ADFilter -- A Web Tool for processing collision events for New Physics Searches with Anomaly Detection

A web-based tool called ADFilter was developed to process collision events using

autoencoders based on a deep unsupervised neural network. The autoencoders are trained on

a small fraction of either collision data or Standard Model Monte Carlo simulations. The tool

calculates loss distributions for input events, helping to determine the degree to which the events

can be considered anomalous. It also calculates two-body invariant masses both before and after

the autoencoders, as well as cross sections. Real-life examples are provided to demonstrate how

the tool can be used to reinterpret existing LHC results with the goal of significantly improving

exclusion limits.