Related papers: Photometric classification and redshift estimation…

We present results exploring the role that probabilistic deep learning models can play in cosmology from large-scale astronomical surveys through photometric redshift (photo-z) estimation. Photo-z uncertainty estimates are critical for the…

A new approach to estimating photometric redshifts - using Artificial Neural Networks (ANNs) - is investigated. Unlike the standard template-fitting photometric redshift technique, a large spectroscopically-identified training set is…

We present space-based ultraviolet/optical photometry and spectroscopy with the Swift Ultra-Violet/Optical Telescope and Hubble Space Telescope, respectively, along with ground-based optical photometry and spectroscopy and near-infrared…

Improvement in the precision of measurements of cosmological parameters with Type Ia Supernovae (SNIa) is expected to come from large photometrically identified (photometric) SN samples. Here we re-analyse the SDSS photometric SN sample,…

We compare two Type Ia supernova (SN Ia) samples that are drawn from a spectroscopically confirmed SN Ia sample: a host-selected sample in which SNe Ia are restricted to those that have a spectroscopic redshift from the host; and a broader,…

SN H0pe is a triply imaged supernova (SN) at redshift $z=1.78$ discovered using the James Webb Space Telescope (JWST). In order to classify the SN spectroscopically and measure the relative time delays of its three images (designated A, B,…

We investigate measuring the peculiar motions of galaxies up to $z=0.5$ using Type Ia supernovae (SNe Ia) from LSST, and predict the subsequent constraints on the growth rate of structure. We consider two cases. Our first is based on…

Using data from the Sloan Digital Sky Supernova Survey-II, we measure the rate of Type Ia Supernovae (SNe Ia) as a function of galaxy properties at intermediate redshift. A sample of 342 SNe Ia with 0.05<z<0.25 is constructed. Using…

We present the discovery of a Type Ia supernova (SN) at redshift $z = 1.914$ from the CANDELS multi-cycle treasury program on the \textit{Hubble Space Telescope (HST)}. This SN was discovered in the infrared using the Wide-Field Camera 3,…

We present the analysis of the first set of low-redshift Type Ia supernovae (SNe Ia) by the Carnegie Supernova Project. Well-sampled, high-precision optical (ugriBV) and near-infrared (NIR; YJHKs) light curves obtained in a well-understood…

Modern time-domain surveys, such as the Zwicky Transient Facility (ZTF), detect far more extragalactic transients than can be spectroscopically classified. Photometric classification offers a scalable alternative, enabling the…

We present multi-band optical photometry of 94 spectroscopically-confirmed Type Ia supernovae (SN Ia) in the redshift range 0.0055 to 0.073, obtained between 2006 and 2011. There are a total of 5522 light curve points. We show that our…

We present a new survey strategy to discover and study high redshift Type Ia supernovae (SNe Ia) using the Hubble Space Telescope (HST). By targeting massive galaxy clusters at 0.9<z<1.5, we obtain a twofold improvement in the efficiency 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…

We present an empirical model of Type Ia supernovae spectro-photometric evolution with time. The model is built using a large data set including light-curves and spectra of both nearby and distant supernovae, the latter being observed by…

We present photometric properties and distance measurements of 252 high redshift Type Ia supernovae (0.15 < z < 1.1) discovered during the first three years of the Supernova Legacy Survey (SNLS). These events were detected and their…

Accurate estimation of photometric redshifts (photo-$z$) is crucial in studies of both galaxy evolution and cosmology using current and future large sky surveys. In this study, we employ Random Forest (RF), a machine learning algorithm, to…

We present the photometric calibration of the Supernova Legacy Survey (SNLS) fields. The SNLS aims at measuring the distances to SNe Ia at (0.3<z<1) using MegaCam, the 1 deg^2 imager on the Canada-France-Hawaii Telescope (CFHT). The…

We develop a method to measure the strength of the absorption features in Type Ia supernova (SN Ia) spectra and use it to make a quantitative comparison between the spectra of Type Ia supernovae at low and high redshifts. In this case…

Context: The Subaru Deep Field (SDF) Supernova Survey discovered 10 Type Ia supernovae (SNe Ia) in the redshift range 1.5<z<2.0, as determined solely from photometric redshifts of the host galaxies. However, photometric redshifts might be…