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    AI4EIC 2023 Annual Workshop

    November 28, 2023 to December 1, 2023
    Catholic University of America, Washington D.C.
    US/Eastern timezone
    Artificial Intelligence for the Electron Ion Collider

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    Contribution List

    49 out of 49 displayed
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    1. 34. Introduction: ePIC Overview
      John Lajoie (Iowa State University)
      11/28/23, 10:00 AM
    2. 2. Performance optimization for a scintillating glass electromagnetic calorimeter at EIC
      Dmitrii Kalinkin (University of Kentucky)
      11/28/23, 10:15 AM

      Successful realization of EIC scientific program requires designing
      and constructing high-performance particle detectors. Recent
      developments in the field of scientific computing and increased
      availability of high performance computing resources have made it
      possible to perform optimization of multi-parameter designs, even when
      the latter require longer computational times (for example...

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    3. 8. Object Condensation for Track Building in a Backward Electron Tagger at the EIC
      Dr Simon Gardner (University of Glasgow)
      11/28/23, 10:40 AM

      Quasi-real photoproduction measurements at the Electron Ion Collider will require a far backward electron tagger to detect electrons scattered at small angles close to the beam line. A high occupancy is expected in the electron tagger, leading to many possible permutations of hits deposited by the electrons into single electron tracks. To avoid a slow and computationally expensive...

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    4. 10. The Optimal use of Segmentation for Sampling Calorimeters
      Fernando Torales Acosta (Staff@lbl.gov;Member@lbl.gov;Other@lbl.gov)
      11/28/23, 11:20 AM

      One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and
      transverse segmentation. To inform this choice, we study the impact of calorimeter segmentation
      on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-
      optimal use of segmentation, we deploy deep neural networks to perform the reconstruction....

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    5. 35. AID(2)E: AI-Assisted Detector Design at EIC
      Cristiano Fanelli (W&M)
      11/28/23, 11:45 AM
    6. 36. Flash Talks
      11/28/23, 12:10 PM
    7. 37. Discussion
      11/28/23, 12:55 PM
    8. 46. LHCb Calibration/Alignment
      Biljana Mitreska (Dortmund Tech. U.)
      11/28/23, 2:00 PM

      In Run II of the LHC, a new scheme for the software trigger at LHCb allows splitting the triggering of events in two stages, allowing to perform the detector alignment and calibration in real time. The real-time alignment and calibration procedure is a fully automatic procedure at LHCb that is executed at the begining of each fill of the LHC. The alignment estimates the position of detector...

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    9. 11. ML-based Calibration and Control of the GlueX Central Drift Chamber
      David Lawrence (Jefferson Lab)
      11/28/23, 2:23 PM

      The GlueX Central Drift Chamber (CDC) in Hall D at Jefferson Lab, used for detecting and tracking charged particles, is calibrated and controlled \textit{during} data taking using a Gaussian process. The system dynamically adjusts the high voltage applied to the anode wires inside the chamber in response to changing environmental and experimental conditions such that the gain is stabilized....

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    10. 48. Towards Fast Calibration with the ePIC Barrel Hadronic Calorimeter
      Derek Anderson (Iowa State University)
      11/28/23, 2:46 PM

      Measurement of jets and their substructure will provide valuable information about the properties of the struck quarks and their radiative properties in Deep-Inelastic Scattering events. The ePIC Barrel Hadronic Calorimeter (BHCal) will be a critical tool for such measurements. By enabling the measurement of the neutral hadronic component of jets, the BHCal will complement the Barrel...

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    11. 49. Hydra: Computer Vision for Data Quality Monitoring
      Thomas Britton (JLAB)
      11/28/23, 3:29 PM

      Hydra is a system which utilizes computer vision to perform near real time data quality management, initially developed for Hall-D in 2019. Since then, it has been deployed across all experimental halls at Jefferson Lab, with the CLAS12 collaboration in Hall-B being the first outside of GlueX to fully utilize Hydra. The system comprises back end processes that manage the models, their...

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    12. 50. Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data
      Yi Huang (Brookhaven national lab)
      11/28/23, 3:52 PM
    13. 51. Autonomous selection of physics events: A RHIC demonstrator for EIC physics
      Cameron Dean (MIT)
      11/28/23, 4:15 PM

      With ever-increasing collision rates producing larger data volumes and the search for rarer physics processes, it is becoming apparent that autonomic decision making can play a key role in tagging physics events of interest to specific groups or filtering data streams to manageable levels. A demonstrator for separating events with a heavy flavor decay from background events in proton-proton...

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    14. 53. Discussion
      11/28/23, 4:38 PM
    15. 26. Tutorial: Continual Learning
      Andrea Cossu (University of Pisa)
      11/29/23, 9:00 AM
    16. 13. Bayesian Optimization Techniques for Accelerator Control and Characterization
      Ryan Roussel (SLAC National Laboratory)
      11/29/23, 10:00 AM

      Future improvements in accelerator performance are predicated on increasing capabilities in online control of beams inside accelerators. Machine learning techniques have been the focus of work at SLAC to increase our ability to autonomously optimize and characterize beam dynamics inside accelerator facilities. Bayesian optimization algorithms, which leverage statistical surrogate models of...

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    17. 14. Machine learning for digital twin development and polarization optimization at BNL hadron injectors
      Lucy Lin (Cornell University)
      11/29/23, 10:20 AM

      Polarization optimization collaboration (BNL, Cornell, JLab, SLAC, RPI)

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    18. 12. Beam Condition Forecasting with non-destructive measurements at FACET-II
      Matt Kilpatrick (RadiaSoft)
      11/29/23, 10:40 AM

      Beam diagnostic technology is one of the foundations of large particle accelerator facilities. A challenge with operating these systems is the measurement of beam dynamics. Many methods such as beam position monitors have an inherent destructive quality to the beam and produce perturbations after the measurement. The ability to measure the beam conditions with non-destructive edge radiation...

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    19. 15. Machine Learning applications for collider luminosity maximization
      Ji Qiang (LBNL), Sherry Li (LBNL), Will Fung (MSU), Xiaofeng Gu (Collider Accelerator Department, BNL), Yi-Kai Kan (LBNL), Yue Hao (Michigan State University / Brookhaven National Laboratory)
      11/29/23, 11:00 AM

      The luminosity of a collider can be affected by many parameters at the same time. It is not easy to distinguish the effects of one parameter from all other parameters separately. Therefore, optimizing the performance of a collider such as RHIC, EIC becomes a multi-objective optimization problem with possible noisy signals and involves many parameters. Therefore, machine learning (Bayesian and...

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    20. 16. Anomaly detection at an X-ray FEL
      Daniel Ratner (staff@stanford.edu;member@stanford.edu)
      11/29/23, 11:35 AM

      Modern light sources produce too many signals for a small operations team to monitor in real time. As a result, recovering from faults can require long downtimes, or even worse subtle performance issues may persist undiscovered. Existing automated methods tend to rely on pre-set limits which either miss subtle problems or produce too many false positives. AI methods can solve both problems,...

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    21. 17. Uncertainty estimation and RL applications at JLab
      Malachi Schram (Thomas Jefferson National Accelerator Facility)
      11/29/23, 11:55 AM

      Standard deep learning models for classification and regression applications are ideal for capturing complex system dynamics.
      Unfortunately, their predictions can be arbitrarily inaccurate when the input samples are not similar to the training data. Implementation of distance aware uncertainty estimation can be used to detect these scenarios and provide a level of confidence associated with...

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    22. 18. Using Machine Learning to Improve Dynamic Aperture Estimates
      Frederik Van der Veken (CERN)
      11/29/23, 12:15 PM

      The dynamic aperture (DA) is an important concept in the study of nonlinear beam dynamics. Several analytical models used to describe the evolution of DA as a function of time, and to extrapolate to realistic time scales that would not be reachable otherwise due to computational limitations, have been successfully developed. Even though these models have been quite successful in the past, the...

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    23. 19. Discussion
      11/29/23, 12:35 PM
    24. 20. The EXCLAIM collaboration approach to deeply virtual exclusive processes
      simonetta liuti (university of virginia)
      11/29/23, 2:00 PM
    25. 21. Decoding inverse problems in QCD with ML-based algorithms
      Yaohang Li (Old Dominion University)
      11/29/23, 2:25 PM
    26. 22. What can AI do for lattice-QCD parton distribution calculations?
      Huey Wen (MSU)
      11/29/23, 2:50 PM
    27. 7. Interpretable Machine Learning applications to Jet Background Subtraction
      Charles Hughes (Iowa State University)
      11/29/23, 3:30 PM

      Reconstructing jets in heavy collisions has always required dealing with the challenges of a high background environment. Traditional techniques, such as the area based method, suffered from poor resolution at low momenta due to the large fluctuating background there. In recent years, the resolution has been improved by using machine learning to estimate the background. While machine learning...

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    28. 24. High Dimensional Unfolding using Machine Learning
      Benjamin Nachman
      11/29/23, 3:55 PM
    29. 23. Hadronization Models Using Machine Learning
      Manuel Szewc (University of Cincinnati)
      11/29/23, 4:20 PM
    30. 25. Discussion
      11/29/23, 4:45 PM
    31. 52. Tutorial: Reinforcement Learning
      11/30/23, 9:00 AM
    32. 38. Introduction and Overview of Foundation Models
      Kazuhiro Terao (SLAC National Accelerator Laboratory)
      11/30/23, 10:00 AM
    33. 39. The Trillion Parameter Consortium
      Rick Stevens (ANL)
      11/30/23, 10:30 AM
    34. 9. A Large Language Model-based Assistant for the Electron Ion Collider
      Dr Karthik Suresh (William & Mary )
      11/30/23, 10:50 AM

      A large-scale physics experiment is distinguished by a wide collaboration and array of research topics, and frequently encounters obstacles in information exchange coordination and data analysis. We introduce an assistant model designed to enhance collaboration and streamline data analysis. This model acts as an intelligent intermediary for research teams, enabling information exchange,...

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    35. 40. Chatlas - the ATLAS AI Assistant
      Daniel Murnane (LBL)
      11/30/23, 11:10 AM
    36. 41. TrackBERT - Generalist Learning of Trackers for Downstream Tasks
      Xiangyang Ju (Lawrence Berkeley National Laboratory)
      11/30/23, 11:45 AM
    37. 42. Adversarial Methods for Generative Data Understanding
      Eric Yeats (Duke)
      11/30/23, 12:05 PM
    38. 43. Toward a generative modeling analysis of CLAS exclusive 2π photoproduction
      Tareq Alghamdi (ODU)
      11/30/23, 12:25 PM
    39. 44. Discussion
      11/30/23, 12:45 PM
    40. 27. Machine learning as a service for HEP using cloud
      Luca Giommi (University of Bologna)
      11/30/23, 2:00 PM
    41. 28. Particle identification with machine learning from incomplete data in the ALICE experiment
      Maja Karwowska (CERN, Warsaw U. of Tech.)
      11/30/23, 2:20 PM
    42. 6. Scalable AI/ML Workflow Management Across Distributed Heterogeneous Resources With PanDA
      Wen Guan (BNL)
      11/30/23, 2:40 PM

      The Production and Distributed Analysis (PanDA) system originating in LHC's ATLAS experiment has been steadily evolving upon a technical foundation of proven scalability and extensibility, extending in recent years to new experiments (Rubin Observatory, sPHENIX) and new capabilities in managing large scale, complex workflows across diverse geographically distributed resources. AI/ML has been a...

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    43. 29. The Exa.TrkX Project
      Xiangyang Ju (Lawrence Berkeley National Laboratory)
      11/30/23, 3:05 PM
    44. 30. ATLAS data analysis using a parallelized workflow on distributed cloud-based services with GPUs
      Jay Sandersara (CERN)
      11/30/23, 3:45 PM
    45. 31. Online Track Reconstruction and Physics Analysis in CLAS12
      Gagik Gavalian (Jefferson Lab)
      11/30/23, 4:10 PM
    46. 32. MLaaS4HEP, Machine Learning for Columnar High Energy Physics Analysis
      Elliott Kaufman (Princeton University)
      11/30/23, 4:30 PM
    47. 33. Discussion
      11/30/23, 4:50 PM
    48. 45. Tutorial: Hackathon
      James Giroux (William & Mary ), Karthik Suresh (William & Mary ), Dr Patrick Moran (William & Mary )
      12/1/23, 9:00 AM
    49. 47. Gaussian process for near real-time calibration and control of GlueX Central Drift Chamber
      David Lawrence (Jefferson Lab)