This thesis describes a timing calibration method for the detector array of the ALTO experiment. ALTO is a project currently at the prototype phase that aims to build a gamma-ray astronomical observatory at high-altitude in the Southern hemisphere. ALTO can be assumed as a hybrid system as each detector consists of a Water Cherenkov Detector (WCD) on top of a Scintillator Detector (SD), providing an increased signal to background discrimination compared to other WCD arrays.

ALTO is planned to complement the Very-High-Energy (VHE) observations by the High Altitude Water Cherenkov (HAWC) gamma ray observatory that collects data from the Northern sky. By the time the full array of 1242 detectors is installed to the proposed site, ALTO together with HAWC and the future Cherenkov Telescope Array (CTA) will serve as a state-of-the-art detection system for VHE gamma-rays combining the WCD and the Imaging Atmospheric Cherenkov Telescope (IACT) techniques.

When a VHE gamma-ray or cosmic-ray enters the Earth’s atmosphere, it initiates an Extensive Air Shower (EAS). These particles are sampled by the detector array and by checking the arrival times of nearby tanks, the method reveals whether a detector suffers from a time-offset.

The data analyzed in this thesis derive from CORSIKA (COsmic Ray SImulation for KAscade) and GEANT4 (GEometry ANd Tracking) simulations of cosmic-ray events within the energy range of 1–1:6TeV, which mainly consist of protons. The high flux of this particular type of cosmic-rays, gives us a tool to statistically evaluate the results generated by the proposed timing calibration method.

In the framework of this thesis, I have written code in Python programming language in order to develop the timing calibration method. The method identifies detectors that suffer from time-offsets and improves the reconstruction accuracy of the ALTO detector array. Different Python packages were used to execute different tasks: astropy to read filter-present-write large datasets, numpy (Numerical Python) to make datasets comprehensiveto functions, scipy (Scientific Python) to develop our models, sympy (Symbolic Python) to find geometrical correlations and matplotlib (Mathematical Plotting Library) to draw figures and diagrams.

The current version of the method achieves sub-nanosecond accuracy. The next stepis to make the timing calibration more intelligent in order to correct itself. This self correction includes an agile adaptation to the data acquired for long periods of time, in order to make different compromises at different time intervals.