Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science - the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs

Search for vector-like quarks with the ATLAS Detector

Vector-like quarks appear in many theories beyond the Standard Model as a way to cancel the mass divergence for the Higgs boson. The current status of the ATLAS searches for the production of vector-like quarks is reviewed for proton-proton collisions with $\sqrt{s}$ = 13 TeV produced by the Large Hadron Collider. The results and the complementarity of the various searches are discussed

Searches for resonances decaying to boson pairs in ATLAS

Many new physics models predict the existence of Higgs-like particles decaying into two bosons (W, Z, photon, or Higgs bosons) making these important signatures in the search for new physics. Searches for spin-0 diboson resonances have been performed in final states with different numbers of leptons, photons, as well as jets and b-jets where new jet substructure techniques are used to disentangle the hadronic decay products in highly boosted configuration. This talk summarises recent ATLAS searches with Run 2 data collected at the LHC and explains the experimental methods used, including vector- and Higgs-boson-tagging techniques

Development of the ATLAS Liquid Argon Calorimeter Readout Electronics for the HL-LHC

A new era of hadron collisions will start around 2028 with the High-Luminosity LHC, that will allow to collect ten times more data that what has been collected so far at the LHC. This is possible thanks to a higher instantaneous luminosity and higher number of collisions per bunch crossing. To meet the new trigger and data acquisition requirements and withstand the high expected radiation doses at the High-Luminosity LHC, the ATLAS Liquid Argon Calorimeter readout electronics will be upgraded. The triangular calorimeter signals are amplified and shaped by analogue electronics over a dynamic range of 16 bits, with low noise and excellent linearity. Developments of low-power preamplifiers and shapers to meet these requirements are ongoing in 130nm CMOS technology. In order to digitize the analogue signals on two gains after shaping, a radiation-hard, low-power 40 MHz 14-bit ADCs is being developed using a pipeline+SAR architecture in 65nm CMOS. The characterization of the prototypes of these on-detector components is promising and will likely fulfill all the requirements. The signals will be sent at 40MHz to the off-detector electronics, where FPGAs connected through high-speed links will perform energy and time reconstruction through the application of corrections and digital filtering. Reduced data are then sent with low latency to the first-level trigger-system, while the full data are buffered until the reception of the trigger decision signal. If an event is triggered, the full data is sent to the ATLAS readout system. The data-processing, control, and timing functions will be realized with dedicated boards using the ATCA technology. The results of tests of prototypes of the on-detector components will be presented. The design of the off-detector boards along with the performance of the first prototypes will be discussed. In addition, the architecture of the firmware and processing algorithms will be shown

Development of the ATLAS Liquid Argon Calorimeter Readout Electronics for the HL-LHC

A new era of hadron collisions will start around 2029 with the High-Luminosity LHC, that will allow to collect ten times more data that what has been collected so far at the LHC. This is possible thanks to a higher instantaneous luminosity and higher number of collisions per bunch crossing. To meet the new trigger and data acquisition requirements and withstand the high expected radiation doses at the High-Luminosity LHC, the ATLAS Liquid Argon Calorimeter readout electronics will be upgraded. The triangular calorimeter signals are amplified and shaped by analogue electronics over a dynamic range of 16 bits, with low noise and excellent linearity. Developments of low-power preamplifiers and shapers to meet these requirements are ongoing in 130nm CMOS technology. In order to digitize the analogue signals on two gains after shaping, a radiation-hard, low-power 40 MHz 14-bit ADCs is being developed using a pipeline+SAR architecture in 65nm CMOS. The characterization of the prototypes of these on-detector components is promising and will likely fulfill all the requirements. The signals will be sent at 40MHz to the off-detector electronics, where FPGAs connected through high-speed links will perform energy and time reconstruction through the application of corrections and digital filtering. Reduced data are then sent with low latency to the first-level trigger-system, while the full data are buffered until the reception of the trigger decision signal. If an event is triggered, the full data is sent to the ATLAS readout system. The data-processing, control, and timing functions will be realized with dedicated boards using the ATCA technology. The results of tests of prototypes of the on-detector components will be presented. The design of the off-detector boards along with the performance of the first prototypes will be discussed. In addition, the architecture of the firmware and processing algorithms will be shown

Search for Vector-Like Quarks with the ATLAS Detector

Vector like quarks appear in many theories beyond the Standard Model as a way to cancel the mass divergence for the Higgs boson. The current status of the ATLAS searches for the production of vector like quarks will be reviewed for proton-proton collisions at 13 TeV. This presentation will address the analysis techniques, in particular the selection criteria, the background modeling and the related experimental uncertainties. The results and the complementarity of the various searches will be discussed

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.ISSN:2624-909

Applications and Techniques for Fast Machine Learning in Science.

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs