Using Machine Learning to Find New Physics
Author Information
Author(s): Grosso Gaia, Letizia Marco
Primary Institution: NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, MA USA
Hypothesis
How can multiple testing strategies enhance signal-agnostic searches for new physics?
Conclusion
Combining different tests improves the detection of new physics signals and provides a more uniform response to various anomalies.
Supporting Evidence
- Combining different tests can achieve performances comparable to the best available test.
- The proposed methodology is valid beyond machine learning approaches.
- Multiple testing strategies can enhance model-agnostic searches for new physics.
Takeaway
This study shows that using different tests together can help scientists find new things in physics better than using just one test.
Methodology
The study explores multiple testing strategies in machine learning for signal-agnostic searches, focusing on the New Physics Learning Machine.
Potential Biases
Model selection can introduce bias towards specific families of new physics signals.
Digital Object Identifier (DOI)
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