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Physics#Nuclear Physics, Particle Physics🔬 ResearchAnalyzed: Jan 3, 2026 06:21

Pion Structure in Dense Nuclear Matter

Published:Dec 31, 2025 15:25
1 min read
ArXiv

Analysis

This paper investigates how the internal structure of a pion (a subatomic particle) changes when it's inside a dense environment of other particles (like in a nucleus). It uses a theoretical model (Nambu--Jona-Lasinio) to calculate these changes, focusing on properties like the pion's electromagnetic form factor and how its quarks are distributed. Understanding these changes is important for understanding how matter behaves under extreme conditions, such as those found in neutron stars or heavy-ion collisions. The paper compares its results with experimental data and other theoretical calculations to validate its approach.
Reference

“The paper focuses on the in-medium electromagnetic form factor, distribution amplitude, and the parton distribution function of the pion.”

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Research Paper#Particle Physics, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 15:53

Understanding PDF Uncertainties with Neural Networks

Published:Dec 30, 2025 09:53
1 min read
ArXiv

Analysis

This paper addresses the crucial need for robust Parton Distribution Function (PDF) determinations with reliable uncertainty quantification in high-precision collider experiments. It leverages Machine Learning (ML) techniques, specifically Neural Networks (NNs), to analyze the training dynamics and uncertainty propagation in PDF fitting. The development of a theoretical framework based on the Neural Tangent Kernel (NTK) provides an analytical understanding of the training process, offering insights into the role of NN architecture and experimental data. This work is significant because it provides a diagnostic tool to assess the robustness of current PDF fitting methodologies and bridges the gap between particle physics and ML research.
Reference

“The paper develops a theoretical framework based on the Neural Tangent Kernel (NTK) to analyse the training dynamics of neural networks, providing a quantitative description of how uncertainties are propagated from the data to the fitted function.”

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Research Paper#Heavy-Ion Physics, Jet Quenching, Parton Energy Loss🔬 ResearchAnalyzed: Jan 3, 2026 17:01

Hadronic Matter's Impact on Jet Energy Loss

Published:Dec 29, 2025 18:51
1 min read
ArXiv

Analysis

This paper investigates how the properties of hadronic matter influence the energy loss of energetic partons (quarks and gluons) as they traverse the hot, dense medium created in heavy-ion collisions. The authors introduce a modification to the dispersion relations of partons, effectively accounting for the interactions with the medium's constituents. This allows them to model jet modification, including the nuclear modification factor and elliptic flow, across different collision energies and centralities, extending the applicability of jet energy loss calculations into the hadronic phase.
Reference

“The paper introduces a multiplicative $(1 + a/T)$ correction to the dispersion relation of quarks and gluons.”

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Physics#High Energy Physics, QCD🔬 ResearchAnalyzed: Jan 3, 2026 19:13

Partonic Entropy of the Proton and DGLAP Evolution

Published:Dec 28, 2025 22:53
1 min read
ArXiv

Analysis

This paper explores the concept of partonic entropy within the context of proton structure, using the DGLAP evolution scheme. The key finding is that this entropy increases with the evolution scale, suggesting a growing complexity in the proton's internal structure as probed at higher energy scales. The paper also touches upon the importance of saturation effects at small x and proposes a connection between partonic entropy and entanglement entropy, potentially offering a new observable for experimental verification.
Reference

“The paper shows that partonic entropy increases monotonically with the evolution scale.”

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Physics#Quantum Chromodynamics (QCD)🔬 ResearchAnalyzed: Jan 4, 2026 06:51

DGLAP evolution at N^3LO with the Candia algorithm

Published:Dec 27, 2025 17:43
1 min read
ArXiv

Analysis

This article discusses the application of the Candia algorithm to perform DGLAP evolution at the N^3LO level. The DGLAP equations are fundamental to understanding the evolution of parton distribution functions (PDFs) in Quantum Chromodynamics (QCD). Achieving N^3LO accuracy is a significant advancement, as it allows for more precise predictions of high-energy particle collisions. The Candia algorithm's efficiency and accuracy are crucial aspects that the article likely explores. The article's impact lies in its contribution to the precision of theoretical calculations in high-energy physics.
Reference

“The Candia algorithm's efficiency and accuracy are crucial aspects.”

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Research Paper#Particle Physics, Quark Model, Meson Spectroscopy🔬 ResearchAnalyzed: Jan 3, 2026 20:01

Quark Coalescence and Excited Meson States

Published:Dec 27, 2025 04:59
1 min read
ArXiv

Analysis

This paper investigates the formation of mesons, including excited states, from coalescing quark-antiquark pairs. It uses a non-relativistic quark model with a harmonic oscillator potential and Gaussian wave packets. The work is significant because it provides a framework for modeling excited meson states, which are often overlooked in simulations, and offers predictions for unconfirmed states. The phase space approach is particularly relevant for Monte Carlo simulations used in high-energy physics.
Reference

“The paper demonstrates that excited meson states are populated abundantly for typical parton configurations expected in jets.”

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Research Paper#Particle Physics, Machine Learning🔬 ResearchAnalyzed: Jan 4, 2026 00:08

Deep Learning for Parton Distribution Extraction

Published:Dec 25, 2025 18:47
1 min read
ArXiv

Analysis

This paper introduces a novel machine-learning method using neural networks to extract Generalized Parton Distributions (GPDs) from experimental data. The method addresses the challenging inverse problem of relating Compton Form Factors (CFFs) to GPDs, incorporating physical constraints like the QCD kernel and endpoint suppression. The approach allows for a probabilistic extraction of GPDs, providing a more complete understanding of hadronic structure. This is significant because it offers a model-independent and scalable strategy for analyzing experimental data from Deeply Virtual Compton Scattering (DVCS) and related processes, potentially leading to a better understanding of the internal structure of hadrons.
Reference

“The method constructs a differentiable representation of the Quantum Chromodynamics (QCD) PV kernel and embeds it as a fixed, physics-preserving layer inside a neural network.”

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Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:48

Connected and disconnected contributions to nucleon form factors and parton distributions

Published:Dec 24, 2025 00:16
1 min read
ArXiv

Analysis

This article likely discusses the theoretical aspects of nucleon structure, focusing on how different components contribute to observable properties. The terms 'connected' and 'disconnected' suggest an analysis of different interaction pathways within the nucleon.

Key Takeaways

    Reference

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    Research#physics🔬 ResearchAnalyzed: Jan 4, 2026 09:27

    CT25: Progress toward next-generation PDFs for precision phenomenology at the LHC

    Published:Dec 22, 2025 19:00
    1 min read
    ArXiv

    Analysis

    This article reports on advancements in the development of next-generation PDFs (Parton Distribution Functions) for high-precision physics analysis at the Large Hadron Collider (LHC). The focus is on improving the accuracy of theoretical predictions for particle collisions, which is crucial for interpreting experimental results and searching for new physics. The use of 'precision phenomenology' suggests a focus on detailed and accurate modeling of particle interactions.
    Reference

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