With higher accelerator energies and beam intensities, searches for New Physics (NP) have been a very active area. While there is motivation for NP, so far model-driven searches have successfully excluded increasing volumes of parameter space, but not yielded evidence for new particles. This has led, over the last few years, to the development of model-independent searches. By now, they have become a useful complement to traditional approaches targeting some specific form of NP. In many of the new searches, Machine Learning has played an important role. The aim of this meeting is to compare and contrast the assumptions and performance on these approaches, and to see what can be learned from Goodness of Fit methodology. This meeting will bring together physicists who are active in this field, those who want to be involved in the future, and Statisticians, to discuss the relevant issues.
The meeting is on 24th and 25th May 2022, and will be remote. The PHYSTAT Seminar on 27th April by Mikael Kuusela (CMU) on "Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests" is also part of the PHYSTAT-Anomalies meeting. See https://indico.cern.ch/event/1148820/
The PHYSTAT series of Workshops started in 2000. They were the first meetings devoted solely to the statistical issues that occur in analyses in Particle Physics and neighbouring fields.The homepage of PHYSTAT with a list of all workshops and seminars is at https://espace.cern.ch/phystat .