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1. FLEET: A Redshift-agnostic Machine Learning Pipeline to Rapidly Identify Hydrogen-poor Superluminous Supernovae

   https://doi.org/10.3847/1538-4357/abbf49  

   Gomez, Sebastian; Berger, Edo; Blanchard, Peter K.; Hosseinzadeh, Griffin; Nicholl, Matt; Villar, V. Ashley; Yin, Yao (November 2020, The Astrophysical Journal)

   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 $$\sim {10}^{3}$$ events per year. 

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2. Identifying Tidal Disruption Events with an Expansion of the FLEET Machine-learning Algorithm

   https://doi.org/10.3847/1538-4357/acc535  

   Gomez, Sebastian; Villar, V. Ashley; Berger, Edo; Gezari, Suvi; van Velzen, Sjoert; Nicholl, Matt; Blanchard, Peter K.; Alexander, Kate. D. (June 2023, The Astrophysical Journal)

   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. 

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3. The Type I superluminous supernova catalogue I: light-curve properties, models, and catalogue description

   https://doi.org/10.1093/mnras/stae2270  

   Gomez, Sebastian; Nicholl, Matt; Berger, Edo; Blanchard, Peter K; Villar, V Ashley; Rest, Sofia; Hosseinzadeh, Griffin; Aamer, Aysha; Ajay, Yukta; Athukoralalage, Wasundara; et al (October 2024, Monthly Notices of the Royal Astronomical Society)

   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. 

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4. Photometrically Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine-learning-based Classification

   https://doi.org/10.3847/1538-4357/ac87ff  

   Hsu, Brian; Hosseinzadeh, Griffin; Villar, V. Ashley; Berger, Edo (September 2022, The Astrophysical Journal)

   Abstract 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 our SuperRAENN and Superphot algorithms. 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. 

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5. The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae

   https://doi.org/10.3847/1538-4365/acbfba  

   Aleo, P. D.; Malanchev, K.; Sharief, S.; Jones, D. O.; Narayan, G.; Foley, R. J.; Villar, V. A.; Angus, C. R.; Baldassare, V. F.; Bustamante-Rosell, M. J.; et al (May 2023, The Astrophysical Journal Supplement Series)

   Abstract We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host–galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z ≈ 0.5. We present relative SN rates from YSE’s magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (∼71%) SNe Ia, 339 (∼23%) SNe II, and 96 (∼6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time. 

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