Related papers: Fuzzy Supernova Templates I: Classification

Given the ever-increasing number of time-domain astronomical surveys, employing robust, interpretative, and automated data-driven classification schemes is pivotal. Based on graph theory, we present new data-driven classification heuristics…

Supernova (SN) rates serve as an important probe of star-formation models and initial mass functions. Near-infrared seeing-limited ground-based surveys typically discover a factor of 3-10 fewer SNe than predicted from far-infrared (FIR)…

The Ultra-Violet Optical Telescope on the Swift spacecraft has observed hundreds of supernovae, covering all major types and most subtypes. Here we introduce the Swift Optical/Ultraviolet Supernova Archive (SOUSA), which will contain all of…

The IfA Deep survey uncovered ~130 thermonuclear supernovae (TNSNe, i.e. Type Ia) candidates at redshifts from z=0.1 out to beyond z=1. The TNSN explosion rates derived from these data have been controversial, conflicting with evidence…

This paper proposes a new approach for the selection of low-energy neutrino bursts, such as the ones detected after a supernova. It exploits the temporal structure of the expected signal with respect to the more diffuse background by…

Type IIP and type IIL supernovae (SNe) are defined on their light curves, but the spectrum criteria in distinguishing these two type SNe remains unclear. We propose a new classification method. Firstly, we subtract the principal components…

Shockwave classification in shadowgraph imaging is challenging due to limited labeled data and complex flow structures. This study presents a hybrid framework that combines unsupervised autoencoder models with a fuzzy inference system to…

Early detection and accurate diagnosis are essential to improving patient outcomes. The use of convolutional neural networks (CNNs) for tumor detection has shown promise, but existing models often suffer from overparameterization, which…

Cosmological analyses with Type Ia Supernovae (SNe Ia) have traditionally been reliant on spectroscopy for both classifying the type of supernova and obtaining reliable redshifts to measure the distance-redshift relation. While obtaining a…

A method is introduced to derive resolution criteria for various a priori defined templates of brightness distribution fitted to represent structures and objects in astronomical images. The method is used for deriving criteria for the…

We present SNIascore, a deep-learning based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R $\sim100$) data. The goal of SNIascore is fully automated classification of SNe Ia with…

Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover…

Type Ia Supernovae (SNeIa) provided the first evidence of an accelerated expansion of the universe and remain a valuable probe to cosmology. They are deemed standardizable candles due to the observed correlations between its luminosity and…

Rapid variability before and near the maximum brightness of supernovae has the potential to provide a better understanding of nearly every aspect of supernovae, from the physics of the explosion up to their progenitors and the circumstellar…

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…

Type Ia supernovae (SNe Ia) are used as distance indicators to infer the cosmological parameters that specify the expansion history of the universe. Parameter inference depends on the criteria by which the analysis SN sample is selected.…

We present new diagnostic tools for distinguishing supernova remnants (SNRs) from HII regions. Up to now, sources with flux ratio [S II]/H$\rm{\alpha}$ higher than 0.4 have been considered as SNRs. Here, we present the combinations of three…

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…

Context. The SDSS-II Supernova Survey, conducted between 2005 and 2007, was designed to detect a large number of Type Ia supernovae (SNe Ia) around z~0.2, the redshift "gap" between low-z and high-z SN searches. The survey has provided…

We present an algorithm to identify the type of an SN spectrum and to determine its redshift and age. This algorithm, based on the correlation techniques of Tonry & Davis, is implemented in the Supernova Identification (SNID) code. It is…