ArticleFrontiers in big data2023
CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals.
Article in Frontiers in big data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Reweighting simulated events using machine-learning techniques in the CMS experiment.The European physical journal. C, Particles and fields · 2025Article
- Robust resonant anomaly detection with NPLM.The European physical journal. C, Particles and fields · 2025Article
- The interplay of machine learning-based resonant anomaly detection methods.The European physical journal. C, Particles and fields · 2024Article
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4 authors.
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Abstract
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 parameterise 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.
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