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Abstract We present a detailed study of SN 2024ahr, a hydrogen-poor superluminous supernova (SLSN-I), for which we determine a redshift ofz= 0.0861. SN 2024ahr has a peak absolute magnitude ofM_g≈M_r≈ −21 mag, rest-frame rise and decline times (50% of peak) of about 40 and 80 days, respectively, and typical spectroscopic evolution in the optical band. Similarly, modeling of the UV/optical light curves with a magnetar spin-down engine leads to typical parameters: an initial spin period of ≈3.3 ms, a magnetic field strength of ≈6 × 10¹³G, and an ejecta mass of ≈9.5M_⊙. Due to its relatively low redshift, we obtained a high signal-to-noise ratio near-IR (NIR) spectrum about 43 rest-frame days postpeak to search for the presence of helium. We do not detect any significant feature at the location of the Heiλ2.058μm feature and place a conservative upper limit of ∼0.05M_⊙on the mass of helium in the outer ejecta. We detect broad features of Mgiλ1.575μm and Mgiiλ2.136μm, which are typical of Type Ic SNe, but with higher velocities. Examining the sample of SLSNe-I with NIR spectroscopy, we find that, unlike SN 2024ahr, these events are generally peculiar. This highlights the need for a large sample of prototypical SLSNe-I with NIR spectroscopy to constrain the fraction of progenitors with helium (Ib-like) and without helium (Ic-like) at the time of explosion, and hence the evolutionary path(s) leading to the rare outcome of SLSNe-I. 

ABSTRACT Hydrogen-poor superluminous supernovae (SLSNe) are among the most energetic explosions in the universe, reaching luminosities up to 100 times greater than those of normal supernovae. This paper presents the largest compilation of SLSN photospheric spectra to date, encompassing data from the advanced Public ESO Spectroscopic Survey of Transient Objects (ePESSTO+), the Finding Luminous and Exotic Extragalactic Transients (FLEET) search, and all published spectra up to December 2022. The data set includes a total of 974 spectra of 234 SLSNe. By constructing average phase binned spectra, we find SLSNe initially exhibit high temperatures (10 000–11 000 K), with blue continua and weak lines. A rapid transformation follows, as temperatures drop to 5000–6000 K by 40 d post-peak, leading to stronger P-Cygni features. Variance within the data set is slightly reduced when defining the phase of spectra relative to explosion, rather than peak, and normalising to the population’s median e-folding decline time. Principal Component Analysis (PCA) supports this, requiring fewer components to explain the same level of variation when binning data by scaled days from explosion, suggesting a more homogeneous grouping. Using PCA and K-means clustering, we identify outlying objects with unusual spectroscopic evolution and evidence for energy input from interaction, but find no support for groupings of two or more statistically significant subpopulations. We find Fe ii  $$\lambda$$5169 line velocities closely track the radius implied from blackbody fits, indicating formation near the photosphere. We also confirm a correlation between velocity and velocity gradient, which can be explained if all SLSNe are in homologous expansion but with different scale velocities. This behaviour aligns with expectations for an internal powering mechanism. 

Abstract With the advent of the Vera C. Rubin Observatory, the discovery rate of supernovae (SNe) will surpass the rate of SNe with real time spectroscopic follow-up by 3 orders of magnitude. Accurate photometric classifiers are essential to both select interesting events for follow-up in real time and for archival population-level studies. In this work, we investigate the impact of observable host-galaxy information on the classification of SNe, both with and without additional light-curve and redshift information. We find that host-galaxy information alone can successfully isolate relatively pure (>90%) samples of Type Ia SNe with or without redshift information. With redshift information, we can additionally produce somewhat pure (>70%) samples of Type II SNe and superluminous SNe. Additionally with redshift information, host-galaxy properties do not significantly improve the accuracy of SN classification when paired with complete light curves. In the absence of redshift information, however, galaxy properties significantly increase the accuracy of photometric classification. As a part of this analysis, we present the first formal application of a new objective function, the weighted hierarchical cross entropy, to the problem of SN classification. This objective function more naturally accounts for the hierarchical nature of SN classes and, more broadly, transients. Finally, we present a new set of SN classifications for the Pan-STARRS Medium Deep Survey of SNe that lack spectroscopic redshift, increasing the full photometric sample to >4400 events. 

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 We present optical photometry and spectroscopy of the Type IIn supernova (SN) 2021qqp. Its unusual light curve is marked by a long precursor for ≈300 days, a rapid increase in brightness for ≈60 days, and then a sharp increase of ≈1.6 mag in only a few days to a first peak ofM_r≈ −19.5 mag. The light curve then declines rapidly until it rebrightens to a second distinct peak ofM_r≈ −17.3 mag centered at ≈335 days after the first peak. The spectra are dominated by Balmer lines with a complex morphology, including a narrow component with a width of ≈1300 km s⁻¹(first peak) and ≈2500 km s⁻¹(second peak) that we associate with the circumstellar medium (CSM) and a P Cygni component with an absorption velocity of ≈8500 km s⁻¹(first peak) and ≈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 ≈10 and ≈5M_⊙yr⁻¹about 0.8 and 2 yr before explosion, respectively, with a total CSM mass of ≈2–4M_⊙. 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 ≈5–30M_⊙for an explosion energy of ≈(3–10) × 10⁵¹erg. We discuss eruptive massive stars (luminous blue variable, pulsational pair instability) and an extreme stellar merger with a compact object as possible progenitor channels. 

Abstract We present an extensive Hubble Space Telescope rest-frame UV imaging study of the locations of Type I superluminous supernovae (SLSNe) within their host galaxies. The sample includes 65 SLSNe with detected host galaxies in the redshift rangez≈ 0.05–2. Using precise astrometric matching with SN images, we determine the distributions of the physical and host-normalized offsets relative to the host centers, as well as the fractional flux distribution relative to the underlying UV light distributions. We find that the host-normalized offsets of SLSNe roughly track an exponential disk profile, but exhibit an overabundance of sources with large offsets of 1.5–4 times their hosts' half-light radii. The SLSNe normalized offsets are systematically larger than those of long gamma-ray bursts (LGRBs), and even Type Ib/c and Type II SNe. Furthermore, we find from a Monte Carlo procedure that about ${37}_{-8}^{+6}\%$of SLSNe occur in the dimmest regions of their host galaxies, with a median fractional flux value of 0.16, in stark contrast to LGRBs and Type Ib/c and Type II SNe. We do not detect any significant trends in the locations of SLSNe as a function of redshift, or as a function of explosion and magnetar engine parameters inferred from modeling of their optical light curves. The significant difference in SLSN locations compared to LGRBs (and normal core-collapse SNe) suggests that at least some of their progenitors follow a different evolutionary path. We speculate that SLSNe arise from massive runaway stars from disrupted binary systems, with velocities of ∼10²km s⁻¹. 

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.