CDS&E: Accelerating Astrophysical Insight at Scale with Likelihood-Free Inference

CDS

• 批准号: 2206744
• 负责人: Joshua Bloom
• 金额: $ 57.29万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2206744&HistoricalAwards=false
• 关键词: CDS& Accelerating; Astrophysical; Insight; Scale

In future surveys to unprecedented depth and image fidelity, the discovery and classification of sources will, by necessity, be fully automated, in part by using machine learning. This project will develop novel algorithms and codebases to accelerate model-based inference in time-domain astrophysics. Inference is becoming a fundamental bottleneck, from both a computation and human cost perspective, and the parameter estimation task can be the main impediment to scientific progress. Often, domain experts are required to supervise fitting in order to narrow search spaces and speed up inference. This work will focus on likelihood-free inference (LFI) approaches using neural networks. It will create a new open-source LFI Python library, and carry out domain-specific inference tasks for gravitational microlensing and for eclipsing binary stars. The software will let non-experts use LFI with smaller computational penalties, and enable on-the-fly inference continuously during data collection. Running a multidisciplinary workshop and adding LFI into undergraduate and graduate courses will improve both the research infrastructure and STEM education.This approach means that the cost and time to compute forward models are covered during training, and then leveraged for parameter estimation on new data. This effort will a) create LFI architectures for irregularly sampled and noisy time-series data, b) advance methods for anomaly detection and posterior calibration, ensuring LFI parameter estimates are unbiased, and c) develop approaches for fast model selection. On-the-fly LFI can inform further data acquisition, and this virtuous feedback loop helps with more efficient use of expensive follow-up resources.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在未来以前所未有的深度和图像保真度进行的调查中，来源的发现和分类必然是完全自动化的，部分是通过使用机器学习。该项目将开发新的算法和代码库，以加速在时间域天体物理学中基于模型的推理。从计算和人力成本的角度来看，推理正在成为一个根本的瓶颈，而参数估计任务可能是科学进步的主要障碍。通常，领域专家需要监督匹配，以缩小搜索空间并加快推理速度。这项工作将集中在使用神经网络的无似然推理(LFI)方法上。它将创建一个新的开源LFI Python库，并执行针对引力微透镜和日食双星的特定领域的推断任务。该软件将允许非专家使用计算惩罚较小的LFI，并允许在数据收集期间连续进行即时推理。举办多学科研讨会并将LFI加入本科和研究生课程将改善研究基础设施和STEM教育。这种方法意味着在培训期间计算向前模型的成本和时间，然后利用这些成本和时间对新数据进行参数估计。这项工作将a)为不规则采样和噪声时间序列数据创建LFI架构，b)改进异常检测和后验校准方法，确保LFI参数估计是无偏的，以及c)开发快速模型选择的方法。即时LFI可以为进一步的数据获取提供信息，这种良性的反馈循环有助于更有效地使用昂贵的后续资源。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。