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Nuclear Binding Energy

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The difference between the calculated and experimentally measured masses is known as the mass defect of the atom. In the case of helium-4, the mass defect indicates a “loss” in mass of 4.0331 amu – 4.0026 amu = 0.0305 amu. The loss in mass accompanying the formation of an atom from protons, neutrons, and electrons is due to the conversion of that mass into energy that is evolved as the atom forms. The nuclear binding energy is the energy produced when the atoms’ nucleons...

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Protons and neutrons, collectively called nucleons, are packed together tightly in a nucleus. With a radius of about 10−15 meters, a nucleus is quite small compared to the radius of the entire atom, which is about 10−10 meters. Nuclei are extremely dense compared to bulk matter, averaging 1.8 × 1014 grams per cubic centimeter. If the earth’s density were equal to the average nuclear density, the earth’s radius would be only about 200 meters.
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Related Experiment Video

Updated: May 7, 2025

Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

Generative Modeling of Nucleon-Nucleon Interactions.

Pengsheng Wen^1,2, Jeremy W Holt^1,2, Maggie Li^3,4,5

¹Cyclotron Institute, <a href="https://ror.org/01f5ytq51">Texas A&M University</a>, College Station, Texas 77843, USA.

Physical Review Letters

|January 3, 2025

Summary

This summary is machine-generated.

Generative machine learning models create new nucleon-nucleon interactions, improving uncertainty quantification in nuclear theory. This advances high-precision nuclear force modeling for quantum many-body calculations.

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Area of Science:

Nuclear Physics
Computational Physics
Machine Learning

Background:

Accurate modeling of the nuclear force and uncertainty propagation are crucial for ab initio nuclear theory.
Current methods face challenges in precisely defining nuclear interactions and their associated uncertainties.

Purpose of the Study:

To demonstrate the capability of generative machine learning models in constructing novel nucleon-nucleon interactions.
To explore the generation of nuclear potentials across a continuous distribution of resolution scale parameters.

Main Methods:

Training generative machine learning models on existing nucleon-nucleon potentials from chiral effective field theory.
Generating new nucleon-nucleon potential samples by sampling from a continuous resolution scale parameter space.

Main Results:

Generative models successfully constructed novel nucleon-nucleon interactions.
Generated potentials yielded high-quality nucleon-nucleon scattering phase shifts.
The approach allows for continuous variation in the resolution scale parameter.

Conclusions:

Generative machine learning offers a powerful tool for creating nuclear interactions.
This work is a significant step towards estimating theoretical uncertainties in nuclear many-body calculations stemming from interaction choices.