On the Energy Distribution of the Galactic Center Excess' Sources

• URL: http://arxiv.org/abs/2507.17804v1
• Date: Wed, 23 Jul 2025 18:00:00 GMT
• Title: On the Energy Distribution of the Galactic Center Excess' Sources
• Authors: Florian List, Yujin Park, Nicholas L. Rodd, Eve Schoen, Florian Wolf,
• Abstract summary: The Galactic Center Excess (GCE) remains one of the defining mysteries uncovered by the Fermi $gamma$-ray Space Telescope.<n>Although it may yet herald the discovery of annihilating dark matter, weighing against that conclusion are analyses showing the spatial structure of the emission appears more consistent with a population of dim point sources.<n>We demonstrate that a neural network-aided simulation-based inference approach can overcome such limitations and thereby confront the point source explanation of the GCE with spatial and spectral data.
• Score: 4.2875024530011085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Galactic Center Excess (GCE) remains one of the defining mysteries uncovered by the Fermi $\gamma$-ray Space Telescope. Although it may yet herald the discovery of annihilating dark matter, weighing against that conclusion are analyses showing the spatial structure of the emission appears more consistent with a population of dim point sources. Technical limitations have restricted prior analyses to studying the point-source hypothesis purely spatially. All spectral information that could help disentangle the GCE from the complex and uncertain astrophysical emission was discarded. We demonstrate that a neural network-aided simulation-based inference approach can overcome such limitations and thereby confront the point source explanation of the GCE with spatial and spectral data. The addition is profound: energy information drives the putative point sources to be significantly dimmer, indicating either the GCE is truly diffuse in nature or made of an exceptionally large number of sources. Quantitatively, for our best fit background model, the excess is essentially consistent with Poisson emission as predicted by dark matter. If the excess is instead due to point sources, our median prediction is ${\cal O}(10^5)$ sources in the Galactic Center, or more than 35,000 sources at 90% confidence, both significantly larger than the hundreds of sources preferred by earlier point-source analyses of the GCE.

Related papers

• Provable Maximum Entropy Manifold Exploration via Diffusion Models [58.89696361871563]
  Exploration is critical for solving real-world decision-making problems such as scientific discovery.<n>We introduce a novel framework that casts exploration as entropy over approximate data manifold implicitly defined by a pre-trained diffusion model.<n>We develop an algorithm based on mirror descent that solves the exploration problem as sequential fine-tuning of a pre-trained diffusion model.
  arXiv  Detail & Related papers  (2025-06-18T11:59:15Z)
• Real-time gravitational-wave inference for binary neutron stars using machine learning [71.29593576787549]
  We present a machine learning framework that performs complete BNS inference in just one second without making any approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $sim30%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses.
  arXiv  Detail & Related papers  (2024-07-12T18:00:02Z)
• Datacube segmentation via Deep Spectral Clustering [76.48544221010424]
  Extended Vision techniques often pose a challenge in their interpretation. The huge dimensionality of data cube spectra poses a complex task in its statistical interpretation. In this paper, we explore the possibility of applying unsupervised clustering methods in encoded space. A statistical dimensional reduction is performed by an ad hoc trained (Variational) AutoEncoder, while the clustering process is performed by a (learnable) iterative K-Means clustering algorithm.
  arXiv  Detail & Related papers  (2024-01-31T09:31:28Z)
• Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources [44.99833362998488]
  We develop an unsupervised machine learning approach to provide probabilistic classes to Chandra Source Catalog sources. We provide a catalog of probabilistic classes for 8,756 sources, comprising a total of 14,507 detections. We investigate the consistency between the distribution of features among classified objects and well-established astrophysical hypotheses.
  arXiv  Detail & Related papers  (2024-01-22T18:42:31Z)
• A deep learning framework for jointly extracting spectra and source-count distributions in astronomy [3.7277730514654555]
  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.
  arXiv  Detail & Related papers  (2024-01-06T23:45:16Z)
• End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics [61.44793171735013]
  We introduce a novel unified architecture, termed latent variation models, which combines the latent learning of cutting-edge generative art approaches with an end-to-end variational framework. Our unified approach achieves a distribution-free distance to the truth of over 20 times less than non-latent state-of-the-art baseline.
  arXiv  Detail & Related papers  (2023-05-17T17:43:10Z)
• Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning [44.99833362998488]
  We train a convolutional neural network on synthetic 2-dimensional sky-maps and incorporate the Fermi-LAT instrumental response functions. The trained neural network is then applied to the Fermi-LAT data, from which we estimate the source count distribution down to flux levels a factor of 50 below the Fermi-LAT threshold.
  arXiv  Detail & Related papers  (2023-02-03T19:00:51Z)
• Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission [61.26321023273399]
  A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data. We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions.
  arXiv  Detail & Related papers  (2022-06-06T18:00:07Z)
• A neural simulation-based inference approach for characterizing the Galactic Center $\gamma$-ray excess [9.101294179203794]
  The Fermi gamma-ray Galactic Center Excess (GCE) has remained a persistent mystery for over a decade. We use recent advancements in the field of simulation-based inference to characterize the contribution of modeled components to the GCE.
  arXiv  Detail & Related papers  (2021-10-13T18:00:00Z)
• AGNet: Weighing Black Holes with Deep Learning [2.4522011090845846]
  Supermassive black holes (SMBHs) are ubiquitously found at the centers of most massive galaxies. Traditional methods require spectroscopic data which is expensive to gather. We present an algorithm that weighs SMBHs using quasar light time series.
  arXiv  Detail & Related papers  (2021-08-17T16:45:11Z)
• Deep learning insights into cosmological structure formation [1.6351557933652356]
  We build a deep learning framework to investigate the role of anisotropic information in the initial conditions in establishing the final mass of dark matter halos. We find that anisotropies add a small, albeit statistically significant amount of information over that contained within spherical averages of the density field about final halo mass.
  arXiv  Detail & Related papers  (2020-11-20T19:00:00Z)
• The GCE in a New Light: Disentangling the $\gamma$-ray Sky with Bayesian Graph Convolutional Neural Networks [5.735035463793008]
  A fundamental question regarding the Galactic Center Excess (GCE) is whether the underlying structure is point-like or smooth. In this work we weigh in on the problem using Bayesian graph convolutional neural networks. We find that the NN estimates for the flux fractions from the background templates are consistent with the NPTF. While suggestive, we do not claim a definitive resolution for the GCE, as the NN tends to underestimate the flux of point-sources peaked near the 1$sigma$ detection threshold.
  arXiv  Detail & Related papers  (2020-06-22T18:00:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.