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Optical surveys have become increasingly adept at identifying candidate tidal disruption events (TDEs) in large numbers, but classifying these generally requires extensive spectroscopic resources. Here we presenttdescore, a simple binary photometric classifier that is trained using a systematic census of ∼3000 nuclear transients from the Zwicky Transient Facility (ZTF). The sample is highly imbalanced, with TDEs representing ∼2% of the total.tdescoreis nonetheless able to reject non-TDEs with 99.6% accuracy, yielding a sample of probable TDEs with recall of 77.5% for a precision of 80.2%.tdescoreis thus substantially better than any available TDE photometric classifier scheme in the literature, with performance not far from spectroscopy as a method for classifying ZTF nuclear transients, despite relying solely on ZTF data and multiwavelength catalog cross matching. In a novel extension, we use “Shapley additive explanations” to provide a human-readable justification for each individualtdescoreclassification, enabling users to understand and form opinions about the underlying classifier reasoning.tdescorecan serve as a model for photometric identification of TDEs with time-domain surveys, such as the upcoming Rubin observatory.

We analyse 33 Type I superluminous supernovae (SLSNe) taken from Zwicky Transient Facility (ZTF)’s Bright Transient Survey to investigate the local environments of their host galaxies. We use a spectroscopic sample of galaxies from the Sloan Digital Sky Survey (SDSS) to determine the large-scale environmental density of the host galaxy. Noting that SLSNe are generally found in galaxies with low stellar masses, high star formation rates (SFRs), and low metallicities, we find that SLSN hosts are also rarely found within high-density environments. Only $3\substack{+9 \\ -1}$ per cent of SLSN hosts were found in regions with two or more bright galaxies within 2 Mpc. For comparison, we generate a sample of 662 SDSS galaxies matched to the photometric properties of the SLSN hosts. This sample is also rarely found within high-density environments, suggesting that galaxies with properties required for SLSN production favour more isolated environments. Furthermore, we select galaxies within the IllustrisTNG simulation to match SLSN host galaxy properties in colour and stellar mass. We find that the fraction of simulated galaxies in high-density environments quantitatively match the observed SLSN hosts only if we restrict to simulated galaxies with metallicity 12 + log (O/H) ≤ 8.12. In contrast, limiting to only the highest specific star formation rate (sSFR) galaxies in the sample leads to an overabundance of SLSN hosts in high-density environments. Thus, our measurement of the environmental density of SLSN host galaxies appears to break the degeneracy between low metallicity and high sSFR as the driver for SLSN hosts and provides evidence that the most constraining factor on SLSN production is low metallicity.

Tidal disruption events (TDEs), in which a star is destroyed by the gravitational field of a supermassive black hole (SMBH), are being observed at a high rate owing to the advanced state of survey science. One of the properties of TDEs that is measured with increasing statistical reliability is the TDE luminosity function,$d{\dot{N}}_{\mathrm{TDE}}/\mathit{dL}$, which is the TDE rate per luminosity (i.e., how many TDEs are within a given luminosity range). Here we show that if the luminous emission from a TDE is directly coupled to the rate of return of tidally destroyed debris to the SMBH, then the TDE luminosity function is in good agreement with observations and scales as ∝L^−2.5for high luminosities, provided that the SMBH mass function${\mathit{dN}}_{•}/{\mathit{dM}}_{•}$—the number of SMBHs (N_•) per SMBH mass (M_•)—is approximately flat in the mass range over which we observe TDEs. We also show that there is a cutoff in the luminosity function at low luminosities that is a result of direct captures, and this cutoff has been tentatively observed. If${\mathit{dN}}_{•}/{\mathit{dM}}_{•}$is flat, which is in agreement with some observational campaigns, these results suggest that the fallback rate feeds the accretion rate in TDEs. Contrarily, if${\mathit{dN}}_{•}/d\log M_{•}$is flat, which has been found theoretically and is suggested by other observational investigations, then the emission from TDEs is likely powered by another mechanism. Future observations and more TDE statistics, provided by the Rubin Observatory/LSST, will provide additional evidence as to the reality of this tension.

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.

Stripped-envelope core-collapse supernovae can be divided into two broad classes: the common Type Ib/c supernovae (SNe Ib/c), powered by the radioactive decay of⁵⁶Ni, and the rare superluminous supernovae (SLSNe), most likely powered by the spin-down of a magnetar central engine. Up to now, the intermediate regime between these two populations has remained mostly unexplored. Here, we present a comprehensive study of 40luminous supernovae(LSNe), SNe with peak magnitudes ofM_r= −19 to −20 mag, bound by SLSNe on the bright end and by SNe Ib/c on the dim end. Spectroscopically, LSNe appear to form a continuum between Type Ic SNe and SLSNe. Given their intermediate nature, we model the light curves of all LSNe using a combined magnetar plus radioactive decay model and find that they are indeed intermediate, not only in terms of their peak luminosity and spectra, but also in their rise times, power sources, and physical parameters. We subclassify LSNe into distinct groups that are either as fast evolving as SNe Ib/c or as slow evolving as SLSNe, and appear to be either radioactively or magnetar powered, respectively. Our findings indicate that LSNe are powered by either an overabundant production of⁵⁶Ni or by weak magnetar engines, and may serve as the missing link between the two populations.

Secure confirmation that a gravitational wave (GW) has been gravitationally lensed would bring together these two pillars of General Relativity for the first time. This breakthrough is challenging for many reasons, including: GW sky localization uncertainties dwarf the angular scale of gravitational lensing, the mass and structure of gravitational lenses is diverse, the mass function of stellar remnant compact objects is not yet well constrained, and GW detectors do not operate continuously. We introduce a new approach that is agnostic to the mass and structure of the lenses, compare the efficiency of different methods for lensed GW discovery, and explore detection of lensed kilonova counterparts as a direct method for localizing candidates. Our main conclusions are: (1) lensed neutron star mergers (NS–NS) are magnified into the ‘mass gap’ between NS and black holes, therefore selecting candidates from public GW alerts with high mass gap probability is efficient, (2) the rate of detectable lensed NS–NS will approach one per year in the mid-2020s, (3) the arrival time difference between lensed NS–NS images is $1\, \rm s\lesssim \Delta \mathit{ t}\lesssim 1\, yr$, and thus well-matched to the operations of GW detectors and optical telescopes, (4) lensed kilonova counterparts are faint at peak (e.g. rAB ≃ 24–26 in the mid-2020s), fade quickly ($d\lt 2\, \rm d$), and are detectable with target of opportunity observations with large wide-field telescopes. For example, just ≲ 0.25 per cent of Vera C. Rubin Observatory’s observing time will be sufficient to follow up one well-localized candidate per year. Our predictions also provide a physically well-defined basis for exploring electromagnetically the exciting new ‘mass gap’ discovery space.

We present new spectroscopic and photometric follow-up observations of the known sample of extreme coronal line-emitting galaxies (ECLEs) identified in the Sloan Digital Sky Survey (SDSS). With these new data, observations of the ECLE sample now span a period of two decades following their initial SDSS detections. We confirm the non-recurrence of the iron coronal line signatures in five of the seven objects, further supporting their identification as the transient light echoes of tidal disruption events (TDEs). Photometric observations of these objects in optical bands show little overall evolution. In contrast, mid-infrared (MIR) observations show ongoing long-term declines consistent with power-law decay. The remaining two objects had been classified as active galactic nuclei (AGNs) with unusually strong coronal lines rather than being TDE related, given the persistence of the coronal lines in earlier follow-up spectra. We confirm this classification, with our spectra continuing to show the presence of strong, unchanged coronal line features and AGN-like MIR colours and behaviour. We have constructed spectral templates of both subtypes of ECLE to aid in distinguishing the likely origin of newly discovered ECLEs. We highlight the need for higher cadence, and more rapid, follow-up observations of such objects to better constrain their properties and evolution. We also discuss the relationships between ECLEs, TDEs, and other identified transients having significant MIR variability.

Recent work has revealed that the light curves of hydrogen-poor (Type I) superluminous supernovae (SLSNe), thought to be powered by magnetar central engines, do not always follow the smooth decline predicted by a simple magnetar spin-down model. Here we present the first systematic study of the prevalence and properties of “bumps” in the post-peak light curves of 34 SLSNe. We find that the majority (44%–76%) of events cannot be explained by a smooth magnetar model alone. We do not find any difference in supernova properties between events with and without bumps. By fitting a simple Gaussian model to the light-curve residuals, we characterize each bump with an amplitude, temperature, phase, and duration. We find that most bumps correspond with an increase in the photospheric temperature of the ejecta, although we do not see drastic changes in spectroscopic features during the bump. We also find a moderate correlation (ρ≈ 0.5;p≈ 0.01) between the phase of the bumps and the rise time, implying that such bumps tend to happen at a certain “evolutionary phase,” (3.7 ± 1.4)t_rise. Most bumps are consistent with having diffused from a central source of variable luminosity, although sources further out in the ejecta are not excluded. With this evidence, we explore whether the cause of these bumps is intrinsic to the supernova (e.g., a variable central engine) or extrinsic (e.g., circumstellar interaction). Both cases are plausible, requiring low-level variability in the magnetar input luminosity, small decreases in the ejecta opacity, or a thin circumstellar shell or disk.