Mini-workshop on Real time tracking : triggering events with tracks

Contribution List

1. Directions in Realtime Tracking : Everything, Everywhere, All at Once

Kristian Hahn (Northwestern University (US))

13/10/2023, 09:00

2. Level-1 Tracking at CMS for the HL-LHC

Sara Fiorendi (University of Tennessee (US))

13/10/2023, 09:30

The success of the CMS physics program at the HL-LHC requires maintaining sufficiently low trigger thresholds to select processes at the electroweak scale. With an average expected 200 pileup interactions, critical to achieve this goal while maintaining manageable trigger rates is in the inclusion of tracking in the L1 trigger. A 40 MHz silicon-tracker based track trigger on the scale of the...

6. Standalone track reconstruction and matching algorithms for the GPU-based High Level Trigger at LHCb

Louis Henry (EPFL - Ecole Polytechnique Federale Lausanne (CH))

13/10/2023, 09:52

The LHCb Upgrade in Run 3 has changed its trigger scheme for a full software selection in two steps. The first step, HLT1, will be entirely implemented on GPUs and run a fast selection aiming at reducing the visible collision rate from 30 MHz to 1 MHz.
This selection relies on a partial reconstruction of the event. A version of this reconstruction starts with two monolithic tracking...

8. FPGA-based architecture for a real-time track reconstruction in the LHCb Scintillating Fibre Tracker beyond Run 3

Michael J. Morello (SNS and INFN-Pisa (IT))

13/10/2023, 10:15

Finding track segments downstream of the magnet is some of the most important and computationally expensive task of the first stage of the new GPU-based software trigger of the LHCb Upgrade I, that has started operation in Run 3. These segments are essential to form all good physics tracks with a very high precision momentum measurement, when combined with those reconstructed in the vertex...

3. A real-time demonstrator of track reconstruction with FPGAs at LHCb

Francesco Terzuoli (Università di Siena & INFN Pisa (IT))

13/10/2023, 11:10

The upgraded LHCb detector has started its Run 3 of data taking in 2022, with a completely overhauled DAQ system, reading out and processing the full detector data at every LHC bunch crossing (30 MHz average rate). At the same, an intense R&D activity is taking place, with the aim of further improving the real-time data processing performance of LHCb, in view of a further luminosity upgrade of...

7. Trigger Level Tracking With Neural Networks on Heterogeneous Computing Systems

Alex Gekow (Ohio State University (US))

13/10/2023, 11:32

The high luminosity upgrade of the LHC aims to better probe the higgs potential and self coupling. The Event Filter task force has been charged with exploring novel approaches to charged particle tracking to be employed in the upgraded ATLAS trigger system, capable of analyzing high luminosity events in real time. We present a neural network (NN) based approach to predicting and identifying...

5. Track reconstruction for the ATLAS Phase-II High-Level Trigger using Graph Neural Networks on FPGAs

Sachin Gupta (Ruprecht Karls Universitaet Heidelberg (DE))

13/10/2023, 11:54

The High-Luminosity LHC (HL-LHC) will provide an order of magnitude increase in integrated luminosity and enhance the discovery reach for new phenomena. The increased pile-up foreseen during the HL-LHC necessitates major upgrades to the ATLAS detector and trigger. The Phase-II trigger will consist of two levels, a hardware-based Level-0 trigger and an Event Filter (EF) with tracking...

9. Studies on track finding algorithms based on machine learning with GPU and FPGA

Francesco Armando Di Bello (INFN e Universita Genova (IT))

13/10/2023, 12:16

Track finding in high-density environments is a key challenge for experiments at modern accelerators. In this presentation we describe the performance obtained running machine learning models studied for the ATLAS Muon High Level Trigger. These models are designed for hit position reconstruction and track pattern recognition with a tracking detector, on a commercially available Xilinx FPGA:...

10. Accelerated Large Graph Creation with CUDA: Empowering Graph Neural Networks in Particle Physics

Nilotpal Kakati (Weizmann Institute of Science (IL))

Graph neural networks have emerged as a powerful tool in various physics studies, particularly in the analysis of sparse and heterogeneous data. However, as the field of particle physics advances towards utilizing graphs in high-luminosity scenarios, a new challenge has emerged: efficient graph creation. While GNN inference is highly optimized, graph creation has not received the same level of...

4. Real time event-level track pattern recognition with Graph Neural Networks in CMS

Miaoyuan Liu (Purdue University (US))

Detecting the signals of very low-pT muons with traditional track reconstruction algorithms, such as Kalman filters, is very challenging. In case of the decay of a tau lepton decaying into three muons, the signature includes three very low pT muons in the forward region of the CMS detector. Some or all of these muons might not carry enough pT to reach all stations of the CMS muon system. Even...