Related papers: The Supernovae Analysis Application (SNAP)

An increasing number of so-called superluminous supernovae (SLSNe) are discovered. It is believed that at least some of them with slowly fading light curves originate in stellar explosions induced by the pair instability mechanism. Recent…

Sequential pattern mining (SPM) has excellent prospects and application spaces and has been widely used in different fields. The non-overlapping SPM, as one of the data mining techniques, has been used to discover patterns that have…

Cosmologists are facing the problem of the analysis of a huge quantity of data when observing the sky. The methods used in cosmology are, for the most of them, relying on astrophysical models, and thus, for the classification, they usually…

We propose a novel deep learning tool in order to study the evolution of dark energy models. The aim is to combine two architectures: the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), we named this full network as…

Despite recent successes, the advances in Deep Learning have not yet been fully translated to Computer Assisted Intervention (CAI) problems such as pose estimation of surgical instruments. Currently, neural architectures for classification…

Type Ia supernovae (SNIa) have been used as approximate standard candles to measure cosmological parameters such as the Hubble constant and the deceleration parameter. These measurements rely on empirical correlations between peak…

The Simple Line Access Protocol (SLAP) is an IVOA Data Access protocol which defines a protocol for retrieving spectral lines coming from various Spectral Line Data Collections through a uniform interface within the VO framework. These…

Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark…

We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods within an active learning framework. Graph-based methods in machine learning are…

Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the state-of-the-art models and have won different machine learning challenges. Although these…

Graphs are widespread data structures used to model a wide variety of problems. The sheer amount of data to be processed has prompted the creation of a myriad of systems that help us cope with massive scale graphs. The pressure to deliver…

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…

Explaining the foundations for predictions obtained from graph neural networks (GNNs) is critical for credible use of GNN models for real-world problems. Owing to the rapid growth of GNN applications, recent progress in explaining…

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…

We present a collection of new, open-source computational tools for numerically modeling recent large-scale observational data sets using modern cosmology theory. Specifically, these tools will allow both students and researchers to…

Although recent advancements in end-to-end learning-based link prediction (LP) methods have shown remarkable capabilities, the significance of traditional similarity-based LP methods persists in unsupervised scenarios where there are no…

The Supernova X-Ray Database (SNaX) was established a few years ago to make X-ray data on supernovae (SNe) publicly available via an elegant searchable web interface. The database has recently been updated to PhP7, had security updates…

I review recent optical observations of supernovae (SNe) conducted by my group. The Lick Observatory Supernova Search with the 0.76-m Katzman Automatic Imaging Telescope is currently the world's most successful search for nearby SNe. We…

The Durham Adaptive Optics Simulation Platform (DASP) is a Monte-Carlo modelling tool used for the simulation of astronomical and solar adaptive optics systems. In recent years, this tool has been used to predict the expected performance of…

Radar-based perception has gained increasing attention in autonomous driving, yet the inherent sparsity of radars poses challenges. Radar raw data often contains excessive noise, whereas radar point clouds retain only limited information.…