DLScanner: A parameter space scanner package assisted by deep learning methods
This work addresses challenges in parameter space scanning for researchers, but it is incremental as it builds on existing DL-based methods.
The paper tackles slow convergence and limited generalization in deep learning-based parameter space scanners by using a similarity learning network and dynamic sampling, achieving substantial gains in performance and efficiency.
In this paper, we introduce a scanner package enhanced by deep learning (DL) techniques. The proposed package addresses two significant challenges associated with previously developed DL-based methods: slow convergence in high-dimensional scans and the limited generalization of the DL network when mapping random points to the target space. To tackle the first issue, we utilize a similarity learning network that maps sampled points into a representation space. In this space, in-target points are grouped together while out-target points are effectively pushed apart. This approach enhances the scan convergence by refining the representation of sampled points. The second challenge is mitigated by integrating a dynamic sampling strategy. Specifically, we employ a VEGAS mapping to adaptively suggest new points for the DL network while also improving the mapping when more points are collected. Our proposed framework demonstrates substantial gains in both performance and efficiency compared to other scanning methods.