Speaker
Description
In this talk we present CURTAINs, a new data driven ML technique for constructing a background template on a resonant spectrum, for use in bump hunts in the search for new physics using a sliding window approach. By employing invertible neural networks to parametrise the distribution of side band data as a function of the resonant observable, we learn a transformation to map any data point from its value of the resonant observable to another chosen value. Optimal transport losses are used to learn the transformation between the two sidebands, conditioned on the invariant masses of the input and target data.
CURTAINs constructs a template for the background data in the signal window by transforming the data from the sidebands into the signal region. This conditional transformation can account for changes in their properties due to the correlation with the resonant observable. With this approach we can improve the reach of bump hunts by training a classifier for anomaly detection on observables which may be correlated to the resonant observable, unlike other approaches which are very sensitive to the presence of correlations. Additionally, by transforming the data itself the correct distribution over features and their correlations are preserved.
We demonstrate the robustness and performance improvements over other ML approaches by performing a sliding window scan for various levels of signal contamination in the QCD dijet background, provided by the LHC Olympis R&D dataset. We compare the performance of our model to the leading approaches and demonstrate improved performance, especially when restricting the amount of training data to narrow sidebands. Furthermore, unlike other approaches, thanks to the invertible networks a single model is trained, which can be validated by transforming each sideband to a separate validation region.
