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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.02194 |
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| _version_ | 1866910730599530496 |
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| author | Heller, Nick Ilten, Phil Menzo, Tony Mrenna, Stephen Nachman, Benjamin Siodmok, Andrzej Szewc, Manuel Youssef, Ahmed |
| author_facet | Heller, Nick Ilten, Phil Menzo, Tony Mrenna, Stephen Nachman, Benjamin Siodmok, Andrzej Szewc, Manuel Youssef, Ahmed |
| contents | We present an autodifferentiable rejection sampling algorithm termed Rejection Sampling with Autodifferentiation (RSA). In conjunction with reweighting, we show that RSA can be used for efficient parameter estimation and model exploration. Additionally, this approach facilitates the use of unbinned machine-learning-based observables, allowing for more precise, data-driven fits. To showcase these capabilities, we apply an RSA-based parameter fit to a simplified hadronization model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02194 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rejection Sampling with Autodifferentiation - Case study: Fitting a Hadronization Model Heller, Nick Ilten, Phil Menzo, Tony Mrenna, Stephen Nachman, Benjamin Siodmok, Andrzej Szewc, Manuel Youssef, Ahmed High Energy Physics - Phenomenology High Energy Physics - Experiment We present an autodifferentiable rejection sampling algorithm termed Rejection Sampling with Autodifferentiation (RSA). In conjunction with reweighting, we show that RSA can be used for efficient parameter estimation and model exploration. Additionally, this approach facilitates the use of unbinned machine-learning-based observables, allowing for more precise, data-driven fits. To showcase these capabilities, we apply an RSA-based parameter fit to a simplified hadronization model. |
| title | Rejection Sampling with Autodifferentiation - Case study: Fitting a Hadronization Model |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2411.02194 |