Posted on 11/25/2024 5:31:19 AM PST by Red Badger
It's no secret.
When I look at that picture, it makes me wonder who drew up the plans to build that thing.
Hang on, guys. My cat Mr. Whiskers almost has it worked out. He claims that all he needs is a few more cat treats to finish the job.
A quantum of Ping!.................
A quantum of ping!...............
Well she does live near Oak Ridge.............
He discovered ‘cations’?...................
How truly awesome God must be to set up all of those, and make them interact with each other just right. He's the Chief Designer at both the macro and the micro level.
This has been a bad year. First I bought a radar detector that didn't detect radar, then I bought a neutron detector that didn't detect neutrons. I have to go to court on those next month.
“I just threw a book at it and WHOOSH, she opened!” from the ghost and Mr. chicken. and that’s how it happened.
How do they know how many particles there are in the Universe if they can’t even see them all to count them?............
How do they know how many particles there are in the Universe if they can’t even see them all to count them?............
> He discovered ‘cations’?................... <
👍🏻
As a retired chemistry teacher, I can appreciate that joke. However, Mr. Whiskers thinks you’re not taking him seriously.
Sigh. I guess you can’t please everyone.
Maybe reversing the polarity of the neutron flow can happen?
https://youtu.be/QDaCMhKPGys?si=TzVtr3H1aLpID5G1
Way back, when the Internet was new, I discovered ‘morons’..............
Automated Neutron Scattering:
Researchers at Oak Ridge National Laboratory (ORNL) are developing an AI-driven platform called Hyperspectral Computed Tomography (HyperCT) for neutron scattering. This system automates sample rotation and optimizes measurement angles, leading to faster and more accurate data collection without human intervention, significantly reducing experiment times and improving data quality Source.
Pattern Recognition in Neutron Scattering Data:
A team at ORNL has trained an artificial neural network (ANN) to analyze neutron scattering data, helping to uncover the microscopic properties of quantum materials. This AI approach aids in interpreting complex data from neutron scattering experiments, which can be challenging for researchers Source.
Addressing the Nuclear Many-Body Problem:
The STREAMLINE project aims to use machine learning to tackle the nuclear many-body problem, which involves understanding nucleon interactions within atomic nuclei. AI techniques are expected to simplify complex calculations and improve predictions about nucleon behavior, which is crucial for various branches of nuclear physics Source.
Improving Nuclear Reactor Performance: At Purdue University, researchers have developed a machine learning algorithm that predicts neutron flux levels in reactors with high accuracy. This AI application enhances monitoring and control of small modular reactors (SMRs), potentially reducing operational costs and improving reactor performance Source.
Enhancing Experimental Nuclear Physics:
Projects at William & Mary involve using AI to optimize experiments related to the strong nuclear force, enhancing the understanding of quark and gluon interactions within nucleons. These projects focus on improving experimental techniques and data analysis through AI-assisted methods Source.
AI-Assisted Neutron Spectroscopy:
A probabilistic active learning approach is being developed for neutron spectroscopy, allowing for autonomous detection of interesting measurement regions in experiments. This method optimizes beam time by focusing on areas with significant signals rather than background noise Source.
Overall, these advancements illustrate how AI is transforming neutron research by increasing efficiency, improving data analysis, and enabling new insights into fundamental nuclear physics questions.
Paging Ant Man!
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