5 M⊙. The host galaxy has a metallicity of ≈0.26 Z⊙, low for SNe Ic but consistent with SLSNe. Overall, we find that SN 2019stc is a transition object between normal SNe Ic and SLSNe. [ABSTRACT FROM AUTHOR]

We present the largest and deepest late-time radio and millimeter survey to date of superluminous supernovae (SLSNe) and long-duration gamma-ray bursts (LGRBs) to search for associated nonthermal synchrotron emission. Using the Karl G. Jansky Very Large Array (VLA) and the Atacama Large Millimeter/submillimeter Array (ALMA), we observed 43 sources at 6 and 100 GHz on a timescale of ∼ 1–19 yr post-explosion. We do not detect radio/millimeter emission from any of the sources, with the exception of a 6 GHz detection of PTF10hgi, as well as the detection of 6 GHz emission near the location of the SLSN PTF12dam, which we associate with its host galaxy. We use our data to place constraints on central engine emission due to magnetar wind nebulae and off-axis relativistic jets. We also explore nonrelativistic emission from the SN ejecta, and place constraints on obscured star formation in the host galaxies. In addition, we conduct a search for fast radio bursts (FRBs) from some of the sources using VLA phased-array observations; no FRBs are detected to a limit of 16 mJy (7σ; 10 ms duration) in about 40 minutes on source per event. A comparison to theoretical models suggests that continued radio monitoring may lead to detections of persistent radio emission on timescales of ≳ a decade. [ABSTRACT FROM AUTHOR]

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 multiwavelength follow-up, as well as archival population studies. Here we present the complete sample of 5243 "SN-like" light curves (in gP1rP1iP1zP1) 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 data set, we train a novel semisupervised machine learning algorithm to photometrically classify 2315 new SN-like light curves with host galaxy spectroscopic redshifts. Our algorithm consists of an RF 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) and macro-averaged purity and completeness of 66% and 69%, respectively. We find the highest accuracy rates for SNe Ia and SLSNe and the lowest for SNe Ibc. 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). [ABSTRACT FROM AUTHOR]

The classification of supernovae (SNe) and its impact on our understanding of 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. [ABSTRACT FROM AUTHOR]

Over the past decade wide-field optical time-domain surveys have increased the discovery rate of transients to the point that ≲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 multiwavelength follow-up. This algorithm is part of the Finding Luminous and Exotic Extragalactic Transients (FLEET) 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 events per year. [ABSTRACT FROM AUTHOR]

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 modeled 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⊙, with a sharp decline at lower masses, and is best fit by a broken power law described by at 3.6–8.6 M⊙ and at 8.6–40 M⊙. 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 ≲ 1/3 Z⊙ 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. [ABSTRACT FROM AUTHOR]