  Aims. We present the application of a fully connected neural network (NN) for
galaxy merger identification using exclusively photometric information. Our
purpose is not only to test the method's efficiency, but also to understand
what merger properties the NN can learn and what their physical interpretation
is. Methods. We created a class-balanced training dataset of 5\,860 galaxies
split into mergers and non-mergers. The galaxy observations came from SDSS DR6
and were visually identified in Galaxy Zoo. The 2$\,$930 mergers were selected
from known SDSS mergers and the respective non-mergers were the closest match
in both redshift and $r$ magnitude. The NN architecture was built by testing a
different number of layers with different sizes and variations of the dropout
rate. We compared input spaces constructed using: the five SDSS filters: $u$,
$g$, $r$, $i$, and $z$; combinations of bands, colours, and their errors; six
magnitude types; and variations of input normalization. Results. We find that
the fibre magnitude errors contribute the most to the training accuracy.
Studying the parameters from which they are calculated, we show that the input
space built from the sky error background in the five SDSS bands alone leads to
92.64 $\pm$ 0.15 \% training accuracy. We also find that the input
normalization, that is to say, how the data are presented to the NN, has a
significant effect on the training performance. Conclusions. We conclude that,
from all the SDSS photometric information, the sky error background is the most
sensitive to merging processes. This finding is supported by an analysis of its
five-band feature space by means of data visualization. Moreover, studying the
plane of the $g$ and $r$ sky error bands shows that a decision boundary line is
enough to achieve an accuracy of 91.59\%.
