A deep learning framework for jointly extracting spectra and source-count distributions in astronomy
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913187852451840 |
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| author | Wolf, Florian List, Florian Rodd, Nicholas L. Hahn, Oliver |
| author_facet | Wolf, Florian List, Florian Rodd, Nicholas L. Hahn, Oliver |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A deep learning framework for jointly extracting spectra and source-count distributions in astronomy Wolf, Florian List, Florian Rodd, Nicholas L. Hahn, Oliver Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena Machine Learning 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. |
| title | A deep learning framework for jointly extracting spectra and source-count distributions in astronomy |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena Machine Learning |
| url | https://arxiv.org/abs/2401.03336 |