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CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals
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We propose a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC. This method, called CURTAINs, uses invertible neural networks to parametrise the distribution of side band data as a function of the resonant observable. The network learns a transformation to map any data point from its value of the resonant observable to another chosen value. Using CURTAINs, a template for the background data in the signal window is constructed by mapping the data from the side-bands into the signal region. We perform anomaly detection using the CURTAINs background template to enhance the sensitivity to new physics in a bump hunt. We demonstrate its performance in a sliding window search across a wide range of mass values. Using the LHC Olympics dataset, we demonstrate that CURTAINs matches the performance of other leading approaches which aim to improve the sensitivity of bump hunts, can be trained on a much smaller range of the invariant mass, and is fully data driven.
Forward citations
Cited by 4 Pith papers
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Optimal Transport Event Representation for Anomaly Detection
Adding a few optimal-transport-based features to standard jet observables nearly doubles anomaly-detection significance at 0.5% signal injection on LHC Olympics benchmarks.
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Robust resonant anomaly detection with NPLM
NPLM-based classifiers and end-to-end NPLM outperform BDT-based anomaly detection at low signal injection on the LHCO and RODEM benchmarks, with lower variance across hyperparameters.
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