arXiv subjects

Myeonghun Park

Publications and source records attributed to Myeonghun Park.

At least 19 recordsLinked to original sources

arXiv2026

High-Frequency Gravitational Wave Detection with Superconducting Qubits

High-frequency gravitational waves (HFGWs) provide a unique window into high-energy and early-universe physics, yet they evade traditional macroscopic interferometry. To bridge this detection gap, we propose a novel quantum-sensing paradigm utilizing superconducting transmon qubits embedded in resonant microwave cavities. Through the inverse Gertsenshtein effect, HFGWs propagating in a static magnetic field resonantly excite a cavity mode. By leveraging the characteristic spin-2 quadrupolar pattern of the induced electromagnetic field, we position qubits directly at the electric-field hot spots of the $\mathrm{TE}_{212}$ mode to act as localized sensors. Crucially, configuring this array as an entangled quantum register via symmetric Dicke states unlocks a fundamental scaling advantage: the signal probability scales quadratically with the qubit number, translating to a $h_{\min} \propto n_q^{-3/4}$ strain sensitivity scaling. We demonstrate that an idealized global register of 800 qubits reaches a strain sensitivity that surpasses standard macroscopic cavity-power limits by five orders of magnitude. Benchmarked against representative axion-haloscope parameters, this collective quantum enhancement decisively mitigates the profound Planck-scale suppression inherent to gravitational interactions, establishing a transformative framework for next-generation HFGW searches in the GHz band.

hep-ph↗

arXiv2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

physics.ins-det↗

arXiv2025

Quantum Integration Networks for Efficient Monte Carlo in High-Energy Physics

Monte Carlo methods play a central role in particle physics, where they are indispensable for simulating scattering processes, modeling detector responses, and performing multi-dimensional integrals. However, traditional Monte Carlo methods often suffer from slow convergence and insufficient precision, particularly for functions with singular features such as rapidly varying regions or narrow peaks. Quantum circuits provide a promising alternative: compared to conventional neural networks, they can achieve rich expressivity with fewer parameters, and the parameter-shift rule provides an exact analytic form for circuit gradients, ensuring precise optimization. Motivated by these advantages, we investigate how sampling strategies and loss functions affect integration efficiency within the \textbf{Quantum Integration Network} (QuInt-Net). We compare adaptive and non-adaptive sampling approaches and examine the impact of different loss functions on accuracy and convergence. Furthermore, we explore three quantum circuit architectures for numerical integration: the data re-uploading model, the quantum signal processing protocol, and deterministic quantum computation with one qubit. The results provide new insights into optimizing QuInt-Nets for applications in high energy physics.

quant-ph↗

arXiv2025

Quantum Sensing Radiative Decays of Neutrinos and Dark Matter Particles

We explore a novel strategy for detecting the radiative decay of very weakly interacting particles by leveraging the extreme sensitivity of quantum devices, such as superconducting transmon qubits and trapped ion systems, to faint electromagnetic signals. By modeling the effective electric field induced by the decay photons, we evaluate the response of quantum sensors across two particle physics scenarios: the cosmic neutrino background and two-component dark matter. We assess the discovery potential of these devices and outline the parameter space accessible under current experimental capabilities. Our analysis demonstrates that quantum sensors can probe radiative decays of dark matter candidates using existing technology, while probing neutrino magnetic moments beyond current limits will require scalable quantum architectures with enhanced coherence.

hep-ph↗

arXiv2024

LeStrat-Net: Lebesgue style stratification for Monte Carlo simulations powered by machine learning

We develop a machine learning algorithm to turn around stratification in Monte Carlo sampling. We use a different way to divide the domain space of the integrand, based on the height of the function being sampled, similar to what is done in Lebesgue integration. This means that isocontours of the function define regions that can have any shape depending on the behavior of the function. We take advantage of the capacity of neural networks to learn complicated functions in order to predict these complicated divisions and preclassify large samples of the domain space. From this preclassification we can select the required number of points to perform a number of tasks such as variance reduction, integration and even event selection. The network ultimately defines the regions with what it learned and is also used to calculate the multi-dimensional volume of each region.

hep-ph↗

arXiv2024

Gravity-Mediated Dark Matter at a low reheating temperature

We present a new study on the Gravity-Mediated Dark Matter (GMDM) scenario, where interactions between dark matter (DM) and the Standard Model are mediated by spin-two particles. Expanding on this established framework, we explore a novel regime characterized by a low reheating temperature that offers an alternative to the conventional thermal relic paradigm. This approach opens new possibilities for understanding the dynamics of the dark sector, encompassing both the dark matter particles (fermion, scalar and vector) and the spin-two mediators. Our analysis examines the constraints imposed by the relic abundance of DM, collider experiments, and direct detection searches, spanning a wide mass range for the dark sector, from very light to extremely heavy states. This work opens new possibilities for the phenomenology of GMDM.

hep-ph↗

arXiv2024

Exploration of Parameter Spaces Assisted by Machine Learning

We demonstrate two sampling procedures assisted by machine learning models via regression and classification. The main objective is the use of a neural network to suggest points likely inside regions of interest, reducing the number of evaluations of time consuming calculations. We compare results from this approach with results from other sampling methods, namely Markov chain Monte Carlo and MultiNest, obtaining results that range from comparably similar to arguably better. In particular, we augment our classifier method with a boosting technique that rapidly increases the efficiency within a few iterations. We show results from our methods applied to a toy model and the type II 2HDM, using 3 and 7 free parameters, respectively. The code used for this paper and instructions are publicly available on the web.

hep-ph↗

arXiv2024

Hybrid quantum-classical approach for combinatorial problems at hadron colliders

In recent years, quantum computing has drawn significant interest within the field of high-energy physics. We explore the potential of quantum algorithms to resolve the combinatorial problems in particle physics experiments. As a concrete example, we consider top quark pair production in the fully hadronic channel at the Large Hadron Collider. We investigate the performance of various quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA), a feedback-based algorithm (FALQON) and a variational quantum imaginary time evolution algorithm (VarQITE). We demonstrate that the efficiency for selecting the correct pairing is greatly improved by utilizing quantum algorithms over conventional kinematic methods. Furthermore, we observe that gate-based universal quantum algorithms perform on par with machine learning techniques and either surpass or match the effectiveness of quantum annealers. Our findings reveal that quantum algorithms not only provide a substantial increase in matching efficiency but also exhibit adaptability and the potential for scalability, making them promising candidates for a variety of high-energy physics applications, as quantum hardware technology matures. Moreover, quantum algorithms eliminate the extensive training processes needed by classical machine learning methods, enabling real-time adjustments based on individual event data.

hep-ph↗

arXiv2024

Exploring Exotic Decays of the Higgs Boson to Multi-Photons at the LHC via Multimodal Learning Approaches

The Standard Model (SM) Higgs boson, the most recently discovered elementary particle, may still serve as a mediator between the SM sector and a new physics sector related to dark matter (DM). The Large Hadron Collider (LHC) has not yet fully constrained the physics associated with the Higgs boson, leaving room for such possibilities. Among the various potential mass scales of the dark sector, the sub-GeV mass range is particularly intriguing. This parameter space presents significant challenges for DM direct detection experiments that rely on nuclear recoils. Various innovative experimental methods are currently under investigation to explore this sub-GeV dark sector. The LHC, functioning as a Higgs factory, could explore this sector once the challenge of identifying DM signals is resolved. Due to the significantly lower mass of particles in the dark sector compared to the Higgs boson, these particles are expected to be highly boosted following the Higgs boson's decay. However, detecting and identifying these highly boosted particles remains a considerable challenge at the LHC, despite their eventual decay into SM particles. We employ a well-motivated leptophobic $Z^{\prime}_B$ model as a prototype to analyze the distinctive signatures from Higgs boson exotic decays into multi-photons. These signatures consist of collimated photons that fail to meet the photon isolation criteria, forming jet-like objects. Conventional analyses relying solely on the purity of energy deposits in the electromagnetic calorimeter would fail to detect these signatures, as they would be overwhelmed by background events from Quantum Chromodynamics. To effectively distinguish between such novel signal signatures and SM background events, we leverage advanced machine learning techniques, specifically the transformer encoder in a multimodal network structure.

hep-ph↗

arXiv2023

Exploring the Synergy of Kinematics and Dynamics for Collider Physics

In collider experiments, an event is characterized by two distinct yet mutually complementary features: the `global features' and the `local features'. Kinematic information such as the event topology of a hard process, masses, and spins of particles comprises global features spanning the entire phase space. This global feature can be inferred from reconstructed objects. In contrast, representations of particles in gauge groups, such as Quantum Chromodynamics (QCD), offer localized features revealing the dynamics of an underlying theory. These local features, particularly observed in the patterns of radiation as raw data in various detector components, complement the global kinematic features. In this letter, we propose a simple but effective neural network architecture that seamlessly integrates information from both kinematics and QCD to enhance the signal sensitivity at colliders.

hep-ph↗

arXiv2023

Quantum Metric Learning for New Physics Searches at the LHC

In the NISQ (Noisy intermediate-scale quantum) area, Quantum computers can be utilized for deep learning by treating variational quantum circuits as neural network models. This can be achieved by first encoding the input data onto quantum computers using nonparametric unitary gates. An alternative approach is to train the data encoding to map input data from different classes to separated locations in the Hilbert space. The separation is achieved with metric loss functions, hence the naming ``Quantum Metric Learning". With the limited number of qubits in the NISQ area, this approach works naturally as a hybrid classical-quantum computation enabling embedding of high-dimensional feature data into a small number of qubits. Here, we consider an example of the global QCD color structure of hard b-jets emerging from color singlet scalar decays to optimize the signal to background discrimination with a hybrid classical-quantum metric learning. Due to the sparsity of data, self-supervised methods with data augmentation have been utilized so far. Compared to the this classical self-supervised approach, our hybrid method shows the better classification performance without data augmentations. We emphasize that performance enhancements independent of data augmentation techniques are devoid of the artificial risks introduced by data augmentation.

hep-ph↗

arXiv2023

Primordial cosmic complexity and effects of reheating

We study the effects of the reheating phase on the evolution of complexities for the primordial curvature perturbation using the squeezed formalism. We examine the evolution of the out-of-time correlator, the quantum discord, and circuit complexity, starting from the inflationary epoch to the radiation-dominated epoch with different reheating scenarios. We find that for a mode that reenters the horizon after reheating, the effect of a finite reheating epoch on the characteristic \textit{freeze-in} amplitude of these primordial complexities can only be distinguished up to three different classes depending on whether the equation of state parameter: $(i)$ $w_\mathrm{re}=1/3$ $(ii)$ $w_\mathrm{re}<1/3$, or, (iii) $w_\mathrm{re}>1/3$. For reheating with different EOS within these classes, the final amplitude will be the same -- hence, the detailed signature of reheating with a class on the complexity measures will be lost. Taking the central value of the scalar spectral index ($n_s=0.9649$) from Planck and the equation of state during reheating $w_\mathrm{re}=0.25$ as benchmark values, we found that the behavior of the complexities for all modes smaller than $1.27\times10^{16}\mathrm{Mpc^{-1}}$ can be classified as above. However, for the small-scale modes reentering the horizon during reheating, the signature of EOS on the evolution of these two complexities will be embedded in each of the cases separately.

astro-ph.CO↗

arXiv2022

Riemannian Data preprocessing in Machine Learning to focus on QCD color structure

Identifying the quantum chromodynamics (QCD) color structure of processes provides additional information to enhance the reach for new physics searches at the Large Hadron Collider (LHC). Analyses of QCD color structure in the decay process of a boosted particle have been spotted as information becomes well localized in the limited phase space. While these kind of a boosted jet analyses provide an efficient way to identify a color structure, the constrained phase space reduces the number of available data, resulting in a low significance. In this letter, we provide a simple but a novel data preprocessing method using a Riemann sphere to utilize a full phase space by decorrelating QCD structure from a kinematics. We can achieve a statistical stability by enlarging the size of testable data set with focusing on QCD structure effectively. We demonstrate the power of our method at the finite statistics of the LHC Run 2. Our method is complementary to conventional boosted jet analyses in utilizing QCD information over the wide range of a phase space.

hep-ph↗

arXiv2022

Kinematic Variables and Feature Engineering for Particle Phenomenology

Kinematic variables have been playing an important role in collider phenomenology, as they expedite discoveries of new particles by separating signal events from unwanted background events and allow for measurements of particle properties such as masses, couplings, spins, etc. For the past 10 years, an enormous number of kinematic variables have been designed and proposed, primarily for the experiments at the Large Hadron Collider, allowing for a drastic reduction of high-dimensional experimental data to lower-dimensional observables, from which one can readily extract underlying features of phase space and develop better-optimized data-analysis strategies. We review these recent developments in the area of phase space kinematics, summarizing the new kinematic variables with important phenomenological implications and physics applications. We also review recently proposed analysis methods and techniques specifically designed to leverage the new kinematic variables. As machine learning is nowadays percolating through many fields of particle physics including collider phenomenology, we discuss the interconnection and mutual complementarity of kinematic variables and machine learning techniques. We finally discuss how the utilization of kinematic variables originally developed for colliders can be extended to other high-energy physics experiments including neutrino experiments.

hep-ph↗

arXiv2021

Leveraging Quantum Annealer to identify an Event-topology at High Energy Colliders

With increasing energy and luminosity available at the Large Hadron collider (LHC), we get a chance to take a pure bottom-up approach solely based on data. This will extend the scope of our understanding about Nature without relying on theoretical prejudices. The required computing resource, however, will increase exponentially with data size and complexities of events if one uses algorithms based on a classical computer. In this letter we propose a simple and well motivated method with a quantum annealer to identify an event-topology, a diagram to describe the history of particles produced at the LHC. We show that a computing complexity can be reduced significantly to the order of polynomials which enables us to decode the "Big" data in a very clear and efficient way. Our method achieves significant improvements in finding a true event-topology, more than by a factor of two compared to a conventional method.

hep-ph↗

arXiv2021

Deep-Learned Event Variables for Collider Phenomenology

The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses. Over time, physicists have derived suitable kinematic variables for many typical event topologies in collider physics. Here we introduce a deep learning technique to design good event variables, which are sensitive over a wide range of values for the unknown model parameters. We demonstrate that the neural networks trained with our technique on some simple event topologies are able to reproduce standard event variables like invariant mass, transverse mass, and stransverse mass. The method is automatable, completely general, and can be used to derive sensitive, previously unknown, event variables for other, more complex event topologies.

hep-ph↗

arXiv2021

Neutrino mass and $(g-2)_μ$ with dark $U(1)_D$ symmetry

We propose an extension of the Standard Model (SM) for radiative neutrino mass by introducing a dark $U(1)_D$ gauge symmetry. The kinetic mixing between the SM gauges and the dark $U(1)_D$ gauge arises at 1-loop mediated by new inert scalar fields. We show that the tiny neutrino mass and dark matter candidates are naturally accommodated. Motivated by the recent measurement of $(g-2)_μ$ indicating $4.2~ σ$ deviation from the SM prediction, we examine how the deviation $Δa_μ$ can be explained in this model.

hep-ph↗

arXiv2020

Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2

We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data.

hep-ph↗