Applying Anomaly Detection To Search For New Physics With The Atlas Detector At The Large Hadron Collider

Applying Anomaly Detection to Search for New Physics with the ATLAS Detector at the Large Hadron Collider

A search for a heavy new particle Y decaying to a Standard Model Higgs boson H and another new particle X is presented. The search is performed using 139 fb−1 of p−p collision data at √s = 13 TeV recorded by the ATLAS detector. The H boson is identified through its decays to bb, with the only assumption applied to X being that it decays hadronically. The X is identified through a novel anomaly detection method via the use of a Variational Recurrent Neural Network trained directly on data collected by the ATLAS detector. This effort marks the first application of a fully unsupervised machine learning method to an ATLAS analysis. An additional benchmark based on interpreting the Y → XH process in the context of a heavy vector triplet model in which the X decays to two quarks defines an additional signal region in which upper limits on the HVT process cross section are reported at 95% confidence level.

General Model Independent Searches for Physics Beyond the Standard Model

This primer describes the general model independent searches for new physics phenomena beyond the Standard Model of particle physics. First, the motivation for performing general model independent experimental searches for new physics is presented by giving an overview of the current theoretical understanding of particle physics in terms of the Standard Model of particle physics and its shortcomings. Then, the concept and features of general model independent search for new physics at collider based experiments is explained. This is followed by an overview of such searches performed in past high energy physics experiments and the current status of such searches, particularly in the context of the experiments at the LHC. Finally, the future prospects of such general model independent searches, with possible improvements using new tools such as machine learning techniques, is discussed.

Novel ideas for accelerators, particle detection and data challenges at future colliders