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1. 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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2. The Dark Energy Survey Supernova Program: Light Curves and 5 Yr Data Release

   https://doi.org/10.3847/1538-4357/ad739a  

   Sánchez, B O; Brout, D; Vincenzi, M; Sako, M; Herner, K; Kessler, R; Davis, T M; Scolnic, D; Acevedo, M; Lee, J; et al (October 2024, The Astrophysical Journal)

   Abstract We presentgrizphotometric light curves for the full 5 yr of the Dark Energy Survey Supernova (DES-SN) program, obtained with both forced point-spread function photometry on difference images (DiffImg) performed during survey operations, and scene modelling photometry (SMP) on search images processed after the survey. This release contains 31,636DiffImgand 19,706 high-quality SMP light curves, the latter of which contain 1635 photometrically classified SNe that pass cosmology quality cuts. This sample spans the largest redshift (z) range ever covered by a single SN survey (0.1 <z< 1.13) and is the largest single sample from a single instrument of SNe ever used for cosmological constraints. We describe in detail the improvements made to obtain the final DES-SN photometry and provide a comparison to what was used in the 3 yr DES-SN spectroscopically confirmed Type Ia SN sample. We also include a comparative analysis of the performance of the SMP photometry with respect to the real-timeDiffImgforced photometry and find that SMP photometry is more precise, more accurate, and less sensitive to the host-galaxy surface brightness anomaly. The public release of the light curves and ancillary data can be found atgithub.com/des-science/DES-SN5YRand doi:10.5281/zenodo.12720777. 

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3. Superphot+: Real-time Fitting and Classification of Supernova Light Curves

   https://doi.org/10.3847/1538-4357/ad6a4f  

   de_Soto, Kaylee M; Villar, V Ashley; Berger, Edo; Gomez, Sebastian; Hosseinzadeh, Griffin; Branton, Doug; Campos, Sandro; DeLucchi, Melissa; Kubica, Jeremy; Lynn, Olivia; et al (October 2024, The Astrophysical Journal)

   Abstract Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averagedF₁-score of 0.61 ± 0.02 and a total accuracy of 0.83 ± 0.01. Including redshift information improves these metrics to 0.71 ± 0.02 and 0.88 ± 0.01, respectively. We assign new class probabilities to 3558 ZTF transients that show SN-like characteristics (based on the ALeRCE Broker light-curve and stamp classifiers) but lack spectroscopic classifications. Finally, we compare our predicted SN labels with those generated by the ALeRCE light-curve classifier, finding that the two classifiers agree on photometric labels for 82% ± 2% of light curves with spectroscopic labels and 72% ± 0% of light curves without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in real time via the ANTARES Broker, and is designed for simple adaptation to six-band Rubin light curves in the future. 

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4. Supernovae at distances <40 Mpc: I. Catalogues and fractions of supernovae in a complete sample

   https://doi.org/10.1051/0004-6361/202452684  

   Ma, Xiaoran; Wang, Xiaofeng; Mo, Jun; Howell, D Andrew; Pellegrino, Craig; Zhang, Jujia; Yan, Shengyu; Arcavi, Iair; Chen, Zhihao; Farah, Joseph; et al (June 2025, Astronomy & Astrophysics)

   Context. This is the first paper in a series aiming to determine the fractions and birth rates of various types of supernovae (SNe) in the local Universe. Aims. In this paper, we aim to construct a complete sample of SNe in the nearby Universe and provide more precise measurements of subtype fractions. Methods. We carefully selected our SN sample at a distance of less than 40 Mpc mainly from wide-field surveys conducted over the years from 2016 to 2023. Results. The sample contains a total of 211 SNe, including 109 SNe II, 69 SNe Ia, and 33 SNe Ibc. With the aid of sufficient spectra, we obtained relatively accurate subtype classifications for all SNe in this sample. After corrections for the Malmquist bias, this volumelimited sample yielded fractions of SNe Ia, SNe Ibc, and SNe II of 30.4_−11.5^+3.7%, 16.3_−7.4^+3.7%, and 53.3_−18.7^+9.5%, respectively. In the SN Ia sample, the fraction of the 91T-like subtype becomes relatively low (~5.4%), while that of the 02cx-like subtype shows a moderate increase (~6.8%). In the SN Ibc sample, we find significant fractions of broadlined SNe Ic (~18.0%) and SNe Ibn (~8.8%). The fraction of the 87A-like subtype was determined to be ~2.3%, indicating rare explosions from blue supergiant stars. We find that SNe Ia show a double peak number distribution in S0- and Sc-type host galaxies, which may serve as straightforward evidence for the presence of “prompt” and “delayed” progenitor components that give rise to SN Ia explosions. Several subtypes of SNe such as 02cx-like SNe Ia, broadlined SNe Ic, and SNe IIn (and perhaps SNe Ibn) are found to occur preferentially in less massive spiral galaxies (i.e., with stellar mass <0.5×10¹⁰M_ʘ), thus favoring their associations with young stellar progenitors. Moreover, the 02cx-like subtype shows a trend of exploding in the outer skirt of their hosts, which is suggestive of metal-poor progenitors. 

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5. The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties

   https://doi.org/10.3847/1538-4357/ad5e6c  

   Vincenzi, M; Brout, D; Armstrong, P; Popovic, B; Taylor, G; Acevedo, M; Camilleri, R; Chen, R; Davis, T M; Lee, J; et al (October 2024, The Astrophysical Journal)

   Abstract We present the full Hubble diagram of photometrically classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7000 host galaxies. Based on the light-curve quality, we select 1635 photometrically identified SNe Ia with spectroscopic redshift 0.10 <z< 1.13, which is the largest sample of supernovae from any single survey and increases the number of knownz> 0.5 supernovae by a factor of 5. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are ${\sigma }_{{\mathrm{\Omega }}_M,\mathrm{stat}+\mathrm{sys}}^{\mathrm{\Lambda }\mathrm{CDM}}=$0.017 in a flat ΛCDM model, and $({\sigma }_{{\mathrm{\Omega }}_M},{\sigma }_w{)}_{\mathrm{stat}+\mathrm{sys}}^{w\mathrm{CDM}}$= (0.082, 0.152) in a flatwCDM model. Combining the DES SN data with the highly complementary cosmic microwave background measurements by Planck Collaboration reduces by a factor of 4 uncertainties on cosmological parameters. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time. 

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