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Combining multiple statistical tests improves signal-agnostic searches in physics. This approach enhances the discovery potential for new physics signals, offering a more uniform response to anomalies.

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Area of Science:

  • High Energy Physics
  • Statistical Analysis
  • Machine Learning

Background:

  • Signal-agnostic searches aim to detect new physics phenomena without prior signal assumptions.
  • Machine learning models can introduce bias in hypothesis testing, potentially favoring specific signal types.
  • The New Physics Learning Machine (NPLM) is a methodology for signal-agnostic likelihood-ratio tests.

Purpose of the Study:

  • To enhance signal-agnostic searches by exploring multiple testing strategies.
  • To mitigate bias introduced by model selection in machine learning-based hypothesis tests.
  • To investigate methods for combining p-values and aggregating test statistics.

Main Methods:

  • Exploration of various multiple testing approaches, including p-value combination and test statistic aggregation.
  • Application of these methods within the framework of the New Physics Learning Machine.
  • Analysis of test performance with distinct hyperparameter configurations.

Main Results:

  • Combining different statistical tests with varied hyperparameters proves beneficial for signal-agnostic searches.
  • Achieved performance comparable to the best single available test.
  • Demonstrated a more uniform sensitivity across diverse anomaly types.

Conclusions:

  • A methodology for enhancing signal-agnostic searches through multiple testing strategies is proposed.
  • The proposed approach offers improved and more uniform detection capabilities for new physics signals.
  • The methodology is applicable beyond machine learning and can be extended to broader model-agnostic analyses.