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Ishizuka Lab
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A lab that unravels nuclear reactions using theory and machine learning, and applies the results to society.
AboutAbout the lab
Nuclear physics for a better society
The Ishizuka Lab studies the mechanisms of reactions between atomic nuclei using both theory and machine learning.
Under the banner of “nuclear physics for a better society,” the lab works to elucidate nuclear reaction mechanisms, with a focus on nuclear fission.
It applies this knowledge to nuclear transmutation in fast reactors and molten salt reactors, cancer treatment with heavy ions, reactor monitoring, and more.
The lab also studies nuclear data, which form the foundation of these applications, with the goal of returning knowledge of nuclear physics to society.
Key terms
- Nuclear fission
- A phenomenon in which a heavy atomic nucleus splits into two or more parts, releasing a large amount of energy and producing other nuclei
- Nuclear data
- Numerical values that compile which nuclei undergo which reactions and how often, forming the foundation of applied research
- Nuclear transmutation
- Changing one atomic nucleus into another by causing a nuclear reaction
NewsLab news
Developed a machine learning model that predicts the energy dependence of fission yields
A doctoral student received the Division Student Paper Award from the Division of Computational Science and Technology, Atomic Energy Society of Japan
A doctoral student received an Excellence Award at the Atomic Energy Society of Japan Student Poster Session
Research results were selected as an Editors' Suggestion paper in Phys. Rev. C
ResearchResearch themes
The lab aims to gain a deep understanding of nuclear reactions themselves, using cutting-edge theoretical nuclear physics and machine learning methods. It then evaluates and verifies the results in application areas such as nuclear power systems and radiation therapy, with the research goal of returning knowledge of nuclear physics to society.
*Illustrative imageReading Fission Products with Deep Learning
Using Bayesian deep learning, a machine learning method, the lab studies the products generated by nuclear fission.
*Illustrative imageImproving the Accuracy of Heavy-Ion Cancer Therapy
Using the theoretical method of antisymmetrized molecular dynamics, the lab aims to make treatment planning for cancer therapy with heavy ions more accurate.
*Illustrative imageUnderstanding the Mechanism of Nuclear Fission
Using the five-dimensional Langevin model, the lab examines what happens when an atomic nucleus splits.
*Illustrative imageMonitoring Reactor Operation
Using neutrinos emitted from nuclear reactors as a clue, the lab studies technology for monitoring the operating status of a reactor in real time.
MembersFaculty
- Associate ProfessorChikako IshizukaPublications ↗
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “HFIR Refueling July )” Oak Ridge National Laboratory / CC BY 2.0 (cropped)
- Reading Fission Products with Deep Learning: “Pleiades two row” NASA Ames Research Center / Public domain (cropped)
- Improving the Accuracy of Heavy-Ion Cancer Therapy: “Newton's Cradle ( )” Sheila Sund / CC BY 2.0 (cropped)
- Understanding the Mechanism of Nuclear Fission: “Depleted Uranium” 范皓程 / CC BY 4.0 (cropped)
- Monitoring Reactor Operation: “MiniBooNE” Fred Ullrich / Public domain (cropped)
- Related lab (片渕研究室): “Particle accelerator DSC09089” David Monniaux / CC BY-SA 3.0 (cropped)
- Related lab (大友順一郎研究室): “Natural Gas Corp. of N.Z. Ammonia-Urea Plant at Kapuni ( )” Archives New Zealand from New Zealand / CC BY 2.0 (cropped)
- Related lab (小原研究室): “Fast Flux Test Facility Reactor Core” ENERGY.GOV / Public domain (cropped)
- Related lab (石川研究室): “Zeolites within an ECM” Savannah River National Laboratory / CC BY-SA 4.0 (cropped)
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