Summary of Toward Model-agnostic Detection Of New Physics Using Data-driven Signal Regions, by Soheun Yi et al.
Toward Model-Agnostic Detection of New Physics Using Data-Driven Signal Regions
by Soheun Yi, John Alison, Mikael Kuusela
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method addresses the challenge of selecting a Signal Region (SR) in high-energy physics when prior domain knowledge is unavailable. It assumes that signal events are concentrated in a specific area of the feature space, allowing for the definition of the SR as an area most affected by random noise. The approach learns the density ratio of potential signal and complementary non-signal events to overcome challenges in density estimation. The method demonstrates efficiency in identifying a data-driven SR in high-dimensional feature spaces where signals concentrate. This paper contributes to the development of novel particle detection methods, enabling searches for new particles that may not fit existing understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re searching for new tiny particles that are really hard to find. Scientists usually rely on what they already know about these particles, but what if they don’t know anything? That’s where this paper comes in. It proposes a new way to find the right area (called the Signal Region) where these particles might be hiding. The method uses a clever trick called a low-pass filter and a special technique to compare signal events with events that look similar but don’t have any signals. By testing it on fake data, the authors show that this method can successfully identify where these new particles might be hiding. |
Keywords
» Artificial intelligence » Density estimation
