Speaker
Description
We present New Physics Learning Machine (NPLM), a machine learning-based strategy to detect data departures from a Reference model, with no prior bias on the source of discrepancy. The main idea behind the method is to approximate the optimal log-likelihood-ratio hypothesis test parametrising the data distribution with a universal approximating function, and solving its maximum-likelihood fit as a machine learning problem with a customised loss function [1]. The method returns a $p$-value that measures the compatibility of the data with the Reference model. The most interesting potential applications are model-independent New Physics searches, validation of new Monte Carlo event generators and data quality monitoring. Using efficient large-scale implementations of kernel methods as universal approximators [2], the NPLM algorithm can be deployed on a GPU-based data acquisition system and be exploited to explore online the readout of an experimental setup. This would allow to spot out detectors malfunctioning or, possibly, unexpected anomalous patters in the data. One crucial advantage of the NPLM algorithm over standard goodness-of-fit tests routinely used in many experiments is its capability of inspecting multiple variables at once, taking care of correlations in the process. It also identifies the most discrepant region of the phase-space and it reconstructs the multidimensional data distribution, allowing for further inspection and interpretation of the results.
Finally, a way for dealing with systematic uncertainties affecting the knowledge of the Reference model has been developed in a neural network framework [3] and is under construction for kernel methods.
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