Related papers: A Convolutional Neural Network Approach to Superno…

Deep convolutional networks based super-resolution is a fast-growing field with numerous practical applications. In this exposition, we extensively compare 30+ state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over…

We present an algorithm to identify the types of supernova spectra, and determine their redshift and phase. This algorithm, based on the correlation techniques of Tonry & Davis, is implemented in the SuperNova IDentification code (SNID). It…

In recent years the amount of publicly available astronomical data has increased exponentially, with a remarkable example being large scale multiepoch photometric surveys. This wealth of data poses challenges to the classical methodologies…

The ultimate fate of the universe, infinite expansion or a big crunch, can be determined by measuring the redshifts, apparent brightnesses, and intrinsic luminosities of very distant supernovae. Recent developments have provided tools that…

Type Ia supernovae (SN Ia) have provided the first evidence for an accelerating universe and for the existence of an unknown ``dark energy'' driving this expansion. The 5-year Supernova Legacy Survey (SNLS) will deliver \~700 type Ia…

The importance of using fast and automatic methods to classify variable stars for large amounts of data is undeniable. There have been many attempts to classify variable stars by traditional algorithms like Random Forest. In recent years,…

Modern supernova (SN) surveys are now uncovering stellar explosions at rates that far surpass what the world's spectroscopic resources can handle. In order to make full use of these SN datasets, it is necessary to use analysis methods that…

Time series anomaly detection plays a critical role in automated monitoring systems. Most previous deep learning efforts related to time series anomaly detection were based on recurrent neural networks (RNN). In this paper, we propose a…

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…

The explosive death of a star as a supernova is one of the most dramatic events in the Universe. Supernovae have an outsized impact on many areas of astrophysics: they are major contributors to the chemical enrichment of the cosmos and…

While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will…

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…

Photometric classification of supernovae (SNe) is imperative as recent and upcoming optical time-domain surveys, such as the Large Synoptic Survey Telescope (LSST), overwhelm the available resources for spectrosopic follow-up. Here we…

Type Ia supernovae provide direct evidence for an accelerating universe, and for the existence of "dark energy" driving this expansion. The Supernova Legacy Survey (SNLS) will deliver many hundreds of SNIa detections, and well-sampled…

Convolutional neural networks (CNNs) define the current state-of-the-art for image recognition. With their emerging popularity, especially for critical applications like medical image analysis or self-driving cars, confirmability is…

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…

Strongly gravitationally lensed supernovae (LSNe) are promising probes for providing absolute distance measurements using gravitational-lens time delays. Spatially unresolved LSNe offer an opportunity to enhance the sample size for…

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is…

In the coming LSST era, we will observe $\mathcal{O}(100)$ of lensed supernovae (SNe). In this paper, we investigate possibility for predicting time and sky position of a supernova using strong lensing. We find that it will be possible to…

Strong gravitationally lensed supernovae (LSNe), though rare, are exceptionally valuable probes for cosmology and astrophysics. Upcoming time-domain surveys like the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) offer a…