Speaker
Description
Mask Transformers, or MaskFormers, have emerged as the current state of the art in a wide range of image and point cloud segmentation tasks. We present the application of this architecture to various reconstruction tasks in high energy physics with the aim of tackling both problem scale and complexity. We consider the popular track reconstruction algorithm benchmark dataset TrackML, which represents a problem scale comparable to what will be encountered by the next generation of detectors at the HL-LHC. Combined with a encoder-only transformer to pre-filter hits, a maskformer is able to reconstruct tracks with ~99% efficiency down to a $p_T$ of 600 MeV, while maintaining a low fake rate, and an inference time comparable to other machine learning tracking approaches. To test the model in complex environments, we consider track reconstruction in highly boosted hadronic ROIs in ATLAS simulation data. In such ROIs, increased collimation leads to high amounts of sharing of clusters between tracks, which leads to markedly reduced tracking performance when using traditional approaches. By using a maskformer, we are not only able to mitigate this, increasing efficiency at high $p_T$ by 40%, but also simplify, unify, and improve the process of identifying and regressing the local positions of particles on shared clusters. Finally, we discuss ongoing efforts to use a maskformer to perform global particle flow reconstruction, where tracker, calorimter, and muon hit assignment with particle regression is performed using one global model, providing a proof-of-concept for single-step global reconstruction.
