# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 9 High-Level Performance Comparison  
In order to provide a final high-level comparison between the new methods considered in this work and those implemented in A-SciSoft, we derived the main performance metrics by means of the standard routine implemented in the ASTRI pipeline [7]. For this high-level comparison, we considered the DL2b samples obtained with the best new methods investigated in this work (namely \"GH SET 3-GH ENS 3\" for the gamma/hadron separation and \"EN SET 1-EN ENS 4\" for the energy estimation) and those produced with A-SciSoft. The main performance metrics that we considered were the differential flux sensitivity (for an exposure time of 50 h), energy resolution, and angular resolution. All of these quantities were comprehensively evaluated from the DL2b data achieved with the best new Machine Learning methods and with A-SciSoft for on-axis source observations in the (reconstructed) energy range between \(10^{-0.5}\simeq 0.3\) TeV and \(10^{2.5}\simeq 300\) TeV. The background (proton) and gamma-ray events of the different Level 2b (DL2b) samples were reweighed according to experimental measurements of their spectra, following the same procedure adopted in [22]. In particular, the gamma-ray events were reweighed considering the Crab Nebula spectrum, as measured by the HEGRA Collaboration [23]. No electron background component was considered in this high-level comparison because for the typical energies for which the ASTRI Mini-Array is sensitive (E \(>\)\(\sim\)1 TeV), it does not contribute significantly to the irreducible gamma-like background (which was by far dominated by proton events). For each DL2b sample, separately, the final analysis cuts were based on the background rejection, shower arrival direction, and event multiplicity parameters. They were defined, in each considered energy bin, by optimizing the sensitivity for a 50 h exposure time. Then, five standard deviations (5\(\sigma\), with \(\sigma\) defined as shown in Equation (17) of [24]) were required for a detection in each energy bin and off-axis bin, considering the same exposure time (as in the cut optimization procedure) and a ratio of the off-source to on-source exposure equal to 5. In the analysis of Cherenkov data, the gamma-ray signal within the \"on-source\" region of interest must always be compared with suitable \"off-source\" background control regions so that the fraction of irreducible background events surviving all analysis cuts in the signal region can be properly estimated and subtracted to determine the final excesses from the gamma-ray source. Typically, for observations of point-like gamma sources, Cherenkov data are taken in such a way that it is always possible to define multiple background control regions (five by default), which all have the same acceptance as the signal region. In addition, the signal excess was required to be larger than ten and at least five times the expected systematic uncertainty in the background estimation (assumed to be \(\sim\)1%). All of these assumptions are commonly adopted in the IACT community (see e.g., [22]) and allow us to derive performance results under coherent analysis conditions. Figures 14-16 show the on-axis point-like source differential sensitivity (in 50 h), angular resolution, and energy resolution, respectively, achieved with A-SciSoft (blue points) and the best Machine Learning methods investigated in this work (orange points). The ratios between the results of the two methods are also shown. Figure 14: **Up**: On-axis differential sensitivity (in 50 h) achieved with the best Machine Learning methods (orange points) against the benchmark (A-SciSoft, blue points) and Crab Nebula [25]. **Bottom**: Comparison between the on-axis differential sensitivity achieved with the best Machine Learning methods and the one achieved with A-SciSoft. The ratio is calculated so that higher values correspond to better performance.