2020/01/14 by Anders Andreassen, Benjamin Nachman, David Shih · 2 citations
Physics and Astronomy · Mathematics · #hep-ph #hep-ex #physics.data-an #stat.ML
paper · pdf · doi:10.1103/physrevd.101.095004
published as Phys. Rev. D 101, 095004 (2020) · 19 pages, 9 figures
arxiv created 2020/01/14 · arxiv updated 2020/05/13
Given the lack of evidence for new particle discoveries at the Large Hadron Collider (LHC), it is critical to broaden the search program. A variety of model-independent searches have been proposed, adding sensitivity to unexpected signals. There are generally two types of such searches: those that rely heavily on simulations and those that are entirely based on (unlabeled) data. This paper introduces a hybrid method that makes the best of both approaches. For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands. This function is then interpolated into the signal region and then the reweighted background-only simulation can be used for supervised learning as well as for background estimation. The background estimation from the reweighted simulation allows for non-trivial correlations between features used for classification and the resonant feature. A dijet search with jet substructure is used to illustrate the new method. Future applications of Simulation Assisted Likelihood-free Anomaly Detection (SALAD) include a variety of final states and potential combinations with other model-independent approaches.