arXiv subjects

Rongrong Ma

Publications and source records attributed to Rongrong Ma.

At least 19 recordsLinked to original sources

arXiv2026

Measure charge transport in high-energy nuclear collisions with an energy scan of isobaric collisions

We present a method to measure electric-charge transport in high-energy nuclear collisions using a beam-energy scan of isobaric systems. Comparing collisions of nuclei with identical mass number but different atomic number allows the charge difference ($ΔQ$) to be extracted with a double-ratio technique that suppresses most experimental systematic uncertainties. By varying the beam energy, the rapidity gap ($Δy$) over which electric charge is transported can be systematically scanned. Simulations of Ru+Ru and Zr+Zr collisions at $\sqrt{s_{\rm NN}}$=19.6-200GeV with UrQMD and PYTHIA Angantyr show that midrapidity $ΔQ$ decreases exponentially with increasing $Δy$, with the slope parameter exhibiting strong model dependence. Comparisons with the baryon number transport reveal distinct patterns. In both UrQMD and PYTHIA Angantyr (with and without final-state baryon junctions), where baryon number is carried solely by valence quarks, the rapidity slope for baryon transport is larger than that for electric-charge transport. In contrast, scenarios that include baryon junctions in the initial state are expected to produce the opposite trend. This demonstrates that an isobar beam-energy scan provides a sensitive probe of electric-charge transport and offers new constraints on the microscopic mechanisms governing conserved-charge redistribution in QCD matter.

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arXiv2026

Heavy Flavors and Quarkonia at RHIC

After the discovery of the strongly coupled quark-gluon plasma (QGP), a nearly perfect fluid, in 200 GeV Au+Au collisions at RHIC, understanding its microscopic structure and transport properties has become a central goal of relativistic heavy-ion physics. Heavy-flavor particles, containing charm or bottom quarks, provide unique sensitivity to the QGP because they are produced predominantly in the initial hard scatterings and interact with the medium throughout its evolution. This review summarizes measurements of open heavy flavor and quarkonia by the PHENIX and STAR experiments, focusing primarily on 200 GeV collisions recorded during the first two decades of RHIC operations. We discuss key results on charm, bottom, and quarkonium production cross sections; cold nuclear matter effects in small collision systems; nuclear modification factors and elliptic flow of open heavy-flavor hadrons; charm baryon-to-meson production ratios; and the suppression patterns of quarkonium states in Au+Au collisions. We further highlight the resulting insights into the properties of the QGP, including heavy-quark transport, hadronization mechanisms, and quarkonium dissociation and regeneration in the medium. Finally, we discuss the future prospects of the RHIC heavy-flavor program, enabled by the large data sets collected by the STAR and sPHENIX experiments, and its strong synergy with the future Electron-Ion Collider, where precision measurements of heavy-flavor production in electron--proton and electron--nucleus collisions will provide complementary constraints on the structure of QCD matter.

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arXiv2026

Inter-Image Pixel Shuffling for Multi-focus Image Fusion

Multi-focus image fusion aims to combine multiple partially focused images into a single all-in-focus image. Although deep learning has shown promise in this task, its effectiveness is often limited by the scarcity of suitable training data. This paper introduces Inter-image Pixel Shuffling (IPS), a novel method that allows neural networks to learn multi-focus image fusion without requiring actual multi-focus images. IPS reformulates the task as a pixel-wise classification problem, where the goal is to identify the focused pixel from a pixel group at each spatial position. In this method, pixels from a clear optical image are treated as focused, while pixels from a low-pass filtered version of the same image are considered defocused. By randomly shuffling the focused and defocused pixels at identical spatial positions in the original and filtered images, IPS generates training data that preserves spatial structure while mixing focus-defocus information. The model is trained to select the focused pixel from each spatially aligned pixel group, thus learning to reconstruct an all-in-focus image by aggregating sharp content from the input. To further enhance fusion quality, IPS adopts a cross-image fusion network that integrates the localized representation power of convolutional neural networks with the long-range modeling capabilities of state space models. This design effectively leverages both spatial detail and contextual information to produce high-quality fused results. Experimental results indicate that IPS significantly outperforms existing multi-focus image fusion methods, even without training on multi-focus images.

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arXiv2025

Projective Imaging of High-Energy Nuclei via Coherent Exclusive Vector Meson Production in Electron-Nucleus Collisions

One of the major goals of modern nuclear experiments is to study the distributions of gluons inside nuclei at high energy. A key measurement is the coherent exclusive vector meson (VM) production in diffractive electron-nucleus collisions, where the gluon spatial distribution inside the nucleus can be obtained through a Fourier transform of the squared nuclear momentum transfer ($|t|$) distribution. This research aims to overcome the two main obstacles of the $|t|$ measurement: limited precision in measuring $|t|$ arising from the momentum resolution of the outgoing electron and the overwhelming incoherent background. We demonstrate that by measuring the projected $|t|$ distribution along the direction perpendicular to the electron scattering plane, the effect of the outgoing electron's momentum resolution can be effectively mitigated, and the diffractive pattern is largely restored. Furthermore, we propose to measure the angular distribution of the VM's decay daughters to statistically remove the incoherent background.

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arXiv2025

Synergies between a U.S.-based Electron-Ion Collider and European Research in Particle Physics

This document is submitted as input to the European Strategy for Particle Physics Update (ESPPU). The U.S.-based Electron-Ion Collider (EIC) aims at understanding how the complex dynamics of confined quarks and gluons makes up nucleons, nuclei and all visible matter, and determines their macroscopic properties. In April 2024, the EIC project received approval for critical-decision 3A (CD-3A) allowing for Long-Lead Procurement, bringing its realization another step closer. The ePIC Collaboration was established in July 2022 around the realization of a general purpose detector at the EIC. The EIC is based in U.S.A. but is characterized as a genuine international project. In fact, a large group of European scientists is already involved in the EIC community: currently, about a quarter of the EIC User Group (consisting of over 1500 scientists) and 29% of the ePIC Collaboration (consisting of $\sim$1000 members) is based in Europe. This European involvement is not only an important driver of the EIC, but can also be beneficial to a number of related ongoing and planned particle physics experiments at CERN. In this document, the connections between the scientific questions addressed at CERN and at the EIC are outlined. The aim is to highlight how the many synergies between the CERN Particle Physics research and the EIC project will foster progress at the forefront of collider physics.

hep-ex↗

arXiv2024

Beam energy dependence of net-hyperon yield and its implication on baryon transport mechanism

In the constituent quark model, each quark inside a baryon carries 1/3 unit of the baryon number. An alternative picture exists where the center of a Y-shaped topology of gluon fields, called the baryon junction, carries a unit baryon number. Studying baryon transport over a large rapidity gap ($δy$) in nuclear collisions provides a possible tool to distinguish these two pictures. A recent analysis of global data on net-proton yield at mid-rapidity in Au+Au collisions showed an exponential dependence on $δy$ and the exponential slope does not vary with event centrality, favoring the baryon junction picture. Since junctions are flavor blind, hyperons -- baryons containing valence strange quarks -- are expected to exhibit a similar behavior as the proton. This study aims to test this prediction by analyzing hyperon yields in Au+Au collisions at various energies. We observe that net-hyperon yields, after correcting for the strangeness production suppression, adhere to the expected exponential form. The extracted slope parameters for net-$Λ$, net-$Ξ$ and net-$Ω$ are consistent with each other and with those of net-proton within uncertainties, and exhibit no centrality dependence, further substantiating the baryon junction picture. Various implementations of the \texttt{PYTHIA} event generator, primarily based on valence quarks for baryon transport, are unable to simultaneously describe the slope parameters for all baryons.

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arXiv2024

Search for baryon junctions in photonuclear processes and isobar collisions at RHIC

During the early development of Quantum Chromodynamics, it was proposed that baryon number could be carried by a non-perturbative Y-shaped topology of gluon fields, called the gluon junction, rather than by the valence quarks as in the QCD standard model. A puzzling feature of ultra-relativistic nucleus-nucleus collisions is the apparent substantial baryon excess in the midrapidity region that could not be adequately accounted for in most conventional models of quark and diquark transport. The transport of baryonic gluon junctions is predicted to lead to a characteristic exponential distribution of net-baryon density with rapidity and could resolve the puzzle. In this context we point out that the rapidity density of net-baryons near midrapidity indeed follows an exponential distribution with a slope of $-0.61\pm0.03$ as a function of beam rapidity in the existing global data from A+A collisions at AGS, SPS and RHIC energies. To further test if quarks or gluon junctions carry the baryon quantum number, we propose to study the absolute magnitude of the baryon vs. charge stopping in isobar collisions at RHIC. We also argue that semi-inclusive photon-induced processes ($γ+p$/A) at RHIC kinematics provide an opportunity to search for the signatures of the baryon junction and to shed light onto the mechanisms of observed baryon excess in the mid-rapidity region in ultra-relativistic nucleus-nucleus collisions. Such measurements can be further validated in A+A collisions at the LHC and $e+p$/A collisions at the EIC.

hep-ph↗

arXiv2024

Deciphering yield modification of hadron-triggered semi-inclusive recoil jets in heavy-ion collisions

In relativistic heavy-ion collisions, a hot and dense state of matter, called the Quark-Gluon Plasma (QGP), is produced. Semi-inclusive jets recoiling from trigger hadrons of high transverse momenta ($p_{\mathrm{T}}$) can serve as an effective probe of the QGP properties, as they are expected to experience jet quenching when traversing the QGP. Recent experimental results on the ratio of recoil jet yields normalized by the trigger counts in heavy-ion collisions to that in $p$+$p$ collisions ($I_{\mathrm{AA}}$) pose an unexpected challenge in its interpretation. It is observed that $I_{\mathrm{AA}}$ rises with the jet $p_{\mathrm{T}}$ and possibly exceeds unity at high $p_{\mathrm{T}}$, while traditionally it is expected that jet quenching would lead to $I_{\mathrm{AA}} < 1$. To address this challenge, we utilize the Linear Boltzmann Transport (LBT) model to simulate jet transport in the QGP, and study the effect of jet quenching for high-$p_{\mathrm{T}}$ triggers and recoil jets separately on $I_{\mathrm{AA}}$. We find that the quenching of the colored triggers alone is responsible for the rising trend and larger-than-unity value observed experimentally.

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arXiv2024

Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks

One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced learning methods, such as oversampling and imbalanced learning loss functions, can be adopted for enabling graph representation learning models to cope with this challenge. However, these methods often directly operate on the graph representations, ignoring rich discriminative information within the graphs and their interactions. To tackle this issue, we introduce a novel multi-scale oversampling graph neural network (MOSGNN) that learns expressive minority graph representations based on intra- and inter-graph semantics resulting from oversampled graphs at multiple scales - subgraph, graph, and pairwise graphs. It achieves this by jointly optimizing subgraph-level, graph-level, and pairwise-graph learning tasks to learn the discriminative information embedded within and between the minority graphs. Extensive experiments on 16 imbalanced graph datasets show that MOSGNN i) significantly outperforms five state-of-the-art models, and ii) offers a generic framework, in which different advanced imbalanced learning loss functions can be easily plugged in and obtain significantly improved classification performance.

cs.LG↗

arXiv2024

Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks

Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are among the most commonly-used GNNs. However, a wealth of structural information of individual nodes and full graphs is often ignored in such process, which restricts the expressive power of GNNs. Various graph data augmentation methods that enable the message passing with richer structure knowledge have been introduced as one main way to tackle this issue, but they are often focused on individual structure features and difficult to scale up with more structure features. In this work we propose a novel approach, namely collective structure knowledge-augmented graph neural network (CoS-GNN), in which a new message passing method is introduced to allow GNNs to harness a diverse set of node- and graph-level structure features, together with original node features/attributes, in augmented graphs. In doing so, our approach largely improves the structural knowledge modeling of GNNs in both node and graph levels, resulting in substantially improved graph representations. This is justified by extensive empirical results where CoS-GNN outperforms state-of-the-art models in various graph-level learning tasks, including graph classification, anomaly detection, and out-of-distribution generalization.

cs.LG↗

arXiv2024

Contribution of coherent electron production to measurements of heavy-flavor decayed electrons in heavy-ion collisions

Heavy quarks, produced at early stages of heavy-ion collisions, are an excellent probe of the Quark-Gluon Plasma (QGP) also created in these collisions. Electrons from open heavy-flavor hadron decays (HFE) are good proxies for heavy quarks, and have been measured extensively in the last two decades to study QGP properties. These measurements are traditionally carried out by subtracting all known background sources from the inclusive electron sample. More recently, a significant enhancement of $e^+e^-$ pair production at very low transverse momenta was observed in peripheral heavy-ion collisions. The production characteristics is consistent with coherent photon-photon interactions, which should also constitute a background source to the HFE measurements. In this article, we provide theoretical predictions for the contribution of coherent electron production to HFE as a function of transverse momentum, centrality and collision energy in Au+Au and Pb+Pb collisions.

hep-ph↗

arXiv2023

Correlations of Baryon and Charge Stopping in Heavy Ion Collisions

Baryon numbers are carried by valence quarks in the standard QCD picture of the baryon structure, while some theory proposed an alternative baryon number carrier, a non-perturbative Y-shaped configuration of the gluon field, called the baryon junction in the 1970s. However, neither of the theories has been verified experimentally. It was recently suggested to search for the baryon junction by investigating the correlation of net-charge and net-baryon yields at midrapidity in heavy-ion collisions. This paper presents studies of such correlations in collisions of various heavy ions from Oxygen to Uranium with the UrQMD Monte Carlo model. The UrQMD model implements valence quark transport as the primary means of charge and baryon stopping at midrapidity. Detailed study are also carried out for isobaric $_{40}^{96}\mathrm{Zr}$ + $_{40}^{96}\mathrm{Zr}$ and $_{44}^{96}\mathrm{Ru}$ + $_{44}^{96}\mathrm{Ru}$ collisions. We found a universal trend of the charge stopping with respect to the baryon stopping, and that the charge stopping is always more than the baryon stopping. This study provides a model baseline in valence quark transport for what is expected in net-charge and net-baryon yields at midrapidity of relativistic heavy-ion collisions.

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arXiv2021

Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation

Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes, as compared to other graphs. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs, i.e., graphs that are abnormal in their fine-grained (node-level) or holistic (graph-level) properties, respectively. To tackle this challenge we introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations. The random distillation is achieved by training one GNN to predict another GNN with randomly initialized network weights. Extensive experiments on 16 real-world graph datasets from diverse domains show that our model significantly outperforms seven state-of-the-art models. Code and datasets are available at https://git.io/GLocalKD.

cs.CV↗

arXiv2019

Transformed $\ell_1$ Regularization for Learning Sparse Deep Neural Networks

Deep neural networks (DNNs) have achieved extraordinary success in numerous areas. However, to attain this success, DNNs often carry a large number of weight parameters, leading to heavy costs of memory and computation resources. Overfitting is also likely to happen in such network when the training data are insufficient. These shortcomings severely hinder the application of DNNs in resource-constrained platforms. In fact, many network weights are known to be redundant and can be removed from the network without much loss of performance. To this end, we introduce a new non-convex integrated transformed $\ell_1$ regularizer to promote sparsity for DNNs, which removes both redundant connections and unnecessary neurons simultaneously. To be specific, we apply the transformed $\ell_1$ to the matrix space of network weights and utilize it to remove redundant connections. Besides, group sparsity is also employed as an auxiliary to remove unnecessary neurons. An efficient stochastic proximal gradient algorithm is presented to solve the new model at the same time. To the best of our knowledge, this is the first work to utilize a non-convex regularizer in sparse optimization based method to promote sparsity for DNNs. Experiments on several public datasets demonstrate the effectiveness of the proposed method.

cs.CV↗

arXiv2018

Quarkonium production in nuclear collisions

In these proceedings, an overview of recent quarkonium measurements in nuclear collisions carried out at both RHIC and LHC is presented. In p+p collisions, despite theoretical progresses made in understanding the production mechanisms for quarkonia, a complete picture is still yet to be achieved. In p+A collisions where measurements are done to quantify the cold nuclear matter effects, significant suppression of quarkonium production is observed for low transverse momentum region at both forward- and mid-rapidities. Furthermore, results from A+A collisions show the interplay of dissociation and regeneration contributions for charmonium at different collision energies, while a correspondence between the suppression level and the binding energy is observed for bottomonium family. Comparisons to experimental data provide stringent tests to model calculations, and help constrain the temperature of the deconfined medium created in heavy-ion collisions.

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arXiv2016

Experimental summary for heavy flavor production

Measurements of heavy flavor production in heavy-ion collisions have played an important role in understanding the properties of the quark-gluon plasma created in such collision. Due to their large masses, heavy flavor quarks present unique sensitivity to the kinematics as well as the dynamics of the hot and dense medium. In this article, a selection of recent measurements on heavy flavor production in p+p, p+A and A+A collisions at both RHIC and LHC energies will be presented. The measurements in p+p collisions serve as benchmarks to fundamental theories, and as references to similar studies in A+A collisions where the hot medium effects are present. On the other hand, the measurements in p+A collisions can help to quantify the cold nuclear matter effects which are also in effect in A+A collisions and thus need to be taken into account when interpreting the measurements in heavy-ion collisions. The experimental results from A+A collisions are discussed and compared to theoretical calculations, which can shed lights on the understanding of the quark-gluon plasma.

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arXiv2015

J/ψ and Υ measurements via the di-muon channel in Au + Au collisions at $\sqrt{s_{\rm{NN}}}$ = 200 GeV with the STAR experiment

Measurements of quarkonium production in heavy-ion collisions have played an essential role in understanding the properties of the Quark Gluon Plasma created in such collisions. In early 2014, the Muon Telescope Detector, designed to trigger on and identify muons based on its precise timing information, was fully installed in STAR. It opens the door to measure quarkonia via the di-muon channel for the first time at the STAR experiment, with the potential to separate different Υ states. In this talk, we present the measurements of J/ψ suppression and elliptic flow at mid-rapidity in Au+Au collisions at $\sqrt{s_{\rm{NN}}}$ = 200 GeV down to low transverse momentum ($p_{\rm{T}}$). The suppression is found to decrease with increasing $p_{\rm{T}}$ while the elliptic flow is consistent with 0 for $p_{\rm{T}}$ above 2 GeV/c. Furthermore, the measurement of different Υ states is explored within the precision of the available statistics.

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arXiv2015

Measurement of jpsi production in p+p collisions at \sqrts = 500 GeV at STAR experiment

Quarkonium measurements in heavy-ion collisions play an essential role in understanding the hot, dense medium created in such collisions. As a reference, their production mechanism in p+p collisions needs to be thoroughly understood. In this paper, we report the measurement of inclusive cross section of jpsi with transverse momentum (pT) above 4 GeV/c at mid-rapidity in p+p collisions at \sqrts = 500 GeV by the STAR experiment. The ratio of the yield of psi(2S) to jpsi integrated over 4 < pT < 12 GeV/c is also presented. Furthermore, the jpsi yields are studied in different event multiplicity bins in different jpsi pT regions, where the low pT measurement is enabled by the newly installed Muon Telescope Detector. A strong increase of the relative jpsi yield with the event multiplicity is observed for all pT with significant pT dependence.

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