Intrinsic dimension of collider events and model-independent searches in 100 dimensions
Year: 2026
Type: article
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.
DOI: 10.1103/b88w-s5kl
Institutions: Centre National de la Recherche Scientifique, Université Paris Cité, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris Sciences et Lettres, Université Paris-Saclay, École Normale Supérieure - PSL, Sorbonne Université, Laboratoire de Physique de l'Ecole Normale Supérieure, Institut de Physique Théorique, Istituto Nazionale di Fisica Nucleare, Sezione di Roma I, University of Basel
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Funders: National Science Foundation, European Commission, Agence Nationale de la Recherche, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Ministero dell'Università e della Ricerca, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, NextGenerationEU
OpenAlex ID: W4416777476
