TY  - RPRT
TI  - Tracking in Dense Environments with Transformers
IS  - ATL-PHYS-PUB-2025-045
M1  - PUBDB-2025-04910
M1  - ATL-PHYS-PUB-2025-045
SP  - 12
PY  - 2025
AB  - This work presents a novel application of machine learning to the pattern-recognition stage of charged-particle reconstruction, enabling learned hit-to-track association within dense environments, such as the cores of high-pTjets. Our Transformer-based architecture is based on a MaskFormer model that jointly optimises hit assignments and the estimation of the charged particles' properties. Trained and evaluated in dense environments the model delivers up to a 30
LB  - PUB:(DE-HGF)29
DO  - DOI:10.3204/PUBDB-2025-04910
UR  - https://bib-pubdb1.desy.de/record/640807
ER  -