[ China Instrument Network Instrument Development ] According to the Russian News Network, the Russian Institute of Economics and the "Yandex" Data Analysis Institute jointly developed an artificial intelligence system that can increase the recognition speed of high-energy particles on the Hadron Collider in an order of magnitude. Related results are published in the journal Science Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment.
There are millions of hadron responses per second on the hadron collider, and the detector records the results produced and determines its performance. Since most of the particles generated are scientifically known particles, the search for new particles is similar to finding a needle in a haystack. This requires understanding and improving the particle recognition speed of the detector, and the more accurate the better.
Considering that millions of particles collide every second on the Hadron Collider, a lower sensitivity detection method has to be used, which results in a low speed of the detector, and the simulation of a process usually takes several seconds. In theory, special program software can be used to improve the detection accuracy and speed of the detector, so that the sensor on the detector can accurately respond to different particles passing through. Researchers use the Generative Adversarial Network (GAN) to solve this problem that has been plaguing high-energy physicists. Because the system consists of two neural networks: the generation network and the discriminant network, a network can be trained to train a network. An image similar to reality is formed, for example, an image of a person or an animal that does not exist at all is generated; and another network tries to find a difference between the virtual image and the real image.
Researchers have been surprised to find that the method developed to generate realistic images can be used to identify the particles generated by hadron collisions and to increase the recognition speed in an order of magnitude. Through training, the researcher church generated the anti-system to predict the result of the hadron collision reaction. The test also proved that the system can accurately describe the whole process of the hadron collision physical phenomenon.
Generating a fast simulation of the network system for detector operation, which will greatly speed up the experiment on the Hadron Collider. In essence, this is the application of advanced training methods to scientific data analysis, plus the mastery of Knowledge and experience with detectors, research and development work done by data scientists and physicists.
(Original title: Russia developed artificial intelligence detection system for Hadron Collider)
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