Phenomenology 2026 Symposium

Session

Computing and Machine Learning

12 May 2026, 14:00

University of Pittsburgh

481. Mini Review: Deep Learning in HEP

Sergei Gleyzer (University of Alabama (US))

12/05/2026, 14:00

479. Memristive tabular variational autoencoder for compression of analog data in high energy physics

Tae Min Hong (University of Pittsburgh (US))

12/05/2026, 14:30

We present an implementation of edge AI to compress data on an in-memory analog content-addressable memory (ACAM) device. A variational autoencoder is trained on a simulated sample of energy measurements from incident high-energy electrons on a generic three-layer scintillator-based calorimeter. The encoding part is distilled into tabular format by regressing the latent space variables using...

299. Many Wrongs Make a Right: Leveraging Biased Simulations Towards Unbiased Parameter Inference

Sean Benevedes (Massachusetts Institute of Technology)

12/05/2026, 14:45

In particle physics, as in many areas of science, parameter inference relies on simulations to bridge the gap between theory and experiment. Recent developments in simulation-based inference have boosted the sensitivity of analyses; however, biases induced by simulation-data mismodeling can be difficult to control within standard inference pipelines. In this talk, we propose a Template-Adapted...

413. Toward Agentic Phenomenology

Tony Menzo (University of Alabama and Fermilab)

12/05/2026, 15:00

Frontier LLMs have transformed from text generators into programmable agents, opening a new paradigm for scientific computing. I will discuss HEPTAPOD, a HEP-focused agentic toolkit that interfaces LLMs with common numerical and symbolic workflows. I'll illustrate the framework on some standard benchmarks. To close, I'll turn to a broader discussion of agentic programming, the range of...

287. Binning Bonanza: Metric Design for Quantum Observables and Efficient Dimensionality Reduction

Mohit Vir Srivastav (Johns Hopkins University (US))

12/05/2026, 15:15

Modern particle physics measurements increasingly rely on precise characterizations of subtle quantum effects, making the identification of optimal observables a significant challenge. We present a framework that defines a new metric for evaluating the performance of observables sensitive to quantum interference. We also demonstrate how most of the relevant multidimensional information to...

386. Generative quantum machine learning for the hadronization process

Jinghong Yang

12/05/2026, 15:30

Phenomenological studies at particle colliders rely on high-fidelity simulation data. These simulations are typically produced using Monte Carlo event generators such as Pythia and Herwig. However, several components of the event-generation pipeline, most notably hadronization, are sensitive to nonperturbative quantum chromodynamics (QCD), where first-principles understanding is limited. As a...

482. -

12/05/2026, 15:45