Similarity hashing for charged particle tracking

Abstract

We propose a novel approach to charged particle tracking at high intensity particle colliders based on Approximate Nearest Neighbors search. With hundreds of thousands of measurements per collision to be reconstructed e.g. at the High Luminosity Large Hadron Collider, the currently employed combinatorial track finding approaches become inadequate. Here, we use hashing techniques to separate measurements into buckets of 20-50 hits and increase their purity using metric learning. Two different approaches are studied to further resolve tracks inside buckets: Local Fisher Discriminant Analysis and Neural Networks for triplet similarity learning. We demonstrate the proposed approach on simulated collisions and show significant speed improvement with bucket tracking efficiency of 96 % and a fake rate of 8 % on unseen particle events. * Corresponding author 2 In the contrast to cars, however, we have quasi-deterministic equations of motions that govern the particle trajectory.

The Tracking Machine Learning Challenge: Accuracy Phase

“…Separately, the availability of the TrackML datasets ( [11] for the Accuracy phase and [10] for this Throughput phase) has been extremely useful to facilitate the collaboration of experts which are usually working within their own experimental team. It is being used for further studies like track seeds finding with similarity hashing [20] or classification with deep learning [21], investigating the use of cluster shape to help seeding [22], investigating tracking with graph net-works [23,24,25,26,27] (including with FPGA [28] ), investigating tracking with simulated annealing on a D-Wave quantum computer [29,30] or Quantum Edge Network [31,32,33], and building a complete generic tracking pipeline [34].…”

Performance of a geometric deep learning pipeline for HL-LHC particle tracking

“…In Ref. [31], metric learning is used to improve the purity in spacepoints buckets formed using similarity hashing. With the advent of quantum computer of increasing size came the development of quantum machine learning techniques, also applied in particle physics [32].…”

“…A growing number of groups are studying the application of graph networks to HEP reconstruction (see [67] for a recent review). Some of these works [24,[27][28][29][30][31]33] have strong connections with the Exa.TrkX project. To promote collaboration and reproducibility, the Exa.TrkX software is available from the HEP Software Foundation's Trigger and Reconstruction GitHub.…”

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

“…ML-based reconstruction approaches using GNNs [19][20][21][22][23] have been proposed for various tasks in particle physics [24], including tracking [25][26][27][28][29], jet finding [30][31][32] and tagging [33][34][35][36], calorimeter reconstruction [37], pileup mitigation [38], and PF reconstruction [39][40][41]. The clustering of energy deposits in detectors with a realistic, irregulargeometry detector using GNNs has been first proposed in Ref.…”