2 papers
astro-ph.IM2026
FPCA-Enhanced Simulation-Based Inference for Robust Type Ia Supernova Cosmology
Moonzarin Reza, Lifan Wang
Precision cosmology is a cornerstone of modern astronomy, and upcoming wide-field photometric surveys make it crucial to develop robust, data-driven inference methods that can hand…
astro-ph.IM2025
An FPCA-enhanced Ensemble Learning Framework for Photometric Identification of Type Ia Supernovae
Moonzarin Reza, Lifan Wang, Lei Hu
Type Ia supernovae (SNe Ia) are essential tools for addressing key cosmic questions, including the Hubble tension and the nature of dark energy. Modern surveys are predominantly ph…