Cosmic rays are constantly entering the Earth's atmosphere, many from outside our Solar System, although (but not always) with a minimal effect. Some of these rays move with much greater energy than others (ultra-high energy cosmic rays, or UHECR, with an energy of more than 10^18 eV), and are still a mystery to researchers.
Researchers from the Laboratory of Methods for Big Data Analysis (LAMBDA) at the Higher School of Economics in Russia have proposed a way of analysing these rays using mobile phones.
UHECR form cascades of secondary particles (extended atmosphere showers, EAS) on entering our atmosphere. They are rare, though; a 1km² detector would detect such an event about once in 100 years.
To improve detection rates, scientists have proposed using a distributed network of mobile phones to detect UHECR. The work would involve an algorithm, developed by the researchers at LAMBDA, to build convolutional neural networks that could record the particles (muons) that form the EAS using mobile phone cameras.
CMOS sensors used in smartphone cameras are similar to those that exist in particle detectors. Muon particles interact with the sensors and leave traces of weakly-activated pixels, although these can be difficult to separate from normal interference. This is where the neural network comes in.
Andrei Ustyuzhanin, head of LAMBDA at HSE, said: "A trigger algorithm is required to eliminate background data. We created a neural network for for the detection of muon signals, which can be used on any mobile phone fast enough to process a video stream. A special feature makes it possible to use the algorithm on something as simple as a mobile phone, meaning that they can now analyse responses to cosmic rays."
Volunteers can install a custom application onto their smartphone and leave them overnight, with the camera facing downwards to hide it from normal light. The phone scans images at a rate of between five and 15 frames per second, sending this information to the server. Detection of UHECR is expected to occur in less than one in 500 such images.
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