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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.

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.

In 2019 November, we began operating Finding Luminous and Exotic Extragalactic Transients (FLEET), a machine-learning algorithm designed to photometrically identify Type I superluminous supernovae (SLSNe) in transient alert streams. Through this observational campaign, we spectroscopically classified 21 of the 50 SLSNe identified worldwide between 2019 November and 2022 January. Based on our original algorithm, we anticipated that FLEET would achieve a purity of about 50% for transients with a probability of being an SLSN,P(SLSN-I) > 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 ≲33%. Here we present FLEET 2.0, an updated version of FLEET trained on 4780 transients (almost three times more than 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 ≈40% for transients withP(SLSN-I) > 0.5. Additionally, we explore the 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.

We present an expansion of FLEET, a machine-learning algorithm optimized to select transients that are most likely tidal disruption events (TDEs). FLEET is based on a random forest algorithm trained on both the light curves and host galaxy information of 4779 spectroscopically classified transients. We find that for transients with a probability of being a TDE,P(TDE) > 0.5, we can successfully recover TDEs with ≈40% completeness and ≈30% purity when using their first 20 days of photometry or a similar completeness and ≈50% purity when including 40 days of photometry, an improvement of almost 2 orders of magnitude compared to random selection. Alternatively, we can recover TDEs with a maximum purity of ≈80% and a completeness of ≈30% when considering only transients withP(TDE) > 0.8. We explore the use of FLEET for future time-domain surveys such as the Legacy Survey of Space and Time on the Vera C. Rubin Observatory (Rubin) and the Nancy Grace Roman Space Telescope (Roman). We estimate that ∼10⁴well-observed TDEs could be discovered every year by Rubin and ∼200 TDEs by Roman. Finally, we run FLEET on the TDEs from our Rubin survey simulation and find that we can recover ∼30% of them at redshiftz< 0.5 withP(TDE) > 0.5, or ∼3000 TDEs yr^–1that FLEET could uncover from the Rubin stream. We have demonstrated that we will be able to run FLEET on Rubin photometry as soon as this survey begins. FLEET is provided as an open source package on GitHub: https://github.com/gmzsebastian/FLEET.

We present preexplosion 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 HSTRband to Spitzer 4.5μm. Fitting with 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 8600R_⊙circumstellar shell of dusty material with a mass ∼5 × 10⁻⁵M_⊙surrounding a$\log (L/L_{\odot })=4.74\pm 0.07$and$T_{\mathrm{eff}}={3920}_{-160}^{+200}$K RSG. This luminosity is consistent with RSG models of initial mass 11M_⊙, depending on assumptions of rotation and overshooting. In addition, the counterpart was significantly variable in preexplosion Spitzer 3.6 and 4.5μm imaging, exhibiting ∼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 withκ-mechanism pulsations observed in RSGs, albeit with a much larger amplitude than RSGs such asαOrionis (Betelgeuse).

With the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only ∼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 ourSuperRAENNandSuperphotalgorithms. We first construct a subsample 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 multiband 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 consistent overall, 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 for an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study.

The core collapse of rapidly rotating massive ∼ 10M_⊙stars (“collapsars”), and the resulting formation of hyperaccreting black holes, comprise a leading model for the central engines of long-duration gamma-ray bursts (GRBs) and promising sources ofr-process nucleosynthesis. Here, we explore the signatures of collapsars from progenitors with helium cores ≳ 130M_⊙above the pair-instability mass gap. While the rapid collapse to a black hole likely precludes prompt explosions in these systems, we demonstrate that disk outflows can generate a large quantity (up to ≳ 50M_⊙) of ejecta, comprised of ≳ 5–10M_⊙inr-process elements and ∼ 0.1–1M_⊙of⁵⁶Ni, expanding at velocities ∼0.1 c. Radioactive heating of the disk wind ejecta powers an optical/IR transient, with a characteristic luminosity ∼ 10⁴²erg s⁻¹and a spectral peak in the near-IR (due to the high optical/UV opacities of lanthanide elements), similar to kilonovae from neutron star mergers, but with longer durations ≳1 month. These “super-kilonovae” (superKNe) herald the birth of massive black holes ≳ 60M_⊙, 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 IR follow-up observations of extremely energetic GRBs. Multiband gravitational waves of ∼ 0.1–50 Hz from nonaxisymmetric instabilities in self-gravitating massive collapsar disks are potentially detectable by proposed observatories out 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”).

The LIGO HET Response (LIGHETR) project is an enterprise to follow up optical transients (OTs) 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 LRS2 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 hr 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 five LVC gravitational-wave events and LRS2 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.