Related papers: STAR NRE: Solving supernova selection effects with…

Recent cosmological analyses (e.g., JLA, Pantheon) of Type Ia Supernova (SNIa) have propagated systematic uncertainties into a covariance matrix and either binned or smoothed the systematic vectors in redshift space. We demonstrate that…

We explore the systematic uncertainties of using Type Ia supernovae (SNe Ia) as cosmological probes, using the Supernova Legacy Survey Three Year data (SNLS3). We focus on studying the possible evolution of the stretch-luminosity parameter…

Large photometric surveys with the aim of identifying many Type Ia supernovae (SNe) at moderate redshift are challenged in separating these SNe from other SN types. We are motivated to identify Type Ia SNe based only on broadband…

In this paper we advance the simple analytic photometric redshift estimator for Type Ia supernovae (SNe Ia) proposed by Wang (2007), and use it to study simulated SN Ia data. We find that better than 0.5% accuracy in z_phot (with…

We study the utility of a large sample of type Ia supernovae that might be observed in an imaging survey that rapidly scans a large fraction of the sky for constraining dark energy. We consider information from the traditional luminosity…

Bayesian Estimation Applied to Multiple Species (BEAMS) is implemented in the BEAMS with Bias Corrections (BBC) framework to produce a redshift-binned Hubble diagram (HD) for Type Ia supernovae (SNe Ia). BBC corrects for selection effects…

The number of type Ia supernova (SNe Ia) observations will grow significantly within the next decade, mainly thanks to the Legacy Survey of Space and Time (LSST) undertaken by the Vera Rubin Observatory in Chile. With this improvement,…

Photometric classification of Type Ia supernovae (SNe Ia) is critical for cosmological studies but remains difficult due to class imbalance and observational noise. While deep learning models have been explored, they are often…

Supernova (SN) classification and redshift estimation using photometric data only have become very important for the Large Synoptic Survey Telescope (LSST), given the large number of SNe that LSST will observe and the impossibility of…

We introduce SuperNNova, an open source supernova photometric classification framework which leverages recent advances in deep neural networks. Our core algorithm is a recurrent neural network (RNN) that is trained to classify light-curves…

Traditional cosmological inference using Type Ia supernovae (SNeIa) have used stretch- and color-corrected fits of SN Ia light curves and assumed a resulting fiducial mean and symmetric intrinsic dispersion for the resulting relative…

Redshift measurement has always been a constant need in modern astronomy and cosmology. And as new surveys have been providing an immense amount of data on astronomical objects, the need to process such data automatically proves to be…

We analyze the mean rest-frame ultraviolet (UV) spectrum of Type Ia Supernovae (SNe Ia) and its dispersion using high signal-to-noise Keck-I/LRIS-B spectroscopy for a sample of 36 events at intermediate redshift (z=0.5) discovered by the…

We present a sample of 485 photometrically identified Type Ia supernova candidates mined from the first three years of data of the CFHT SuperNova Legacy Survey (SNLS). The images were submitted to a deferred processing independent of the…

Using data from the Sloan Digital Sky Supernova Survey-II, we measure the rate of Type Ia Supernovae (SNe Ia) as a function of galaxy properties at intermediate redshift. A sample of 342 SNe Ia with 0.05<z<0.25 is constructed. Using…

We examine three SNe Type Ia datasets: Union2.1, JLA and Panstarrs to check their consistency using cosmology blind statistical analyses as well as cosmological parameter fitting. We find that Panstarrs dataset is the most stable of the…

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…

Current and future Type Ia Supernova (SN Ia) surveys will need to adopt new approaches to classifying SNe and obtaining their redshifts without spectra if they wish to reach their full potential. We present here a novel approach that uses…

Using type Ia supernovae as cosmological probes requires empirical corrections that are correlated with their host environment. Here we present a unified Bayesian hierarchical model designed to infer, from purely photometric observations,…

We have constructed a comprehensive statistical model for Type Ia supernova (SN Ia) light curves spanning optical through near infrared (NIR) data. A hierarchical framework coherently models multiple random and uncertain effects, including…