Book ; Online: Optimising HEP parameter fits via Monte Carlo weight derivative regression
2020
Abstract: HEP event selection is traditionally considered a binary classification problem, involving the dichotomous categories of signal and background. In distribution fits for particle masses or couplings, however, signal events are not all equivalent, as the ... ...
| Abstract | HEP event selection is traditionally considered a binary classification problem, involving the dichotomous categories of signal and background. In distribution fits for particle masses or couplings, however, signal events are not all equivalent, as the signal differential cross section has different sensitivities to the measured parameter in different regions of phase space. In this paper, I describe a mathematical framework for the evaluation and optimization of HEP parameter fits, where this sensitivity is defined on an event-by-event basis, and for MC events it is modeled in terms of their MC weight derivatives with respect to the measured parameter. Minimising the statistical error on a measurement implies the need to resolve (i.e. separate) events with different sensitivities, which ultimately represents a non-dichotomous classification problem. Since MC weight derivatives are not available for real data, the practical strategy I suggest consists in training a regressor of weight derivatives against MC events, and then using it as an optimal partitioning variable for 1-dimensional fits of data events. This CHEP2019 paper is an extension of the study presented at CHEP2018: in particular, event-by-event sensitivities allow the exact computation of the "FIP" ratio between the Fisher information obtained from an analysis and the maximum information that could possibly be obtained with an ideal detector. Using this expression, I discuss the relationship between FIP and two metrics commonly used in Meteorology (Brier score and MSE), and the importance of "sharpness" both in HEP and in that domain. I finally point out that HEP distribution fits should be optimized and evaluated using probabilistic metrics (like FIP or MSE), whereas ranking metrics (like AUC) or threshold metrics (like accuracy) are of limited relevance for these specific problems. Comment: 15 pages, 1 figure, submitted to CHEP2019 proceedings in EPJ Web of Conferences; revised version addressing referee's comments |
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| Keywords | Physics - Data Analysis ; Statistics and Probability ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; 62F10 ; G.3 ; I.2.6 ; J.2 |
| Subject code | 006 |
| Publishing date | 2020-03-28 |
| Publishing country | us |
| Document type | Book ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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