A deep learning framework for jointly extracting spectra and source-count distributions in astronomy

Author(s)

Florian Wolf, Florian List, Nicholas L. Rodd, Oliver Hahn

Abstract

Astronomical observations typically provide three-dimensional maps,

encoding the distribution of the observed flux in (1) the two angles of

the celestial sphere and (2) energy/frequency. An important task

regarding such maps is to statistically characterize populations of

point sources too dim to be individually detected. As the properties of

a single dim source will be poorly constrained, instead one commonly

studies the population as a whole, inferring a source-count distribution

(SCD) that describes the number density of sources as a function of

their brightness. Statistical and machine learning methods for

recovering SCDs exist; however, they typically entirely neglect spectral

information associated with the energy distribution of the flux. We

present a deep learning framework able to jointly reconstruct the

spectra of different emission components and the SCD of point-source

populations. In a proof-of-concept example, we show that our method

accurately extracts even complex-shaped spectra and SCDs from simulated

maps.

Organisation(s)

Department of Astrophysics, Department of Mathematics

No. of pages

8

Publication date

01-2024

Peer reviewed

Yes

Austrian Fields of Science 2012

103003 Astronomy, 103004 Astrophysics

Keywords

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