Speaker
Description
Over the past year, the ATLAS muon group has successfully incorporated machine learning (ML) techniques to improve the identification of hits in the ATLAS Muon Spectrometer (MS) originating from primary vertices, while effectively rejecting noise and background muons. These advancements are critical to address the challenges posed by the extreme operating conditions during Phase-2 of the High Luminosity Large Hadron Collider (HL-LHC), when event reconstruction will have to be carefully optimized to operate within the stringent latency constraints of the Event Filter, ensuring scalability to the upgraded trigger system running at an input rate of 1 MHz.
Searches for long-lived particles (LLPs) are among the most promising avenues for discovering yet unseen physics beyond the Standard Model at the HL-LHC. However, displaced signatures are notoriously difficult to identify as a result of their ability to evade standard object-reconstruction strategies. In particular, searches for LLPs with large proper lifetimes (cτ), such as low-mass Heavy Neutral Lepton (HNL) or Hidden Abelian Higgs Model (HAHM) dark photons, currently rely on fully reconstructed tracklets (segments) inside the ATLAS muon spectrometer as an input to displaced vertex reconstruction. Although this algorithm achieves position resolutions at the order of 100 millimeters and mass resolutions of a few GeV, it faces important limitations. In particular, it is inefficient in reconstructing highly collimated decay signatures, such as those expected from phenomenologically favored low-mass HNLs produced in W decays, as well as scenarios with similarly favored long lifetimes, where decays occur inside the muon spectrometer volume and prevent segment reconstruction.
To overcome these challenges, this project proposes the development of transformer-based models for the identification and triggering of LLP decay signatures directly from the measurements in the muon spectrometer (drift circle and strip), targeting large proper lifetimes currently inaccessible. Originally designed for natural language processing, transformer architectures are particularly well suited to capture long-range dependencies and complex correlations in sparse, high-dimensional data such as that produced in the MS. Our goal is to leverage these capabilities for the reconstruction of displaced muon vertices, with a particular focus on both trigger-level and offline reconstruction. The focus is on the new L0 global system of the upgraded ATLAS experiment, which will enable the deployment of the ML model to run directly in the first stage of the trigger. This advancement opens the possibility of identifying relevant events in real time, without relying on conventional first-level trigger signatures as required in the current system. Success in this area could enable the exploration of leptonic vertex-based LLP channels for the Phase-2 HL-LHC that are currently beyond the technical reach of ATLAS.
CERN group/ Experiment
CERN ATLAS Team
| Working area | Area 2: Optimal AI deployment for Online Data Processing |
|---|---|
| Project goals | AI/DL models for displaced vertex reconstruction and triggering in the Atlas Muon Spectrometer to unlock the discovery potential of leptonic vertex-based LLP channels that are currently inaccessible to ATLAS. |
| Timeline | Year 1 - Development of AI/ML models for offline identification of LLP events: - Design and evaluation of transformer-based architectures tailored to sparse detector data; - Benchmarking of model accuracy, robustness, and computational efficiency; - Initial integration and testing within the ATHENA software framework. Year 2 - Development of AI/ML models for trigger-level LLP identification: - Design of low-latency transformer architectures optimized for real-time inference; - Benchmarking inference performance across multiple hardware platforms (CPU, GPU, FPGA); - Validation of model performance within the ATLAS Event Filter environment. Year 3 - Integration of LLP reconstruction into the L0 global trigger: - Model optimization and compression techniques for FPGA deployment; - Comprehensive benchmarking of trigger-level efficiency and latency; - Final validation of physics performance and readiness for HL-LHC operations. |
| Available person power | 0.3 FTE |
| Additional person power request | 36 GRAP months, 36 DOCT months |
| Is this an already ongoing activity? | No |
| Indicative hardware resources needs | Cluster with CPU/GPU, FPGA boards, and high-capacity storage |