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Yi-Lun Du

Publications and source records attributed to Yi-Lun Du.

At least 19 recordsLinked to original sources
arXiv2026

Deep learning for jet modification in the presence of the QGP background

Jet interactions with the color-deconfined QCD medium in relativistic heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to their baselines in proton-proton collisions. Deep learning methods enable per-jet evaluation of these modifications, enhancing the use of jets as precision probes of the nuclear medium. In this work, we predict the jet-by-jet fractional energy loss $χ$ for jets evolving through a quark-gluon plasma (QGP) medium using a Linear Boltzmann Transport (LBT) model. To approximate realistic experimental conditions, we embed medium-modified jets in a thermal background and apply Constituent Subtraction for background removal. Two network architectures are studied: convolutional neural networks (CNNs) using jet images, and dynamic graph convolutional neural networks (DGCNNs) using particle clouds. We find that CNNs achieve accurate predictions for background-free jets but degrade in the presence of the QGP background and remain below the background-free baseline even after background subtraction. In contrast, DGCNNs applied to background-subtracted particle clouds maintain high accuracy across the entire $χ$ range, demonstrating the advantage of point-cloud-based graph neural networks that exploit full jet structure under realistic conditions.

hep-ph↗
arXiv2026

Discovering quasiorder parameters in the Potts model: A bridge between machine learning and critical phenomena

Machine-learning (ML) models trained on Ising spin configurations have demonstrated surprising effectiveness in classifying phases of Potts models, even when processing severely reduced representations that retain only two spin states. To unravel this remarkable capability, we identify a family of alternative order parameters for the $q=3$ and $q=4$ Potts models on a square lattice, constructed from the occupancies of secondary and minimal spin states rather than the conventional dominant-state order parameter. Through systematic finite-size scaling analyses, we demonstrate that these quantities, along with a magnetization-like quantity derived from a reduced spin representation, accurately capture critical behavior, yielding critical temperatures and exponents consistent with established theoretical predictions and numerical benchmarks. Furthermore, we rigorously establish the fundamental relationships between these alternative (quasi)order parameters, demonstrating how they collectively encode criticality through different aspects of spin configurations. Our results clarify, within this specific setting, how reduced spin representations can retain the essential thermodynamic information needed for identifying critical behavior. Taken together, this work establishes a concrete bridge between Ising-trained ML models and critical phenomena in Potts systems by showing that Potts criticality can be encoded in more compact, non-traditional forms, thereby opening avenues for discovering analogous order parameters in broader spin systems.

cond-mat.stat-mech↗
arXiv2025

Jet momentum reconstruction in the QGP background with machine learning

We apply a Dense Neural Network (DNN) approach to reconstruct jet momentum within a quark-gluon plasma (QGP) background, using simulated data from PYTHIA and Linear Boltzmann Transport (LBT) Models for comparative analysis. We find that medium response particles from the LBT simulation, scattered out of the QGP background but belonging to medium-modified jets, lead to oversubtraction of the background if the DNN model is trained on vacuum jets from PYTHIA simulation. By training the DNN model on quenched jets generated using LBT or the combination of jet samples from PYTHIA and LBT, we significantly reduce this prediction bias and achieve more accurate background subtraction compared to conventional Area-based and Constituent Subtraction methods widely adopted in experimental measurements. We further study the performance of these machine learning models on evaluating the nuclear modification factor of jets, and find that while the unfolding procedure is necessary for correcting residuals in reconstructed jet momenta, models trained on samples incorporating quenched jets still achieve superior accuracy than those trained on vacuum jets even after unfolding.

hep-ph↗
arXiv2025

Deep Learning the Forecast of Galactic Cosmic-Ray Spectra

We introduce a novel deep learning framework based on Long Short-Term Memory (LSTM) networks to predict galactic cosmic-ray spectra on a one-day-ahead basis by leveraging historical solar activity data, overcoming limitations inherent in traditional transport models. By flexibly incorporating multiple solar parameters, such as the heliospheric magnetic field, solar wind speed, and sunspot numbers, our model achieves accurate short-term and long-term predictions of cosmic-ray flux. The addition of historical cosmic-ray flux data significantly enhances prediction accuracy, allowing the model to capture complex dependencies between past and future flux variations. Additionally, the model reliably predicts full cosmic-ray spectra for different particle species, enhancing its utility for comprehensive space weather forecasting. Our approach offers a scalable, data-driven alternative to traditional physics-based methods, ensuring robust daily and long-term forecasts. This work opens avenues for advanced models that can integrate broader observational data, with significant implications for space weather monitoring and mission planning.

astro-ph.HE↗
arXiv2024

Community detection by simulated bifurcation

Community detection, also known as graph partitioning, is a well-known NP-hard combinatorial optimization problem with applications in diverse fields such as complex network theory, transportation, and smart power grids. The problem's solution space grows drastically with the number of vertices and subgroups, making efficient algorithms crucial. In recent years, quantum computing has emerged as a promising approach to tackling NP-hard problems. This study explores the use of a quantum-inspired algorithm, Simulated Bifurcation (SB), for community detection. Modularity is employed as both the objective function and a metric to evaluate the solutions. The community detection problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling seamless integration with the SB algorithm. Experimental results demonstrate that SB effectively identifies community structures in benchmark networks such as Zachary's Karate Club and the IEEE 33-bus system. Remarkably, SB achieved the highest modularity, matching the performance of Fujitsu's Digital Annealer, while surpassing results obtained from two quantum machines, D-Wave and IBM. These findings highlight the potential of Simulated Bifurcation as a powerful tool for solving community detection problems.

math.OC↗
arXiv2024

Spectral sum rules and phase transition in strongly coupled QCD

By incorporating contributions from both the (chromo)electric scale $gT$ and (chromo)magnetic scale $g^2T$, we establish spectral sum rules of quarks for strongly coupled QCD that respect Fermi-Dirac statistics as required by quantum mechanics. In sharp contrast to QED and weakly coupled QCD whose spectral functions consist of discontinuous zero-dimensional (poles) and one-dimensional (branch cuts) non-analytic contributions from real energy $p_0 \in \mathbb{R}$, the derived spectral function for strongly coupled quarks features continuous but non-analytic contributions from complex energy $p_0 \in \mathbb{C}$ that are two-dimensional in nature. In light of the novel sum rules, we uncover an intrinsic QCD transition between a three-mode phase at small coupling and a one-mode phase at large coupling. The transition is induced by the magnetic scale that generates a massless hydro-like mode with the genuine non-Abelian feature of positivity violation and serving as the Goldstone mode of the Lorentz symmetry breaking. The thermal mass serves as an order parameter of the transition and vanishes at large coupling in line with phenomenological predictions from Dyson-Schwinger equations and gauge/gravity duality. This result provides novel insights into the mechanism of the QCD deconfinement transition.

hep-th↗
arXiv2024

Heavy flavor-asymmetric pseudoscalar mesons on the light front

We extract the leading Fock-state light front wave functions (LF-LFWFs) of heavy flavor-asymmetric pseudoscalar mesons $D$, $B$ and $B_c$ from their Bethe-Salpeter wave functions based on Dyson-Schwinger equations approach, and study their leading twist parton distribution amplitudes, generalized parton distribution functions and transverse momentum dependent parton distributions. The spatial distributions of the quark and antiquark on the transverse plane are given, along with their charge and energy distributions on the light front. We find that in the considered mesons, the heavier quarks carry most longitudinal momentum fraction and yield narrow $x$-distributions, while the lighter quarks play an active role in shaping the transverse distributions within both spatial and momentum space, exhibiting a duality embodying characteristics from both light mesons and heavy quarkonium.

hep-ph↗
arXiv2023

Overview: Jet quenching with machine learning

Jets are suppressed and modified in heavy ion collisions, which serve as powerful probes to the properties of the quark-gluon plasma (QGP). Attributed to the abundant information carried by the jet constituents and reconstructed substructures, plenty of interesting applications of machine learning techniques have been made on a jet-by-jet basis to study the jet quenching phenomena. Here we review recent proceedings on this topic including the tasks of reconstructing jet momentum in heavy ion collisions, classifying quenched jets and unquenched jets, identifying jet energy loss, locating the jet creation points as well as distinguishing between quark- and gluon-initiated jets in the QGP. Such jet-by-jet analyses will allow us to have a better handle on the jet reconstruction and selections to investigate the effects of jet modifications and push forward the long-standing goal of jet tomographic probes of the QGP.

hep-ph↗
arXiv2022

Jet tomography in heavy ion collisions with deep learning

Deep learning techniques have the power to identify the degree of modification of high energy jets traversing deconfined QCD matter on a jet-by-jet basis. Such knowledge allows us to study jets based on their initial, rather than final energy. We show how this new technique provides unique access to the genuine configuration profile of jets over the transverse plane of the nuclear collision, both with respect to their production point and their orientation. Effectively removing the selection biases induced by final-state interactions, one can in this way analyse the potential azimuthal anisotropies of jet production associated to initial-state effects. Additionally, we demonstrate the capability of our new method to locate with precision the production point of a dijet pair in the nuclear overlap region, in what constitutes an important step forward towards the long term quest of using jets as tomographic probes of the quark-gluon plasma.

hep-ph↗
arXiv2022

Classification of quark and gluon jets in hot QCD medium with deep learning

Deep learning techniques have shown the capability to identify the degree of energy loss of high-energy jets traversing hot QCD medium on a jet-by-jet basis. The average amount of quenching of quark and gluon jets in hot QCD medium actually have different characteristics, such as their dependence on the in-medium traversed length and the early-developed jet substructures in the evolution. These observations motivate us to consider these two types of jets separately and classify them from jet images with deep learning techniques. We find that the classification performance gradually decreases with increasing degree of jet modification. In addition, we discuss the predictive power of different jet observables, such as the jet shape, jet fragmentation function, jet substructures as well as their combinations, in order to address the interpretability of the classification task.

hep-ph↗
arXiv2022

Jet tomography in hot QCD medium with deep learning

With deep learning techniques, the degree of modification of energetic jets that traversed hot QCD medium can be identified on a jet-by-jet basis. Due to the strong correlations between the degree of jet modification and its traversed length in the medium, we demonstrate the power of our novel method to locate the creation point of a dijet pair in the nuclear overlap region. In particular, jet properties, such as jet width and orientation can serve as additional handles to locate the creation points to a higher level of precision, which constitutes a significant development towards the long-standing goal of using jets as tomographic probes of the quark-gluon plasma.

hep-ph↗
arXiv2021

Deep learning jet modifications in heavy-ion collisions

Jet interactions in a hot QCD medium created in heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to the proton-proton baseline. However, the steeply falling production spectrum introduces a strong bias toward small energy losses that obfuscates a direct interpretation of the impact of medium effects in the measured jet ensemble. Modern machine learning techniques offer the potential to tackle this issue on a jet-by-jet basis. In this paper, we employ a convolutional neural network (CNN) to diagnose such modifications from jet images where the training and validation is performed using the hybrid strong/weak coupling model. By analyzing measured jets in heavy-ion collisions, we extract the original jet transverse momentum, i.e., the transverse momentum of an identical jet that did not pass through a medium, in terms of an energy loss ratio. Despite many sources of fluctuations, we achieve good performance and put emphasis on the interpretability of our results. We observe that the angular distribution of soft particles in the jet cone and their relative contribution to the total jet energy contain significant discriminating power, which can be exploited to tailor observables that provide a good estimate of the energy loss ratio. With a well-predicted energy loss ratio, we study a set of jet observables to estimate their sensitivity to bias effects and reveal their medium modifications when compared to a more equivalent jet population, i.e., a set of jets with similar initial energy. Finally, we also show the potential of deep learning techniques in the analysis of the geometrical aspects of jet quenching such as the in-medium traversed length or the position of the hard scattering in the transverse plane, opening up new possibilities for tomographic studies.

hep-ph↗
arXiv2020

Identifying the nature of the QCD transition in heavy-ion collisions with deep learning

In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade "after-burner". As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.

nucl-th↗
arXiv2020

Heavy quark radiative energy loss in nuclei within the improved high-twist approach

In this proceedings, we review our recent work on the heavy quark radiative energy loss in nuclei due to multiple parton scattering within the recently improved high-twist approach, where gauge invariance can be ensured by a delicate setup of the initial partons' transverse momenta. Our new result is consistent with the previous calculations of light quark energy loss in the massless limit and heavy quark energy loss in the soft gluon radiation limit, respectively. We show numerically the correction to the heavy quark energy loss as compared with previous result and with soft gluon radiation approximation. The necessity to go beyond soft gluon radiation limit is demonstrated for a global description of light and heavy flavor data in heavy-ion collisions.

hep-ph↗
arXiv2020

Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning

Using deep convolutional neural network (CNN), the nature of the QCD transition can be identified from the final-state pion spectra from hybrid model simulations of heavy-ion collisions that combines a viscous hydrodynamic model with a hadronic cascade "after-burner". Two different types of equations of state (EoS) of the medium are used in the hydrodynamic evolution. The resulting spectra in transverse momentum and azimuthal angle are used as the input data to train the neural network to distinguish different EoS. Different scenarios for the input data are studied and compared in a systematic way. A clear hierarchy is observed in the prediction accuracy when using the event-by-event, cascade-coarse-grained and event-fine-averaged spectra as input for the network, which are about 80%, 90% and 99%, respectively. A comparison with the prediction performance by deep neural network (DNN) with only the normalized pion transverse momentum spectra is also made. High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.

hep-ph↗
arXiv2018

Fermion Self-energy and Pseudovector Condensate in NJL Model with External Magnetic Field

In this paper, we aim to study the complete self-energy in the fermion propagator within two-flavor NJL model in the case of finite temperature, chemical potential and external magnetic field. Through Fierz transformation we prove that the self-energy is not simply proportional to dynamical mass in the presence of chemical potential, moreover, it contains four kinds of condensates after introducing external magnetic field. We find out the appropriate and complete form of self-energy and establish new gap equations. We take two of the four condensates (scalar and pseudovector condensates) to make an approximation and simplify the gap equations. The numerical results show that not only the dynamical mass get quantitative modification, but also the properties of Nambu phase and Wigner phase are significantly different with classic results. Instead of classic Wigner phase with zero dynamic mass in the massless NJL model, we propose a new phase - quasi-Wigner phase in this article, it has small but nonzero dynamic mass, with increasing chemical potential, eventually Nambu phase will turn into quasi-Wigner phase with first-order phase transition, therefore the chiral symmetry can never be fully restored but be partially restored. Furthermore, we prove that pseudovector condensate in self-energy can generate energy splitting in dispersion relation, it will cause minor differences of particle numbers with the split energy levels.

hep-th↗
arXiv2018

Revisiting heavy quark radiative energy loss in nuclei within the high-twist approach

We revisit the calculation of multiple parton scattering of a heavy quark in nuclei within the framework of recently improved high-twist factorization formalism, in which gauge invariance is ensured by a delicate setup of the initial partons' transverse momenta. We derive a new result for medium modified heavy quark fragmentation functions in deeply inelastic scattering. It is consistent with the previous calculation of light quark energy loss in the massless limit, but leads to a new correction term in the heavy quark case, which vanishes in the soft gluon radiation limit. We show numerically the significance of the new correction term in the calculation of heavy quark energy loss as compared to previous studies and with soft gluon radiation approximation.

hep-ph↗