Identifying the Quantum Properties of Hadronic Resonances using Machine Learning

Thank you for your feedback! We are glad to hear that you find this to be an interesting study. We appreciate that CNNs and images are no longer state of the art. However, our paper was submitted in early 2021 and the research itself actually started in 2018 (!) and due to a number of factors, it took a long time to converge. Point cloud methods were increasingly used around the time this paper was posted to arXiv, but we are no longer able to add additional studies at the level of training new models. While using state of the art methods would surely improve the quantitative performance, we do not believe it would change the qualitative results as images are still useful representation. In particular, the trends in Table 1 are likely not affected by the representation as long as it is good enough (e.g. able to resolve colorflows within the jet). Thank you for the suggestion of multiclass classification; while that would allow us to combine various options into one model, it should not qualitatively change the performance. It would essentially let us share weights across the tasks, increasing the dataset size across the board. It would still be possible to do a fit to multiple resonant hypotheses with individual models. We have clarified in the text how we envision this classifier is used, which hopefully addresses this point. Thank you again and we are sorry that we are unable to do extensive studies given how long this paper has been in review.

Thank you for your feedback! We are glad to hear that you find this to be an interesting question and that it uses state-of-the-art tools in a new way. For your main question - we have expanded the introduction to explain how we envision this tool would be used in practice. In particular, while the jet-by-jet classification performance is weak, even a small amount of separation can be used in a template fit to extract the quantum numbers. Thus, we envision that this approach will be used in a post-discovery phase to categorize the resonance without needing excellent per-event distinguishing power. The exact separability required depends on the amount of signal present.