通过将Bilby集成到PyFstat中，为连续波分析启用新的采样策略
Enabling new sampling strategies for continuous-wave analyses by integrating Bilby into PyFstat
浏览论文内容
中文总结 AI 辅助
本文通过将贝叶斯推断库BILBY集成到PyFstat，实现多种随机采样策略，验证了DYNESTY在连续波候选体跟进中的有效性与可靠性。
中文摘要 AI 辅助
旋转中子星的连续波搜索通常覆盖广泛的参数空间，并产生大量候选信号。因此，随机采样方法在候选体跟进和参数估计中扮演着重要角色。我们展示了将贝叶斯推断库BILBY集成到开源连续波分析包PyFstat中的工作，这使得在保留现有PyFstat分析基础设施的同时，能够访问BILBY提供的广泛随机采样器。该实现通过在高斯噪声中的注入研究得到验证，针对两种具有代表性的单阶段跟进配置：来自定向搜索的候选体和来自全天搜索的候选体。使用嵌套采样器DYNESTY，我们获得的探测效率与理论灵敏度预测一致。在这种设置下，对于定向情况，DYNESTY的性能与PyFstat现有的基于PTEMCEE的实现相当，并且对于此处考虑的全天候选体跟进，它也提供了一种有效的单阶段方法，而我们未找到具有类似恢复性能的PTEMCEE设置。DYNESTY的百分位-百分位检验进一步显示出校准良好的可信区间，证明了可靠的参数估计。虽然对DYNESTY设置、多阶段配置和其他采样器的全面探索留待未来工作，但这些结果表明，这一灵活且公开可用的框架为贝叶斯连续波分析开辟了有用的新可能性。
英文摘要
Continuous-wave searches for rotating neutron stars usually cover wide parameter spaces and produce a large number of candidate signals. Stochastic sampling methods therefore play an important role in candidate follow-up and parameter estimation. We present the integration of the Bayesian inference library BILBY into the open-source continuous-wave analysis package PyFstat, enabling access to the broad range of stochastic samplers available through BILBY while preserving the existing PyFstat analysis infrastructure. The implementation is validated through injection studies in Gaussian noise for two representative single-stage follow-up configurations: candidates from a directed search and candidates from an all-sky search. Using the nested sampler DYNESTY, we obtain detection efficiencies consistent with the theoretical sensitivity predictions. In this setup, DYNESTY performs comparably to the existing PTEMCEE-based implementation of PyFstat for the directed case, and also provides an effective single-stage approach for the all-sky candidate follow-up considered here, where we did not identify a PTEMCEE setup with similar recovery performance. Percentile-percentile tests for DYNESTY further show well-calibrated credible intervals, demonstrating reliable parameter estimation. While a full exploration of DYNESTY settings, multi-stage setups, and other samplers is left for future work, these results demonstrate that this flexible and publicly available framework opens useful new possibilities for Bayesian continuous-wave analyses.
发表机构
- Universitat de les Illes Balears（巴利阿里群岛大学）
- Università di Milano and INFN, sezione di Milano（米兰大学与意大利国家核物理研究所米兰分部）
机构由 AI 辅助整理，请以论文原文为准。
