تقرير
Optimizing Observables with Machine Learning for Better Unfolding
| العنوان: | Optimizing Observables with Machine Learning for Better Unfolding |
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| المؤلفون: | Arratia, Miguel, Britzger, Daniel, Long, Owen, Nachman, Benjamin |
| المصدر: | 2022 JINST 17 P07009 |
| سنة النشر: | 2022 |
| المجموعة: | High Energy Physics - Experiment High Energy Physics - Phenomenology |
| مصطلحات موضوعية: | High Energy Physics - Experiment, High Energy Physics - Phenomenology |
| الوصف: | Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while the particle-level observable needs to be physically motivated to link with theory, the detector-level need not be and can be optimized. We show that using deep learning to define detector-level observables has the capability to improve the measurement when combined with standard unfolding methods. Comment: This is the version that was published on July 5, 2022 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1088/1748-0221/17/07/P07009 |
| URL الوصول: | http://arxiv.org/abs/2203.16722 |
| رقم الأكسشن: | edsarx.2203.16722 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1088/1748-0221/17/07/P07009 |
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