4.7 Article

Efficient gravitational wave template bank generation with differentiable waveforms

期刊

PHYSICAL REVIEW D
卷 106, 期 12, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevD.106.122001

关键词

-

资金

  1. Schmidt Futures foundation
  2. Netherlands eScience Center [638-2013-8993]
  3. Vetenskapsradet (Swedish Research Council) [DE-SC0022021]
  4. Oskar Klein Centre for Cosmoparticle Physics
  5. Rubicon Fellowship awarded by the Netherlands Organisation for Scientific Research (NWO)
  6. Jeff & Gail Kodosky Endowed Chair in Physics at the University of Texas at Austin
  7. U.S. Department of Energy, Office of Science, Office of High Energy Physics program [PHY-1912578]
  8. NSF [864035]
  9. European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program
  10. NSF's LIGO Laboratory - National Science Foundation
  11. Calcul Quebec
  12. Digital Research Alliance of Canada
  13. Digital Research Alliance of Canada
  14. [ETEC.2019.018]

向作者/读者索取更多资源

This study proposes a new method for generating template banks using automatic differentiation and demonstrates its efficiency and accuracy. By combining random template placement and Monte Carlo methods, search-ready template banks for frequency-domain waveforms can be rapidly generated. The study also suggests that differentiable waveforms can accelerate stochastic placement algorithms.
The most sensitive search pipelines for gravitational waves from compact binary mergers use matched filters to extract signals from the noisy data stream coming from gravitational wave detectors. Matched-filter searches require banks of template waveforms covering the physical parameter space of the binary system. Unfortunately, template bank construction can be a time-consuming task. Here we present a new method for efficiently generating template banks that utilizes automatic differentiation to calculate the parameter space metric. Principally, we demonstrate that automatic differentiation enables accurate computation of the metric for waveforms currently used in search pipelines, whilst being computationally cheap. Additionally, by combining random template placement and a Monte Carlo method for evaluating the fraction of the parameter space that is currently covered, we show that search-ready template banks for frequency-domain waveforms can be rapidly generated. Finally, we argue that differentiable waveforms offer a pathway to accelerating stochastic placement algorithms. We implement all our methods into an easy-to-use PYTHON package based on the JAX framework, diffbank, to allow the community to easily take advantage of differentiable waveforms for future searches.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据