AI News, Applying machine learning to the universe's mysteries
- On Sunday, June 3, 2018
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Applying machine learning to the universe's mysteries
The researchers programmed powerful arrays known as neural networks to serve as a sort of hivelike digital brain in analyzing and interpreting the images of the simulated particle debris left over from the collisions.
The images used in this study -- relevant to particle-collider nuclear physics experiments at Brookhaven National Laboratory's Relativistic Heavy Ion Collider and CERN's Large Hadron Collider -- recreate the conditions of a subatomic particle 'soup,' which is a superhot fluid state known as the quark-gluon plasma believed to exist just millionths of a second after the birth of the universe.
These collisions are believed to liberate particles inside the atoms' nuclei, forming a fleeting, subatomic-scale fireball that breaks down even protons and neutrons into a free-floating form of their typically bound-up building blocks: quarks and gluons.
Researchers hope that by learning the precise conditions under which this quark-gluon plasma forms, such as how much energy is packed in, and its temperature and pressure as it transitions into a fluid state, they will gain new insights about its component particles of matter and their properties, and about the universe's formative stages.
'We thought, 'If we have some visual scientific data, maybe we can get an abstract concept or valuable physical information from this.'' Wang added, 'With this type of machine learning, we are trying to identify a certain pattern or correlation of patterns that is a unique signature of the equation of state.'
When researchers employed an array of GPUs that work in parallel -- GPUs are graphics processing units that were first created to enhance video game effects and have since exploded into a variety of uses -- they cut that time down to about 20 minutes per image.
Discussions are already underway to apply the machine learning tools to data from actual heavy-ion collision experiments, and the simulated results should be helpful in training neural networks to interpret the real data.
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