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

Characterising authors on the extent of their paper acceptance: A case study of the Journal of High Energy Physics