arXiv:2409.06399v4 Announce Type: replace-cross 
Abstract: Searches for signals of new physics in particle physics are usually done by training a supervised classifier to separate a signal model from the known Standard Model physics (also called the background model). However, even when the signal model is correct, systematic errors in the background model can influence supervised classifiers and might adversely affect the signal detection procedure. To tackle this problem, one approach is to use the (possibly misspecified) classifier only to perform a preliminary signal-enrichment step and then to carry out a signal detection test on the signal-rich sample. For this procedure to work, we need a classifier constrained to be decorrelated with one or more protected variables used for the signal-detection step. We do this by considering an optimal transport map of the classifier output that makes it independent of the protected variable(s) for the background. We then fit a semiparametric mixture model to the distribution of the protected variable after making cuts on the transformed classifier to detect the presence of a signal. We compare and contrast this decorrelation method with previous approaches, show that the decorrelation procedure is robust to moderate background misspecification, and analyze the power and validity of the signal detection test as a function of the cut on the classifier both with and without decorrelation. We conclude that decorrelation and signal enrichment help produce a stable, robust, valid, and more powerful test.

تقدم دراسة حديثة طريقة قوية لاكتشاف إشارات الفيزياء الجديدة في فيزياء الجسيمات من خلال استخدام مصنفات غير مرتبطة عبر النقل الأمثل. تتناول هذه الطريقة التحديات التي تطرحها الأخطاء النظامية في نماذج الخلفية، والتي يمكن أن تؤثر على النتائج حتى عندما يكون نموذج الإشارة دقيقًا. من خلال تحسين موثوقية اكتشاف الإشارات، يمكن أن تعزز هذه الأبحاث فهمنا للفيزياء الأساسية وقد تؤدي إلى اكتشافات رائدة.

Un estudio reciente presenta un método robusto para detectar señales de nueva física en la física de partículas utilizando clasificadores decorrelacionados a través del transporte óptimo. Este enfoque aborda los desafíos que plantean los errores sistemáticos en los modelos de fondo, que pueden distorsionar los resultados incluso cuando el modelo de señal es preciso. Al mejorar la fiabilidad de la detección de señales, esta investigación podría enriquecer significativamente nuestra comprensión de la física fundamental y potencialmente conducir a descubrimientos innovadores.

Une étude récente présente une méthode robuste pour détecter des signaux de nouvelle physique en physique des particules en utilisant des classificateurs décorrelés par transport optimal. Cette approche répond aux défis posés par les erreurs systématiques dans les modèles de fond, qui peuvent fausser les résultats même lorsque le modèle de signal est précis. En améliorant la fiabilité de la détection des signaux, cette recherche pourrait considérablement enrichir notre compréhension de la physique fondamentale et mener à des découvertes révolutionnaires.

A recent study introduces a robust method for detecting signals of new physics in particle physics by utilizing classifiers that are decorrelated through optimal transport. This approach addresses the challenges posed by systematic errors in background models, which can skew results even when the signal model is accurate. By improving the reliability of signal detection, this research could significantly enhance our understanding of fundamental physics and potentially lead to groundbreaking discoveries.

Robust semi-parametric signal detection in particle physics with classifiers decorrelated via optimal transport

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