REVIEW 5 cited by
A Language Model for Particle Tracking
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Particle tracking is crucial for almost all physics analysis programs at the Large Hadron Collider. Deep learning models are pervasively used in particle tracking related tasks. However, the current practice is to design and train one deep learning model for one task with supervised learning techniques. The trained models work well for tasks they are trained on but show no or little generalization capabilities. We propose to unify these models with a language model. In this paper, we present a tokenized detector representation that allows us to train a BERT model for particle tracking. The trained BERT model, namely TrackingBERT, offers latent detector module embedding that can be used for other tasks. This work represents the first step towards developing a foundational model for particle detector understanding.
Forward citations
Cited by 5 Pith papers
-
Learning Standard Model structure from LHC data with Riemannian flow matching
ShellFlow, a Riemannian flow-matching transformer fed only on-shell and invariant-mass priors and ~8×10^8 recorded ATLAS events, reproduces the SM's dilepton resonances, Weinberg angle, and top/W mass peaks in a singl...
-
End-to-End Multi-Track Reconstruction using Graph Neural Networks at Belle II
A GNN-based end-to-end track finder for the Belle II drift chamber reconstructs displaced tracks at 85.4% efficiency with a 2.5% fake rate, outperforming the baseline algorithm at 52.2%.
-
Transformer-Based Approach to Enhance Positron Tracking Performance in MEG II
A Transformer-based hit classifier improves MEG II positron tracking efficiency and resolution, yielding an expected ~10% gain in μ→eγ sensitivity.
-
Towards Foundation Models for Experimental Readout Systems Combining Discrete and Continuous Data
A single transformer with separate pixel and time vocabularies generates realistic Cherenkov detector hits and supports particle identification and noise filtering after fine-tuning.
-
Pretrained Event Classification Model for High Energy Physics Analysis
A pretrained GNN on 120M simulated LHC events improves downstream event-classification accuracy when training data are scarce, with benefits shrinking as data grow.
Discussion (0). Continue with ORCID to comment.