Related papers: Integrating Temporal and Spectral Features of Astr…
Astronomical source deblending is the process of separating the contribution of individual stars or galaxies (sources) to an image comprised of multiple, possibly overlapping sources. Astronomical sources display a wide range of sizes and…
We propose a new method for the classification of optically dark gamma-ray bursts (GRBs), based on the X-ray and optical-to-X-ray spectral indices of GRB afterglows, and utilizing the spectral capabilities of Swift. This method depends less…
We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…
Cosmic photons can be efficiently collected by broadband intensity mapping but information on their emission redshift and frequency is largely lost. We introduce a technique to statistically recover these otherwise collapsed dimensions by…
We developed a Python based framework for astronomical image processing and analysis. Astronomical image loading, normalizing, stacking, and filtering processes represent visible range images from grayscale. Besides, the blending process…
Multi-wavelength spectroscopy can be used to constrain the dust and gas properties in debris disks. Circumstellar dust absorbs and scatters incident stellar light. The scattered light is sometimes resolved spatially at visual and…
In some models of quantum gravity, space-time is thought to have a foamy structure with non-trivial optical properties. We probe the possibility that photons propagating in vacuum may exhibit a non-trivial refractive index, by analyzing the…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
Optical and infrared observations have thus far detected more celestial cataclysms than have been seen in gravity waves (GW). This argues that we should search for gravity wave signatures that correspond to flux variability seen at optical…
We present a powerful new algorithm that combines both spatial information (event locations and the point spread function) and spectral information (photon energies) to separate photons from overlapping sources. We use Bayesian statistical…
How to analyse Terabytes of photometric data, and extract knowledge on variable stars? How to detect variable phenomena? How to combine different photometric bands? Which algorithm to search for periods? How to characterize and classify the…
The wavelength calibration of spectrographs is an essential but challenging task in many disciplines. Calibration is traditionally accomplished by imaging the spectrum of a light source containing features that are known to appear at…
Astronomical observations typically provide three-dimensional maps, encoding the distribution of the observed flux in (1) the two angles of the celestial sphere and (2) energy/frequency. An important task regarding such maps is to…
Optical spectroscopy is an important and widely used technique, for instance, to characterize new materials and to identify unknown compounds. Spectra are typically reported as a function of the wavelength of light, yet the information…
In cosmic-ray physics, large field of view experiments are triggered by a number of signals laying on different angular scales: point-like and extended gamma-ray sources, diffuse emissions, as well as large and intermediate scale cosmic-ray…
Over the next few years new spectroscopic surveys (from the optical surveys of the Sloan Digital Sky Survey and the 2 degree Field survey through to space-based ultraviolet satellites such as GALEX) will provide the opportunity and…
Stellar photometric variability and instrumental effects, like cosmic ray hits, data discontinuities, data leaks, instrument aging etc. cause difficulties in the characterization of exoplanets and have an impact on the accuracy and…
Astronomy light curves are sparse, gappy, and heteroscedastic. As a result standard time series methods regularly used for financial and similar datasets are of little help and astronomers are usually left to their own instruments and…
Observations of present and future X-ray telescopes include a large number of serendipidious sources of unknown types. They are a rich source of knowledge about X-ray dominated astronomical objects, their distribution, and their evolution.…
In the era of rapidly increasing amounts of time series data, classification of variable objects has become the main objective of time-domain astronomy. Classification of irregularly sampled time series is particularly difficult because the…