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arXiv Machine Learning By Soheun Yi, John Alison, Mikael Kuusela

Model-Agnostic Search for New Physics in the Presence of Imperfect Background by Smearing the Data

Read the original on arXiv Machine Learning →

The paper introduces MASSIB, a model‑agnostic search technique for new physics that learns a density ratio between data and a misspecified background distribution. By smearing the density ratio, the method defines a signal region with high signal concentration while mitigating bias in background estimation. Applied to simulated LHC events with realistic background mismodeling, MASSIB consistently identifies data‑driven signal regions and maintains strong statistical power without relying on strong assumptions about signal or background.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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