DLScanner: A parameter space scanner package assisted by deep learning methods

TL;DR

DLScanner is a deep learning-assisted parameter space scanner that improves convergence speed and generalization in high-dimensional scans through similarity learning and adaptive sampling strategies.

Contribution

It introduces a novel framework combining similarity learning and dynamic sampling to enhance the efficiency and accuracy of parameter space scanning.

Findings

Significant improvement in scan convergence speed.

Enhanced generalization of the DL network in high-dimensional spaces.

Better performance compared to existing scanning methods.

Abstract

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.