CPAD Workshop 2022

Session

WG6: TDAQ and AI/ML

29 Nov 2022, 11:30

Wang Center, Stony Brook University

Conveners

WG6: TDAQ and AI/ML

• Xin Qian (BNL)
• Williams Mike (MIT)
• Alexander Paramonov (Argonne National Laboratory)

WG6: TDAQ and AI/ML

• Williams Mike (MIT)
• Alexander Paramonov (Argonne National Laboratory)
• Xin Qian (BNL)

36. Neural network accelerator for quantum control

Giuseppe Di Guglielmo (Fermilab)

29/11/2022, 11:30

WG6: TDAQ and AI/ML

Contribution Talk

Efficient quantum control is necessary for practical quantum computing implementations with current technologies. However, conventional algorithms for determining optimal control parameters are computationally expensive, mainly excluding them from use outside of the simulation. Furthermore, existing hardware solutions structured as lookup tables are imprecise and costly. A more efficient...

97. Nonlinear On-Chip Waveform Processing for Detector ASICs

Soumyajit Mandal (Brookhaven National Laboratory)

29/11/2022, 11:50

WG6: TDAQ and AI/ML

Contribution Talk

Modern high energy and nuclear physics experiments generate massive (and steadily increasing) amounts of real-time data, much of which cannot be acquired and processed online and is thus discarded using a variety of hardware triggering schemes. This situation is not optimal since the triggering logic is based on established signal models and may discard unexpected signals (some of which may...

55. Quality control assessment of silicon detector construction using deep learning

Kamal Lamichhane (Texas Tech University)

29/11/2022, 12:10

WG6: TDAQ and AI/ML

Contribution Talk

The wire-bond between the silicon sensor and the circuit board is the primary mode to collect the signal in many silicon detectors. Hence, the clean and unbroken bond is one of the vital demands in the silicon module during construction. Each of these bond holes needs to be inspected at least three times during the assembly i.e., before and after wire-bonding, and after encapsulation....

81. A Generic Data Acquisition System for Multidisciplinary Detector R&D

Samuel Watkins (Los Alamos National Laboratory)

30/11/2022, 10:35

WG6: TDAQ and AI/ML

Contribution Talk

Time-domain triggering on a raw stream of data, where the triggering is generally threshold-based or randomly acquired, is critical for many scientific applications from rare-event searches to condensed matter system characterization to high-rate high-energy physics experiments. Generic data acquisition (DAQ) systems that quickly and efficiently process such data are thus necessary for...

76. The upgrade of the versatile data acquisition system - CaRIBOu

Shaochun Tang (BNL)

30/11/2022, 10:55

WG6: TDAQ and AI/ML

Contribution Talk

Developing new detectors requires the design of an adequate readout and control system. Such a system typically consists of hardware in form of a readout board containing programmable logic to provide an interface to the detector chip, power supplies for biasing the sensor, as well as DACs and ADCs for setting and measuring operation parameters, generating test pulses, etc. The CaRIBOu system,...

65. The LUX-ZEPLIN Data Acquisition and Real Time Monitoring System

DEV ASHISH KHAITAN

30/11/2022, 11:15

WG6: TDAQ and AI/ML

Contribution Talk

The LUX-ZEPLIN (LZ) direct-detection dark matter experiment has been deployed at the Sanford Underground Research Facility (SURF), has successfully completed it's first science campaign, and boasts world leading sensitivity. The LZ signal chain comprises of 1395 photomultiplier and auxiliary channels. These signals are digitized on custom FPGA-based digitizers with custom firmware. Digital...

41. Real-Time Object Detection and Identification for DUNE’s Data Selection System

Judicael Clair (Columbia University)

30/11/2022, 11:35

WG6: TDAQ and AI/ML

Contribution Talk

Modern-day particle and astro-particle physics experiments call for detectors with increasingly higher imaging resolution to be deployed in often inaccessible, remote locations, e.g., deep underground, or in-flight on balloons or satellites. The inherent limitations in available on-detector power and computational resources, combined with the need to operate these detectors continually, which...

85. Usage of Machine Learning in CMS Level-1 Endcap Muon Trigger

Efe Yigitbasi (staff@rice.edu;member@rice.edu)

30/11/2022, 11:55

WG6: TDAQ and AI/ML

Contribution Talk

The Level-1 trigger of the CMS experiment at the LHC uses custom hardware processors to select up to 100 kHz of interesting events out of a possible 40 MHz. Correct measurement of the transverse momentum of particles is crucial to correctly identify which events to keep. This task is particularly challenging in the endcaps of the CMS experiment due to the non-uniform magnetic field, reduced...