Name: Connecting The Dots 2020
Start: 2020-04-20T04:00:00+02:00
End: 2020-04-30T18:05:00+02:00
Location: Virtual/Digital only workshop

Connecting The Dots 2020

from Monday 20 April 2020 (04:00) to Thursday 30 April 2020 (18:05)

Monday 20 April 2020
13:00 Recording session 1
Recording session 1
13:00 - 16:30
Contributions

13:00 Hashing and similarity learning for track reconstruction for the HL-LHC ATLAS detector - Moritz Kiehn (Universite de Geneve (CH))

13:20 The Hybrid Seeding at LHCb - Louis Henry (Instituto de Física Corpuscular (IFIC))

13:35 Fast parallel Primary Vertex reconstruction for the LHCb Upgrade - Florian Reiss (Centre National de la Recherche Scientifique (FR))

13:55 A 30 MHz software trigger and reconstruction for the LHCb upgrade - Louis Henry (Instituto de Física Corpuscular (IFIC))

14:25 Using an Optical Processing Unit for tracking and calorimetry at the LHC - Laurent Basara (LAL/LRI, Université Paris Saclay)

14:55 A Quantum Graph Network Approach to Particle Track Reconstruction - Cenk Tuysuz (Middle East Technical University (TR))

15:15 An updated hybrid deep learning algorithm for identifying and locating primary vertices - Marian Stahl (University of Cincinnati (US))

15:30 Allen: A high level trigger on GPUs for LHCb - Thomas Julian Boettcher (Massachusetts Inst. of Technology (US))
18:59 Workshop introduction
Workshop introduction
18:59 - 19:00
19:00 Recording session 2
Recording session 2
19:00 - 22:00
Contributions

19:00 The Track finder algorithm for the Trigger System of the Mu2e experiment at Fermilab - Gianantonio Pezzullo (Yale University)

19:30 Track Clustering with a Quantum Annealer for Primary Vertexing - Souvik Das (Purdue University (US))

20:00 A muon tracking algorithm for Level 1 trigger in the CMS barrel muon chambers during HL-LHC - Jaime Leon Holgado (Centro de Investigaciones Energéti cas Medioambientales y Tecno)

20:15 Track Reconstruction on Free Streaming Data at PANDA - Jenny Regina (Uppsala University)

20:45 Minimum Pt Track Reconstruction in ATLAS - William Patrick Mccormack (Lawrence Berkeley National Lab. (US))

21:05 Parallelizable Track Pattern Recognition in High-Luminosity LHC - Philip Chang (Univ. of California San Diego (US))
Tuesday 21 April 2020
13:00 Recording session 3
Recording session 3
13:00 - 16:40
Contributions

13:00 Level-1 Track Finding at CMS for the HL-LHC - Louise Skinnari (Northeastern University (US))

13:30 Displaced Event Classification Using Graph Networks - Kim Albertsson (Lulea University of Technology (SE))

13:50 Tracking performance with the HL-LHC ATLAS detector - Zachary Michael Schillaci (Brandeis University (US))

14:05 Performance of Belle II tracking on collision data. - Simon Thomas Kurz

14:35 Progress towards a 4D fast tracking pixel detector - Marco Petruzzo (Università degli Studi e INFN Milano (IT))

15:05 Fast tracking for the HL-LHC ATLAS detector - Fabian Klimpel (Max-Planck-Institut fur Physik (DE))

15:35 Exploring (Quantum) Track Reconstruction Algorithms for non-HEP applications - Kristiane Novotny (GluoNNet) Kristiane Novotny
19:00 Recording session 4
Recording session 4
19:00 - 22:00
Contributions

19:00 Application of the Deep Sets architecture to track-based flavour tagging with the ATLAS detector - Nicole Michelle Hartman (SLAC National Accelerator Laboratory (US))

19:30 Graph Neural Networks for Track Finding - Daniel Murnane (Lawrence Berkeley National Laboratory)

20:00 Kinematic Kalman Filter Track Fit - David Brown (Lawrence Berkeley Lab)

20:30 Learned Representations from Lower-Order Interactions for Efficient Clustering - Nicholas Choma (Lawrence Berkeley National Laboratory)

20:50 Performance of the Z Trigger under Luminosity Conditions: First Experience - Felix Meggendorfer (Max-Planck-Institute for Physics)

21:05 Requirements, Status and plans for track reconstruction of the sPHENIX experiment - Joseph Osborn (Oak Ridge National Laboratory)

21:35 Rescuing VBF Higgs Invisible Events with Novel Vertex Selection - Murtaza Safdari (SLAC National Accelerator Laboratory (US))
Wednesday 22 April 2020
13:00 Recording session 5
Recording session 5
13:00 - 16:45
Contributions

13:00 Overview of online and offline reconstruction in ALICE for LHC Run 3 - David Rohr (CERN)

13:30 40 MHz Scouting with Deep Learning in CMS - Dejan Golubovic (University of Belgrade (RS))

13:50 A novel reconstruction framework for an imaging calorimeter for HL-LHC - Loukas Gouskos (CERN) Marco Rovere (CERN) Arabella Martelli (Imperial College (GB)) Leonardo Cristella (CERN) Felice Pantaleo (CERN)

14:10 Global least squares alignment with Kalman Filter fitted tracks - Pawel Bruckman De Renstrom (Polish Academy of Sciences (PL))

14:40 A Machine Learning based 3D Track Trigger for Belle II - Sebastian Skambraks (Max-Planck-Institut für Physik)

15:10 An optical network for accelerating real-time tracking with FPGAs - Federico Lazzari (Universita & INFN Pisa (IT))

15:30 ACTS Vertexing and Deep Learning Vertex Finding - Bastian Schlag (CERN / JGU Mainz)

15:50 Fast pattern recognition for ATLAS track triggers in HL-LHC - William Kalderon (Brookhaven National Laboratory (US))
19:00 Recording session 6
Recording session 6
19:00 - 22:20
Contributions

19:00 Graph Neural Networks for Reconstruction in Liquid Argon Time Projection Chambers - Jeremy Edmund Hewes (University of Cincinnati (US))

19:20 Data Reconstruction Using Deep Neural Networks for Particle Imaging Neutrino Detectors - François Drielsma (Universite de Geneve (CH))

19:50 Graph neural networks for FPGAs - Yutaro Iiyama (CERN)

20:20 Particle Clustering and Flow Reconstruction for Particle Imaging Neutrino Detectors Using Graph Neural Networks - François Drielsma (Universite de Geneve (CH))

20:35 Applying Submanifold Sparse CNN in MicroBooNE - Ran Itay (SLAC)

21:25 Parallelizing the unpacking and clustering of detector data for reconstruction of charged particle tracks on multi-core CPUs and many-core GPUs - Bei Wang (Princeton University (US))

21:40 Tracking performance with ACTS - Xiaocong Ai (UC Berkeley)

22:10 Manifold reconstruction using linear approximations - Panchali Nag (Duke University)
Thursday 23 April 2020
08:00 Detect New Physics with Deep Learning Trigger at the LHC - Zhenbin Wu (University of Illinois at Chicago (US))
Detect New Physics with Deep Learning Trigger at the LHC
- Zhenbin Wu (University of Illinois at Chicago (US))
08:00 - 08:20
The Large Hadron Collider has an enormous potential of discovering physics beyond the Standard Model, given the unprecedented collision energy and the large variety of production mechanisms that proton-proton collisions can probe. Unfortunately, only a small fraction of the produced events can be studied, while the majority of the events are rejected by the online filtering system. One is then forced to decide upfront what to search and miss a new physics that might hide in unexplored "corners" of the search region. We propose a model-independent anomaly detection technique, based on deep autoencoders, to identify new physics events as outliers of the standard event distribution in some latent space. We discuss how this algorithm could be designed, trained, and operated within the tight latency of the first trigger level of a typical general-purpose LHC experiment.
Friday 24 April 2020
Saturday 25 April 2020
Sunday 26 April 2020

Choose timezone