Simulation-Based Inference for Direction Reconstruction of Ultra-High-Energy Cosmic Rays with Radio Arrays
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914383494381568 |
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| author | Macias, Oscar Mason, Zachary Ho, Matthew Ferrière, Arsène Benoit-Lévy, Aurélien Tueros, Matías |
| author_facet | Macias, Oscar Mason, Zachary Ho, Matthew Ferrière, Arsène Benoit-Lévy, Aurélien Tueros, Matías |
| contents | Ultra-high-energy cosmic-ray (UHECR) observatories require unbiased direction reconstruction to enable multi-messenger astronomy with sparse, nanosecond-scale radio pulses. Explicit likelihood methods often rely on simplified models, which may bias results and understate uncertainties. We introduce a simulation-based inference pipeline that couples a physics-informed graph neural network (GNN) to a normalizing-flow posterior within the Learning the Universe Implicit Likelihood Inference framework. Each event is seeded by an analytic plane-wavefront fit; the GNN refines this estimate by learning spatiotemporal correlations among antenna signals, and its frozen embedding conditions an eight-block autoregressive flow that returns the full Bayesian posterior. Trained on about $8,000$ realistic UHECR air-shower simulations generated with the ZHAireS code, the posteriors are temperature-calibrated to meet empirical coverage targets. We demonstrate a sub-degree median angular resolution on test UHECR events, and find that the nominal 68% highest-posterior-density contours capture $71\% \pm 2\%$ of true arrival directions, indicating a mildly conservative uncertainty calibration. This approach provides physically interpretable reconstructions, well-calibrated uncertainties, and rapid inference, making it ideally suited for upcoming experiments targeting highly inclined events, such as GRAND, AugerPrime Radio, and BEACON. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15991 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulation-Based Inference for Direction Reconstruction of Ultra-High-Energy Cosmic Rays with Radio Arrays Macias, Oscar Mason, Zachary Ho, Matthew Ferrière, Arsène Benoit-Lévy, Aurélien Tueros, Matías High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology Ultra-high-energy cosmic-ray (UHECR) observatories require unbiased direction reconstruction to enable multi-messenger astronomy with sparse, nanosecond-scale radio pulses. Explicit likelihood methods often rely on simplified models, which may bias results and understate uncertainties. We introduce a simulation-based inference pipeline that couples a physics-informed graph neural network (GNN) to a normalizing-flow posterior within the Learning the Universe Implicit Likelihood Inference framework. Each event is seeded by an analytic plane-wavefront fit; the GNN refines this estimate by learning spatiotemporal correlations among antenna signals, and its frozen embedding conditions an eight-block autoregressive flow that returns the full Bayesian posterior. Trained on about $8,000$ realistic UHECR air-shower simulations generated with the ZHAireS code, the posteriors are temperature-calibrated to meet empirical coverage targets. We demonstrate a sub-degree median angular resolution on test UHECR events, and find that the nominal 68% highest-posterior-density contours capture $71\% \pm 2\%$ of true arrival directions, indicating a mildly conservative uncertainty calibration. This approach provides physically interpretable reconstructions, well-calibrated uncertainties, and rapid inference, making it ideally suited for upcoming experiments targeting highly inclined events, such as GRAND, AugerPrime Radio, and BEACON. |
| title | Simulation-Based Inference for Direction Reconstruction of Ultra-High-Energy Cosmic Rays with Radio Arrays |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2508.15991 |