AI in Astrophysics

Massive astrophysical data analysis and modeling.

Introduction

Artificial Intelligence (AI) and related technologies are beginning to revolutionize astronomy and astrophysics. As facilities like the GAIA mission, the Large Synoptic Survey Telescope and the Wide Field InfraRed Telescope come online, data volumes in astronomy are going to increase. Cooperation between AI speciallists and astronomers need to cooperate to identify and categorize astronomical objects in enormous datasets with more fidelity than ever. New applications of AI in astrophysics, including data analysis and numerical simulation are going to be developed and it will be an excellent application field for many researchers.

The UPM has a long term cooperation with INTA Carmenes consortium and other missions, allowing to cooperate with outstanding researchers in the field.

Machine Learning techniques and in particular Deep Learning modelling have been tested as tools able to estimate stellar parameters. However, we are just at the beginning, where new architectures, learning procedures, cooperation with reinforced and transfer learning, etc. are expected, in particular when models become trained with synthetic datasets.

PhD candidates with solid programming skills are expected to contribute to bring new models able to accurately predict the stellar parameters, but also to implement processing pipelines helping in carrying out workloads in a smart way.

Related Publications

Some of the interesting papers produced in this research can be found underneath,

  • [DOI] A. }. {González-Marcos, L. ~M. }. {Sarro, J. }. {Ordieres-Meré, and A. }. {Bello-García, “Evaluation of data compression techniques for the inference of stellar atmospheric parameters from high-resolution spectra,” Mnras, vol. 465, pp. 4556-4571, 2017.
    [Bibtex] 
    @article{2017MNRAS.465.4556G,
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {http://adsabs.harvard.edu/abs/2017MNRAS.465.4556G},
author = {{González-Marcos, A.} and {Sarro, L.~M.} and {Ordieres-Meré, J.} and {Bello-García, A.}},
citations = {8},
doi = {10.1093/mnras/stw3031},
gsid = {12250023724957398743},
journal = {mnras},
keywords = {methods: data analysis, methods: statistical, stars: fundamental parameters},
month = {mar},
ncites = {8},
note = {\textbf{Q1}; 5.194; Astronomy \& Astrophysics},
pages = {4556-4571},
title = {Evaluation of data compression techniques for the inference of stellar atmospheric parameters from high-resolution spectra},
volume = {465},
year = {2017}
}
  • A. G. Brown, A. Vallenari, T. Prusti, J. De Bruijne, F. Mignard, R. Drimmel, C. Babusiaux, C. Bailer-Jones, U. Bastian, M. Biermann, J. Ordieres-Meré, and others, “Gaia data release 1-summary of the astrometric, photometric, and survey properties,” Astronomy & astrophysics, vol. 595, p. A2, 2016.
    [Bibtex] 
    @article{brown2016gaia,
author = {Anthony GA Brown and A Vallenari and T Prusti and JHJ {De Bruijne} and F Mignard and R Drimmel and C Babusiaux and CAL Bailer-Jones and U Bastian and M Biermann and J. Ordieres-Meré and others},
citations = {2250},
gsid = {13164961930711353607},
journal = {Astronomy \& Astrophysics},
ncites = {1619},
note = {\textbf{Q1}; 5.651; Astronomy \& Astrophysics},
pages = {A2},
publisher = {EDP Sciences},
title = {Gaia Data Release 1-Summary of the astrometric, photometric, and survey properties},
volume = {595},
year = {2016}
}
  • [DOI] Gaia Collaboration, T. }. {Prusti, J. H. J. }. {de Bruijne, A. G. A. }. {Brown, A. }. {Vallenari, C. }. {Babusiaux, C. A. L. }. {Bailer-Jones, J. }. {Ordieres-Meré, and Others, “The gaia mission,” A&a, vol. 595, p. A1, 2016.
    [Bibtex] 
    @article{refId0,
author = {{Gaia Collaboration} and {Prusti, T.} and {de Bruijne, J. H. J.} and {Brown, A. G. A.} and {Vallenari, A.} and {Babusiaux, C.} and {Bailer-Jones, C. A. L.} and {Ordieres-Meré, J.} and {Others}},
citations = {5405},
doi = {10.1051/0004-6361/201629272},
gsid = {8813586658938847684,1963209609205969636},
journal = {A\&A},
ncites = {4643},
note = {\textbf{Q1}; 5.014; Astronomy \& Astrophysics},
pages = {A1},
title = {The Gaia mission},
url = {https://doi.org/10.1051/0004-6361/201629272},
volume = {595},
year = {2016}
}
  • [DOI] L. Sarro, J. Ordieres-Meré, A. Bello-García, A. González-Marcos, and E. Solano, “Estimates of the atmospheric parameters of m-type stars: a machine-learning perspective,” Monthly notices of the royal astronomical society, vol. 476, iss. 1, p. 1120–1139, 2018.
    [Bibtex] 
    @article{sarro2018estimates,
author = {LM Sarro and J Ordieres-Meré and A Bello-Garc\'ia and A González-Marcos and E Solano},
citations = {18},
doi = {10.1093/mnras/sty165},
gsid = {9078124872158782866},
journal = {Monthly Notices of the Royal Astronomical Society},
ncites = {17},
note = {\textbf{Q1}; 5.321; Astronomy \& Astrophysics},
number = {1},
pages = {1120--1139},
publisher = {Oxford University Press},
title = {Estimates of the atmospheric parameters of M-type stars: a machine-learning perspective},
url = {https://academic.oup.com/mnras/article-abstract/476/1/1120/4923340?redirectedFrom=fulltext},
volume = {476},
year = {2018}
}

Related Research Projects

Some research projects are aligned to this research line, such as,

CodeURL of the project / TitleFunding source
AyA2011-24052http://www.upm.es/observatorio/vi/index.jsp?pageac=actividad.jsp&id_actividad=160620National