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Exploration of Parameter Spaces Assisted by Machine Learning
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We demonstrate two sampling procedures assisted by machine learning models via regression and classification. The main objective is the use of a neural network to suggest points likely inside regions of interest, reducing the number of evaluations of time consuming calculations. We compare results from this approach with results from other sampling methods, namely Markov chain Monte Carlo and MultiNest, obtaining results that range from comparably similar to arguably better. In particular, we augment our classifier method with a boosting technique that rapidly increases the efficiency within a few iterations. We show results from our methods applied to a toy model and the type II 2HDM, using 3 and 7 free parameters, respectively. The code used for this paper and instructions are publicly available on the web.
Forward citations
Cited by 2 Pith papers
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Normalizing Flow-Assisted Nested Sampling on Type-II Seesaw Model
A RealNVP normalizing flow trained inside nested sampling accelerates Bayesian scans of the Type-II seesaw parameter space and yields posterior constraints on scalar masses and couplings.
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DLScanner: A parameter space scanner package assisted by deep learning methods
A new scanner package combines a similarity-learning neural network with VEGAS adaptive sampling to collect valid points in BSM parameter scans faster than earlier ML-based methods.
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