The upgrade I of LHCb VELO—towards an intelligent monitoring platform

Kopciewicz, P.; Szumlak, T.; Majewski, M. W.; Akiba, K. Carvalho; Augusto, Oscar; Back, J. J.; Bobulska, D.; Bogdanova, G. A.; Bowcock, T. J. V.; Buytaert, J.; Cid, E. Lemos; Coco, V.; Collins, P.; Dall’Occo, E.; Bruyn, K. De; Capua, S. De; Dettori, F.; Dreimanis, K.; Dutta, D.; Eklund, L.; Evans, T.; Ferro‐Luzzi, M.; Funk, W.; Garcia, L. Meyer; García, O. Boente; Gersabeck, M.; Gershon, T.; Graz, Cristina Sanchez; Hennessy, K.; Hulsbergen, W.; Hutchcroft, D.; Hynds, D.; Jałocha, P.; John, M.; Jurik, N.; Kostiuk, I.; Latham, T.; Leflat, A.; Lima, V. Franco; Marinho, F.; Mendes, Larissa Helena; Merk, M.; Morris, A. G.; Murray, D.; Naik, Sneha; Nasteva, I.; Obłąkowska-Mucha, A.; Otalora, Juan; Parkes, C.; Plana, B. Garcia; Prieto, A. Fernández; Rachwał, B.; Regueiro, P. Vázquez; Rodrigues, Gabriel Dias; Scantlebury-Smead, Luke; Schiller, M.; Schindler, H.; Shears, T.; Snoch, A.; Švihra, Peter; Torreira, A. Gallas; Beuzekom, M. van; Velthuis, J. J.; Volkov, V.; Williams, Mark Richard James

doi:10.1088/1748-0221/15/06/c06009

Cited by 5 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The critical part of the upgrade is an innovative, flexible fully-software trigger system that can maximize efficiencies for hadronic decay channels, requiring the new trigger-less front-end electronics capable of reading out the full detector at 40 MHz [ 28 , 29 ]. The upgraded VELO will be adjusted to that readout [ 30 , 31 ]. One should note that the principal role of the VELO will remain the same.…”

Section: Methodsmentioning

confidence: 99%

Simulation and Optimization Studies of the LHCb Beetle Readout ASIC and Machine Learning Approach for Pulse Shape Reconstruction

Kopciewicz

Szumlak

Sitko

et al. 2021

Sensors

View full text Add to dashboard Cite

The optimization of the Beetle readout ASIC and the performance of the software for the signal processing based on machine learning methods are presented. The Beetle readout chip was developed for the LHCb (Large Hadron Collider beauty) tracking detectors and was used in the VELO (Vertex Locator) during Run 1 and 2 of LHC data taking. The VELO, surrounding the LHC beam crossing region, was a leading part of the LHCb tracking system. The Beetle chip was used to read out the signal from silicon microstrips, integrating and amplifying it. The studies presented in this paper cover the optimization of its electronic configuration to achieve the lower power consumption footprint and the lower operational temperature of the sensors, while maintaining a good condition of the analogue response of the whole chip. The studies have shown that optimizing the operational temperature is possible and can be beneficial when the detector is highly irradiated. Even a single degree drop in silicon temperature can result in a significant reduction in the leakage current. Similar studies are being performed for the future silicon tracker, the Upstream Tracker (UT), which will start operating at LHC in 2021. It is expected that the inner part of the UT detector will suffer radiation damage similar to the most irradiated VELO sensors in Run 2. In the course of analysis we also developed a general approach for the pulse shape reconstruction using an ANN approach. This technique can be reused in case of any type of front-end readout chip.

show abstract

Section: Methodsmentioning

confidence: 99%

Simulation and Optimization Studies of the LHCb Beetle Readout ASIC and Machine Learning Approach for Pulse Shape Reconstruction

Kopciewicz

Szumlak

Sitko

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Neural Networks are therefore widely used in High Energy Physics not only as a classification tool, but also for other tasks like intelligent data reduction and time series analysis [27] or reconstruction of a pulse shape from the front-end electronics [26]. They are used as well to optimize processes in various environments(Reinforcement Learning), for example automate the management of resources in a computing cloud [20].…”

Section: Machine Learning Based Track Reconstructionmentioning

confidence: 99%

Machine Learning based Reconstruction for the MUonE Experiment

Zdybał,

Kucharczyk,

Wolter

2024

csci

View full text Add to dashboard Cite

As currently operating high energy physics experiments produce a huge amount of data, new methods of fast and efficient event reconstruction are necessary to handle the immense load. Storing the unprocessed data is not feasible, forcing experiments to process the data online employing the algorithms of quality provided for the offline analysis, but within strict time constraints. In the MUonE experiment the machine learning based event reconstruction techniques are being implemented and tested in order to provide efficient online reduction of data and to maximize the statistical power of the final physics measurement.

show abstract

“…A long-time monitoring of those provides input for the machine learning applications, which proved to be useful for the trend analysis and state prediction in the old VELO. A comprehensive intelligent monitoring platform for similar cases is planned to be part of the upgrade I [35]. The noise scan described in section 2 can be useful for various studies related to the ASIC performance.…”

Section: The Calibrated Detectormentioning

confidence: 99%

The LHCb vertex locator upgrade — the detector calibration overview

2022

Self Cite

View full text Add to dashboard Cite

The Vertex Locator (VELO) is a silicon tracking detector in the spectrometer of the Large Hadron Collider beauty (LHCb) experiment. LHCb explores and investigates CP violation phenomena in b- and c- hadron decays and is one of the experiments operating on the Large Hadron Collider (LHC) at CERN. After run 1 and run 2 of LHC data taking (2011–2018), the LHCb detectors are being modernized within the LHCb upgrade I program. The upgrade aims to adjust the spectrometer to readout at full LHC 40 MHz frequency, which requires radical changes to the technologies currently used in LHCb. The hardware trigger is removed, and some of the detectors replaced. The VELO changes its tracking technology and silicon strips are replaced by 55 μm pitch silicon pixels. The readout chip for the VELO upgrade is the VeloPix ASIC. The number of readout channels increases to over 40 million, and the hottest ASIC is expected to produce the output data rate of 15 Gbit/s. New conditions challenge the software and the hardware side of the readout system and put special attention on the detector monitoring. This paper presents the upgraded VELO design and outlines the software aspects of the detector calibration in the upgrade I. An overview of the challenges foreseen for the upgrade II is given.

show abstract

The upgrade I of LHCb VELO—towards an intelligent monitoring platform

Cited by 5 publications

References 6 publications

Simulation and Optimization Studies of the LHCb Beetle Readout ASIC and Machine Learning Approach for Pulse Shape Reconstruction

Simulation and Optimization Studies of the LHCb Beetle Readout ASIC and Machine Learning Approach for Pulse Shape Reconstruction

Machine Learning based Reconstruction for the MUonE Experiment

The LHCb vertex locator upgrade — the detector calibration overview

Contact Info

Product

Resources

About