Importance-based signal detection and parameter estimation with applications to new particle search

Abstract

One of the hardest challenges in data analysis is perhaps the detection of rare anomalous data buried ina huge normal background. We study this problem by constructing a novel method, which is a combination of theKullback–Leibler importance estimation procedure based anomaly detection algorithm and linear discriminant classifier.We choose to illustrate it with the example of charged Higgs boson (CHB) search in particle physics. Indeed, the LargeHadron Collider experiments at CERN ensure that CHB signal must be a tiny effect within the irreducible W-bosonbackground. In simulations, different CHB events with different characteristics are produced and judiciously mixed withthe non-CHB data, and the proposed method is applied. Our results show that distribution parameters of weak CHBsignals can be estimated with high performance. This anomaly detection method is general enough to apply to similarproblems in other fields (e.g., astrophysical, medical, engineering problems).