AstroAI Lunch Talks - December 2, 2024 - Sina Taamoli & He Jia
02 Dec 2024 - Joshua Wing
The video can be found here: https://www.youtube.com/watch?v=8uVy_1y2Gtg
Speaker 1: Sina Taamoli (UC Riverside)
Title: Disentangling the Role of Mass and Environment in Star Formation Activity of Galaxies at z between 0.4 and 4
Speaker 2: He Jia (Princeton)
Title: Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators
Abstract: A major challenge in extracting information from current and upcoming surveys of cosmological Large-Scale Structure (LSS) is the limited availability of computationally expensive high-fidelity simulations. We introduce Neural Quantile Estimation (NQE), a new Simulation-Based Inference (SBI) method that leverages a large number of approximate simulations for training and a small number of high-fidelity simulations for calibration. This approach guarantees an unbiased posterior and achieves near-optimal constraining power when the approximate simulations are reasonably accurate. As a proof of concept, we demonstrate that cosmological parameters can be inferred at field level from projected 2-dim dark matter density maps up to kmax∼1.5h/Mpc at z=0 by training on ∼104 Particle-Mesh (PM) simulations with transfer function correction and calibrating with ∼102 Particle-Particle (PP) simulations. The calibrated posteriors closely match those obtained by directly training on ∼104 expensive PP simulations, but at a fraction of the computational cost. Our method offers a practical and scalable framework for SBI of cosmological LSS, enabling precise inference across vast volumes and down to small scales.