Shedding Light on Dark Matter: Using Machine Learning to Unravel Physics’ Hardest Questions
Almost 600 feet below the surface near Geneva, Switzerland lies the Large Hadron Collider (LHC), the world’s largest and most powerful particle collider. At over sixteen miles in circumference, the LHC is the largest machine in the world, and it is being used by scientists to answer some of the most daunting questions physics has to offer.
Within the tunnel, particles are accelerated to a speed close to the speed of light and collide together, and, upon these collisions, emit new, smaller particles and bits of matter. Collisions occur at four different particle detectors, and, back in 2012, this process lead to the discovery of the long theorized Higgs boson particle.
The discovery of the Higgs boson particle was made with the help of machine learning. At the Large Hadron Collider, the particle itself emerges only from roughly one out of 1 billion collisions, after which it decays almost immediately into other particles. Scientists utilized artificial intelligence and machine learning algorithms to help distinguish between the different particles resulting from the collisions [1]. In addition to this, algorithms are also being used to recognize patterns produced by the decay of the post-collision particles to determine what kind of particle they originated from [2].
Given the success of the LHC in discovering and confirming the existence of the Higgs boson particle, CERN, or the European Organization for Nuclear Research, the organization that runs the LHC, are expecting an increase in the amount of data collected from the machine. Because of this, machine learning will be implemented more and more to help sift through all of the data the LHC collects. It is also possible that it will aid in the identification of dark matter.
The goal of experiments at the Large Hadron Collider is to discover particles that previously were undetected. Like in the case of the Higgs bosons, the particles decay after the collision and leave signatures in the detectors. Dark matter, however, is a different story. Outnumbering visible matter in the universe five-to-one, dark matter has shown very weak interactions with conventional matter, and, because of this, could pass through the detectors unnoticed. However, due to the conservation of energy of collisions, it is clear that something emerged from a collision that the detectors could not see when there is an energy imbalance [3].
Collision rates are about to increase at the LHC, and scientists are hopeful that the use of machine learning algorithms to cut through the collected collision data and highlight the runs that exhibit properties consistent with those of the presence of dark matter. A scientific breakthrough could be on the horizon, and discovering the mystery behind dark matter would, in turn, help unravel the secrets of the make up of our universe.
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Written by Rachel Weissman & Edited by Alexander Fleiss
[1] Cho, A. (2017, July 26). AI is changing how we do science. Get a glimpse. Retrieved from http://www.sciencemag.org/news/2017/07/ai-changing-how-we-do-science-get-glimpse
[2] Castelvecchi, D. (2015, December 1). Artificial intelligence called in to tackle LHC data deluge. Retrieved from https://www.nature.com/news/artificial-intelligence-called-in-to-tackle-lhc-data-deluge-1.18922
[3] Jepsen, K., & WennersHerron, A. (2014, May 16). The search for dark matter at the LHC. Retrieved from https://www.symmetrymagazine.org/article/the-search-for-dark-matter-at-the-lhc