SciPost Submission Page
Semi-visible jets, energy-based models, and self-supervision
by Luigi Favaro, Michael Krämer, Tanmoy Modak, Tilman Plehn, and Jan Rüschkamp
Submission summary
| Authors (as registered SciPost users): | Luigi Favaro · Tilman Plehn |
| Submission information | |
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| Preprint Link: | scipost_202312_00024v1 (pdf) |
| Code repository: | https://github.com/luigifvr/dark-clr |
| Date submitted: | 2023-12-14 15:42 |
| Submitted by: | Favaro, Luigi |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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Abstract
We present DarkCLR, a novel framework for detecting semi-visible jets at the LHC. DarkCLR uses a self-supervised contrastive learning approach to create observables that are approximately invariant under relevant transformations. We use background-enhanced data to create a sensitive representation and evaluate the representations using a normalized autoencoder as a density estimator. Our results show a remarkable sensitivity for a wide range of semi-visible jets and are more robust than a supervised classifier trained on a specific signal.
Current status:
Reports on this Submission
Strengths
The paper describes a new tagging algorithm for identifying semi-visible jets based on a contrastive learning representation.
1 - The algorithm presented is interesting and relevant, since it only relies on minimal physical features of the signal and it is not trained on specific signal data.
2 - In addition, the background rejection is superior to supervised classifiers and more stable with respect to changes in the model parameters.
3 - The text is well written and the results presented strongly support the paper claims.
Weaknesses
1 - The paper mostly focus on the technical aspects of the algorithm, but it is not very accessible for readers which are not familiar with current machine learning developments.
2 - The paper results go up to the ROC curves and do not address the physics impact of the proposed algorithm, i.e. how much the sensitivity of current searches can be improved by the algorithm.
Report
The paper contains relevant results and illustrate a novel approach for tagging semi-visible jets. In addition, the ROC curves obtained are more stable with respect to variations of the BSM parameters than supervised learning methods.
However, a few improvements in the text are needed in order to make the paper more accessible.
Requested changes
1 - I suggest to include in Figure 1 an example of positive augmentation, so it is easier to compare the effect of positive and anomalous augmentations.
2 - The authors should comment on whether including momentum smearing for the jet constituents would have any relevant impact on the results.
3 - The dimensionality of the head network's input and output should be made explicit is Sec.3.3.
4 -I suggest including a schematic figure showing the main steps of the network architecture along with the dimensions of the input and output of each step. It would help the reader who is not familiar with the CLR setup.
5 - The definition of the AUC and LCT acronyms are missing.
Recommendation
Ask for minor revision