Speaker
Description
A central goal in experimental high energy physics is to detect new physics signals that are not explained by known physics. In this work, we aim to search for new signals that appear as deviations from known Standard Model physics in high-dimensional particle physics data. To do this, we determine whether there is any statistically significant difference between the distribution of Standard Model background samples and the distribution of the experimental observations, which are a mixture of the background and a potential new signal. Traditionally, one also assumes access to a sample from a model for the hypothesized signal distribution. Here we instead investigate a model-independent method that does not make any assumptions about the signal and uses a semi-supervised classifier to detect the presence of the signal in the experimental data. We construct two test statistics using the classifier: an estimated likelihood ratio test statistic and a test based on the area under the ROC curve (AUC). Additionally, we propose a method for estimating the signal strength parameter and explore active subspace methods to interpret the proposed semi-supervised classifier in order to understand the properties of the detected signal. We investigate the performance of the methods on a data set related to the search for the Higgs boson at the Large Hadron Collider at CERN. We demonstrate that the semi-supervised tests have power comparable to the classical methods for a well-specified signal, but much higher power for an unexpected signal which might be entirely missed by the supervised tests.
