Related papers: SuperNNova: an open-source framework for Bayesian,…

Encouraged by the success of deep learning in a variety of domains, we investigate the suitability and effectiveness of Recurrent Neural Networks (RNNs) in a domain where deep learning has not yet been used; namely detecting confusion from…

Type Ia supernovae (SNe Ia) can be calibrated to be good standard candles at cosmological distances. We propose a supernova pencil beam survey that could yield between dozens to hundreds of SNe Ia in redshift bins of 0.1 up to $z=1.5$,…

In the last decade, special purpose computing systems, such as Neuromorphic computing, have become very popular in the field of computer vision and machine learning for classification tasks. In 2015, IBM's released the TrueNorth…

We use the BayeSN hierarchical probabilistic SED model to analyse the optical-NIR ($BVriYJH$) light curves of 86 Type Ia supernovae (SNe Ia) from the Carnegie Supernova Project to investigate the SN Ia host galaxy dust law distribution and…

We present a sample of 485 photometrically identified Type Ia supernova candidates mined from the first three years of data of the CFHT SuperNova Legacy Survey (SNLS). The images were submitted to a deferred processing independent of the…

Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given…

While the spectroscopic classification scheme for Stripped envelope supernovae (SESNe) is clear, and we know that they originate from massive stars that lost some or all their envelopes of Hydrogen and Helium, the photometric evolution of…

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…

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…

We propose a new sequential classification model for astronomical objects based on a recurrent convolutional neural network (RCNN) which uses sequences of images as inputs. This approach avoids the computation of light curves or difference…

We introduce the Hawai'i Supernova Flows project and present summary statistics of the first 1,217 astronomical transients observed, 668 of which are spectroscopically classified Type Ia Supernovae (SNe Ia). Our project is designed to…

Convolutional neural networks (CNNs) have been established as the main workhorse in image data processing; nonetheless, they require large amounts of data to train, often produce overconfident predictions, and frequently lack the ability to…

The use of advanced statistical analysis tools is crucial in order to improve cosmological parameter estimates via removal of systematic errors and identification of previously unaccounted for cosmological signals. Here we demonstrate the…

This work presents and analyzes three convolutional neural network (CNN) models for efficient pixelwise classification of images. When using convolutional neural networks to classify single pixels in patches of a whole image, a lot of…

We introduce a novel weighted convolution operator that enhances traditional convolutional neural networks (CNNs) by integrating a spatial density function into the convolution operator. This extension enables the network to differentially…

In the era of multi-messenger astronomy, early classification of photometric alerts from wide-field and high-cadence surveys is a necessity to trigger spectroscopic follow-ups. These classifications are expected to play a key role in…

The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to…

The Internet of Things (IoT) is reshaping modern society by allowing a decent number of RF devices to connect and share information through RF channels. However, such an open nature also brings obstacles to surveillance. For alleviation, a…

The radio astronomy community is rapidly adopting deep learning techniques to deal with the huge data volumes expected from the next generation of radio observatories. Bayesian neural networks (BNNs) provide a principled way to model…

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…