arXiv subjects

Wonyong Chung

Publications and source records attributed to Wonyong Chung.

6 recordsLinked to original sources

arXiv2026

Agentic-AI Detector Co-design and Optimization in Vertically-Integrated Differentiable Full Simulations

We present the first implementation of AI agents into the design and optimization of detectors in high-energy physics experiments via a bi-level optimization framework that vertically integrates detector geometry, front-end digitization, and high-level reconstruction algorithm parameters in differentiable full simulations. Using the example of a dual-readout, segmented crystal EM calorimeter with a baseline resolution of $3\%/\sqrt{E}$, we investigate the capabilities and value propositions of AI agents in the identification and reduction of key detector parameters and in the nonlinear traversal of design space. We find that frontier LLM reasoning-models today, without being given additional experiment-specific context, are able to effectively execute complex workflows and proactively suggest generic but relevant avenues for further study or improvement. Here, we demonstrate an AI agent's ability to find an optimal design point amidst three competing performance criteria, showing that effective integration of agents into the complex workflows of frontier research areas can yield higher performance for key physics goals while reducing labor and compute. This study establishes the foundation for a future demonstration of the first fully AI-designed detector for future scientific facilities.

physics.ins-det↗

arXiv2026

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.

hep-ex↗

arXiv2025

Detector Designs for Frontier Measurements in Neutrino and Collider Physics in the 21st Century

The last energy-frontier lepton collider, LEP, established several limits that still hold today. A key one is the counting of three light neutrino species from the invisible decay width of the Z boson. From a collider calorimetry standpoint, the missing energy is an invitation to design an experiment to directly measure the neutrino mass. We present a new type of EM spectrometer which leverages the first adiabatic invariant in magnetic gradient drift to achieve exponentially bounded resolution in a highly compact and scalable format, enabling the PTOLEMY experiment to not only measure the neutrino mass at the tritium endpoint, but one day directly detect the Cosmic Neutrino Background. Meanwhile, the next lepton collider promises to expose the Higgs self-coupling and complete the accounting of lepton universality. We present a dual-readout, segmented crystal calorimeter for future collider detectors, combining new hardware capabilities with novel AI/ML reconstruction techniques towards realizing a detector that must definitively and unambiguously surpass its predecessors. Together, these studies confront the most pressing challenges for 21st century particle physics experiments to achieve the sensitivities needed to bridge the gap between the largest and smallest scales of reality.

physics.ins-det↗

arXiv2025

Synthetic Training and Representation Bridging in Reconstruction Domains

Reconstructing low-dimensional truth labels from high-dimensional experimental data is a central challenge in any scenario that relies on robust mappings across this so-called domain gap, from multi-particle final states in high-energy physics to large-scale early-universe structure in cosmological surveys. We introduce a new method to bridge this domain gap with an intermediate, synthetic representation of truth that differs from methods operating purely in latent space, such as normalizing flows or invertible approaches, in that the synthetic data is specifically engineered to represent intrinsic detector hardware capabilities of the system at hand. The hypothesis is that by encoding physical properties of the detector response available only in full simulation, such synthetic representations result in a less lossy compression and recovery than a direct mapping from truth to experimental data. We demonstrate a first implementation of this concept with full simulation of a dual-readout crystal electromagnetic calorimeter for future collider detectors, in which the synthetic data is constructed to be the simulated detector hits corresponding to photon tracks of scintillation and Cerenkov photons. We refer to these signals as simulated observables as they would not be physical observables in a real detector, but are nonetheless representations of a real physical process. First results show that the synthetic representation naturally anchors the neural network architecture to a known physical method, in this case the dual-readout correction. We believe this strategy opens new avenues for machinistic interpretability and explainability of ML-based reconstruction methods. In the case of anomalous signal detection, we hypothesize that anomalous signals detected in networks trained on synthetic data rooted in a physical process are more likely to be indicative of a genuinely physical anomaly.

hep-ph↗

arXiv2024

Differentiable Full Detector Simulation of a Projective Dual-Readout Crystal Electromagnetic Calorimeter with Longitudinal Segmentation and Precision Timing

A differentiable full detector simulation has been implemented in the key4hep software stack for future colliders. A fully automated and configurable geometry enabling differentiation of all detector dimensions, including crystal widths and thicknesses, is presented. The software architecture, development environment, and necessary components to implement a new detector concept from scratch are described. General AI/ML reconstruction strategies for future collider detectors are discussed, based around the idea of picking the right neural network for each detector.

physics.ins-det↗

arXiv2020

New perspectives on segmented crystal calorimeters for future colliders

Crystal calorimeters have a long history of pushing the frontier on high-resolution electromagnetic (EM) calorimetry. We explore in this paper major innovations in collider detector performance that can be achieved with crystal calorimetry when longitudinal segmentation and dual-readout capabilities are combined with a new high EM resolution approach to PFA in multi-jet events, such as $e^+e^+\rightarrow HZ$ events in all-hadronic final-states at Higgs factories. We demonstrate a new technique for pre-processing $π^0$ momenta through combinatoric di-photon pairing in advance of applying jet algorithms. This procedure significantly reduces $π^0$ photon splitting across jets in multi-jet events. The correct photon-to-jet assignment efficiency improves by a factor of 3 with a $3\%/\sqrt{E}$ EM resolution. In addition, the technique of bremsstrahlung photon recovery significantly improves electron momentum measurements. A high EM resolution calorimeter increases the Z boson recoil mass resolution in Higgstrahlung events for decays into electron pairs to 80% of that for muon pairs. We present the design and optimization of a highly segmented crystal detector concept that achieves the required energy resolution, and a time resolution better than 30 ps providing exceptional particle identification capabilities. We demonstrate that, contrary to previous detector designs that suffered from large neutral hadron resolution degradation from one interaction length of crystals in front of a sampling hadron calorimeter, the implementation of dual-readout on crystals permits to achieve a resolution better than $30\%/\sqrt{E}\oplus 2\%$ for neutral hadrons. Our studies find that the integration of crystal calorimetry into future Higgs factory collider detectors can open new perspectives by yielding the highest level of combined EM and neutral hadron resolution in the PFA paradigm.

physics.ins-det↗