Hostname: page-component-cb9f654ff-9knnw Total loading time: 0 Render date: 2025-09-10T17:21:53.743Z Has data issue: false hasContentIssue false
  • English
  • Français

The entropy of galaxy spectra

Published online by Cambridge University Press:  01 August 2025

I. Ferreras Open the ORCID record for I. Ferreras [Opens in a new window] ,
O. Lahav Open the ORCID record for O. Lahav [Opens in a new window] ,
R. S. Somerville  and
J. Silk
I. Ferreras*
Affiliation:
Instituto de Astrofísica de Canarias, C/ Vía Láctea s/n, E38205, La Laguna, Tenerife, Spain Dept. of Physics and Astronomy, University College London, London WC1E 6BT, UK
O. Lahav
Affiliation:
Dept. of Physics and Astronomy, University College London, London WC1E 6BT, UK
R. S. Somerville
Affiliation:
Center for Computational Astrophysics, Flatiron Institute, New York, NY 10010, USA
J. Silk
Affiliation:
Dept. of Physics and Astronomy, The Johns Hopkins University, Baltimore, MD 21218, USA Institut d’Astrophysique de Paris: CNRS&UPMC, Sorbonne University, F-75014, Paris, France Beecroft Institute, Dept. of Physics, University of Oxford, Keble Road, Oxford OX1 3RH, UK
*
email: iferreras@iac.es
Get access Rights & Permissions [Opens in a new window]

Abstract

The ability of any Machine Learning method to classify the spectra of galaxies depending on the properties of the stellar component rests on the information content of the data. The well-known degeneracies found in population synthesis models suggest this information might be so entangled as to challenge the most sophisticated Deep Learning approaches. This contribution focuses on the traditional definition of entropy to explore this problem from a fundamental viewpoint. We find that the information content – when interpreting the spectrum as a probability distribution function – is reduced to a few spectral intervals that are strongly correlated. Dimensionality reduction via PCA suggests the standard 4000Å break strength and Balmer absorption are the two most informative regions in the analysis of galaxy spectra.

Keywords

methods: data analysismethods: statisticaltechniques: spectroscopicgalaxies: stellar content

Information

Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 58 - 62
Check for updates
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Angthopo, J., Ferreras, I., Silk, J., 2019, MNRAS Lett., 488, L99 Google Scholar
Bruzual, G., Charlot, S., 2003, MNRAS, 344, 1000 Google Scholar
Conroy, C., 2013, ARA&A, 51, 393 Google Scholar
Fabbro, S., Venn, K. A., O’Briain, T., Bialek, S., Kielty, C. L., Jahandar, F., Monty, S., 2018, MNRAS, 475, 2978 CrossRefGoogle Scholar
Ferreras, I., Lahav, O., Somerville, R. S., Silk, J., 2022, RASTI, submitted, arXiv:2208.05489Google Scholar
Hawkins, K., Jofré, P., Gilmore, G., Masseron, T., 2014, MNRAS, 445, 2575 CrossRefGoogle Scholar
Liew-Cain, C. L., Kawata, D., Sánchez-Blázquez, P., Ferreras, I., Symeonidis, M., 2021, MNRAS, 502, 1355 CrossRefGoogle Scholar
Lovell, C. C., Acquaviva, V., Thomas, P. A., Iyer, K. G., Gawiser, E., Wilkins, S. M., 2019, MNRAS, 490, 5503 CrossRefGoogle Scholar
Portillo, S. K. N., Parejko, J. K., Vergara, J. R., Connolly, A. J., 2020, AJ, 160, 45 Google Scholar
Rogers, B., Ferreras, I., Peletier, R., Silk, J., 2010, MNRAS, 402, 447 CrossRefGoogle Scholar
Shannon, C. E., Weaver, W., 1975, The mathematical theory of communication, University of Illinois Press, Urbana Google Scholar
Slonim, N., Somerville, R., Tishby, N., Lahav, O., 2001, MNRAS, 323, 270 CrossRefGoogle Scholar
Smee, S. A., et al., 2013, AJ, 146, 32 Google Scholar
Ting, Y.-S., Conroy, C., Rix, H.-W., Cargile, P., 2019, ApJ, 879, 69 CrossRefGoogle Scholar
Trager, S. C., Worthey, G., Faber, S. M., Burstein, D., González, J. J., 1998, ApJS, 116, 1 CrossRefGoogle Scholar
Walcher, J., Groves, B., Budavári, T., Dale, D., 2011, Ap&SS, 331, 1 Google Scholar
Worthey, G., 1994, ApJS, 95, 107 CrossRefGoogle Scholar
Wu, J. F., Boada, S., 2019, MNRAS, 484, 4683 CrossRefGoogle Scholar