Related papers: Reduction of supernova light curves by vector Gaus…

Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine…

Machine learning has become widely used in astronomy. Gaussian Process (GP) regression in particular has been employed a number of times to fit or re-sample supernova (SN) light-curves, however by their nature typical GP models are not…

Through the creation of spectral energy distributions for well-observed literature core-collapse supernovae (CCSNe), we present corrections to transform optical light curves to full bolometric light curves. These corrections take the form…

Many synoptic surveys are observing large parts of the sky multiple times. The resulting lightcurves provide a wonderful window to the dynamic nature of the universe. However, there are many significant challenges in analyzing these light…

We present GausSN, a Bayesian semi-parametric Gaussian Process (GP) model for time-delay estimation with resolved systems of gravitationally lensed supernovae (glSNe). GausSN models the underlying light curve non-parametrically using a GP.…

Time-domain astronomy is entering a new era as wide-field surveys with higher cadences allow for more discoveries than ever before. The field has seen an increased use of machine learning and deep learning for automated classification of…

Gaussian processes are a fully Bayesian smoothing technique that allows for the reconstruction of a function and its derivatives directly from observational data, without assuming a specific model or choosing a parameterization. This is…

The Swift UV-Optical Telescope (UVOT) has been observing Core-Collapse Supernovae (CCSNe) of all subtypes in the UV and optical since 2005. We present here 50 CCSNe observed with the Swift UVOT, analyzing their UV properties and behavior.…

A rapidly spinning, millisecond magnetar is widely considered one of the most plausible power sources for gamma-ray burst-associated supernovae (GRB-SNe). Recent studies have demonstrated that the magnetar model can effectively explain the…

We examine the bolometric lightcurves of five Type II-P supernovae (SNe 1998A, 2000cb, 2006V, 2006au and 2009E) which are thought to originate from blue supergiant progenitors using a new python package named SuperBoL. With this code, we…

Temporal variations of apparent magnitude, called light curves, are observational statistics of interest captured by telescopes over long periods of time. Light curves afford the exploration of Space Domain Awareness (SDA) objectives such…

Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties. Spectroscopic follow-ups are…

We present an analytic model for bolometric light curves which are powered by the interaction between supernova ejecta and a dense circumstellar medium. This model is aimed at modeling Type IIn supernovae to determine the properties of…

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…

Photometry is the most easily acquired information about supernovae. The light curves constructed from regular imaging provide signatures not only for the energy input, the radiation escape, the local environment and the progenitor stars,…

Strong-lensing images provide a wealth of information both about the magnified source and about the dark matter distribution in the lens. Precision analyses of these images can be used to constrain the nature of dark matter. However, this…

Recent efforts in using 3D Gaussians for scene reconstruction and novel view synthesis can achieve impressive results on curated benchmarks; however, images captured in real life are often blurry. In this work, we analyze the robustness of…

We propose to recover spectral details from RGB images of known spectral quantization by modeling natural spectra under Gaussian Processes and combining them with the RGB images. Our technique exploits Process Kernels to model the relative…

Mitigating data gaps in Gamma-ray bursts (GRBs) light curves (LCs) is crucial for cosmological research, enhancing the precision of parameters, assuming perfect satellite conditions for complete LC coverage with no gaps. This analysis…

The use of Gaussian processes (GPs) as models for astronomical time series datasets has recently become almost ubiquitous, given their ease of use and flexibility. GPs excel in particular at marginalization over the stellar signal in cases…