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Context. Ground-based γ-ray astronomy is still a rather young field of research, with strong historical connections to particle physics. This is why most observations are conducted by experiments with proprietary data and analysis software, as is usual in the particle physics field. However, in recent years, this paradigm has been slowly shifting toward the development and use of open-source data formats and tools, driven by upcoming observatories such as the Cherenkov Telescope Array (CTA). In this context, a community-driven, shared data format (the gamma-astro-data-format , or GADF) and analysis tools such as Gammapy and ctools have been developed. So far, these efforts have been led by the Imaging Atmospheric Cherenkov Telescope community, leaving out other types of ground-based γ -ray instruments. Aims. We aim to show that the data from ground particle arrays, such as the High-Altitude Water Cherenkov (HAWC) observatory, are also compatible with the GADF and can thus be fully analyzed using the related tools, in this case, Gammapy. Methods. We reproduced several published HAWC results using Gammapy and data products compliant with GADF standard. We also illustrate the capabilities of the shared format and tools by producing a joint fit of the Crab spectrum including data from six different γ -ray experiments. Results. We find excellent agreement with the reference results, a powerful confirmation of both the published results and the tools involved. Conclusions. The data from particle detector arrays such as the HAWC observatory can be adapted to the GADF and thus analyzed with Gammapy. A common data format and shared analysis tools allow multi-instrument joint analysis and effective data sharing. To emphasize this, a sample of Crab nebula event lists is made public with this paper. Because of the complementary nature of pointing and wide-field instruments, this synergy will be distinctly beneficial for the joint scientific exploitation of future observatories such as the Southern Wide-field Gamma-ray Observatory and CTA.
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This content will become publicly available on October 10, 2026
HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation
Abstract Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With more than a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich data set, we introduce a machine learning approach for gamma-hadron separation. A multilayer perceptron shows the best performance, surpassing traditional and other machine learning–based methods. This approach shows a notable improvement in the detector’s sensitivity, supported by results from both simulated and real HAWC data. In particular, it achieves a 19% increase in significance for the Crab Nebula, commonly used as a benchmark. These improvements highlight the potential of machine learning to significantly enhance the performance of HAWC and provide a valuable reference for ground-based observatories, such as the Large High Altitude Air Shower Observatory and the upcoming Southern Wide-field Gamma-ray Observatory.
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- PAR ID:
- 10710369
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- The Astrophysical Journal
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 992
- Issue:
- 1
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 156
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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