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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.22930 |
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| _version_ | 1866911541240004608 |
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| author | Fauvel, G. Arefiev, A. Manuel, M. Tangtartharakul, K. Weber, S. Condamine, F. P. |
| author_facet | Fauvel, G. Arefiev, A. Manuel, M. Tangtartharakul, K. Weber, S. Condamine, F. P. |
| contents | Stacking calorimeters also refered as bremsstrahlung cannons widely used in inertial confinement fusion and ultra-intense laser plasma experiments have become essential diagnostics for characterizing short bursts of high-energy photons and charged particles. Extracting the underlying energy spectrum from these detectors requires solving an ill-posed inverse problem, often complicated by noise, secondary particle contamination, and uncertainties in the detector response. In this work, we introduce an open-source unfolding framework available under the username ggfauvel under CMA-unfold based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), designed to reconstruct photon spectra directly from depth-dose profiles without imposing restrictive parametric assumptions. The algorithm demonstrates high robustness, accurately recovering complex spectral shapes while tolerating percent-level deviations in individual detector layers. This approach provides a flexible and noise-resilient tool for the analysis of stacking calorimeter data, with particular relevance for bremsstrahlung diagnostics in inertial confinement fusion and high-intensity laser applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22930 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CMA-Unfold A Covariance Matrix Adaptation unfolding algorithm for stacked calorimeter detectors Fauvel, G. Arefiev, A. Manuel, M. Tangtartharakul, K. Weber, S. Condamine, F. P. Plasma Physics Stacking calorimeters also refered as bremsstrahlung cannons widely used in inertial confinement fusion and ultra-intense laser plasma experiments have become essential diagnostics for characterizing short bursts of high-energy photons and charged particles. Extracting the underlying energy spectrum from these detectors requires solving an ill-posed inverse problem, often complicated by noise, secondary particle contamination, and uncertainties in the detector response. In this work, we introduce an open-source unfolding framework available under the username ggfauvel under CMA-unfold based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), designed to reconstruct photon spectra directly from depth-dose profiles without imposing restrictive parametric assumptions. The algorithm demonstrates high robustness, accurately recovering complex spectral shapes while tolerating percent-level deviations in individual detector layers. This approach provides a flexible and noise-resilient tool for the analysis of stacking calorimeter data, with particular relevance for bremsstrahlung diagnostics in inertial confinement fusion and high-intensity laser applications. |
| title | CMA-Unfold A Covariance Matrix Adaptation unfolding algorithm for stacked calorimeter detectors |
| topic | Plasma Physics |
| url | https://arxiv.org/abs/2603.22930 |