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

One of the hardest challenges in data analysis is perhaps the detection of rare anomalous data buried in a huge normal background. We study this problem by constructing a novel method, which is a combination of the Kullback-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 Large Hadron Collider experiments at CERN ensure that CHB signal must be a tiny effect within the irreducible W-boson background. In simulations, different CHB events with different characteristics are produced and judiciously mixed with the non-CHB data, and the proposed method is applied. Our results show that distribution parameters of weak CHB signals can be estimated with high performance. This anomaly detection method is general enough to apply to similar problems in other fields (e.g., astrophysical, medical, engineering problems).