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The modern study of astrophysical transients has been transformed by an exponentially growing volume of data. Within the last decade, the transient discovery rate has increased by a factor of ∼20, with associated survey data, archival data, and metadata also increasing with the number of discoveries. To manage the data at this increased rate, we require new tools. Here we presentYSE-PZ, a transient survey management platform that ingests multiple live streams of transient discovery alerts, identifies the host galaxies of those transients, downloads coincident archival data, and retrieves photometry and spectra from ongoing surveys.YSE-PZalso presents a user with a range of tools to make and support timely and informed transient follow-up decisions. Those subsequent observations enhance transient science and can reveal physics only accessible with rapid follow-up observations. Rather than automating out human interaction,YSE-PZfocuses on accelerating and enhancing human decision making, a role we describe as empowering the human-in-the-loop. Finally,YSE-PZis built to be flexibly used and deployed;YSE-PZcan support multiple, simultaneous, and independent transient collaborations through group-level data permissions, allowing a user to view the data associated with the union of all groups in which they are a member.YSE-PZcan be used as a local instance installed via Docker or deployed as a service hosted in the cloud. We provideYSE-PZas an open-source tool for the community.

 

Abstract We present panchromatic observations and modeling of supernova (SN) 2020tlf, the first normal Type II-P/L SN with confirmed precursor emission, as detected by the Young Supernova Experiment transient survey. Pre-SN activity was detected in riz -bands at −130 days and persisted at relatively constant flux until first light. Soon after discovery, “flash” spectroscopy of SN 2020tlf revealed narrow, symmetric emission lines that resulted from the photoionization of circumstellar material (CSM) shed in progenitor mass-loss episodes before explosion. Surprisingly, this novel display of pre-SN emission and associated mass loss occurred in a red supergiant (RSG) progenitor with zero-age main-sequence mass of only 10–12 M ⊙ , as inferred from nebular spectra. Modeling of the light curve and multi-epoch spectra with the non-LTE radiative-transfer code CMFGEN and radiation-hydrodynamical code HERACLES suggests a dense CSM limited to r ≈ 10 15 cm, and mass-loss rate of 10 −2 M ⊙ yr −1 . The luminous light-curve plateau and persistent blue excess indicates an extended progenitor, compatible with an RSG model with R ⋆ = 1100 R ⊙ . Limits on the shock-powered X-ray and radio luminosity are consistent with model conclusions and suggest a CSM density of ρ < 2 × 10 −16 g cm −3 for distances from the progenitor star of r ≈ 5 × 10 15 cm, as well as a mass-loss rate of M ̇ < 1.3 × 10 − 5 M ☉ yr − 1 at larger distances. A promising power source for the observed precursor emission is the ejection of stellar material following energy disposition into the stellar envelope as a result of gravity waves emitted during either neon/oxygen burning or a nuclear flash from silicon combustion. 

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.