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Register a new userReal-time reconstruction of long-lived particles at LHCb using FPGAs
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Added by
Michael Joseph Morello
Publication date
2020
fields
Physics
and research's language is
English
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Finding tracks downstream of the magnet at the earliest LHCb trigger level is not part of the baseline plan of the upgrade trigger, on account of the significant CPU time required to execute the search. Many long-lived particles, such as $K^0_S$ and strange baryons, decay after the vertex track detector, so that their reconstruction efficiency is limited. We present a study of the performance of a future innovative real-time tracking system based on FPGAs, developed within a R&D effort in the context of the LHCb Upgrade Ib (LHC Run~4), dedicated to the reconstruction of the particles downstream of the magnet in the forward tracking detector (Scintillating Fibre Tracker), that is capable of processing events at the full LHC collision rate of 30 MHz.
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The LHCb experiment is dedicated to the study of the $c-$ and $b-$hadron decays, including long-lived particles such as $K_s$ and strange baryons ($Lambda^0$, $Xi^-$, etc... ). These kind of particles are difficult to reconstruct by the LHCb tracking system since they escape detection in the first tracker. A new method to evaluate the performance of the different tracking algorithms for long-lived particles using real data samples has been developed. Special emphasis is laid on particles hitting only part of the tracking system of the new LHCb upgrade detector.
In this paper, we show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely challenging environment of a real-time event selection system at the Large Hadron Collider (LHC). We demonstrate that new physics signatures can be enhanced by three orders of magnitude, while staying within the strict latency and resource constraints of a typical LHC event filtering system. This would allow for collecting datasets potentially enriched with high-purity contributions from new physics processes. Through per-layer, highly parallel implementations of network layers, support for autoencoder-specific losses on FPGAs and latent space based inference, we demonstrate that anomaly detection can be performed in as little as $80,$ns using less than 3% of the logic resources in the Xilinx Virtex VU9P FPGA. Opening the way to real-life applications of this idea during the next data-taking campaign of the LHC.
We report on recent progress in the design of the proposed MATHUSLA Long Lived Particle (LLP) detector for the HL-LHC, updating the information in the original Letter of Intent (LoI), see CDS:LHCC-I-031, arXiv:1811.00927. A suitable site has been identified at LHC Point 5 that is closer to the CMS Interaction Point (IP) than assumed in the LoI. The decay volume has been increased from 20 m to 25 m in height. Engineering studies have been made in order to locate much of the decay volume below ground, bringing the detector even closer to the IP. With these changes, a 100 m x 100 m detector has the same physics reach for large c$tau$ as the 200 m x 200 m detector described in the LoI and other studies. The performance for small c$tau$ is improved because of the proximity to the IP. Detector technology has also evolved while retaining the strip-like sensor geometry in Resistive Plate Chambers (RPC) described in the LoI. The present design uses extruded scintillator bars read out using wavelength shifting fibers and silicon photomultipliers (SiPM). Operations will be simpler and more robust with much lower operating voltages and without the use of greenhouse gases. Manufacturing is straightforward and should result in cost savings. Understanding of backgrounds has also significantly advanced, thanks to new simulation studies and measurements taken at the MATHUSLA test stand operating above ATLAS in 2018. We discuss next steps for the MATHUSLA collaboration, and identify areas where new members can make particularly important contributions.
We describe the Hybrid seeding, a standalone pattern recognition algorithm aiming at finding charged particle trajectories for the LHCb upgrade. A significant improvement to the charged particle reconstruction efficiency is accomplished by exploiting the knowledge of the LHCb magnetic field and the position of energy deposits in the scintillating fibre tracker detector. Moreover, we achieve a low fake rate and a small contribution to the overall timing budget of the LHCb real-time data processing.
A search is performed for heavy long-lived charged particles using 3.0 fb$^{-1}$ of pp collisions collected at $sqrt{s}$= 7 and 8 TeV with the LHCb detector. The search is mainly based on the response of the ring imaging Cherenkovdetectors to distinguish the heavy, slow-moving particles from muons. No evidence is found for the production of such long-lived states. The results are expressed as limits on the Drell-Yan production of pairs of long-lived particles, with both particles in the LHCb pseudorapidity acceptance, $1.8 < eta < 4.9$. The mass-dependent cross-section upper limits are in the range 2-4 fb (at 95% CL) for masses between 124 and 309 GeV/c$^2$.
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