تقرير
Tree boosting for learning EFT parameters
| العنوان: | Tree boosting for learning EFT parameters |
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| المؤلفون: | Chatterjee, Suman, Frohner, Nikolaus, Lechner, Lukas, Schöfbeck, Robert, Schwarz, Dennis |
| المصدر: | Comput.Phys.Commun. 277 (2022) 108385 |
| سنة النشر: | 2021 |
| المجموعة: | High Energy Physics - Phenomenology |
| مصطلحات موضوعية: | High Energy Physics - Phenomenology |
| الوصف: | We present a new tree boosting algorithm designed for the measurement of parameters in the context of effective field theory (EFT). To construct the algorithm, we interpret the optimized loss function of a traditional decision tree as the maximal Fisher information in Poisson counting experiments. We promote the interpretation to general EFT predictions and develop a suitable boosting method. The resulting ``Boosted Information Tree'' algorithm approximates the score, the derivative of the log-likelihood function with respect to the parameter. It thus provides a sufficient statistic in the vicinity of a reference point in parameter space where the estimator is trained. The training exploits per-event information of likelihood ratios for different theory parameter values available in the simulated EFT data sets. Comment: 23 pages, 3 figures. Updated with referee comments |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1016/j.cpc.2022.108385 |
| URL الوصول: | http://arxiv.org/abs/2107.10859 |
| رقم الأكسشن: | edsarx.2107.10859 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1016/j.cpc.2022.108385 |
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