More Like this

1. ASAS-SN follow-up of IceCube high-energy neutrino alerts

   https://doi.org/10.1093/mnras/stac2261  

   Necker, Jannis ; de Jaeger, Thomas ; Stein, Robert ; Franckowiak, Anna ; Shappee, Benjamin J. ; Kowalski, Marek ; Kochanek, Christopher S. ; Stanek, Krzysztof Z. ; Beacom, John F. ; Desai, Dhvanil D. ; et al ( August 2022 , Monthly Notices of the Royal Astronomical Society)

   We report on the search for optical counterparts to IceCube neutrino alerts released between 2016 April and 2021 August with the All-Sky Automated Survey for SuperNovae (ASAS-SN). Despite the discovery of a diffuse astrophysical high-energy neutrino flux in 2013, the source of those neutrinos remains largely unknown. Since 2016, IceCube has published likely astrophysical neutrinos as public real-time alerts. Through a combination of normal survey and triggered target-of-opportunity observations, ASAS-SN obtained images within 1 h of the neutrino detection for 20 per cent (11) of all observable IceCube alerts and within one day for another 57 per cent (32). For all observable alerts, we obtained images within at least two weeks from the neutrino alert. ASAS-SN provides the only optical follow-up for about 17 per cent of IceCube’s neutrino alerts. We recover the two previously claimed counterparts to neutrino alerts, the flaring-blazar TXS 0506 + 056 and the tidal disruption event AT2019dsg. We investigate the light curves of previously detected transients in the alert footprints, but do not identify any further candidate neutrino sources. We also analysed the optical light curves of Fermi 4FGL sources coincident with high-energy neutrino alerts, but do not identify any contemporaneous flaring activity. Finally, we derive constraints on the luminosity functions of neutrinomore »sources for a range of assumed evolution models.

   « less
2. The Hyper Suprime-Cam SSP transient survey in COSMOS: Overview

   https://doi.org/10.1093/pasj/psz050  

   Yasuda, Naoki ; Tanaka, Masaomi ; Tominaga, Nozomu ; Jiang, Ji-an ; Moriya, Takashi J. ; Morokuma, Tomoki ; Suzuki, Nao ; Takahashi, Ichiro ; Yamaguchi, Masaki S. ; Maeda, Keiichi ; et al ( May 2019 , Publications of the Astronomical Society of Japan)

   We present an overview of a deep transient survey of the COSMOS field with the Subaru Hyper Suprime-Cam (HSC). The survey was performed for the 1.77 deg2 ultra-deep layer and 5.78 deg2 deep layer in the Subaru Strategic Program over six- and four-month periods from 2016 to 2017, respectively. The ultra-deep layer reaches a median depth per epoch of 26.4, 26.3, 26.0, 25.6, and 24.6 mag in g, r, i, z, and y bands, respectively; the deep layer is ∼0.6 mag shallower. In total, 1824 supernova candidates were identified. Based on light-curve fitting and derived light-curve shape parameter, we classified 433 objects as Type Ia supernovae (SNe); among these candidates, 129 objects have spectroscopic or COSMOS2015 photometric redshifts and 58 objects are located at z > 1. Our unique data set doubles the number of Type Ia SNe at z > 1 and enables various time-domain analyses of Type II SNe, high-redshift superluminous SNe, variable stars, and active galactic nuclei.
3. Photometric Classification of Early-time Supernova Light Curves with SCONE

   https://doi.org/10.3847/1538-3881/ac39a1  

   Qu, Helen ; Sako, Masao ( January 2022 , The Astronomical Journal)

   In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe (r< 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% five days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometricmore »transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package¹1

   github.com/helenqu/scone

   located at github.com/helenqu/scone (Qu 2021).

   « less
4. Supernova 2020wnt: An Atypical Superluminous Supernova with a Hidden Central Engine

   https://doi.org/10.3847/1538-4357/acc6c3  

   Tinyanont, Samaporn ; Woosley, Stan E. ; Taggart, Kirsty ; Foley, Ryan J. ; Yan, Lin ; Lunnan, Ragnhild ; Davis, Kyle W. ; Kilpatrick, Charles D. ; Siebert, Matthew R. ; Schulze, Steve ; et al ( June 2023 , The Astrophysical Journal)

   We present observations of a peculiar hydrogen- and helium-poor stripped-envelope (SE) supernova (SN) 2020wnt, primarily in the optical and near-infrared (near-IR). Its peak absolute bolometric magnitude of −20.9 mag (L_{bol, peak}= (6.8 ± 0.3) × 10⁴³erg s⁻¹) and a rise time of 69 days are reminiscent of hydrogen-poor superluminous SNe (SLSNe I), luminous transients potentially powered by spinning-down magnetars. Before the main peak, there is a brief peak lasting <10 days post explosion, likely caused by interaction with circumstellar medium (CSM) ejected ∼years before the SN explosion. The optical spectra near peak lack a hot continuum and Oiiabsorptions, which are signs of heating from a central engine; they quantitatively resemble those of radioactivity-powered hydrogen/helium-poor Type Ic SESNe. At ∼1 yr after peak, nebular spectra reveal a blue pseudo-continuum and narrow Oirecombination lines associated with magnetar heating. Radio observations rule out strong CSM interactions as the dominant energy source at +266 days post peak. Near-IR observations at +200–300 days reveal carbon monoxide and dust formation, which causes a dramatic optical light-curve dip. Pair-instability explosion models predict slow light curve and spectral features incompatible with observations. SN 2020wnt is best explained as a magnetar-powered core-collapse explosion of a 28M_⊙pre-SN star. Themore »explosion kinetic energy is significantly larger than the magnetar energy at peak, effectively concealing the magnetar-heated inner ejecta until well after peak. SN 2020wnt falls into a continuum between normal SNe Ic and SLSNe I, and demonstrates that optical spectra at peak alone cannot rule out the presence of a central engine.

   « less
5. Cosmological Results from the RAISIN Survey: Using Type Ia Supernovae in the Near Infrared as a Novel Path to Measure the Dark Energy Equation of State

   https://doi.org/10.3847/1538-4357/ac755b  

   Jones, D. O. ; Mandel, K. S. ; Kirshner, R. P. ; Thorp, S. ; Challis, P. M. ; Avelino, A. ; Brout, D. ; Burns, C. ; Foley, R. J. ; Pan, Y. -C. ; et al ( July 2022 , The Astrophysical Journal)

   Type Ia supernovae (SNe Ia) are more precise standardizable candles when measured in the near-infrared (NIR) than in the optical. With this motivation, from 2012 to 2017 we embarked on the RAISIN program with the Hubble Space Telescope (HST) to obtain rest-frame NIR light curves for a cosmologically distant sample of 37 SNe Ia (0.2 ≲z≲ 0.6) discovered by Pan-STARRS and the Dark Energy Survey. By comparing higher-zHST data with 42 SNe Ia atz< 0.1 observed in the NIR by the Carnegie Supernova Project, we construct a Hubble diagram from NIR observations (with only time of maximum light and some selection cuts from optical photometry) to pursue a unique avenue to constrain the dark energy equation-of-state parameter,w. We analyze the dependence of the full set of Hubble residuals on the SN Ia host galaxy mass and find Hubble residual steps of size ∼0.06-0.1 mag with 1.5σ−2.5σsignificance depending on the method and step location used. Combining our NIR sample with cosmic microwave background constraints, we find 1 +w= −0.17 ± 0.12 (statistical + systematic errors). The largest systematic errors are the redshift-dependent SN selection biases and the properties of the NIR mass step. We also use these data to measureH₀=more »75.9 ± 2.2 km s⁻¹Mpc⁻¹from stars with geometric distance calibration in the hosts of eight SNe Ia observed in the NIR versusH₀= 71.2 ± 3.8 km s⁻¹Mpc⁻¹using an inverse distance ladder approach tied to Planck. Using optical data, we find 1 +w= −0.10 ± 0.09, and with optical and NIR data combined, we find 1 +w= −0.06 ± 0.07; these shifts of up to ∼0.11 inwcould point to inconsistency in the optical versus NIR SN models. There will be many opportunities to improve this NIR measurement and better understand systematic uncertainties through larger low-zsamples, new light-curve models, calibration improvements, and eventually by building high-zsamples from the Roman Space Telescope.

   « less