Related papers: deepSIP: Linking Type Ia Supernova Spectra to Phot…
In providing an independent measure of the expansion history of the Universe, the Carnegie Supernova Project (CSP) has observed 71 high-z Type Ia supernovae (SNe Ia) in the near-infrared bands Y and J. These can be used to construct…
Deep learning based methods have achieved remarkable success in image restoration and enhancement, but most such methods rely on RGB input images. These methods fail to take into account the rich spectral distribution of natural images. We…
In this work, we assess several deep learning strategies for hyperspectral pansharpening. First, we present a new dataset with a greater extent than any other in the state of the art. This dataset, collected using the ASI PRISMA satellite,…
In order to investigate non-uniformity in the luminosity evolution of Type Ia supernovae, we fit the lightcurves with a multi-parameter empirical model. The model provides a quantitative method of analyzing the lightcurves of Type I…
Deep learning based methods have seen a massive rise in popularity for hyperspectral image classification over the past few years. However, the success of deep learning is attributed greatly to numerous labeled samples. It is still very…
Type Ia supernovae (SNe Ia) provide us with a unique tool for measuring extragalactic distances and determining cosmological parameters. As a result, the precise and effective calibration for peak luminosities of SNe Ia becomes extremely…
The deep learning (DL)-based methods of low-level tasks have many advantages over the traditional camera in terms of hardware prospects, error accumulation and imaging effects. Recently, the application of deep learning to replace the image…
We discuss the possibility to identify anisotropic and/or inhomogeneous cosmological models using type Ia supernova data. A search for correlations in current type Ia peak magnitudes over a large range of angular scales yields a null…
Deep neural networks (DNNs) have recently become the leading method for low-light image enhancement (LLIE). However, despite significant progress, their outputs may still exhibit issues such as amplified noise, incorrect white balance, or…
Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmentation can maximize brain tumor resection, improving patient…
Herein we analyse late-time (post-plateau; 103 < t < 1229 d) optical spectra of low-redshift (z < 0.016), hydrogen-rich Type IIP supernovae (SNe IIP). Our newly constructed sample contains 91 nebular spectra of 38 SNe IIP, which is the…
The information recoverable from galaxy spectra depends fundamentally on spectral resolution, yet assembling large samples at high resolution remains observationally expensive. We present a deep-learning framework for spectral…
Type IIP and type IIL supernovae (SNe) are defined on their light curves, but the spectrum criteria in distinguishing these two type SNe remains unclear. We propose a new classification method. Firstly, we subtract the principal components…
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…
Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that…
The distance and redshift of a type Ia supernova can be determined simultaneously through its multi-band light curves. This fact may be used for imaging surveys that discover and obtain photometry for large numbers of supernovae; so many…
We estimate systematic errors due to K-corrections in standard photometric analyses of high redshift Type Ia supernovae. Errors due to K-correction occur when the spectral template model underlying the lightcurve fitter poorly represents…
We present multiband photometry of 60 spectroscopically-confirmed supernovae (SN): 39 SN II/IIP, 19 IIn, one IIb and one that was originally classified as a IIn but later as a Ibn. Forty-six have only optical photometry, six have only near…
We present an updated version of the Multicolor Light Curve Shape method to measure distances to type Ia supernovae (SN Ia), incorporating new procedures for K-correction and extinction corrections. We also develop a simple model to…
Deep learning computer vision techniques have achieved many successes in recent years across numerous imaging domains. However, the application of deep learning to spectral data remains a complex task due to the need for augmentation…