Related papers: A graph-based spectral classification of Type II s…

Large time-domain sky surveys generate extensive multi-year catalogs of light curves in which scientifically valuable transients, such as supernovae (SNe), are vastly outnumbered by artifacts and routine star variability. While supervised…

We present a comprehensive analysis of 32 type II supernovae (SNe II) with plateau phase photometry and late phase ($nebular$) spectroscopy available, aiming to bridge the gap between the surface and core of their red supergiant (RSG)…

There are now hundreds of publicly available supernova spectral time series. Radiative transfer modeling of this data gives insights into the physical properties of these explosions such as the composition, the density structure, or the…

We propose a new strategy of finding strongly-lensed supernovae (SNe) by monitoring known galaxy-scale strong-lens systems. Strongly lensed SNe are potentially powerful tools for the study of cosmology, galaxy evolution, and stellar…

We investigate the application of neural networks to the automation of MK spectral classification. The data set for this project consists of a set of over 5000 optical (3800-5200 AA) spectra obtained from objective prism plates from the…

SN 2011fe was the nearest and best-observed type Ia supernova in a generation, and brought previous incomplete datasets into sharp contrast with the detailed new data. In retrospect, documenting spectroscopic behaviors of type Ia supernovae…

The design and analysis of time-domain sky surveys requires the ability to simulate accurately realistic populations of core collapse supernova (SN) events. We present a set of spectral time-series templates designed for this purpose, for…

We construct spectra of supernovae interacting strongly with a circumstellar medium (CSM) by adding SN templates, a black-body continuum and an emission-line spectrum. In a Monte Carlo simulation we generate more than 800 spectra,…

We report the results from spectroscopic observations of the multiple images of the strongly lensed Type Ia supernova (SN Ia), iPTF16geu, obtained with ground based telescopes and the Hubble Space Telescope (HST). From a single epoch of…

A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data. In this paper we present a novel graph-based framework, which aims to tackle this problem in the…

A systematic survey of ions that could be responsible for features in the optical spectra of supernovae is carried out. Six different compositions that could be encountered in supernovae are considered. For each composition, the LTE optical…

Using a method to discover and classify supernovae (SNe) in galaxy spectra, we find 90 Type Ia SNe (SNe Ia) and 10 Type II SNe among the ~700,000 galaxy spectra in the Sloan Digital Sky Survey Data Release 7 that have VESPA-derived…

The volume of data generated by internet and social networks is increasing every day, and there is a clear need for efficient ways of extracting useful information from them. As those data can take different forms, it is important to use…

We examine the relationship between three parameters of Type Ia supernovae (SNe~Ia): peak magnitude, rise time, and photospheric velocity at the time of peak brightness. The peak magnitude is corrected for extinction using an estimate…

Context. Spectroscopic analyses to interpret the spectra of the brightest supernovae from the UV to the near-IR provide a powerful tool with great astrophysical potential for the determination of the physical state of the ejecta, their…

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional…

Atomic data determined by analysis of observed atomic spectra are essential for plasma diagnostics. For each low-ionisation open d- and f-subshell atomic species, around $10^3$ fine structure level energies can be determined through years…

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…

We present the most comprehensive catalog to date of Type I Superluminous Supernovae (SLSNe), a class of stripped envelope supernovae (SNe) characterized by exceptionally high luminosities. We have compiled a sample of 262 SLSNe reported…

A comparative study of optical spectra of Type Ia supernovae (SNe Ia) obtained near 1 week, 3 weeks, and 3 months after maximum light is presented. Most members of the four groups that were defined on the basis of maximum light spectra in…