Results 11 to 20 of about 119 (87)

Classification of Chandra X-Ray Sources in Cygnus OB2 [PDF]

The Astrophysical Journal Supplement Series, 2023
We have devised a predominantly Naive Bayes−based method to classify X-ray sources detected by Chandra in the Cygnus OB2 association into members, foreground objects, and background objects.
Vinay L. Kashyap, Mario G. Guarcello, Nicholas J. Wright, Jeremy J. Drake, Ettore Flaccomio, Tom L. Aldcroft, Juan F. Albacete Colombo, Kevin Briggs, Francesco Damiani, Janet E. Drew, Eduardo L. Martin, Giusi Micela, Tim Naylor, Salvatore Sciortino   +13 more
doaj   +2 more sources

Bayesian and Machine Learning Methods in the Big Data Era for Astronomical Imaging [PDF]

, 2023
The Atacama large millimeter/submillimeter array with the planned electronic upgrades will deliver an unprecedented number of deep and high resolution observations.
Philipp Arras, Eric Villard, Torsten Enßlin, Giuseppe Longo, Michele Delli Veneri, Lukasz Tychoniec, Fabrizia Guglielmetti   +6 more
core   +2 more sources

A BRAIN Study to Tackle Image Analysis with Artificial Intelligence in the ALMA 2030 Era

, 2023
An ESO internal ALMA development study, BRAIN, is addressing the ill-posed inverse problem of synthesis image analysis, employing astrostatistics and astroinformatics.
Jakob Roth, Eric Villard, Andrea Dosi, Torsten Enßlin, Vishal Johnson, Łukasz Tychoniec, Ivano Baronchelli, Giuseppe Longo, Felix Stoehr, Michele Delli Veneri, Carmen Blanco, Fabrizia Guglielmetti   +11 more
core   +2 more sources

Pylira: deconvolution of images in the presence of Poisson noise

, 2022
SciPy 2022 21st Python in Science Conference - Austin, Texas (July 11 - 17, 2022)All physical and astronomical imaging observations are degraded by the finite angular resolution of the camera and telescope systems.
Siemiginowska, Aneta, Burke, Douglas, Donath, Axel, Kashyap, Vinay, Solipuram, Karthik Reddy, Dyk, David van   +5 more
core   +1 more source

SDSS-IV MaNGA: Unveiling Galaxy Interaction by Merger Stages with Machine Learning

, 2023
We use machine-learning techniques to classify galaxy merger stages, which can unveil physical processes that drive the star formation and active galactic nucleus (AGN) activities during galaxy interaction.
Chang, Yu-Yen;Lin, Lihwai;al, Hsi-An Pan et
core   +1 more source

Automatic Classification of Galaxy Morphology: A Rotationally-invariant Supervised Machine-learning Method Based on the Unsupervised Machine-learning Data Set

The Astronomical Journal, 2023
Classification of galaxy morphology is a challenging but meaningful task for the enormous amount of data produced by the next-generation telescope.
GuanWen Fang, Shuo Ba, Yizhou Gu, Zesen Lin, Yuejie Hou, Chenxin Qin, Chichun Zhou, Jun Xu, Yao Dai, Jie Song, Xu Kong   +10 more
doaj   +1 more source

The Classification of Galaxy Morphology in the H Band of the COSMOS-DASH Field: A Combination-based Machine-learning Clustering Model

The Astrophysical Journal Supplement Series, 2023
By applying our previously developed two-step scheme for galaxy morphology classification, we present a catalog of galaxy morphology for H -band-selected massive galaxies in the COSMOS-DASH field, which includes 17,292 galaxies with stellar mass M _ ...
Yao Dai, Jun Xu, Jie Song, Guanwen Fang, Chichun Zhou, Shuo Ba, Yizhou Gu, Zesen Lin, Xu Kong   +8 more
doaj   +1 more source

Astrostatistics for luminosity calibration in the Gaia era

, 2015
With Gaia currently in nominal mission mode and sending data to earth, the challenge for the astronomical community is to prepare for the use of what will be at the time of release one of the largest and most complex astronomical catalogues ever produced.
X. Luri, F. Arenou, E. Masana, M. Palmer
core   +1 more source

Statistical Issues Often Overlooked when Analyzing Astronomical Data

The Astrophysical Journal Supplement Series
The main topics covered in this paper are (1) controlling significance levels when applying the same hypothesis test to many (possibly millions) of datasets; (2) dealing with the fact that for very large datasets hypotheses are rejected for trivially ...
C. Koen
doaj   +1 more source

Stratified learning: A general‐purpose statistical method for improved learning under covariate shift

Statistical Analysis and Data Mining: The ASA Data Science Journal, Volume 17, Issue 1, February 2024.
Abstract We propose a simple, statistically principled, and theoretically justified method to improve supervised learning when the training set is not representative, a situation known as covariate shift. We build upon a well‐established methodology in causal inference and show that the effects of covariate shift can be reduced or eliminated by ...
Maximilian Autenrieth, David A. van Dyk, Roberto Trotta, David C. Stenning   +3 more
wiley   +1 more source