First Impressions: Early-Time Classification of Supernovae using Host Galaxy Information and Shallow Learning

Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase-coverage of an event, limiting their use for triggering rapid follow-up of ongoing phenomena. In this work, we introduce a neural network with a single recurrent layer designed explicitly for early photometric classification of supernovae. Our algorithm leverages transfer learning to account for model misspecification, host galaxy photometry to solve the data scarcity problem soon after discovery, and a custom weighted loss to prioritize accurate early classification. We first train our algorithm using state-of-the-art transient and host galaxy simulations, then adapt its weights and validate it on the spectroscopically-confirmed SNe Ia, SNe II, and SNe Ib/c from the Zwicky Transient Facility Bright Transient Survey. On observed data, our method achieves an overall accuracy of $82 \pm 2$% within 3 days of an event's discovery, and an accuracy of $87 \pm 5$% within 30 days of discovery. At both early and late phases, our method achieves comparable or superior results to the leading classification algorithms with a simpler network architecture. These results help pave the way for rapid photometric and spectroscopic follow-up of scientifically-valuable transients discovered in massive synoptic surveys.Comment: 24 pages, 8 figures. Accepted to Ap

Physical and Morphological Properties of [O II] Emitting Galaxies in the HETDEX Pilot Survey

The Hobby-Eberly Dark Energy Experiment pilot survey identified 284 [O II] 3727 emitting galaxies in a 169 square-arcminute field of sky in the redshift range 0 < z < 0.57. This line flux limited sample provides a bridge between studies in the local universe and higher-redshift [O II] surveys. We present an analysis of the star formation rates (SFRs) of these galaxies as a function of stellar mass as determined via spectral energy distribution fitting. The [O II] emitters fall on the "main sequence" of star-forming galaxies with SFR decreasing at lower masses and redshifts. However, the slope of our relation is flatter than that found for most other samples, a result of the metallicity dependence of the [O II] star formation rate indicator. The mass specific SFR is higher for lower mass objects, supporting the idea that massive galaxies formed more quickly and efficiently than their lower mass counterparts. This is confirmed by the fact that the equivalent widths of the [O II] emission lines trend smaller with larger stellar mass. Examination of the morphologies of the [O II] emitters reveals that their star formation is not a result of mergers, and the galaxies' half-light radii do not indicate evolution of physical sizes.Comment: 36 pages, 16 figures, 4 tables, accepted to Ap

Superphot+: Realtime Fitting and Classification of Supernova Light Curves

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 6,061 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-averaged F1-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 3,558 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% 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.Comment: 37 pages, 25 figures. Submitted to AAS Journal

Stress testing the dark energy equation of state imprint on supernova data

International audienceThis work determines the degree to which a traditional analysis of the standard model of cosmology (ΛCDM) based on type Ia supernovae can identify deviations from a cosmological constant in the form of a redshift-dependent dark energy equation of state w(z). We introduce and apply a novel random curve generator to simulate instances of w(z) from constraint families with increasing distinction from a cosmological constant. After producing a series of mock catalogs of binned type Ia supernovae corresponding to each w(z) curve, we perform a standard ΛCDM analysis to estimate the corresponding posterior densities of the absolute magnitude of type Ia supernovae, the present-day matter density, and the equation of state parameter. Using the Kullback-Leibler divergence between posterior densities as a difference measure, we demonstrate that a standard type Ia supernova cosmology analysis has limited sensitivity to extensive redshift dependencies of the dark energy equation of state. In addition, we report that larger redshift-dependent departures from a cosmological constant do not necessarily manifest easier-detectable incompatibilities with the ΛCDM model. Our results suggest that physics beyond the standard model may simply be hidden in plain sight