Variational autoencoders for new physics mining at the Large Hadron Collider

Abstract

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn't make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.

Uncovering latent jet substructure

“…refs. [33][34][35][36][37][38] and [39][40][41][42][43]. They rely on categorizing and comparing datasets with different expected signal and background admixtures or identifying anomalous events inside large datasets.…”

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.…”