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Register a new userEstimation of the exposure for the air shower detection mode of EUSO-SPB1
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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.
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The latest and most advanced effort towards a space-based optical cosmic ray detector developed within the Joint Experiment Mission for the Extreme Universe Space Observatory (JEM-EUSO) collaboration was the Extreme Universe Space Observatory on a Super Pressure Balloon (EUSO-SPB1) mission. The EUSO-SPB1 instrument looks for UV light emitted by extensive air showers above the detectors energy threshold of unit[3]{EeV}. This detector was launched in 2017 out of Wanaka, New Zealand as a mission of opportunity on a NASA SPB. Over 27 hours of data was taken in air shower detection mode during the 12-day flight over the Pacific Ocean. Besides an overview of the instrument and the mission details, we will show the results of the data analysis of the flight. Methods to search for tracks and other interesting signals were developed and applied to the flight data set revealing different types of events. But no obvious track of a cosmic ray candidate was found. This result is in agreement with a detailed simulation study performed after the flight to include the different conditions. Data of the flown IR camera and weather forecast model were used to determine the cloud conditions within the telescopes FoV. The presented results are also discussed in various separate contributions at this conference. The experience gained during this flight is essential for the preparation of the follow-up mission EUSO-SPB2 which is planned to launch in 2022.
We evaluate the exposure during nadir observations with JEM-EUSO, the Extreme Universe Space Observatory, on-board the Japanese Experiment Module of the International Space Station. Designed as a mission to explore the extreme energy Universe from space, JEM-EUSO will monitor the Earths nighttime atmosphere to record the ultraviolet light from tracks generated by extensive air showers initiated by ultra-high energy cosmic rays. In the present work, we discuss the particularities of space-based observation and we compute the annual exposure in nadir observation. The results are based on studies of the expected trigger aperture and observational duty cycle, as well as, on the investigations of the effects of clouds and different types of background light. We show that the annual exposure is about one order of magnitude higher than those of the presently operating ground-based observatories.
Air-shower radio arrays operate in low signal-to-noise ratio conditions, which complicates the autonomous measurement of air-shower signals without using an external trigger from optical or scintillator detectors. A simple threshold trigger for radio detector can be efficiently applied onlyin radio-quiet conditions, because for other cases this trigger detects a high fraction of noise pulses. In the present work, we study aspects of independent air-shower detection by dense antenna clusters with a complex real-time trigger system. For choosing the optimal procedures for the real-time analysis, we study the dependence between trigger efficiency, count rate, detector hardware and geometry. For this study, we develop a framework for testing various methods of signal detection and noise filtration for arrays with various specifications and the hardware implementation of these methods based on field programmable gate arrays. The framework provides flexible settings for the management of station-level and cluster-level steps of detecting the signal, optimized for the hardware implementation for real-time processing. It includes data-processing tools for the initialconfiguration and tests on pre-recorded data, tools for configuring the trigger architecture andtools for preliminary estimates of the trigger efficiency at given thresholds of cosmic-ray energyand air-shower pulse amplitude. We show examples of the trigger pipeline developed with this framework and discuss the results of tests on simulated data.
The Moscow State University Extensive Air Shower (EAS-MSU) array studied high-energy cosmic rays with primary energies ~(1-500) PeV in the Northern hemisphere. The EAS-MSU data are being revisited following recently found indications to an excess of muonless showers, which may be interpreted as the first observation of cosmic gamma rays at ~100 PeV. In this paper, we present a complete Monte-Carlo model of the surface detector which results in a good agreement between data and simulations. The model allows us to study the performance of the detector and will be used to obtain physical results in further studies.
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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