Georges Aad, Braden Keim Abbott, Kira Abeling, Nils Julius Abicht, Haider Abidi, Asmaa Aboulhorma, Halina Abramowicz, Henso Abreu, Yiming Abulaiti, Angel Abusleme, Bobby Samir Acharya, Claire Adam Bourdarios, Leszek Adamczyk, Lukas Adamek, Sagar Addepalli, Matt Addison, Jahred Adelman, Aytul Adiguzel, Tim Adye, Tony Affolder, Yoav Afik, Merve Nazlim Agaras, Jinky Agarwala, Anamika Aggarwal, Catalin Agheorghiesei, Ammara Ahmad, Faig Ahmadov, Waleed Syed Ahmed, Sudha Ahuja, Xiaocong Ai, Giulio Aielli, Arya Aikot, Malak Ait Tamlihat, Brahim Aitbenchikh, Iakov Aizenberg, Melike Akbiyik, Torsten Akesson, Andrei Akimov, Daiya Akiyama, Nilima Nilesh Akolkar, Konie Al Khoury, Gian Luigi Alberghi, Justin Albert, Pietro Albicocco, Guillaume Lucas Albouy, Sara Alderweireldt, Martin Aleksa, Igor Alexandrov, Calin Alexa, Theodoros Alexopoulos, Fabrizio Alfonsi, Malte Algren, Muhammad Alhroob, Babar Ali, Hanadi Ali, Shahzad Ali, Samuel William Alibocus, Malik Aliev, Gianluca Alimonti, Wael Alkakhi, Corentin Allaire, Benedict Allbrooke, Julia Frances Allen, Cristian Andres Allendes Flores, Philip Patrick Allport, Alberto Aloisio, Francisco Alonso, Cristiano Alpigiani, Manuel Alvarez Estevez, Adrian Alvarez Fernandez, Mario Alves Cardoso, Mariagrazia Alviggi, Mohamed Aly, Yara Do Amaral Coutinho, Alessandro Ambler, Christoph Amelung, Maximilian Amerl, Christoph Ames, Dante Amidei, Susana Patricia Amor dos Santos, Kieran Robert Amos, Viktor Ananiev, Christos Anastopoulos, Timothy Robert Andeen, John Kenneth Anders, Stefio Yosse Andrean, Attilio Andreazza, Stylianos Angelidakis, Aaron Angerami, Alexey Anisenkov, Alberto Annovi, Claire Antel, Matthew Thomas Anthony, Egor Antipov, Mario Antonelli, Fabio Anulli, Masato Aoki, Takumi Aoki, Javier Alberto Aparisi Pozo, Marco Aparo
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These algorithms require track reconstruction, which is computationally expensive and could overwhelm the high-level-trigger farm, even at the reduced event rate that passes the ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAS has mitigated these computational demands by introducing a fast neural-network-based b -tagger, which acts as a low-precision filter using input from hadronic jets and tracks. It runs after a hardware trigger and before the remaining high-level-trigger reconstruction. This design relies on the negligible cost of neural-network inference as compared to track reconstruction, and the cost reduction from limiting tracking to specific regions of the detector. In the case of Standard Model HH → bb̅bb̅ , a key signature relying on b -jet triggers, the filter lowers the input rate to the remaining high-level trigger by a factor of five at the small cost of reducing the overall signal efficiency by roughly 2%.","PeriodicalId":16184,"journal":{"name":"Journal of Instrumentation","volume":"212 ","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3\",\"authors\":\"Georges Aad, Braden Keim Abbott, Kira Abeling, Nils Julius Abicht, Haider Abidi, Asmaa Aboulhorma, Halina Abramowicz, Henso Abreu, Yiming Abulaiti, Angel Abusleme, Bobby Samir Acharya, Claire Adam Bourdarios, Leszek Adamczyk, Lukas Adamek, Sagar Addepalli, Matt Addison, Jahred Adelman, Aytul Adiguzel, Tim Adye, Tony Affolder, Yoav Afik, Merve Nazlim Agaras, Jinky Agarwala, Anamika Aggarwal, Catalin Agheorghiesei, Ammara Ahmad, Faig Ahmadov, Waleed Syed Ahmed, Sudha Ahuja, Xiaocong Ai, Giulio Aielli, Arya Aikot, Malak Ait Tamlihat, Brahim Aitbenchikh, Iakov Aizenberg, Melike Akbiyik, Torsten Akesson, Andrei Akimov, Daiya Akiyama, Nilima Nilesh Akolkar, Konie Al Khoury, Gian Luigi Alberghi, Justin Albert, Pietro Albicocco, Guillaume Lucas Albouy, Sara Alderweireldt, Martin Aleksa, Igor Alexandrov, Calin Alexa, Theodoros Alexopoulos, Fabrizio Alfonsi, Malte Algren, Muhammad Alhroob, Babar Ali, Hanadi Ali, Shahzad Ali, Samuel William Alibocus, Malik Aliev, Gianluca Alimonti, Wael Alkakhi, Corentin Allaire, Benedict Allbrooke, Julia Frances Allen, Cristian Andres Allendes Flores, Philip Patrick Allport, Alberto Aloisio, Francisco Alonso, Cristiano Alpigiani, Manuel Alvarez Estevez, Adrian Alvarez Fernandez, Mario Alves Cardoso, Mariagrazia Alviggi, Mohamed Aly, Yara Do Amaral Coutinho, Alessandro Ambler, Christoph Amelung, Maximilian Amerl, Christoph Ames, Dante Amidei, Susana Patricia Amor dos Santos, Kieran Robert Amos, Viktor Ananiev, Christos Anastopoulos, Timothy Robert Andeen, John Kenneth Anders, Stefio Yosse Andrean, Attilio Andreazza, Stylianos Angelidakis, Aaron Angerami, Alexey Anisenkov, Alberto Annovi, Claire Antel, Matthew Thomas Anthony, Egor Antipov, Mario Antonelli, Fabio Anulli, Masato Aoki, Takumi Aoki, Javier Alberto Aparisi Pozo, Marco Aparo\",\"doi\":\"10.1088/1748-0221/18/11/p11006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The ATLAS experiment relies on real-time hadronic jet reconstruction and b -tagging to record fully hadronic events containing b -jets. 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引用次数: 0
摘要
ATLAS实验依靠实时强子射流重建和b -标记来记录包含b -射流的全强子事件。这些算法需要跟踪重建,这在计算上是昂贵的,并且可能会压倒高级触发场,即使在通过ATLAS第一阶段基于硬件的触发器的降低事件率时也是如此。在LHC Run 3中,ATLAS通过引入一个快速的基于神经网络的b -标记器来减轻这些计算需求,该标记器使用来自强子射流和轨道的输入作为低精度滤波器。它在硬件触发器之后和剩余的高级触发器重构之前运行。与轨道重建相比,该设计依赖于神经网络推理的可忽略不计的成本,以及将跟踪限制在检测器的特定区域的成本降低。在标准模型HH→bb′bb′的情况下,一个依赖于b射流触发器的关键签名,滤波器将输入率降低到剩余的高电平触发器的五倍,而整体信号效率降低了大约2%。
Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3
Abstract The ATLAS experiment relies on real-time hadronic jet reconstruction and b -tagging to record fully hadronic events containing b -jets. These algorithms require track reconstruction, which is computationally expensive and could overwhelm the high-level-trigger farm, even at the reduced event rate that passes the ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAS has mitigated these computational demands by introducing a fast neural-network-based b -tagger, which acts as a low-precision filter using input from hadronic jets and tracks. It runs after a hardware trigger and before the remaining high-level-trigger reconstruction. This design relies on the negligible cost of neural-network inference as compared to track reconstruction, and the cost reduction from limiting tracking to specific regions of the detector. In the case of Standard Model HH → bb̅bb̅ , a key signature relying on b -jet triggers, the filter lowers the input rate to the remaining high-level trigger by a factor of five at the small cost of reducing the overall signal efficiency by roughly 2%.
期刊介绍:
Journal of Instrumentation (JINST) covers major areas related to concepts and instrumentation in detector physics, accelerator science and associated experimental methods and techniques, theory, modelling and simulations. The main subject areas include.
-Accelerators: concepts, modelling, simulations and sources-
Instrumentation and hardware for accelerators: particles, synchrotron radiation, neutrons-
Detector physics: concepts, processes, methods, modelling and simulations-
Detectors, apparatus and methods for particle, astroparticle, nuclear, atomic, and molecular physics-
Instrumentation and methods for plasma research-
Methods and apparatus for astronomy and astrophysics-
Detectors, methods and apparatus for biomedical applications, life sciences and material research-
Instrumentation and techniques for medical imaging, diagnostics and therapy-
Instrumentation and techniques for dosimetry, monitoring and radiation damage-
Detectors, instrumentation and methods for non-destructive tests (NDT)-
Detector readout concepts, electronics and data acquisition methods-
Algorithms, software and data reduction methods-
Materials and associated technologies, etc.-
Engineering and technical issues.
JINST also includes a section dedicated to technical reports and instrumentation theses.