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

• URL: http://arxiv.org/abs/2401.03336v1
• Date: Sat, 6 Jan 2024 23:45:16 GMT
• Title: A deep learning framework for jointly extracting spectra and source-count distributions in astronomy
• Authors: Florian Wolf, Florian List, Nicholas L. Rodd, Oliver Hahn
• Abstract summary: We present a 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.
• Score: 3.7277730514654555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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