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Towards a method to anticipate dark matter signals with deep learning at the LHC
by Ernesto Arganda, Anibal D. Medina, Andres D. Perez, Alejandro Szynkman
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Submission summary
| Authors (as registered SciPost users): | Andres Daniel Perez |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2105.12018v2 (pdf) |
| Date submitted: | 2021-08-25 17:18 |
| Submitted by: | Perez, Andres Daniel |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
Abstract
We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of $S/\sqrt{B}$, where $S$ and $B$ are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC run.
Current status:
Reports on this Submission
Anonymous Report 1 on 2021-11-21 (Invited Report)
- Cite as: Anonymous, Report on arXiv:2105.12018v2, delivered 2021-11-21, doi: 10.21468/SciPost.Report.3880
Report
This paper presents a robust Machine Learning based method for collider dark matter searches. In particular, they focus on the monojet channel and consider several possible new physics models which could provide dark matter candidates. The problem is timely, and the method suggested is very interesting. As the authors mentioned, the idea of histograms was suggested in Ref 26, but this work thoroughly investigated the robustness of this approach. Overall, I am happy with the scientific content of the paper and believe it is suitable for publication in SciPost, but before recommending it for publication, I have the following comments:
1. Table 2, please mention how many neurons were used in the dense layer of the CNN.
2. I am not very sure about the statement that histograms method is totally independent of the background number of events. For example, in Fig. 10, I think there is not a significant difference because you are comparing the performance of histograms with 1000 events with 50K, 1000 is already a large number. Could you please also compare the performance of histograms with 20 events or 100 events? It seems the conclusion about the robustness against the background events is most likely true when you form a histogram of a reasonably large number of events. As mentioned in the paper, the total number of background events is decided by the luminosity, but a priori, there is no fixed number for the events to form a histogram.
3. Section 4.3, Fig. 12, is it true that trained models should have accuracy greater than or equal to 70% because DNN trained on ALP model provides more variation?
Minor comments regarding the presentation:
If possible, could you please provide better quality (pdf files) images? Though better quality images will not add scientific value, I find it distracting and difficult to read these images (see e.g. Figure 8).
I think “Machine Learning algorithm” is a more suitable name for section 3.
I also noticed a few typos in the paper. Please correct them.