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Register a new userMachine Learning Approach for Air Shower Recognition in EUSO-SPB Data
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The main goal of The Extreme Universe Space Observatory on a Super Pressure Balloon (EUSO-SPB1) was to observe from above extensive air showers caused by ultra-high energy cosmic rays. EUSO-SPB1 uses a fluorescence detector that observes the atmosphere in a nadir observation mode from a near space altitude. During the 12-day flight, an onboard first level trigger detected more than um{175000} candidate events. This paper presents an approach to recognize air showers in this dataset. The approach uses a feature extraction method to create a simpler representation of an event and then it uses established machine learning techniques to classify data into at least two classes - shower and noise. The machine learning models are trained on a set of air shower simulations put on top of the background observed during the flight and a set of events from the flight. We present the efficiency of the method on datasets of simulated events. The flight data events are also used in unsupervised learning methods to identify groups of events with similar features. The presented methods allow us to shorten the candidate events list and, thanks to the groups of similar events identified by the unsupervised methods, the classification of the triggered events is made simpler.
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EUSO-SPB1 was a balloon-borne pathfinder mission of the JEM-EUSO (Joint Experiment Missions for the Extreme Universe Space Observatory) program. A 12-day long flight started from New Zealand on April 25th, 2017 on-board the NASAs Super Pressure Balloon. With capability of detecting EeV energy air showers, the data acquisition was performed using a 1 m^2 two-Fresnel-lens UV-sensitive telescope with fast readout electronics in the air shower detection mode over ~30 hours at ~16--30 km above South Pacific. Using a variety of approaches, we searched for air shower events. Up to now, no air shower events have been identified. The effective exposure, regarding the role of the clouds in particular, was estimated based on the air shower and detector simulations together with a numerical weather forecast model. Compared with the case assuming the fully clear atmosphere conditions, more than ~60% of showers are detectable regardless the presence of the clouds. The studies in the present work will be applied in the follow-up pathfinders and in the future full-scale missions in the JEM-EUSO program.
We present a machine learning (ML) based method for automated detection of Gamma-Ray Burst (GRB) candidate events in the range 60 keV - 250 keV from the AstroSat Cadmium Zinc Telluride Imager data. We use density-based spatial clustering to detect excess power and carry out an unsupervised hierarchical clustering across all such events to identify the different light curves present in the data. This representation helps understand the instruments sensitivity to the various GRB populations and identify the major non-astrophysical noise artefacts present in the data. We use Dynamic Time Warping (DTW) to carry out template matching, which ensures the morphological similarity of the detected events with known typical GRB light curves. DTW alleviates the need for a dense template repository often required in matched filtering like searches. The use of a similarity metric facilitates outlier detection suitable for capturing previously unmodelled events. We briefly discuss the characteristics of 35 long GRB candidates detected using the pipeline and show that with minor modifications such as adaptive binning, the method is also sensitive to short GRB events. Augmenting the existing data analysis pipeline with such ML capabilities alleviates the need for extensive manual inspection, enabling quicker response to alerts received from other observatories such as the gravitational-wave detectors.
The Experimental complex NEVOD includes several different setups for studying various components of extensive air showers (EAS) in the energy range from 10^10 to 10^18 eV. The NEVOD-EAS array for detection of the EAS electron-photon component began its data taking in 2018. It is a distributed system of scintillation detectors installed over an area of about 10^4 m^2. A distinctive feature of this array is its cluster organization with different-altitude layout of the detecting elements. The main goal of the NEVOD-EAS array is to obtain an estimation of the primary particle energy for events measured by various detectors of the Experimental complex NEVOD. This paper describes the design, operation principles and data processing of the NEVOD-EAS array. The criteria for the event selection and the accuracy of the EAS parameters reconstruction obtained on the simulated events are discussed. The results of the preliminary analysis of experimental data obtained during a half-year operation are presented.
Sparse digital antenna arrays constitute a promising detection technique for future large-scale cosmic-ray observatories. It has recently been shown that this kind of instrumentation can provide a resolution of the energy and of the shower maximum on the level of other cosmic-ray detection methods. Due to the dominant geomagnetic nature of the air-shower radio emission in the traditional frequency band of 30 to 80 MHz, the amplitude and polarization of the radio signal strongly depend on the azimuth and zenith angle of the arrival direction. Thus, the estimation of the efficiency and subsequently of the aperture of an antenna array is more complex than for particle or Cherenkov-light detectors. We have built a new efficiency model based on utilizing a lateral distribution function as a shower model, and a probabilistic treatment of the detection process. The model is compared to the data measured by the Tunka Radio Extension (Tunka-Rex), a digital antenna array with an area of about 1 km$^2$ located in Siberia at the Tunka Advanced Instrument for Cosmic rays and Gamma Ray Astronomy (TAIGA). Tunka-Rex detects radio emission of air showers using trigger from air-Cherenkov and particle detectors. The present study is an essential step towards the measurement of the cosmic-ray flux with Tunka-Rex, and is important for radio measurements of air showers in general.
We observe a correlation between the slope of radio lateral distributions, and the mean muon pseudorapidity of 59 individual cosmic-ray-air-shower events. The radio lateral distributions are measured with LOPES, a digital radio interferometer co-located with the multi-detector-air-shower array KASCADE-Grande, which includes a muon-tracking detector. The result proves experimentally that radio measurements are sensitive to the longitudinal development of cosmic-ray air-showers. This is one of the main prerequisites for using radio arrays for ultra-high-energy particle physics and astrophysics.
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