arXiv subjects

M. Malekhosseini

Publications and source records attributed to M. Malekhosseini.

4 recordsLinked to original sources

arXiv2026

Comprehensive Mass Predictions: From Triply Heavy Baryons to Pentaquarks

In this article, we use two different methods for studying the mass spectra of fully-heavy baryons and pentaquarks. In the first section, we use state-of-the-art machine learning methods, such as deep neural networks and the Particle Transformer model architecture, to predict baryon masses directly from their quantum numbers, based on experimental information on hadrons from the Particle Data Group (PDG). We use this data-driven approach for the case of fully heavy baryons, and a large number of exotic pentaquark states, going much beyond the well-known $ P_c^+(4380) $ and $ P_c^+(4457) $ candidates. Subsequently,we extend the Gürsey-Radicati mass formula to incorporate the contributions of charm and bottom quarks, enabling analytical calculations for both ground and radially excited states of baryons and pentaquarks. The results obtained from both approaches demonstrate strong agreement with experimental data where available and make predictions for a number of unobserved states, including higher radial excitations. By addressing the question through both data-driven prediction and analytical modeling in different frameworks, this study offers complementary insights into the mass spectrum of conventional and exotic hadrons, guiding future experimental searches.

hep-ph↗

arXiv2025

CGAN-Based Framework for Meson Mass and Width Prediction

Mesons play a crucial role in understanding the strong interaction in the framework of quantum chromodynamics (QCD). However, the mass and decay width of several ordinary and exotic mesons remain experimentally undetermined. In this work, we propose a novel application of advanced machine learning techniques to deal with this challenge. Due to the limited available meson datasets, traditional data-driven methods are norm To overcome this, we employ a Conditional Generative Adversarial Network (CGAN) to generate synthetic meson data based on known physical parameters. This not only augments the dataset but also retain the underlying physics of the original mesons data. With the extended dataset, we train multiple copies of CGAN and apply a bagging technique to predict uncertainties, improving the robustness and reliability of the predictions. As our findings indicate, the CGAN models are capable of well describing meson properties and their structure relations, offering a potent novel instrument for hadron spectroscopy. This calculation opens a promising future for data-driven hadron physics studies.

hep-ph↗

arXiv2025

Exploring fully-heavy tetraquarks through the CGAN framework: Mass and width

Fully-heavy tetraquark states, $QQ\bar{Q}\bar{Q} (Q=c,b)$, have garnered significant attention both experimentally and theoretically, due to their unique properties and potential to provide new insights into Quantum Chromodynamics (QCD). In this study, we employ Conditional Generative Adversarial Networks (CGANs) to predict the masses and decay widths of fully-heavy tetraquarks. To deepen our understanding of heavy multiquark structures, we prepare datasets based on two distinct approaches and train the CGAN model using both. The CGAN framework allows us to capture the complex relationships between input features, such as quark content, quantum numbers, and Clebsch-Gordan coefficients, and output properties, including mass and decay width. Our predictions, based on the CGAN framework, are consistent with existing data. By combining fundamental knowledge of QCD with advanced machine learning techniques, this work represents a significant step forward in the theoretical understanding of fully-heavy tetraquark states. Our CGAN approach has the potential to become a strong contender for future studies in heavy tetraquark systems, complementing existing theoretical models to deliver more precise results. Additionally, our findings could assist in the search for fully-heavy tetraquark systems in future experiments.

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arXiv2024

Meson mass and width: Deep learning approach

It is fascinating to predict the mass and width of the ordinary and exotic mesons solely based on their quark content and quantum numbers. Such prediction goes beyond conventional methodologies traditionally employed in hadron physics for calculating or estimating these quantities. The relation between the quantum numbers and the properties of the mesons, such as the mass and width, is complicated in the world of particle physics. However, the deep neural network (DNN) as a subfield of machine learning techniques provides a solution to this problem. By analyzing large datasets, deep learning algorithms can automatically identify complex patterns among the particles' quantum numbers, and their mass and width, that would otherwise require complex calculations. In this study, we present two approaches using the DNNs to estimate the mass of some ordinary and exotic mesons. Also for the first time, the DNNs are trained to predict the width of ordinary and exotic mesons, whose widths have not been experimentally known. Our predictions obtained through the DNNs, will be useful for future experimental searches.

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