Researchers from the Laboratory of the Methods for Big Data Analysis (LAMBDA) at the Higher School of Economics have improved the way of analyzing ultra-high energy cosmic rays (UHECR) with the use of mobile phones.
Cosmic rays are constantly entering the earth’s atmosphere. Ultra-high energy cosmic ray (UHECR, rays with an energy of more than 1018 ev) properties remain somewhat of a mystery to scientists. They originate from supernovae and black holes. When they interact with air particles, cascades of secondary particles with lower energy are formed. These particles are called extended atmospheric showers (EAS). Scientists have calculated that with a detector with a surface area of 1 km², it is possible to detect approximately one event in 100 years. In order for a full study to be conducted, the surface area the size of a small European country would be required.
The CRAYFIS project proposes using a distributed mobile phone network to detect ultra-high energy cosmic rays (UHECR). In order to do this, researchers from LAMBDA have developed an algorithm for constructing convolutional neural networks that can be used with conventional mobile phones in order to record the muons making up the atmospheric showers.
Mobile phone cameras use technology similar to that of particle detectors. This means that they are able to detect EAS. The particles interact with the CMOS camera and leave traces of weakly activated pixels. This can be difficult to distinguish between interference and random noise.
Volunteers for the experiment are asked to install the application on their smartphones and leave the phone, with the camera facing down, overnight so that normal light doesn’t fall on it. The smartphone scans megapixel images at a speed of 5-15 frames per second and then sends the necessary information to the server.
Scientists expect signals from the interaction of cosmic rays to occur in less than one of the 500 image frames. Due to the fact that millions of phones will potentially participate in the experiment, a problem pops up when separating those images where the muon tracks are recorded from all the others.
"A trigger algorithm is required to eliminate background data. We created a neural network 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 analyze responses to cosmic rays," says Andrei Ustyuzhanin, head of LAMBDA at HSE.
The network is divided into cascades. The first cascade works with a high-resolution image and each cascade after that works with an image 4 times smaller. It works only on those parts that the previous cascade detected as interesting. If there are no interesting sites, the cascade can stop the network from analyzing this part of the image.
The mathematical model is currently undergoing beta testing. Anyone can participate as a volunteer by registering at crayfis.io. If the project is successful, researchers hope the information obtained will enable astrophysicists around the world to clarify where ultra-high energy cosmic rays from and to develop theories around their properties.