Anomaly Detection for Resonant New Physics with Machine Learning

Abstract

Despite extensive theoretical motivation for physics beyond the Standard Model (BSM) of particle physics, searches at the Large Hadron Collider (LHC) have found no significant evidence for BSM physics. Therefore, it is essential to broaden the sensitivity of the search program to include unexpected scenarios. We present a new model-agnostic anomaly detection technique that naturally benefits from modern machine learning algorithms. The only requirement on the signal for this new procedure is that it is localized in at least one known direction in phase space. Any other directions of phase space that are uncorrelated with the localized one can be used to search for unexpected features. This new method is applied to the dijet resonance search to show that it can turn a modest 2σ excess into a 7σ excess for a model with an intermediate BSM particle that is not currently targeted by a dedicated search.

Anomaly detection with density estimation

“…For example, this was used in the γγ channel of the recent tth observation, [19,20] and General Search [21][22][23] strategies are from CMS and ATLAS, respectively. LDA stands for Latent Dirichlet Allocation [37,78], ANOmaly detection with Density Estimation (ANODE) is the method presented in this paper, and CWoLa stands for Classification Without Labels [32,33,77]. Direct density estimation is a form of side-banding where the multidimensional feature space density is learned conditional on the resonant feature (see Sec.…”

“…• 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.…”

“…The tradeoff is that there is no optimality guarantee for the autoencoder -any signals that it does find will be found in a rather uncontrolled manner. Meanwhile, CWoLa hunting [32,33] is somewhat more signal and background model-dependent than autoencoders, since this approach assumes that the signal is localized in a particular feature, and that there is an uncorrelated set of additional features on which one can train a classifier to distinguish signal region and sideband. In return, one obtains a guarantee of asymptotic optimality -the classifier approaches the likelihood ratio [81] in the limit of infinite statistics.…”

“…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.…”

“…In order to benchmark the performance of ANODE, it is compared with the CWoLa hunting method [32,33]. The CWoLa approach is also a neural network-based resonance search, but does not involve density estimation.…”