LHCb Upgrade II: Maximising HL-LHC Discovery Potential (Bridging Funding)

Lead Research Organisation: University of Bristol
Department Name: Physics

Abstract

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Publications

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Blake Thomas TORCH reconstruction and particle identification algorithm

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Cicala M (2022) Picosecond timing of charged particles using the TORCH detector in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

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Gao R (2022) A precision time of flight readout system for the TORCH prototype detector in Journal of Instrumentation

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Hadavizadeh T (2020) Status of the TORCH time-of-flight detector

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Harnew N (2020) Status of the TORCH Project in Journal of Instrumentation

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Harnew N (2023) The TORCH time-of-flight detector in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

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Jones T (2023) New developments from the TORCH R&D project in Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

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Kreps M (2021) Test-beam performance of a TORCH prototype module

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LHCb Collaboration (2021) Framework TDR for the LHCb Upgrade II - Opportunities in flavour physics, and beyond, in the HL-LHC era

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Rademacker J (2022) TORCH, a novel time of flight detector for LHCb upgrade II

 
Description As part of the award, we performed (a) testbeam and calibration studies for the TORCH detector, (b) developed new TORCH reconstruction algorithms, and (c) we ported tracking reconstruction algorithms on Intelligence Processing Units.
For (a): initial results are promising, but not yet final
For (b): our new algorithm is about 100x faster than the previous one.
For (c): IPUs do not lend themselves well for VELO the tracking algorithm under study.
Exploitation Route Our industrial partners directly benefit from our calibration system, a copy of which they will use. Future particle physics experiments, in particular the LHCb upgrade II, will benefit from the progress we made on making TORCH a functioning detector, and by speeding up its complex pattern recognition. The community using (and making) IPUs has shown great interest in the use of IPUs outside the classical machine learning paradigm they have been designed for. The disappointing performance we achieved when porting a tracking algorithm to IPUs that works well on GPUs is relevant in that context, even though it is disappointing.
Sectors Digital/Communication/Information Technologies (including Software),Electronics