Dr. Yan He , Nuclear Chemistry , Student at RIKEN , Japan
Yan He is a Ph.D. student at Lanzhou University and a researcher at RIKEN, Japan, specializing in hyper nuclear physics. His work focuses on double-strangeness hyper nuclear event searches using machine learning and nuclear emulsion detectors in the J-PARC E07 experiment. By integrating AI-driven methodologies, he enhances event reconstruction and nuclear emulsion analysis, pushing the boundaries of experimental nuclear physics and particle physics. As a corresponding author, he has made significant contributions to high-impact journals and international collaborations. His expertise spans nuclear detector technology, computational physics, and hyper nuclear decay studies. At RIKEN, he collaborates with leading physicists, leveraging advanced data analysis techniques for high-precision tracking of hyper nuclei. His innovative research bridges the gap between theoretical nuclear models and experimental detection, contributing to our understanding of strong interactions and exotic nuclei. With numerous accolades, he continues to make significant strides in hyper nuclear physics and AI applications.
Professional Profile:
Orcid
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Summary of Suitability for Award:
Yan He, a Ph.D. student at Lanzhou University and RIKEN, has demonstrated outstanding research capabilities in hyper nuclear physics, particularly in the double-strangeness hyper nuclear event search using machine learning and nuclear emulsion detectors. His work is crucial in advancing nuclear reactions research, contributing significantly to the J-PARC E07 experiment. As a corresponding author, he has published in high-impact journals, showcasing his ability to lead and contribute to cutting-edge discoveries. His research aligns with the award criteria, as it demonstrates innovation, scientific impact, and academic excellence in nuclear physics. Given his contributions to nuclear physics, expertise in advanced detection techniques, and significant journal publications, Yan He is a strong candidate for the “Best Researcher Award”. His research not only advances fundamental knowledge in nuclear reactions but also integrates modern computational techniques, making a profound impact on the field.
🎓Education:
Yan He pursued his academic journey in nuclear physics, laying a strong foundation in experimental techniques and particle interactions. He is currently a Ph.D. candidate at Lanzhou University, focusing on hyper nuclear event detection and machine learning applications in nuclear physics. His doctoral research at RIKEN, Japan, involves hyper nuclear event searches in the J-PARC E07 experiment, using nuclear emulsion detectors for precise particle tracking. He holds a Master’s degree in Physics from Lanzhou University, where he specialized in nuclear detector technology and data-driven particle physics experiments. During this time, he developed skills in computational simulations, AI-based event classification, and nuclear reaction modeling. His Bachelor’s degree in Physics from Lanzhou University provided a strong background in quantum mechanics, nuclear interactions, and experimental physics methodologies. With expertise in nuclear emulsion analysis, machine learning algorithms, and high-energy physics, his education has been instrumental in shaping his cutting-edge research in hyper nuclear physics.
🏢Work Experience:
As a Ph.D. researcher at RIKEN, Japan, Yan He is actively involved in hyper nuclear event searches, utilizing machine learning techniques for high-precision nuclear emulsion analysis in the J-PARC E07 experiment. His expertise in nuclear detector technology enables him to develop AI-based event reconstruction algorithms, significantly improving detection accuracy. At Lanzhou University, he worked on nuclear reaction modeling and particle physics simulations, contributing to major high-energy physics collaborations. His work in nuclear emulsion detectors has enhanced hyper nuclear decay event tracking, providing critical insights into ΛΛ hyper nuclei. His experience also includes collaborations with J-PARC (Japan Proton Accelerator Research Complex), where he assists in data acquisition, detector calibration, and experimental setup optimization. Through his research, he integrates computational physics, experimental nuclear techniques, and artificial intelligence, making significant contributions to hyper nuclear event reconstruction and fundamental particle interactions.
🏅Awards:
Yan He has received multiple accolades for his outstanding contributions to hyper nuclear physics and machine learning applications in nuclear event detection. He was honored with the Young Scientist Recognition Award at RIKEN for his groundbreaking work in hyper nuclear event searches . At Lanzhou University, he received the Best Research Scholar Award for his excellence in Ph.D. research and experimental nuclear studies . He also secured a Travel Grant Award to present his work at international particle physics conferences, showcasing his research in hyper nuclear event detection . His work on AI-driven nuclear emulsion analysis earned him the Outstanding Presentation Award at J-PARC Collaboration Meetings, recognizing his innovative approach to machine learning applications in nuclear physics 🎤. He was also awarded the RIKEN International Research Fellowship, enabling him to collaborate with global experts in nuclear physics .
🔬Research Focus:
Yan He’s research is centered on hyper nuclear physics, with a specific focus on the double-strangeness hyper nuclear event search using machine learning and nuclear emulsion detectors. His work plays a crucial role in the J-PARC E07 experiment, which aims to explore the fundamental properties of hyper nuclei—exotic nuclear states containing strange quarks. By leveraging advanced computational techniques, including deep learning algorithms, he enhances the precision and efficiency of event detection in nuclear emulsions, leading to improved data analysis and interpretation. His research not only advances our understanding of strangeness nuclear interactions but also contributes to nuclear astrophysics, particle physics, and future high-energy physics experiments. Through interdisciplinary collaboration, Yan He’s work bridges experimental nuclear physics and artificial intelligence, fostering innovation in nuclear detection technologies and paving the way for new discoveries in subatomic physics. His contributions have significant implications for both theoretical models and practical applications in nuclear science.
Publication Top Notes:
“A novel application of machine learning to detect double-Λ hypernuclear events in nuclear emulsions”
Journal: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Publication Date: April 2025
DOI: 10.1016/j.nima.2024.170196
“A compact start time counter using plastic scintillators readout with MPPC arrays for the WASA-FRS HypHI experiment”
Journal: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Publication Date: July 2024
DOI: 10.1016/j.nima.2024.169384
“Hypernuclear event detection in the nuclear emulsion with Monte Carlo simulation and machine learning”
Journal: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Publication Date: November 2023
DOI: 10.1016/j.nima.2023.168663
Authors: A. Kasagi, W. Dou, V. Drozd, H. Ekawa, S. Escrig, Y. Gao, Y. He, E. Liu, A. Muneem, M. Nakagawa, K. Nakazawa, C. Rappold, N. Saito, T. R. Saito, S. Sugimoto, M. Taki, Y. K. Tanaka, A. Yanai, J. Yoshida, M. Yoshimoto, H. Wang
“Development of machine learning analyses with graph neural network for the WASA-FRS experiment”
Journal: The European Physical Journal A
Publication Date: May 12, 2023
DOI: 10.1140/epja/s10050-023-01016-5
“Unique approach for precise determination of binding energies of hypernuclei with nuclear emulsion and machine learning”
Journal: EPJ Web of Conferences
Publication Date: 2022
DOI: 10.1051/epjconf/202227111006
“New directions in hypernuclear physics”
Journal: Nature Reviews Physics
Publication Date: September 14, 2021
DOI: 10.1038/s42254-021-00371-w
“Improved empirical formula for α particle preformation factor”
Journal: Chinese Physics C
Publication Date: January 1, 2021
DOI: 10.1088/1674-1137/abc684