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Fundamental Physics

erhtjhtyhy. Co-Leads Marcel Demarteau – Oak Ridge National Laboratory Torre Weanus – Brookhaven National Laboratory Bronson Messer– Oak Ridge National Laboratory Participants – 29 with full list in the xls. Fundamental Physics. A possible taxonomy…. Adapted from Prabhat.

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Fundamental Physics

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  1. erhtjhtyhy Co-Leads Marcel Demarteau– Oak Ridge National Laboratory Torre Weanus– Brookhaven National Laboratory Bronson Messer– Oak Ridge National Laboratory Participants – 29 with full list in the xls. Fundamental Physics

  2. A possible taxonomy… Adapted from Prabhat

  3. A possible taxonomy… Adapted from Prabhat

  4. A possible taxonomy… CNNs, Graph NNs, RNNs Auto-encoders, PCA, random forest ??? VAEs, GANs RL Adapted from Prabhat

  5. FRIB (2022) • Challenges: Event characterization (physics based), moving beyond tracking-based analysis (TPC, gamma-ray spectroscopy), which rates to measure? • Possible Research Areas: • Regression and classification for combined detector analyses • Detector response deconvolution • 30 orders of magnitude in reaction rates; present approaches not robust to this • Transfer learning for short-duration (O(week)) experiments • Beam tuning and delivery (RL) -- too expensive currently • Analysis: Decision support for level analysis; automation? • Significant validation question; including constraints • Design of experiments: Identify thermonuclear reactions for further study that are critical in cosmic element creation • Push back to simulation: Use models to replace interpolation in derived tables (e.g. equation of state, nuclear networks)

  6. LHC (2021; HL 2026), DUNE • Challenges: Data volumes that are 10x, complexity much greater; event generation and simulation at scale; training at scale; data transfer and workflows; • Possible Research Areas: • ”Training as a (set of) Service(s)” – enabling scalable training for fast-turnaround physics analyses • How to integrate new tools into the (well-established) workflow? • Data models • Moving towards a knowledge base/registry of useful “building blocks” (e.g. in the TaaS workflow) • Anomaly detection – optimized selection and implementation of methods • Training and validation of models with simulated and experimental data • Bias and uncertainty of the model(s) • Use of fast simulation (informed by ML models) to make use of trained models • How to use full simulations to train (active learning)? • Do we need new AI methods/algorithms or enhancements to current approaches to capture rare events? to enable this validation? • Improved trigger processing – more physics and faster (inference) • Training at scale (to decrease turnaround time) is a prerequisite • ASICs (maybe more realistic [and just as useful] for EIC) –latency and power constraints are strong

  7. EIC (2030) • Challenges: One big inverse problem—determine the structure of the nucleon • Integrating data from other facilities (universal analysis) • What does the question mean? What is the way to understand the physics deliverable? Imaging? • Possible Research Areas: • Using ML/DL to find correlations in disparate data sets • EIC can enable new ideas here with respect to data curation (cf. ATLAS) • Using AI or ML to constrain parameter-rich theories • Try to close the experiment-theory gap • What is observable? What do we need to measure? (hypothesis testing) • Fast theoretical calculation (properly validated)  design of experiments • Streaming readout (see LHC; large data volume at high cadence) • Using models to guide HW design for accelerator and detectors

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