Related papers: CCSNscore: A multi-input deep learning tool for cl…

A method is presented for automated photometric classification of supernovae (SNe) as Type-Ia or non-Ia. A two-step approach is adopted in which: (i) the SN lightcurve flux measurements in each observing filter are fitted separately; and…

We discuss results of analyzing a time series of selected photospheric-optical spectra of core collapse supernovae (CCSNe). This is accomplished by means of the parameterized supernovae synthetic spectrum (SSp) code ``SYNOW''. Special…

We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. The simulation was…

In this work, we present classification results on early supernova lightcurves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using lightcurve data. SCONE is able to…

The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) and Cluster Lensing And Supernova survey with Hubble (CLASH) multi-cycle treasury programs with the Hubble Space Telescope (HST) have provided new opportunities to…

The classification of supernovae (SNe) and its impact on our understanding of the explosion physics and progenitors have traditionally been based on the presence or absence of certain spectral features. However, current and upcoming…

One of the brightest objects in the universe, supernovae (SNe) are powerful explosions marking the end of a star's lifetime. Supernova (SN) type is defined by spectroscopic emission lines, but obtaining spectroscopy is often logistically…

Type IIn supernovae (SNeIIn) are a highly heterogeneous subclass of core-collapse supernovae, spectroscopically characterized by signatures of interaction with a dense circumstellar medium (CSM). Here we systematically model the light…

Large numbers of supernovae (SNe) have been discovered in recent years, and many more will be found in the near future. Once discovered, further study of a SN and its possible use as an astronomical tool (e.g., as a distance estimator)…

The Zwicky Transient Facility (ZTF) was expected to detect more than one strong gravitationally-lensed supernova (glSN) per year, but only one event was identified in the first four years of the survey. This work investigates selection…

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…

Massive stars die an explosive death as a core-collapse supernova (CCSN). The exact physical processes that cause the collapsing star to rebound into an explosion are not well-understood, and the key in resolving this issue may lie in the…

Accurate classification of transients obtained from spectroscopic data are important to understand their nature and discover new classes of astronomical objects. For supernovae (SNe), SNID, NGSF (a Python version of SuperFit), and DASH are…

A Galactic core-collapse supernova (CCSN) is likely to be observed in neutrino detectors around the world minutes to hours before the electromagnetic radiation arrives. The SNEWS2.0 network of neutrino and dark matter detectors aims to use…

A fundamental property determining the transient behaviour of core-collapse supernovae (CCSNe) is the amount of radioactive $^{56}$Ni synthesised in the explosion. Using established methods, this is a relatively easy parameter to extract…

A large fraction of core-collapse supernovae (CCSNe), 30-50%, are expected to originate from the low-mass end of progenitors with $M_{\rm ZAMS}~= 8-12~M_\odot$. However, degeneracy effects make stellar evolution modelling of such stars…

With the upcoming Vera C.~Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only $\sim 0.1\%$ of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I…

Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of…

We present a method for selecting high-redshift type Ia supernovae (SNe Ia) located via rolling SN searches. The technique, using both color and magnitude information of events from only 2-3 epochs of multi-band real-time photometry, is…

Core-collapse supernovae (CCSNe) offer extremely valuable insights into the dynamics of galaxies. Neutrino time profiles from CCSNe, in particular, could reveal unique details about collapsing stars and particle behavior in dense…