# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 1 Introduction  
### General Overview  
Gamma-ray astronomy has been one of the last branches of astrophysics to arise (the first telescope for observing gamma rays was sent into orbit aboard the Explorer 11 satellite in 1961). Gamma photons cannot reach the Earth's surface. Up to energies of a few hundred GeV, we can collect them by sending instruments aboard satellites in orbit above the atmosphere (see, for example, the Fermi Gamma-ray Space Telescope). However, for higher energies, these technologies are unsuccessful due to the low photon flux, which would require an unthinkably large collection area.  
Imaging Atmospheric Cherenkov Telescopes (IACTs), with their specific characteristics [1], permit us to perform very high-energy (VHE) gamma astronomy (above a few tenths GeVs) from the ground with a very large effective collection area (above the order of \(10^{5}\) m\({}^{2}\)). The keyidea is to collect the Cherenkov light induced by the atmospheric cascade ignited by the VHE photon that interacts with the atoms of the atmosphere.  
An atmospheric cascade is started when relativistic high energy particles (in this document, we will sometimes refer to them as primaries), such as cosmic rays and photons, reach the Earth's atmosphere. Cherenkov light is produced when secondary charged relativistic particles in the cascade, travelling through the atmosphere, disturb the equilibrium of the atoms in the atmosphere, which subsequently emit a faint blue-ultraviolet radiation as they regain their equilibrium.  
As a result, on the focal plane of IACTs we do not have a direct image of the gamma rays coming from the outer space (and therefore of the astrophysical source) but a mere collection of footprints of the Cherenkov radiation triggered by each relativistic high-energy particle. The morphological and temporal features of these footprints can then be used to reconstruct the image of the source for scientific analysis. Since the showers are intrinsically blurred due to their development in the atmosphere, the footprints are approximate ellipsoids extended on a few degrees of arc, so that IACTs usually do not need high-performance optics. On the other hand, since the Cherenkov flash lasts only a few nanoseconds, very fast electronics are required [1].  
One of the factors limiting IACTs sensitivity is how challenging it is to suppress the enormous number of cosmic ray background events. In fact, gamma rays make up a very minor fraction of the flux of cosmic rays, and IACTs detect and image the Cherenkov radiation regardless of its origin, either from a gamma photon or from a high-energy charged particle such as a proton. In order to detect gamma-ray sources, any IACT analysis method must therefore be capable of performing an effective background rejection. This involves separating the gamma-ray signal from the far more abundant background of hadron driven showers by exploiting the recognition of shape features in the image.  
Usually, the image of a shower initiated by a gamma photon has a quite regular elliptical shape. On the other hand, a hadronic shower, which forms if a cosmic ray is the primary, even though it may contain electromagnetic sub-shower components, typically has a more irregular shape (see Figure 1).  
Fortunately, most images have enough information to distinguish the gamma-ray signal from the predominant cosmic-ray background and to reconstruct the arrival direction and energy of the primary.  
The extraction of pertinent features, such as the Hillas parameters [2] and more features that will be described later, from the camera pixel data is a prerequisite for IACT image analysis.