17–31 Jul 2025
Orthodox Academy of Crete, Kolymbari, Crete, Greece
Europe/Athens timezone
Please see new information on proceedings at the link "Scientific Information"-> "Proceedings"

Advancements and Challenges in the ATLAS Liquid Argon Calorimeter Trigger and Readout Electronics for HL-LHC

Not scheduled
25m
Orthodox Academy of Crete, Kolymbari, Crete, Greece

Orthodox Academy of Crete, Kolymbari, Crete, Greece

Talk Workshop on Instruments and Methods Workshop on Instruments and Methods

Speaker

LAr speaker committee

Description

To meet the demands of increased instantaneous luminosity at the Large Hadron Collider (LHC), significant upgrades have been implemented on the ATLAS Liquid Argon (LAr) Calorimeters. This presentation will cover the performance of the upgraded trigger readout electronics, currently running, and the status of the readout electronics upgrade for the High-Luminosity LHC (HL-LHC), in preparation.

New trigger readout electronics have been installed during the last LHC long shutdown (LS2) to handle the increased data throughput. On the detector side, 124 new electronic boards digitize at high speed ten times more signals than the legacy system. Downstream, large FPGAs process up to 20 Tbps of data to compute deposited energies. Additionally, a new control and monitoring infrastructure has been developed. This contribution will detail the performance of the new system and the milestones achieved in phasing out the legacy analog trigger in favor of the new digital trigger for Run 3.

Looking into the future, the ATLAS LAr Calorimeter readout electronics are being upgraded to support the High-Luminosity LHC (HL-LHC). This includes the development of custom preamplifiers and shapers with low noise and excellent linearity, a new ADC chip with two gains, and new calibration boards with minimal non-linearity and uniformity issues across all calorimeter channels. New ATCA-compliant signal processing boards equipped with FPGAs and high-speed links receive detector data and perform energy and time reconstruction. A new timing and control system has also been designed to ensure seamless operation. Machine learning approaches, including convolutional and recurrent neural networks, are being explored to outperform the optimal signal filter currently used in energy resolution. The latest developments towards the full production of the upgrade components will be presented.

Details

N/A

Internet talk No
Is this an abstract from experimental collaboration? Yes
Name of experiment and experimental site ATLAS
Is the speaker for that presentation defined? No

Author

LAr speaker committee

Presentation materials

There are no materials yet.