The Intrinsic Dimension of Collider Events and Model-Independent Searches in 100 Dimensions
Abstract
The phase space of hadron collider events spans hundreds of dimensions, generating an intricate geometry that we are just starting to explore. The number of possible new physics signals is exponential in the number of dimensions and detecting all of them is currently impossible for any human or artificial intelligence. In this work we introduce a method to search for new physics model-independently in this high-dimensional space. It is based on the measurement of the most basic property of the manifold of collider events, its dimensionality. Our proposed technique does not suffer from a look-elsewhere effect that grows exponentially with the number of dimensions of the dataset, and by construction is insensitive to energy scale uncertainties. We illustrate its potential by finding new physics in simulated events with hundreds of phase space dimensions, taking as input single particles rather than jets. This study sets the stage for new model-independent search strategies based on global properties of collider data manifolds.
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BibTeX
@article{dagnolo2026intrinsic,
title = {{The Intrinsic Dimension of Collider Events and Model-Independent Searches in 100 Dimensions}},
author = {Raffaele Tito d'Agnolo and Alfredo Glioti and Gabriele Rigo and Alessandro Valenti},
journal = {Physical Review D},
year = {2026},
month = oct,
doi = {10.1103/b88w-s5kl},
eprint = {2511.20760},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05409723v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The Intrinsic Dimension of Collider Events and Model-Independent Searches in 100 Dimensions
AU - Raffaele Tito d'Agnolo
AU - Alfredo Glioti
AU - Gabriele Rigo
AU - Alessandro Valenti
PY - 2026
DA - 2026/10/13
JO - Physical Review D
DO - 10.1103/b88w-s5kl
UR - https://hal.science/hal-05409723v1
AB - The phase space of hadron collider events spans hundreds of dimensions, generating an intricate geometry that we are just starting to explore. The number of possible new physics signals is exponential in the number of dimensions and detecting all of them is currently impossible for any human or artificial intelligence. In this work we introduce a method to search for new physics model-independently in this high-dimensional space. It is based on the measurement of the most basic property of the manifold of collider events, its dimensionality. Our proposed technique does not suffer from a look-elsewhere effect that grows exponentially with the number of dimensions of the dataset, and by construction is insensitive to energy scale uncertainties. We illustrate its potential by finding new physics in simulated events with hundreds of phase space dimensions, taking as input single particles rather than jets. This study sets the stage for new model-independent search strategies based on global properties of collider data manifolds.
SN - 2470-0010
SN - 2470-0029
ER -