Periodic Reporting for period 1 - ALPaCA (AcceLerated PreCision Tests of Lepton UniversAlity)
Période du rapport: 2022-10-01 au 2025-03-31
The Standard Model (SM) of particle physics provides an exceptionally accurate framework for describing most fundamental processes in nature. However, it leaves some critical questions unanswered, such as the origin of the observed matter-antimatter asymmetry in the universe. Physicists suspect that "New Physics" (NP) beyond the SM could offer solutions, to address these gaps, but no direct evidence has yet been observed at particle colliders. This implies that NP may either be extremely rare or manifest only at energy scales accessible through highly precise SM measurements. A particularly stringent prediction of the SM is lepton flavor universality (LFU), which says that all types of leptons interact similarly with other particles, the only difference being their mass. However, recent measurements in the decays of B hadrons have revealed potential deviations from LFU.
To explore this and other paths probing the SM in more detail, the LHCb experiment at the LHC at CERN has undergone a major upgrade for Run 3 of the LHC, which started in 2022. The upgraded experiment will produce an unprecedented number of beauty hadrons in proton-proton collisions with a rate five times higher than before. To select decays of interest, efficient real-time analysis systems are necessary. This poses exceptional computing demands, which can be met by heterogeneous systems based on many-core architectures, such as the Allen framework co-developed by ALPaCA members.
The ALPACA project aims to achieve three key objectives. First, it seeks to enhance the performance of the Allen framework, a cutting-edge real-time data processing system, to maximize its physics potential. Second, it will pioneer the study of lepton flavor universality (LFU) in specific semileptonic particle decays (b → c l ν) involving electrons. Finally, the project will guide the design of future experiments by generalising and expanding the Allen framework, ensuring it can meet the evolving demands of next-generation particle physics research.
To explore this and other paths probing the SM in more detail, the LHCb experiment at the LHC at CERN has undergone a major upgrade for Run 3 of the LHC, which started in 2022. The upgraded experiment will produce an unprecedented number of beauty hadrons in proton-proton collisions with a rate five times higher than before. To select decays of interest, efficient real-time analysis systems are necessary. This poses exceptional computing demands, which can be met by heterogeneous systems based on many-core architectures, such as the Allen framework co-developed by ALPaCA members.
The ALPACA project aims to achieve three key objectives. First, it seeks to enhance the performance of the Allen framework, a cutting-edge real-time data processing system, to maximize its physics potential. Second, it will pioneer the study of lepton flavor universality (LFU) in specific semileptonic particle decays (b → c l ν) involving electrons. Finally, the project will guide the design of future experiments by generalising and expanding the Allen framework, ensuring it can meet the evolving demands of next-generation particle physics research.
From 2022 to 2024, the Allen system has been fully validated, showing it can handle the data flow of 30 million proton-proton collisions per second from the LHCb experiment, a first for a high energy physics experiment. Allen's flexible design proved invaluable as new detector components were added and fine-tuned during the commissioning phase, enabling adjustments to maintain data quality under complex conditions.
Originally planned for fast, less efficient particle track reconstruction, Allen’s performance improvements allowed the addition of a more precise method for reconstructing low-momentum particles. This enhanced the efficiency of LHCb’s data selection process, doubling its effectiveness compared to the previous phase of the experiment. These advancements mark a significant step forward in maximizing LHCb’s physics potential in Run 3.
For Run 3, the LHCb Semileptonics physics group has adopted a new strategy for efficiently selecting and reconstructing b → c l ν particle decays in real time.
Common selections are applied for various excited states of charm hadrons, while individual excited states are then reconstructed offline, using as input the ground state particles selected inclusively and extra particles that are stored. To select these extra particles, an innovative method based on Neural Networks was co-developed and implemented by ALPaCA team members, which significantly improves data collection efficiency compared to state-of-the art selection methods.
This novel method will be crucial for the test of LFU with b → c l ν decays involving electrons.
Originally planned for fast, less efficient particle track reconstruction, Allen’s performance improvements allowed the addition of a more precise method for reconstructing low-momentum particles. This enhanced the efficiency of LHCb’s data selection process, doubling its effectiveness compared to the previous phase of the experiment. These advancements mark a significant step forward in maximizing LHCb’s physics potential in Run 3.
For Run 3, the LHCb Semileptonics physics group has adopted a new strategy for efficiently selecting and reconstructing b → c l ν particle decays in real time.
Common selections are applied for various excited states of charm hadrons, while individual excited states are then reconstructed offline, using as input the ground state particles selected inclusively and extra particles that are stored. To select these extra particles, an innovative method based on Neural Networks was co-developed and implemented by ALPaCA team members, which significantly improves data collection efficiency compared to state-of-the art selection methods.
This novel method will be crucial for the test of LFU with b → c l ν decays involving electrons.
The ALPaCA project has played an important role in demonstrating the first working system of a fully GPU-based real-time analysis system, performing reconstruction and selections at 30 million proton bunch collisions per second. The system has outperformed the performance described in the Technical Design Report, providing more efficient particle trajectory reconstruction at low momentum.