A systematic assessment of Data Volume Reduction for IACTs

Abstract

High-energy cosmic rays generate air showers of secondary particles when they enter the Earth’s atmosphere. These highly energetic particles emit Cherenkov light that can be detected by Imaging Air Cherenkov Telescopes (IACTs) or Water-Cherenkov Detectors at mountain altitudes. Advances in the technique and larger collection areas have increased the rate at which air shower events can be captured, and the amount of data produced by modern high-time-resolution Cherenkov cameras. Therefore, Data Volume Reduction (DVR) has become critical for such telescope arrays, ensuring that only relevant information regarding the air shower is stored long-term. Given the vast amount of raw data, owing to the highest resolution and sensitivity, the upcoming Cherenkov Telescope Array Observatory (CTAO) will need robust data reduction strategies to ensure efficient data handling and a sustainable data analysis. The CTAO data rates needs to be reduced from hundreds of Petabytes (PB) per year to a few PB/year.

This paper presents DVR algorithms tailored for CTAO but also applicable for other existing IACT arrays, focusing on the selection of pixels likely to contain Cherenkov light from the air shower. It describes and evaluates multiple algorithms based on their signal efficiency, noise rejection, and shower reconstruction. With a focus on a time-based clustering algorithm which demonstrates a notable enhancement in the retention of low level signal pixels. Moreover, the robustness is assessed under different observing conditions, including detector defects. Through testing and analysis, it is shown that these algorithms offer promising solutions for efficient volume reduction in CTAO. They effectively address the challenges posed by the array’s very large data volume and ensure reliable data storage amidst varying observational conditions and hardware issues.