Baikal-GVD is a large-scale underwater neutrino telescope currently under construction in Lake Baikal. Its principal component is a three-dimensional array of optical modules (OMs) registering Cherenkov light associated with the neutrino-induced particles. The OMs are organized in clusters, each containing 8 vertical strings with 36 OMs per string.
Located in a natural water reservoir, the...
KASCADE was an air-shower detector located in Karlsruhe Institute of Technology. It consisted of scintillating detectors which were arranged in a 16×16 grid and recorded signals from secondary particles of air-showers. Data has been acquired from 1996 till 2013 and then has been made available online. Our goal is to find out, whether we can accurately reconstruct the initial particle by that...
CTLearn is a project that aims at IACT event reconstruction through the usage deep-learning models. The associated software packages include modules for loading and manipulating IACT data, and handling the training and test of deep-learning architectures with TensorFlow, using pixel-wise camera data as input. In this contribution we will comment on the challeges we faced so far, the lessons...
We focus on the novel data analysis from KASCADE, one of the most successful cosmic ray detectors in the >PeV range. The detector operated for about 15 years, its data are publicly accessible. The data archive includes about half a billion recorded air showers. Extensive air showers generated by ultrahigh-energy gamma-rays (not detected at the moment) are of particular research interest, since...
The IceCube Neutrino Observatory is a unique experiment located at the geographic South Pole. It is composed of two detectors: an optical array deep in the ice and an array of ice-Cerenkov tanks at the surface called IceTop. The combination of the two detectors can be exploited for the study of cosmic rays and the search for PeV photons. In particular, the in-ice detector measures the...
Nowadays the deep learning techniques are broadly applied for the processing of radio signals generated in air-showers. The majority of the implementations are based on the convolutional neural networks (CNN) running of 1D arrays containing finite waveforms with radio impulses. This approach has shown its feasibility and is able to be implemented for the both trigger- and high- levels of data...
Radio emission, produced mainly as a result of the geomagnetic deflection of oppositely charged particles within the cosmic-ray air showers, is contaminated by backgrounds such as the continuous Galactic background and thermal noise. This irreducible background poses a significant challenge for radio detection of air showers. To mitigate this effect of background we employ machine learning...
Cosmic-ray air showers produce radio signals which can be detected from Earth’s surface. However, the radio background that is detected along with these signals can make it difficult to identify an air shower signal from the local background. To solve this problem, this project aims to train two convolutional neural networks (CNNs): a “classifier” and a “denoiser”. The classifier distinguishes...
In this presentation, I will describe the Zooniverse.org citizen science platform as a tool to gather labels from over 2.5 million dedicated volunteers worldwide who are motivated to participate in scientific research. Hundreds of research teams now turn to Zooniverse for crowdsourcing tasks such as image classification and annotation which provide the large labeled data sets needed for...
Cosmic ray analysis relies on multiple steps including calibration, simulation, event reconstruction and interpretation, etc. Because of their broad energy coverage and sophisticated analysis and simulation techniques, large cosmic ray projects often suffer from their science analysis falling behind data collection. This challenge may be more severe in next generation multi-messenger...
Summary of the 3-day workshop and outlook into the future of cosmic rays analysis using state of the art machine learning techniques.
