We propose a new scientific application of unsupervised learning techniques to boost our ability to search for new phenomena in data, by detecting discrepancies between two datasets. These could be, for example, a simulated standard-model background, and an observed dataset containing a potential hidden signal of New Physics. We build a statistical test upon a test statistic which measures deviations between two samples, using a Nearest Neighbors approach to estimate the local ratio of the density of points. The test is model-independent and non-parametric, requiring no knowledge of the shape of the underlying distributions, and it does not bin the data, thus retaining full information from the multidimensional feature space. As a proof-of-concept, we apply our method to synthetic Gaussian data, and to a simulated dark matter signal at the Large Hadron Collider. Even in the case where the background can not be simulated accurately enough to claim discovery, the technique is a powerful tool to identify regions of interest for further study.
|Titolo:||Guiding new physics searches with unsupervised learning|
|Autori:||De Simone, A.; Jacques, T.|
|Data di pubblicazione:||2019|
|Numero di Articolo:||289|
|Digital Object Identifier (DOI):||10.1140/epjc/s10052-019-6787-3|
|Appare nelle tipologie:||1.1 Journal article|