Related papers: A Method of Rapidly Deriving Late-type Contact Bin…

We present the physical parameters of 2335 late-type contact binary (CB) systems extracted from the Catalina Sky Survey (CSS). Our sample was selected from the CSS Data Release 1 by strictly limiting the prevailing temperature uncertainties…

The advent of large-scale photometric surveys has led to the discovery of over a million contact binary systems. Conventional light curve analysis methods are no longer adequate for handling such massive datasets. To address this challenge,…

Light curve analysis of W UMa-type contact binary systems using MCMC or MC methods can be time-consuming, primarily because the repeated generation of synthetic light curves tends to be relatively slow during the fitting process. Although…

Precise estimation of cosmological parameters from the cosmic microwave background (CMB) remains a central goal of modern cosmology and a key test of inflationary physics. However, this task is fundamentally limited by strong foreground…

We present the first light curve solutions of the four binary systems observed and catalogued by the All Sky Automated Survey program. The light curves are analysed by using PHOEBE software, which is based on the Wilson-Devinney method. The…

We present the identification and photometric analysis of 30 new low mass ratio (LMR) totally eclipsing contact binaries found in Catalina Sky Survey data. The LMR candidates are identified using Fourier coefficients and visual inspection.…

We present a deep neural network (DNN) accelerated Hamiltonian Monte Carlo (HMC) algorithm called DeepHMC for the inference of binary neutron star systems. The HMC is a non-random walk sampler that uses background gradient information to…

In the era of astronomical big data, more than one million contact binaries have been discovered. Traditional approaches of light curve analysis are inadequate for investigating such an extensive number of systems. This paper builds on…

We carried out high-precision photometric observations of three eclipsing ultrashortperiod contact binaries (USPCBs). Theoretical models were fitted to the light-curves by means of the Wilson-Devinney code. The solutions suggest that the…

In this work we present a Neural Network (NN) algorithm for the identification of the appropriate parametrization of diffuse polarized Galactic emissions in the context of Cosmic Microwave Background (CMB) $B$-mode multi-frequency…

We present a comparison of two methods for cosmological parameter inference from supernovae Ia lightcurves fitted with the SALT2 technique. The standard chi-square methodology and the recently proposed Bayesian hierarchical method (BHM) are…

We present a new technique to create a bin-averaged Hubble Diagram (HD) from photometrically identified SN~Ia data. The resulting HD is corrected for selection biases and contamination from core collapse (CC) SNe, and can be used to infer…

Cosmological parameter estimation techniques that robustly account for systematic measurement uncertainties will be crucial for the next generation of cosmological surveys. We present a new analysis method, superABC, for obtaining…

We report the methods of and initial scientific inferences from the extraction of precision photometric information for the $>800$ trans-Neptunian objects (TNOs) discovered in the images of the Dark Energy Survey (DES). Scene-modelling…

This study examines five models derived from the Pacif parametrization scheme of the Hubble parameter ($H$), yielding various linear to quintic forms of the deceleration parameter (DP). Our goal is to explore the impact of these DP…

Optical and near-IR (NIR) line profiles of many ageing core-collapse supernovae (CCSNe) exhibit an apparently asymmetric bluewards shift often attributed to greater extinction by internal dust of redshifted radiation emitted from the…

In this paper, we present the first study that compares different models of Bayesian Neural Networks (BNNs) to predict the posterior distribution of the cosmological parameters directly from the Cosmic Microwave Background temperature and…

Deep learning (DL)-based methods have achieved state-of-the-art performance for many medical image segmentation tasks. Nevertheless, recent studies show that deep neural networks (DNNs) can be miscalibrated and overconfident, leading to…

Rapid parameter estimation is critical when dealing with short lived signals such as kilonovae. We present a parameter estimation algorithm that combines likelihood-free inference with a pre-trained embedding network, optimized to…

We present a novel method for Cosmic Microwave Background (CMB) foreground removal based on deep learning techniques. This method employs a Transformer model, referred to as \texttt{TCMB}, which is specifically designed to effectively…