AstroEBSD: exploring new space in pattern indexing with methods launched from an astronomical approach [PDF]
Journal of Applied Crystallography, 2018 Electron backscatter diffraction (EBSD) is a technique used to measure crystallographic features in the scanning electron microscope. The technique is highly automated and readily accessible in many laboratories.
T. B. Britton +4 more
semanticscholar +1 more source
An Astronomical Image Content-based Recommendation System Using Combined Deep Learning Models in a Fully Unsupervised Mode [PDF]
Astronomical Journal, 2021 We have developed a method that maps large astronomical images onto a two-dimensional map and clusters them. A combination of various state-of-the-art machine-learning algorithms is used to develop a fully unsupervised image-quality assessment and ...
H. Teimoorinia +6 more
semanticscholar +1 more source
The Astrophysical Journal Supplement Series, 2023 We investigate the uncertainties of fitted X-ray model parameters and fluxes for relatively faint Chandra ACIS-I source spectra. Monte Carlo (MC) simulations are employed to construct a large set of 150,000 fake X-ray spectra in the low photon count ...
J. F. Albacete-Colombo +5 more
doaj +1 more source
Self-supervised Representation Learning for Astronomical Images [PDF]
Astrophysical Journal Letters, 2020 Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity.
Md. Abul Hayat +4 more
semanticscholar +1 more source
Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series [PDF]
, 2017 The growing field of large-scale time domain astronomy requires methods for probabilistic data analysis that are computationally tractable, even with large data sets. Gaussian processes (GPs) are a popular class of models used for this purpose, but since
D. Foreman-Mackey +3 more
semanticscholar +1 more source
Detecting outliers in astronomical images with deep generative networks [PDF]
Monthly notices of the Royal Astronomical Society, 2020 With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging data sets. The main
B. Margalef-Bentabol +7 more
semanticscholar +1 more source
Incorporating Measurement Error in Astronomical Object Classification [PDF]
Astronomical Journal, 2021 Most general-purpose classification methods, such as support-vector machine (SVM) and random forest (RF), fail to account for an unusual characteristic of astronomical data: known measurement error uncertainties. In astronomical data, this information is
Sarah Shy +4 more
semanticscholar +1 more source
The LAEX and NASA portals for CoRoT public data [PDF]
, 2009 * Aims. We describe here the main functionalities of the LAEX (Laboratorio de Astrofisica Estelar y Exoplanetas/Laboratory for Stellar Astrophysics and Exoplanets) and NASA portals for CoRoT Public Data.
A. C. Laity +39 more
core +2 more sources
An analysis of feature relevance in the classification of astronomical transients with machine learning methods [PDF]
, 2016 The exploitation of present and future synoptic (multiband and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation.
Antonio D'Isanto +6 more
semanticscholar +1 more source
The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch [PDF]
, 2010 Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers.
McCollum, Bruce +2 more
core +4 more sources