Novelty detection meets collider physics

Abstract

Novelty detection is the machine learning task to recognize data, which belong to an unknown pattern. Complementary to supervised learning, it allows to analyze data model-independently. We demonstrate the potential role of novelty detection in collider physics, using autoencoder-based deep neural network. Explicitly, we develop a set of density-based novelty evaluators, which are sensitive to the clustering of unknown-pattern testing data or new-physics signal events, for the design of detection algorithms. We also explore the influence of the known-pattern data fluctuations, arising from non-signal regions, on detection sensitivity. Strategies to address it are proposed. The algorithms are applied to detecting fermionic di-top partner and resonant di-top productions at LHC, and exotic Higgs decays of two specific modes at a future e + e − collider. With parton-level analysis, we conclude that potentially the new-physics benchmarks can be recognized with high efficiency.

Exploring the space of jets with CMS open data

“…In terms of quantitative measures, we showed how to use the EMD to characterize the impact of detector effects and to calculate the intrinsic dimension of a jet ensemble. For qualitative studies, we showed how to use the EMD to identify the most representative jets in a histogram bin and the least representative jets in the ensemble as a whole, where the latter analysis is particularly interesting in the context of anomaly detection for new physics searches [171][172][173][174][175][176][177].…”

Searching for periodic signals in kinematic distributions using continuous wavelet transforms

“…Part of these approaches use machine learning techniques, which is another direction into which new physics searches at the LHC can expand, as has been also proposed recently in several other contexts (e.g., refs. [34][35][36][37][38][39][40][41][42][43]).…”

Anomaly detection with density estimation

“…• More recently, a variety of approaches have been proposed, often relying on sophisticated deep learning techniques, that attempt to be both signal and background model agnostic, to varying degrees. These include approaches based on autoencoders [26][27][28][29][30][31], weak supervision [32,33], nearest neighbor algorithms [34][35][36], probabilistic modeling [37], and others [38]. These are indicated in the upper-right corner of Fig.…”

“…Yet, they have varying degrees of both signal-model and background-model independence, as there is often a tradeoff between the broadness of a search and how sensitive it is to particular classes of signal scenarios. Existing and proposed model-agnostic searches range from fully-signal-model independent but fully-background model dependent [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] (because they compare data to SM simulation); to varying degrees of partial signal-model and background-model independence [26][27][28][29][30][31][32][33][34][35][36][37][38]. A comprehensive overview of existing model-agnostic approaches and how they are classified in terms of signal and background model independence will be given in Section 2.…”