QCD or What?

(1) The authors should add a discussion about experimental effects such as imperfect calibration and badly measured input objects

(2) The authors should directly test the impact of imperfect calibration and badly measured input objects by injecting a known miscalibration to the particle-flow objects to see the impact on the weakly-supervised adversarial network. It would build confidence if such effects were shown to be negligible or could be mitigated in some fashion.

1. There are a few typos/missing words, and the paper would benefit from careful proofreading
2. The paper does not consider the robustness of the results to e.g. non-perturbative effects.
3. The loss functions used suffer from some drawbacks, such as sensitivity to rotations or soft and collinear splittings, and the authors do not discuss the impact of these limitations.

1- On page 2, when saying "we can choose our input format to deep learning analysis tools", do the authors mean here choose an input format best adapted for deep learning frameworks? 2- On page 2, regarding the use of jet images: it seems to me that while jet images have historically been the first representation used in conjunction with deep learning networks, there is no particular consensus on which input type is preferred, and in fact there has been substantial work in exploring other techniques. I would suggest citing some of these other methods in this paragraph as well, such as: * arXiv:1702.00748 * arXiv:1704.02124 * arXiv:1704.08249 * arXiv:1710.01305 * arXiv:1712.07124 * arXiv:1807.04758 * arXiv:1810.05165 3- On page 2, regarding how to address systematic uncertainties: While I agree that this article presents an interesting angle, using adversarial networks to study some of these limitations, I think the statement is too broad. There are certainly other systematic uncertainties beyond those considered here. 4- On page 5, equation (2). An obvious downside to this loss function, and to the jet image approach in general, is that it is very sensitive to rotations: a small rotation, while leaving the physical properties mostly unchanged, will lead to a large value of the loss function. A discussion of this point and whether the authors have any insights into how it impacts the results would be useful. 5- On page 6, equation (3). The $(k_{\mu,i})$ matrix is not IRC safe: for example, a collinear splitting will result in a reshuffle of the columns, as well as a change of the values in eight of the entries. Did the authors study the impact of this unsafety? 6- On page 7, equation (7). Since the matrix compared before and after autoencoding can change substantially due to effects that are not physically relevant, e.g. soft or collinear splittings, does this impact the performance of the loss function? 7- On page 8. It would be interesting to see this study done on groomed jets, to remove the impact of soft wide angle partons on the jet mass considered as input. 8- On page 8, just after the middle of the page: "We know from many studies that the jet mass is the single most powerful observable in separating QCD jets from hadronically decaying heavy states". This is only true at parton level, without considering non-perturbative or pile-up effects. Otherwise, some of the many studies should be cited. 9- On page 12, the last sentence of section 2 is missing a "to". 10- On page 16, the second sentence of the second paragraph in the Outlook section is missing an "of".

1 - show the MC statistics are high enough to support the conclusions, or generate more
2 - show or discuss how well the method should work on groomed (pile-up suppressed) jets.
3 - show or discuss impact of the detector simulation used
4 - address questions/requests for clarification in the attached PDF (which include the above as the most significant) (I also so highlighted some bits of text which look like typos or may need rephrasing)