Did AI Prove Our Proton Model WRONG?
PBS Space Time・2 minutes read
Atoms are made up of electrons, protons, and neutrons, which are composed of smaller particles like quarks. Physicists use high-energy particles to study the smallest scales of nature, such as the interior of protons, revealing uncertainty about the existence of charm quarks and advancements in understanding space-time through the collaboration of artificial and natural intelligences.
Insights
- Protons, essential components of visible matter in the universe, are composed of smaller particles called quarks, with the charm quark sometimes present, highlighting the intricate structure of matter at the subatomic level.
- The integration of AI and machine learning techniques in experimental physics, exemplified by the NNPDF collaboration's use of Neural Networks, accelerates model testing and generation, offering promising avenues for enhancing scientific understanding by combining artificial and natural intelligences.
Get key ideas from YouTube videos. It’s free
Recent questions
What are protons made of?
Protons are made of three quarks, including valence quarks and virtual quarks from the quark sea, forming a complex network with gluons.
How do physicists study small objects?
Physicists use scattering experiments with high-energy particles to probe nature's smallest scales and see objects smaller than atoms.
What did Earnest Rutherford discover about atoms?
Earnest Rutherford discovered the nucleus of an atom by shooting alpha particles at a gold foil, revealing that atoms are mostly empty space.
How do electron microscopes work?
Electron microscopes shoot electrons into samples to study structures larger than electrons, depending on the energy of the electrons used.
What technology allowed for rapid testing of proton models?
Artificial Intelligence, specifically a Neural Network, allowed the NNPDF collaboration to test numerous models of the proton's interior simultaneously, revealing uncertainty about the existence of charm quarks in protons.
Related videos
Tyler DeWitt
Basic Atomic Structure: A Look Inside the Atom
Khan Academy
Introduction to the atom | Chemistry of life | Biology | Khan Academy
Math and Science
The Discovery of Atomic Structure
Garrison Student Hub
Structure of an atom ch#4 pg# 48 secondary Science class 7 explanation with Test yourself
IDS ACC
Atoms, Molecules, and Bonds
Summary
00:00
Unraveling the Proton's Quark Composition
- People are made of cells, cells are made of molecules, molecules are made of atoms, atoms are made of electrons, protons, and neutrons, and those last two are made up of three quarks.
- Protons and neutrons are actually made of many quarks, sometimes even 5 quarks including the charm quark.
- Protons are common and make up most visible matter in the universe, either as hydrogen nuclei or bound with neutrons in elements.
- Physicists probe nature's smallest scales through scattering experiments, using high-energy particles to see smaller objects.
- Earnest Rutherford discovered the nucleus by shooting alpha particles at a gold foil, revealing the atom's mostly empty space.
- Electron microscopes shoot electrons into samples to study structures larger than electrons.
- Electrons can study anything larger than their wavelength, depending on their energy.
- SLAC physicists discovered protons are made of three quarks, matching the quark model proposed by Gell-Mann and Zweig.
- The interior of the proton is a complex network of gluons and quarks, including valence quarks and virtual quarks from the quark sea.
- AI allowed the NNPDF collaboration to test numerous models of the proton's interior simultaneously, revealing the uncertainty surrounding the existence of charm quarks in protons.
14:12
"Neural Network Advances Physics Model Predictions"
- A Neural Network was utilized to test thousands of models in a few days, identifying a model with intrinsic charm that outperformed previous ones in predicting data, with a reported 3-sigma result indicating a 1 in 1000 chance of error.
- Despite the need for further testing, the rapid development of machine learning technology in experimental physics allows for quicker model generation and testing, potentially leading to advancements in understanding space-time through the collaboration of artificial and natural intelligences.
