  We present the SEMLA (Signal Extraction using Machine Learning for ALTO)
analysis method, developed for the detection of $\rm E>200\,GeV$ $\gamma$ rays
in the context of the ALTO wide-field-of-view atmospheric shower array R&D
project. The scientific focus of ALTO is extragalactic $\gamma$-ray astronomy,
so primarily the detection of soft-spectrum $\gamma$-ray sources such as Active
Galactic Nuclei and Gamma Ray Bursts. The current phase of the ALTO R&D project
is the optimization of sensitivity for such sources and includes a number of
ideas which are tested and evaluated through the analysis of dedicated Monte
Carlo simulations and hardware testing. In this context, it is important to
clarify how data are analysed and how results are being obtained. SEMLA takes
advantage of machine learning and comprises four stages: initial event cleaning
(stage A), filtering out of poorly reconstructed $\gamma$-ray events (stage B),
followed by $\gamma$-ray signal extraction from proton background events (stage
C) and finally reconstructing the energy of the events (stage D). The
performance achieved through SEMLA is evaluated in terms of the angular, shower
core position, and energy resolution, together with the effective detection
area, and background suppression. Our methodology can be easily generalized to
any experiment, provided that the signal extraction variables for the specific
analysis project are considered.
