Identification of new long-lived particles using deep neural networks

Matthias Komm,C Doglioni,W Kamleh,L Silvestris,G.A Stewart,P Jackson,D Kim

doi:10.1051/epjconf/202024506013

Matthias Komm, C Doglioni + Show 5 more

Open Access

https://doi.org/10.1051/epjconf/202024506013

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Abstract

We present the development of a deep neural network for identifying generic displaced jets arising from the decays of exotic long-lived particles in data recorded by the CMS detector at the CERN LHC. Various jet features including detailed information about each clustered particle candidate as well as reconstructed secondary vertices are refined through the use of 1-dimensional convolution layers before being combined with high-level engineered features and passed through a series of fully-connected layers. The proper lifetime of the long-lived particle,cτ0, is treated as a parameter of the neural network model, which allows for hypothesis testing over several orders of magnitude ranging fromcτ0=1 µm to 10 m. Domain adaptation by backward propagation is performed to construct domain-independent features at an intermediate layer of the network to mitiage difference between simulation and data. The training is performed by streaming ROOT trees containingO(100M) jets directly into the TensorFlow queue system, which allows for a flexible selection of input features and asynchronous preprocessing. The application of the tagger is showcased in a search for long-lived gluinos as predicted by split supersymmetric models demonstrating significant gains in sensitivity over a reference analysis.

Highlights

Machine-learned algorithms are routinely deployed to perform event reconstruction, particle identification, event classification, and other tasks [1] when analysing data samples recorded by experiments at the CERN LHC
This note, based on Ref. [6], summarises the development and application of a novel algorithm for identifying jets originating from the decay of long-lived particles (LLPs)
The algorithm is based on a deep neural network (DNN) that is inspired by the CMS DeepJet approach [7], albeit several aspects required an extension of the DeepJet architecture and training procedure

Summary

Introduction

Machine-learned algorithms are routinely deployed to perform event reconstruction, particle identification, event classification, and other tasks [1] when analysing data samples recorded by experiments at the CERN LHC. The ATLAS [2] and CMS [3] Collaborations have developed numerous algorithms based on boosted decision trees or neural networks to identify jets originating from the hadronisation of bottom quarks with unprecedented performance [4, 5]. [6], summarises the development and application of a novel algorithm for identifying jets originating from the decay of long-lived particles (LLPs). The algorithm is based on a deep neural network (DNN) that is inspired by the CMS DeepJet approach [7], albeit several aspects required an extension of the DeepJet architecture and training procedure. The application of the resulting DNN is demonstrated in a search for long-lived gluino production as predicted by split supersymmetric (SUSY) models [9]

Simulated samples and jet labelling

Deep neural network architecture and training

Performance

Findings

Showcase search for long-lived gluinos