  New researchers are usually very curious about the recipe that could
accelerate the chances of their paper getting accepted in a reputed forum
(journal/conference). In search of such a recipe, we investigate the profile
and peer review text of authors whose papers almost always get accepted at a
venue (Journal of High Energy Physics in our current work). We find authors
with high acceptance rate are likely to have a high number of citations, high
$h$-index, higher number of collaborators etc. We notice that they receive
relatively lengthy and positive reviews for their papers. In addition, we also
construct three networks -- co-reviewer, co-citation and collaboration network
and study the network-centric features and intra- and inter-category edge
interactions. We find that the authors with high acceptance rate are more
`central' in these networks; the volume of intra- and inter-category
interactions are also drastically different for the authors with high
acceptance rate compared to the other authors. Finally, using the above set of
features, we train standard machine learning models (random forest, XGBoost)
and obtain very high class wise precision and recall. In a followup discussion
we also narrate how apart from the author characteristics, the peer-review
system might itself have a role in propelling the distinction among the
different categories which could lead to potential discrimination and
unfairness and calls for further investigation by the system admins.
