Supervised Deep Learning in High Energy Phenomenology: a Mini Review *
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Supervised Deep Learning in High Energy Phenomenology: a Mini Review * |
| Murat Abdughani,Jie Ren,Lei Wu,Jin-Min Yang,Jun Zhao |
| Fig. 14 (Color online) Sampling efficiencies of the CMSSM parameter space: the MLS uses 5000 initial samples and the MultiNest uses 5000 live samples. Both methods perform the calculation for $2 \times 10^5$ parameter samples. This figure is taken from Ref. [ |
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