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...
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...
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...
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...
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...
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...
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:...
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...
