Connecting The Dots 2022

Session

Plenary

31 May 2022, 09:00

Princeton University

Conveners

Plenary

• Salvador Marti I Garcia (IFIC-Valencia (UV/EG-CSIC))

Plenary

• Salvador Marti I Garcia (IFIC-Valencia (UV/EG-CSIC))

Plenary

• Markus Elsing (CERN)

Plenary

• Markus Elsing (CERN)

Plenary

• Andreas Salzburger (CERN)

Plenary

• Isobel Ojalvo (Princeton University (US))

Plenary

• Isobel Ojalvo (Princeton University (US))

Plenary

• Paolo Calafiura (Lawrence Berkeley National Lab. (US))

Plenary

• Michel De Cian (EPFL - Ecole Polytechnique Federale Lausanne (CH))

44. Welcome and Introduction

David Lange (Princeton University (US))

31/05/2022, 09:00

33. Allen in the First Days of Run 3

Thomas Boettcher (University of Cincinnati (US))

31/05/2022, 09:30

Plenary

The LHCb collaboration recently began commisioning an upgraded detector that will be read out at the full 30 MHz LHC event rate. Events will be reconstructed and selected in real time using a GPU-based software trigger called Allen. In this talk, I'll present the status of Allen and discuss how it is evolving as Run 3 begins. In particular, I'll focus on the process of preparing Allen to...

19. Deep Learning Track Reconstruction for TeV-PeV Cosmic Rays in Space

Andrii Tykhonov (Universite de Geneve (CH))

31/05/2022, 10:00

Plenary

Astroparticle physics is experiencing a new era of direct precision measurements in space at the highest energies. DArk Matter Particle Explorer (DAMPE) launched in 2015 has recently published the first results on Cosmic Ray proton and helium spectra up to 100 TeV and 80 TeV kinetic energy respectively. The successor mission to be launched in the nearest future, the High Energy Radiation...

18. Track Finding and Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System

Christopher Edward Brown (Imperial College (GB))

31/05/2022, 11:00

Plenary

The CMS experiment will be upgraded to take advantage of the rich and ambitious physics opportunities provided by the High Luminosity LHC. Part of this upgrade will see the first level (Level-1) trigger use charged particle tracks from the full outer silicon tracker as an input for the first time. The reconstruction of these tracks begins with on-detector hit suppression, identifying hits...

46. The Tracking at Belle II

Radek Zlebcik (Charles University), Radek Zlebcik (Deutsches Elektronen-Synchrotron (DE))

31/05/2022, 11:30

Plenary

The tracking system of Belle II consists of a silicon vertex detector (VXD) and a cylindrical drift chamber (CDC), both operating in a magnetic field created by the main solenoid of 1.5 T and final focusing magnets. The experiment is taking data since 2019 with high-quality and stable tracking performance. We present the tracking-based calibration of the beams characteristics and their...

43. Learning to Discover workshops

Andreas Salzburger (CERN)

31/05/2022, 12:00

Plenary

This talk summarizes the recently concluded Learning to Discover workshop series on Artificial Intelligence and High Energy Physics.

14. Implementation of ACTS into LDMX track reconstruction

Pierfrancesco Butti (SLAC National Accelerator Laboratory (US))

31/05/2022, 14:00

Plenary

The Light Dark Matter eXperiment (LDMX) is a planned electron-beam fixed-target missing-momentum experiment that has unique sensitivity to light Dark Matter in the sub-GeV range.
The tracker is a low-mass, fast, silicon-based detector divided into two sub-detectors: a tagger tracker upstream of the target used to accurately measure the incoming electron and a recoil tracker downstream...

10. Line Segment Tracking in the HL-LHC

Balaji Venkat Sathia Narayanan (Univ. of California San Diego (US))

31/05/2022, 14:30

Plenary

The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks in a high pile-up environment. While there have been efforts to use modern techniques like vectorization to improve the existing classic Kalman Filter based reconstruction algorithms, we take a fundamentally different...

9. traccc - GPU Track reconstruction demonstrator for HEP

Beomki Yeo (Lawrence Berkeley National Lab. (US))

31/05/2022, 15:00

Plenary

In the future HEP experiments, there will be a significant increase in computing power required for track reconstruction due to the large data size. As track reconstruction is inherently parallelizable, heterogeneous computing with GPU hardware is expected to outperform the conventional CPUs. To achieve better maintainability and high quality of track reconstruction, a host-device compatible...

21. ATLAS ITk Track Reconstruction with a GNN-based Pipeline

Charline Rougier (Laboratoire des 2 Infinis - Toulouse, CNRS / Univ. Paul Sabatier (FR))

31/05/2022, 16:00

Plenary

Graph Neural Networks (GNNs) have been shown to produce high accuracy performance on track reconstruction in the TrackML challenge. However, GNNs are less explored in applications with noisy, heterogeneous or ambiguous data. These elements are expected from HL-LHC ATLAS Inner Tracker (ITk) detector data, when it is reformulated as a graph. We present the first comprehensive studies of a...

1. Graph Neural Networks for Pattern Recognition & Fast Track Finding

Nisha Lad (UCL)

31/05/2022, 16:30

Plenary

Particle track reconstruction is a challenging problem in modern high-energy physics detectors where existing algorithms do not scale well with a growing data stream. The development of novel methods incorporating machine learning techniques is a vibrant and ongoing area of research. In the past two years, algorithms for track pattern recognition based on graph neural networks (GNNs) have...

22. Graph Neural Networks for Charged Particle Tracking on FPGAs

Abdelrahman Elabd (IRIS-HEP)

31/05/2022, 17:00

Plenary

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task...

24. Vecpar - a portable parallelization library

Georgiana Mania (Deutsches Elektronen-Synchrotron (DE))

01/06/2022, 09:00

Plenary

As High Energy Physics collider experiments continue to push the boundaries of instantaneous luminosity, the corresponding increase in particle multiplicities pose significant computing challenges. Although most of today’s supercomputers provide shared-memory nodes and accelerators to boost the performance of scientific applications, the usage of latest hardware has little impact unless the...

47. Present and future of online tracking in CMS

Adriano Di Florio (Politecnico e INFN, Bari)

01/06/2022, 09:30

Plenary

We describe the expected evolution of tracking algorithms in the CMS high-level trigger including Run 3 and HL-LHC. Results will include those from the recent CMS DAQ technical design report and how the adoption of heterogeneous architectures enables novel tracking approaches.

37. Accelerated graph building for particle tracking graph neural nets

Liv Helen Vage (Imperial College (GB))

01/06/2022, 10:00

Plenary

The CMS experiment is undergoing upgrades that will increase the average pileup from 50 to 140, and eventually 200. The high level trigger at CMS will experience an increase in data size by a factor of five. With current algorithms, this means that almost 50% of the high level trigger time budget is spent on particle track reconstruction. Graph neural nets have shown promise as an alternative...

13. Event Filter Tracking for the Upgrade of the ATLAS Trigger and Data Acquisition System

Jiri Masik (University of Manchester (GB))

01/06/2022, 14:00

Plenary

This submission describes revised plans for Event Filter Tracking in the upgrade of the ATLAS Trigger and Data Acquisition System for the high pileup environment of the High-Luminosity Large Hadron Collider (HL-LHC). The new Event Filter Tracking system is a flexible, heterogeneous commercial system consisting of CPU cores and possibly accelerators (e.g., FPGAs or GPUs) to perform the...

20. 4D Track Reconstruction at the sPHENIX Experiment

Joe Osborn (Oak Ridge National Laboratory)

01/06/2022, 14:30

Plenary

The sPHENIX detector is a next generation QCD experiment being constructed for operation at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory. sPHENIX will collect high statistics $p+p$, $p$+Au, and Au+Au data starting in 2023. The high luminosities that RHIC will deliver create a complex track reconstruction environment that is comparable to the High Luminosity LHC....

6. The ATLAS Inner Detector tracking trigger at 13 TeV in LHC Run-2 and new developments on standard and unconventional tracking signatures for the upcoming LHC Run-3

Jonathan Long (Univ. Illinois at Urbana Champaign (US))

01/06/2022, 14:55

Plenary

The performance of the Inner Detector tracking trigger of the ATLAS experiment at the LHC is evaluated for the data-taking period of Run-2 (2015-2018). The Inner Detector tracking was used for the muon, electron, tau, and b-jet triggers, and its high performance is essential for a wide variety of ATLAS physics programs such as many precision measurements of the Standard Model and searches for...

35. Towards a Track Condensation Network

Gage DeZoort (Princeton University (US))

01/06/2022, 16:00

Plenary

Tracker data is naturally represented as a graph by embedding tracker hits as nodes and hypothesized particle trajectories as edges. Edge-classifying graph neural networks (GNNs) have demonstrated powerful performance in rejecting unphysical edges from such graphs, yielding a set of disjoint subgraphs that ideally correspond to individual tracks. Post-processing modules, for example clustering...

27. Graph Neural Network for Three Dimensional Object Reconstruction in Liquid Argon Time Projection Chambers

Kaushal Gumpula (UChicago)

01/06/2022, 16:20

Plenary

The Exa.TrkX project presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. Graphs describing particle interactions are formed by treating each detector hit as a node, with edges...

29. Fast and flexible data structures for the LHCb run 3 software trigger

Sevda Esen (Universitaet Zuerich (CH))

01/06/2022, 16:50

Plenary

Starting this year, the upgraded LHCb detector will collection data with a pure software trigger. In its first stage, reducing the rate from 30MHz to about 1MHz, GPUs are used to reconstruct and trigger on B and D meson topologies and high-pT objects in the event. In its second stage, a CPU farm is used to reconstruct the full event and perform candidate selections, which are persisted for...

28. Tracking on GPU at LHCb’s fully software trigger

Alessandro Scarabotto (Centre National de la Recherche Scientifique (FR))

02/06/2022, 09:00

Plenary

The LHCb experiment will use a fully software trigger to collect data from 2022 at an event rate of 30 MHz. During the first stage of High-Level Trigger (HLT1), a partial track reconstruction is performed on charged particles to select interesting events using efficient parallelisation techniques on GPU cards. This stage will already help reduce the event rates by at least a factor 30....

32. Track reconstruction in the LUXE experiment using quantum algorithms

David Spataro

02/06/2022, 09:30

Plenary

LUXE (Laser Und XFEL Experiment) is a proposed experiment at DESY which will study Quantum Electrodynamics (QED) in the strong-field regime, where QED becomes non-perturbative. The measurement of the rate of electron-positron pair creation, an essential ingredient to study this regime, is enabled by the use of a silicon tracking detector. Precision tracking of positrons traversing the four...

45. Track Reconstruction at the Electron-Ion Collider

Shujie Li (University of New Hampshire)

02/06/2022, 13:30

Plenary

During the past year, substantial progress has been made on the design of Detector 1 for the Electron-Ion Collider (EIC). All proposed detector configurations used a combination of silicon trackers and gas detectors for particle tracking and vertex reconstruction. A DD4hep + Gaudi + Acts + EDM4hep simulation and reconstruction framework was developed by the ATHENA proposal collaboration to...

40. Application of machine learning in muon scattering tomography for better image reconstruction

Dr Sridhar Tripathy (York College, New York)

02/06/2022, 14:00

Plenary

Muon Scattering Tomography (MST) is a non-destructive imaging technique that uses cosmic ray muon to probe three-dimensional objects. It is based on the multiple Coulomb scattering suffered by the muons while crossing an object. Muons deflect and decelerated depending upon the density and the atomic number of the material of the object. Therefore, by studying the deflection of the muons, the...

31. Real-time alignment procedure at the LHCb experiment for Run3

Florian Reiss (University of Manchester (GB))

02/06/2022, 14:30

Plenary

The LHCb detector at the LHC is a general purpose detector in the forward region with a focus on studying decays of c- and b-hadrons. For Run 3 of the LHC, LHCb will take data at an instantaneous luminosity of $2\times {10}^{33}c{m}^{-2}{s}^{-1}$, five times higher than in Run 2 (2015-2018). To cope with the harsher data taking conditions, LHCb will deploy a purely software based trigger with a 30 MHz...

26. Charged Particle Tracking with Machine Learning on FPGAs

Haider Abidi (Brookhaven National Laboratory (US))

Plenary

Triggers at high luminosity colliders play an important role in ensuring high sensitivity to new physics and signatures while keeping the data storage requirements at acceptable levels. This will be especially crucial in the next runs of the LHC and at the HL-LHC. Rather than simply imposing stricter pT or isolation requirements to keep trigger rates low, we explore the use of tracking...