Connecting The Dots 2018

Session

Session1

Mar 20, 2018, 8:50 AM

Physics-Astronomy Auditorium A118 (University of Washington Seattle)

Conveners

Session1: Session1

• Markus Elsing (CERN)

Presentation materials

Vidyo Recording March 20 morning I

Vidyo Recording March 20 morning II

32. Welcome

Shih-Chieh Hsu (University of Washington Seattle (US))

3/20/18, 8:50 AM

Oral

31. Developments in pileup suppression techniques at the LHC

Simone Pagan Griso (University of California Berkeley (US))

3/20/18, 9:00 AM

Oral

The LHC accelerator is running at unprecedented high instantaneous
luminosities, allowing the experiments to collect a vast amount of
data. However this ashonishing performance comes with a
larger-than-designed number of interactions per crossing of proton
bunches (pile-up). During 2017 values up to 60 interactions per bunch
crossing were routinely achieved and capped by the ability...

30. First Steps Towards Four-Dimensional Tracking: Timing Layers at the HL-LHC

Lindsey Gray (Fermi National Accelerator Lab. (US))

3/20/18, 9:30 AM

2: Real-time pattern recognition and fast tracking

Oral

The projected proton beam intensity of the High Luminosity Large Hadron Collider (HL-LHC), slated to begin operation in 2026, will result in between 140 and 200 concurrent proton-proton interactions per 25 ns bunch crossing. The scientific program of the HL-LHC, which includes precision Higgs coupling measurements, measurements of vector boson scattering, and searches for new heavy or exotic...

29. Machine Learning for transient noise event classification in LIGO and Virgo

Elena Cuoco, Elena Cuoco (INFN - National Institute for Nuclear Physics), Dr Elena Cuoco (EGO & INFN Pisa)

3/20/18, 10:00 AM

6: Beyond the conventional tracking

Oral

Noise of non-astrophysical origin contaminates science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory and Advanced Virgo gravitational-wave detectors. Characterization of instrumental and environmental noise transients has proven critical in identifying false positives in the first observing runs. Machine-Learning techniques have, in recent years, become more...

33. Fast Reconstruction and Data Scouting

Javier Mauricio Duarte (Fermi National Accelerator Lab. (US))

3/20/18, 11:00 AM

2: Real-time pattern recognition and fast tracking

Oral

5. The Fast TracKer - A hardware track processor for the ATLAS trigger system

Karolos Potamianos (Deutsches Elektronen-Synchrotron (DE))

3/20/18, 11:30 AM

2: Real-time pattern recognition and fast tracking

Oral

The Fast Tracker (FTK) is a hardware upgrade to the ATLAS trigger and data acquisition system providing global track reconstruction to the High-Level Trigger (HLT) with the goal to improve pile-up rejection. The FTK processes incoming data from the Pixel and SCT detectors (part of the Inner Detector, ID) at up to 100 kHz using custom electronic boards. ID hits are matched to pre-defined track...

8. Neural Networks in FPGAs for Trigger and DAQ

Nhan Viet Tran (Fermi National Accelerator Lab. (US))

3/20/18, 12:00 PM

3: Machine learning approaches

Oral

Machine learning methods are becoming ubiquitous across the LHC and particle physics. However, the exploration of such techniques within the field in low latency, low power FPGA hardware has only just begun. There is great potential to improve trigger and data acquisition performance, more generally for pattern recognition problems, and potentially beyond. We present a case study for using...