Debottam Bakshi Gupta

In the hunt for new and unobserved phenomena in particle physics, attention has turned in recent years to using advanced machine learning techniques for model independent searches. In this paper we highlight the main challenge of applying anomaly detection to jet physics, where preserving an unbiased estimator of the jet mass remains a critical piece of any model independent search. Using Variational Autoencoders and multiple industry-standard anomaly detection metrics, we demonstrate the unavoidable nature of this problem.

One of the most important developments in Monte Carlo simulation of collider events for the LHC has been the arrival of schemes and codes for matching of parton showers to matrix elements calculated at next-to-leading order in the QCD coupling. The POWHEG scheme, and particularly its implementation in the POWHEG-BOX code, has attracted most attention due to ease of use and effective portability between parton shower algorithms. But formal accuracy to NLO does not guarantee predictivity, and the beyond-fixed-order corrections associated with the shower may be large. Further, there are open questions over which is the "best" variant of the POWHEG matching procedure to use, and how to evaluate systematic uncertainties due to the degrees of freedom in the scheme. In this paper we empirically explore the scheme variations allowed in Pythia8 matching to POWHEG-BOX dijet events, demonstrating the effects of both discrete and continuous freedoms in emission vetoing details for both tuning to data and for estimation of systematic uncertainties from the matching and parton shower aspects of the POWHEG-BOX+Pythia8 generator combination.