Results 11 to 20 of about 5,261,538 (109)

The next decade of astroinformatics and astrostatistics [PDF]

, 2019
Over the past century, major advances in astronomy and astrophysics have been largely driven by improvements in instrumentation and data collection. With the amassing of high quality data from new telescopes, and especially with the advent of deep and ...
Siemiginowska, Aneta, Ford, Eric B., Kuhn, Michael, Fabbiano, Giuseppina, Law, Casey, Huppenkothen, Daniela, Mandel, Kaisey, Eadie, Gwendolyn, Scargle, Jeff, Avelino, Arturo, Burkhart, Blakesley, de Souza, Rafael S., James, David J., Finkbeiner, Douglas P., López-Morales, Mercedes, Yanamandra-Fisher, Padma A., Lee, Thomas, Lazio, Joseph, Borne, Kirk, van Dyk, David A., Cisewski-Kehe, Jessi, Moustakas, John, Stenning, David C., Ntampaka, Michelle, Fruscione, Antonella, Muna, Demitri, Graham, Matthew, Kashyap, Vinay, Peek, J. E. G., Feigelson, Eric, Civano, Francesca, Richards, Gordon, Trimble, Virginia, Stevens, Abbie, Goodman, Alyssa A., Mahabal, Ashish A., Taylor, Stephen R., Speagle, Joshua S., Budavari, Tamas, Hernquist, Lars, Chilingarian, Igor, Foreman-Mackey, Daniel, Loredo, Tom, Stassun, Keivan G., Portillo, Stephen K. N., Czekala, Ian, Freeman, Peter, Guenther, Hans Moritz, Meng, Xiao-Li, Hakkila, Jon, Tremblay, Grant R., Young, C. Alex   +51 more
core   +4 more sources

Galaxies in haystacks: astrostatistics and the Gaia mission

, 2023
Talk given at the 2023 Joint Statistical Meetings "True North Strong and…Amazing at Astrostatistics!" invited paper session.
Pauline Barmby (498323)
core   +1 more source

Classification of Chandra X-Ray Sources in Cygnus OB2

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   +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

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

Realizing the potential of astrostatistics and astroinformatics [PDF]

, 2019
This Astro2020 State of the Profession Consideration White Paper highlights the growth of astrostatistics and astroinformatics in astronomy, identifies key issues hampering the maturation of these new subfields, and makes recommendations for structural ...
Siemiginowska, Aneta, Ford, Eric B., Schafer, Chad, Eadie, Gwendolyn, Loredo, Thomas J., Cisewski-Kehe, Jessi, Mahabal, Ashish A., Djorgovski, S. G., Graham, Matthew, Ivezić, Željko, Yanamandra-Fisher, Padma A., Peek, J. E. G., Feigelson, Eric, Borne, Kirk, Young, C. Alex   +14 more
core  

Multifidelity Emulator for Large-scale 21 cm Lightcone Images: A Few-shot Transfer Learning Approach with Generative Adversarial Network

The Astrophysical Journal
Emulators using machine learning techniques have emerged to efficiently generate mock data matching the large survey volume for upcoming experiments, as an alternative approach to large-scale numerical simulations.
Kangning Diao, Yi Mao
doaj   +1 more source

The next decade of astroinformatics and astrostatistics [PDF]

Over the past century, major advances in astronomy and astrophysics have been largely driven by improvements in instrumentation and data collection. With the amassing of high quality data from new telescopes, and especially with the advent of deep and ...
Siemiginowska, Aneta, Ford, Eric B., Kuhn, Michael, Fabbiano, Giuseppina, Law, Casey, Huppenkothen, Daniela, Mandel, Kaisey, Eadie, Gwendolyn, Scargle, Jeff, Avelino, Arturo, Burkhart, Blakesley, de Souza, Rafael S., James, David J., Finkbeiner, Douglas P., López-Morales, Mercedes, Yanamandra-Fisher, Padma A., Lee, Thomas, Lazio, Joseph, Borne, Kirk, van Dyk, David A., Cisewski-Kehe, Jessi, Moustakas, John, Stenning, David C., Ntampaka, Michelle, Fruscione, Antonella, Muna, Demitri, Graham, Matthew, Kashyap, Vinay, Peek, J. E. G., Feigelson, Eric, Civano, Francesca, Richards, Gordon, Trimble, Virginia, Stevens, Abbie, Goodman, Alyssa A., Mahabal, Ashish A., Taylor, Stephen R., Speagle, Joshua S., Budavari, Tamas, Hernquist, Lars, Chilingarian, Igor, Foreman-Mackey, Daniel, Loredo, Tom, Stassun, Keivan G., Portillo, Stephen K. N., Czekala, Ian, Freeman, Peter, Guenther, Hans Moritz, Meng, Xiao-Li, Hakkila, Jon, Tremblay, Grant R., Young, C. Alex   +51 more
core   +2 more sources