Particle classification in the LAGO water Cherenkov detectors using clustering algorithms

Abstract

The Latin American Giant Observatory (LAGO) is a ground-based observatory studying solar or high-energy astrophysics transient events. LAGO takes advantage of its distributed network of Water Cherenkov Detectors (WCDs) in Latin America as a tool to measure the secondary particle flux reaching the ground. These secondary particles are produced during the interaction between the modulated cosmic rays flux and the atmosphere.The LAGO WCDs are sensitive to secondary charged particles, high energy photons through pair creation and Compton scattering, and even neutrons thanks to, e.g., the deuteration of protons in the water volume. The pulse shape generated by these particles depends on several factors, such as the detector geometry, the water purity, the sensor response, or the reflectivity and diffusivity of the inner coating. Due to the decentralized nature of LAGO, these properties are different for each node. Additionally, the pulse shape depends on the convolution between the response of the central photomultiplier (PMT) to individual photons and the time distribution of the Cherenkov photons reaching the PMT. Typically, a WCD gives pulses with a sharp rise time (∼10ns) and a longer decay time (∼70ns).In this work, the WCD data used is acquired using the original LAGO data-acquisition system that digitizes pulses at a sampling rate of 40 MHz and 10 bits resolution on time windows of 400ns. Here, we apply unsupervised machine learning techniques to find patterns in the WCDs data and subsequently create groups, through clustering, that can be used to provide particle separation. We use data acquired from an individual WCD, showing that density-based clustering algorithms are suitable for automatic particle separation producing good candidate groups. Improved separation would help LAGO to reconstruct in situ the properties of primary cosmic rays flux. These results open the possibility to deploy machine learning-based models in our distributed detection network for onboard data analysis as an operative prototype, allowing detectors to be installed at very remote sites.