Related papers: PHotometry Assisted Spectral Extraction (PHASE) an…

Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin…

The improvements in spectral and spatial resolution of the satellite images have facilitated the automatic extraction and identification of the features from satellite images and aerial photographs. An automatic object extraction method is…

Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of…

We present a novel method of classifying Type Ia supernovae using convolutional neural networks, a neural network framework typically used for image recognition. Our model is trained on photometric information only, eliminating the need for…

Aims: Spectroscopic observations of Type Ia supernovae obtained at the New Technology Telescope (NTT) and the Nordic Optical Telescope (NOT), in conjunction with the SDSS-II Supernova Survey, are analysed. We use spectral indicators…

We report results from the Supernova Photometric Classification Challenge (SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rate. The simulation was…

New images are presented of the supernova remnant (SNR) HB9 based on 408 MHz and 1420 MHz continuum emission and HI-line emission data of the Canadian Galactic Plane Survey (CGPS) by the Dominion Radio Astrophysical Observatory (DRAO). Two…

(Abridged) We describe a new method to extract spectra of stars from observations of crowded stellar fields with integral field spectroscopy (IFS). Our approach extends the well-established concept of crowded field photometry in images into…

The Foundation Supernova Survey aims to provide a large, high-fidelity, homogeneous, and precisely-calibrated low-redshift Type Ia supernova (SN Ia) sample for cosmology. The calibration of the current low-redshift SN sample is the largest…

Supernova cosmology without spectroscopic confirmation is an exciting new frontier which we address here with the Bayesian Estimation Applied to Multiple Species (BEAMS) algorithm and the full three years of data from the Sloan Digital Sky…

We present the cosmological analysis of 752 photometrically-classified Type Ia Supernovae (SNe Ia) obtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented with host-galaxy spectroscopy from the…

The price of instruments and observing time on modern telescopes is quickly increasing with the size of the primary mirror. Therefore, it is worth revisiting the data reduction algorithms to extract every bit of scientific information from…

We present a new database of absorption and emission-line measurements based on the entire spectral atlas from the Sloan Digital Sky Survey (SDSS) 7th data release of galaxies within a redshift of 0.2. Our work makes use of the publicly…

Tissue surface shape and reflectance spectra provide rich intra-operative information useful in surgical guidance. We propose a hybrid system which displays an endoscopic image with a fast joint inspection of tissue surface shape using…

In recent years, astronomical photometry has been revolutionised by space missions such as MOST, CoRoT and Kepler. However, despite this progress, high-quality spectroscopy is still required as well. Unfortunately, high-resolution spectra…

We present a new, multi-dimensional implementation of the Advanced Spectral Leakage (ASL) scheme with the purpose of modelling neutrino-matter interactions in neutron star mergers. A major challenge is the neutrino absorption in the…

We investigate strongly gravitationally lensed type II supernovae (LSNe II) for time-delay cosmography incorporating microlensing effects, which expands on previous microlensing studies of type Ia supernovae (SNe Ia). We use the…

Optical hyperspectral imaging based on absorption and scattering of photons at the visible and adjacent frequencies denotes one of the most informative and inclusive characterization methods in material research. Unfortunately, restricted…

Spectroscopy is an important tool for providing insights into the structure of core-collapse supernova explosions. We use the Monte Carlo radiative transfer code ARTIS to compute synthetic spectra and light curves based on a two-dimensional…

In the first two papers of this series (Rhea et al. 2020; Rhea et al. 2021), we demonstrated the dynamism of machine learning applied to optical spectral analysis by using neural networks to extract kinematic parameters and emission-line…