  The statistical analysis of large sample of strong lensing events can be a
powerful tool to extract astrophysical and/or cosmological valuable
information. However, the number of such events is still relatively low, mostly
because of the lengthily observational validation process on individual events.
In this work we propose a new methodology with a statistical selection approach
in order to increase by a factor of $\sim 5$ the number of such events.
Although the methodology can be applied to address several selection problems,
it has particular benefits in the case of the identification of strongly lensed
galaxies: objectivity, minimal initial constrains in the main parameter space,
preservation of the statistical properties. The proposed methodology is based
on the Bhattacharyya distance as a measure of the similarity between
probability distributions of properties of two different cross-matched
galaxies. The particular implementation for the aim of this work is called
SHALOS and it combines the information of four different properties of the pair
of galaxies: angular separation, luminosity percentile, redshift and
optical/sub-mm flux density ratio. The SHALOS method provided a ranked list of
strongly lensed galaxies. The number of candidates for the final associated
probability, $P_{tot}>0.7$, is 447 with an estimated mean amplification factor
of 3.12 for an halo with a typical cluster mass. Additional statistical
properties of the SHALOS candidates, as the correlation function or the source
number counts, are in agreement with previous results indicating the
statistical lensing nature of the selected sample.
