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

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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.