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Extracting the Galactic Center excess’ source-count distribution with neural nets

The two leading hypotheses for the Galactic Center excess (GCE) in the Fermi data are an unresolved population of faint millisecond pulsars (MSPs) and dark-matter (DM) annihilation. The dichotomy between these explanations is typically reflected by modeling them as two separate emission components....

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Detalles Bibliográficos
Autores principales: List, Florian, Rodd, Nicholas L., Lewis, Geraint F.
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:https://dx.doi.org/10.1103/PhysRevD.104.123022
http://cds.cern.ch/record/2776534
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author List, Florian
Rodd, Nicholas L.
Lewis, Geraint F.
author_facet List, Florian
Rodd, Nicholas L.
Lewis, Geraint F.
author_sort List, Florian
collection CERN
description The two leading hypotheses for the Galactic Center excess (GCE) in the Fermi data are an unresolved population of faint millisecond pulsars (MSPs) and dark-matter (DM) annihilation. The dichotomy between these explanations is typically reflected by modeling them as two separate emission components. However, point sources (PSs) such as MSPs become statistically degenerate with smooth Poisson emission in the ultrafaint limit (formally where each source is expected to contribute much less than one photon on average), leading to an ambiguity that can render questions such as whether the emission is PS-like or Poissonian in nature ill defined. We present a conceptually new approach that describes the PS and Poisson emission in a unified manner and only afterwards derives constraints on the Poissonian component from the so obtained results. For the implementation of this approach, we leverage deep learning techniques, centered around a neural network-based method for histogram regression that expresses uncertainties in terms of quantiles. We demonstrate that our method is robust against a number of systematics that have plagued previous approaches, in particular DM/PS misattribution. In the Fermi data, we find a faint GCE described by a median source-count distribution (SCD) peaked at a flux of <math display="inline"><mrow><mo>∼</mo><mn>4</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>11</mn></mrow></msup><mtext> </mtext><mtext> </mtext><mtext>counts</mtext><mtext> </mtext><msup><mrow><mi>cm</mi></mrow><mrow><mo>-</mo><mn>2</mn></mrow></msup><mtext> </mtext><msup><mrow><mi mathvariant="normal">s</mi></mrow><mrow><mo>-</mo><mn>1</mn></mrow></msup></mrow></math> (corresponding to <math display="inline"><mo>∼</mo><mn>3</mn><mi>–</mi><mn>4</mn></math> expected counts per PS), which would require <math display="inline"><mi>N</mi><mo>∼</mo><mi mathvariant="script">O</mi><mo stretchy="false">(</mo><msup><mn>10</mn><mn>4</mn></msup><mo stretchy="false">)</mo></math> sources to explain the entire excess (median value <math display="inline"><mrow><mi>N</mi><mo>=</mo><mn>29</mn></mrow></math>,300 across the sky). Although faint, this SCD allows us to derive the constraint <math display="inline"><msub><mi>η</mi><mi>P</mi></msub><mo>≤</mo><mn>66</mn><mo>%</mo></math> for the Poissonian fraction of the GCE flux <math display="inline"><msub><mi>η</mi><mi>P</mi></msub></math> at 95% confidence, suggesting that a substantial amount of the GCE flux is due to PSs.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2021
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spelling cern-27765342023-03-17T03:36:44Zdoi:10.1103/PhysRevD.104.123022http://cds.cern.ch/record/2776534engList, FlorianRodd, Nicholas L.Lewis, Geraint F.Extracting the Galactic Center excess’ source-count distribution with neural netshep-phParticle Physics - Phenomenologycs.LGComputing and Computersastro-ph.IMAstrophysics and Astronomyastro-ph.COastro-ph.HEThe two leading hypotheses for the Galactic Center excess (GCE) in the Fermi data are an unresolved population of faint millisecond pulsars (MSPs) and dark-matter (DM) annihilation. The dichotomy between these explanations is typically reflected by modeling them as two separate emission components. However, point sources (PSs) such as MSPs become statistically degenerate with smooth Poisson emission in the ultrafaint limit (formally where each source is expected to contribute much less than one photon on average), leading to an ambiguity that can render questions such as whether the emission is PS-like or Poissonian in nature ill defined. We present a conceptually new approach that describes the PS and Poisson emission in a unified manner and only afterwards derives constraints on the Poissonian component from the so obtained results. For the implementation of this approach, we leverage deep learning techniques, centered around a neural network-based method for histogram regression that expresses uncertainties in terms of quantiles. We demonstrate that our method is robust against a number of systematics that have plagued previous approaches, in particular DM/PS misattribution. In the Fermi data, we find a faint GCE described by a median source-count distribution (SCD) peaked at a flux of <math display="inline"><mrow><mo>∼</mo><mn>4</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>-</mo><mn>11</mn></mrow></msup><mtext> </mtext><mtext> </mtext><mtext>counts</mtext><mtext> </mtext><msup><mrow><mi>cm</mi></mrow><mrow><mo>-</mo><mn>2</mn></mrow></msup><mtext> </mtext><msup><mrow><mi mathvariant="normal">s</mi></mrow><mrow><mo>-</mo><mn>1</mn></mrow></msup></mrow></math> (corresponding to <math display="inline"><mo>∼</mo><mn>3</mn><mi>–</mi><mn>4</mn></math> expected counts per PS), which would require <math display="inline"><mi>N</mi><mo>∼</mo><mi mathvariant="script">O</mi><mo stretchy="false">(</mo><msup><mn>10</mn><mn>4</mn></msup><mo stretchy="false">)</mo></math> sources to explain the entire excess (median value <math display="inline"><mrow><mi>N</mi><mo>=</mo><mn>29</mn></mrow></math>,300 across the sky). Although faint, this SCD allows us to derive the constraint <math display="inline"><msub><mi>η</mi><mi>P</mi></msub><mo>≤</mo><mn>66</mn><mo>%</mo></math> for the Poissonian fraction of the GCE flux <math display="inline"><msub><mi>η</mi><mi>P</mi></msub></math> at 95% confidence, suggesting that a substantial amount of the GCE flux is due to PSs.The two leading hypotheses for the Galactic Center Excess (GCE) in the $\textit{Fermi}$ data are an unresolved population of faint millisecond pulsars (MSPs) and dark-matter (DM) annihilation. The dichotomy between these explanations is typically reflected by modeling them as two separate emission components. However, point-sources (PSs) such as MSPs become statistically degenerate with smooth Poisson emission in the ultra-faint limit (formally where each source is expected to contribute much less than one photon on average), leading to an ambiguity that can render questions such as whether the emission is PS-like or Poissonian in nature ill-defined. We present a conceptually new approach that describes the PS and Poisson emission in a unified manner and only afterwards derives constraints on the Poissonian component from the so obtained results. For the implementation of this approach, we leverage deep learning techniques, centered around a neural network-based method for histogram regression that expresses uncertainties in terms of quantiles. We demonstrate that our method is robust against a number of systematics that have plagued previous approaches, in particular DM / PS misattribution. In the $\textit{Fermi}$ data, we find a faint GCE described by a median source-count distribution (SCD) peaked at a flux of $\sim4 \times 10^{-11} \ \text{counts} \ \text{cm}^{-2} \ \text{s}^{-1}$ (corresponding to $\sim3 - 4$ expected counts per PS), which would require $N \sim \mathcal{O}(10^4)$ sources to explain the entire excess (median value $N = \text{29,300}$ across the sky). Although faint, this SCD allows us to derive the constraint $\eta_P \leq 66\%$ for the Poissonian fraction of the GCE flux $\eta_P$ at 95% confidence, suggesting that a substantial amount of the GCE flux is due to PSs.arXiv:2107.09070oai:cds.cern.ch:27765342021-07-19
spellingShingle hep-ph
Particle Physics - Phenomenology
cs.LG
Computing and Computers
astro-ph.IM
Astrophysics and Astronomy
astro-ph.CO
astro-ph.HE
List, Florian
Rodd, Nicholas L.
Lewis, Geraint F.
Extracting the Galactic Center excess’ source-count distribution with neural nets
title Extracting the Galactic Center excess’ source-count distribution with neural nets
title_full Extracting the Galactic Center excess’ source-count distribution with neural nets
title_fullStr Extracting the Galactic Center excess’ source-count distribution with neural nets
title_full_unstemmed Extracting the Galactic Center excess’ source-count distribution with neural nets
title_short Extracting the Galactic Center excess’ source-count distribution with neural nets
title_sort extracting the galactic center excess’ source-count distribution with neural nets
topic hep-ph
Particle Physics - Phenomenology
cs.LG
Computing and Computers
astro-ph.IM
Astrophysics and Astronomy
astro-ph.CO
astro-ph.HE
url https://dx.doi.org/10.1103/PhysRevD.104.123022
http://cds.cern.ch/record/2776534
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