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Abstract 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. 

Abstract 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. 

ABSTRACT We present the most comprehensive catalogue 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 ultraviolet, optical, and infrared photometry totalling over 30 000 photometric detections. Using these data, we derive observational parameters such as the peak absolute magnitudes, rise and decline time-scales, 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 subtypes of SLSNe. We find that only a small fraction of SLSNe, $$\lt 3$$ per cent, are best fit with a significant radioactive decay component $$\gtrsim 50$$ per cent. 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. 

Abstract 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. 

Abstract 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. 

Abstract 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. 

Abstract Dust associated with various stellar sources in galaxies at all cosmic epochs remains a controversial topic, particularly whether supernovae play an important role in dust production. We report evidence of dust formation in the cold, dense shell behind the ejecta–circumstellar medium (CSM) interaction in the Type Ia-CSM supernova (SN) 2018evt three years after the explosion, characterized by a rise in mid-infrared emission accompanied by an accelerated decline in the optical radiation of the SN. Such a dust-formation picture is also corroborated by the concurrent evolution of the profiles of the Hα emission line. Our model suggests enhanced CSM dust concentration at increasing distances from the SN as compared to what can be expected from the density profile of the mass loss from a steady stellar wind. By the time of the last mid-infrared observations at day +1,041, a total amount of 1.2 ± 0.2 × 10⁻² M_⊙of new dust has been formed by SN 2018evt, making SN 2018evt one of the most prolific dust factories among supernovae with evidence of dust formation. The unprecedented witness of the intense production procedure of dust may shed light on the perceptions of dust formation in cosmic history. 

Abstract AT 2019azh is a H+He tidal disruption event (TDE) with one of the most extensive ultraviolet and optical data sets available to date. We present our photometric and spectroscopic observations of this event starting several weeks before and out to approximately 2 yr after theg-band's peak brightness and combine them with public photometric data. This extensive data set robustly reveals a change in the light-curve slope and a possible bump in the rising light curve of a TDE for the first time, which may indicate more than one dominant emission mechanism contributing to the pre-peak light curve. Indeed, we find that theMOSFiT-derived parameters of AT 2019azh, which assume reprocessed accretion as the sole source of emission, are not entirely self-consistent. We further confirm the relation seen in previous TDEs whereby the redder emission peaks later than the bluer emission. The post-peak bolometric light curve of AT 2019azh is better described by an exponential decline than by the canonicalt^−5/3(and in fact any) power-law decline. We find a possible mid-infrared excess around the peak optical luminosity, but cannot determine its origin. In addition, we provide the earliest measurements of the Hαemission-line evolution and find no significant time delay between the peak of theV-band light curve and that of the Hαluminosity. These results can be used to constrain future models of TDE line formation and emission mechanisms in general. More pre-peak 1–2 days cadence observations of TDEs are required to determine whether the characteristics observed here are common among TDEs. More importantly, detailed emission models are needed to fully exploit such observations for understanding the emission physics of TDEs. 

Photometry shown in Figure Extended Data 4 (a) of Wang, Lingzhi, et al. 2024, Nature Astronomy, https://doi.org/10.1038/s41550-024-02197-9.Phase is days since B-band maximum MJD 58352.BVgri-band photometry from 1-m network at Las Cumbres Observatory.SN2018evt_lcogt_lc.datBVgri-band photometry from 2.4-m LiJiang Telescope (LJT) and 60/90-cm XingLong Schmidt Telescope (XLST)SN2018evt_xlt_ljt_lc.datOptical and NIR spectra data shown in Figures Extended Data 2, 3, and Table Extended Data 2 of Wang, Lingzhi, et al. 2024, Nature Astronomy, NIR spectraSN2018evt_181224_spex.txt SN2018evt_190511_spex.txtSN2018evt_190617_spex.txtSN2018evt_200119_spex.txtSN2018evt_20190101_gnirs.txtSN2018evt_20190108_gnirs.txtSN2018evt_20190516_fire.datSN2018evt_20190712_fire.datOptical spectraOptical spectra observed with 2.4-m LiJiang Telescope (LJT)SN2018evt_190104_LJT_G3.datSN2018evt_190131_LJT_G3.datSN2018evt_190328_LJT_G3.datSN2018evt_190520_LJT_G3.datOptical spectra observed with 2.16-m XingLong Telescope (XLT)SN2018evt_20190208_2458551.3570_bao_bfosc.txtSN2018evt_20190220_2458563.3588_bao-bfosc.txtSN2018evt_20190413_2458587.2169_bao-bfosc.txtOptical spectra observed with 3.6-m ESO New Technology Telescope (NTT)SN2018evt_20180812_NTT_Gr13_Free_slit1.0_58346_1_e.asciSN2018evt_20190425_NTT_Gr13_Free_slit1.0_58599_1_e.asciSN2018evt_20190512_NTT_Gr13_Free_slit1.0_58616_1_e.asciSN2018evt_20190608_NTT_Gr13_Free_slit1.0_58643_1_e.asciSN2018evt_20200218_NTT_Gr13_Free_slit1.0_58899_1_e.asciSN2018evt_20200322_NTT_Gr13_Free_slit1.0_58931_1_e.asciOptical spectrum observed with WiFes mounted on 2.3-m telescope at the Siding Spring Observatory (WiFeS)SN2018evt_20190624_ANU_Wifes.datOptical spectrum observed with 2.0-m Faulkes Telescope North (FTN)/FLOYDSSN2018evt_20191224_FTN-floyds-redblu_145742.306.asciiSN2018evt_20200119_FTN-floyds-redblu_133856.906.asciiSN2018evt_20200203_FTN-floyds-redblu_125905.990.ascii