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

arXiv: https://arxiv.org/abs/2412.19675 · HTML: https://mlsanity.com/paper/2412.19675/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Anomalies, Representations, and Self-Supervision](https://mlsanity.com/paper/2301.04660/) (2023)
- [Constraining the Parameters of High-Dimensional Models with Active Learning](https://mlsanity.com/paper/1905.08628/) (2019)
- [Exploration of Parameter Spaces Assisted by Machine Learning](https://mlsanity.com/paper/2207.09959/) (2022)
- [Universal New Physics Latent Space](https://mlsanity.com/paper/2407.20315/) (2024)

## Built on by (most cited first, 1 of 1)

- [Symbolic Classification-Enabled LHC Limits for BSM Global Fits](https://mlsanity.com/paper/2605.22330/) (2026)

## Abstract (arXiv)

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.