Related papers: SuperSNEC: Fast and Accurate Light Curve Productio…
We present the SuperNova Explosion Code (SNEC), an open-source Lagrangian code for the hydrodynamics and equilibrium-diffusion radiation transport in the expanding envelopes of supernovae. Given a model of a progenitor star, an explosion…
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…
We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae\footnote{Code available at \href{https://github.com/adammoss/supernovae}{https://github.com/adammoss/supernovae}}.…
We present an improved version of a light curve model, which is able to estimate the physical properties of different types of core-collapse supernovae having double-peaked light curves, in a quick and efficient way. The model is based on a…
We present improved photometric supernovae classification using deep recurrent neural networks. The main improvements over previous work are (i) the introduction of a time gate in the recurrent cell that uses the observational time as an…
Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin…
A two-dimensional hydrodynamics code for Type Ia supernovae (SNIa) simulations is presented. The code includes a fifth-order shock-capturing scheme WENO, detailed nuclear reaction network, flame-capturing scheme and sub-grid turbulence. For…
A new model grid containing 228,016 synthetic red supergiant explosions (Type II supernovae) is introduced. Time evolution of spectral energy distributions from 1 A to 50,000 A (100 frequency bins in a log scale) is computed at each time…
The X-ray signal from hydrogen-rich supernovae (SNe II) in the first tens to hundreds of days after the shock breakout encodes important information about the circumstellar material (CSM) surrounding their progenitors before explosion. In…
We develop a method to compute synthetic kilonova light curves that combines numerical relativity simulations of neutron star mergers and the $\texttt{SNEC}$ radiation-hydrodynamics code. We describe our implementation of initial and…
The aim of this work is to present a semi-analytical light curve modeling code which can be used for estimating physical properties of core collapse supernovae (SNe) in a quick and efficient way. To verify our code we fit light curves of…
We present fits of generalized semi-analytic supernova (SN) light curve (LC) models for a variety of power inputs including Ni-56 and Co-56 radioactive decay, magnetar spin-down, and forward and reverse shock heating due to supernova…
The early rise of Type IIP supernovae (SN IIP) provides important information for constraining the properties of their progenitors. This can in turn be compared to pre-explosion imaging constraints and stellar models to develop a more…
A new, fully dynamic and self-consistent radiation hydrodynamics code, suitable for the calculation of supernovae light curves and continuum spectra, is described. It is a multigroup (frequency-dependent) code and includes all important…
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 HYDE, a new one-dimensional hydrodynamical code, and use it to construct a grid of supernova (SN) models based on solar-metallicity bare helium-core models evolved to the verge of core-collapse with MESA STAR. This grid is suited…
We study supernova (SN) classification using the machine learning method of the Recurrent Neural Network (RNN) in the Chinese Space Station Survey Telescope Ultra-Deep Field (CSST-UDF) photometric survey, and explore the improvement of the…
We present 1,800 multiwavelength Type Ib/c supernovae light curve models obtained by running the radiation transport code Sedona and varying the mass distribution, velocity profile, and abundance ejecta profiles of helium star progenitors.…
We explore and demonstrate the capabilities of LSST to study Type I superluminous supernovae (SLSNe). We first fit the light curves of 58 known SLSNe at z~0.1-1.6, using an analytical magnetar spin-down model implemented in MOSFiT. We then…
A number of Type I (hydrogenless) superluminous supernova (SLSN) events have been discovered recently. However, their nature remains debatable. One of the most promising ideas is the shock-interaction mechanism, but only simplified…