Better Radial velocities from Stellar Spectroscopy via Machine Learning
The goal of this project is to demonstrate how anyone, even those without any prior astronomical knowledge, can learn about the study of star variability using the well-known method of "light curves" inspection in a virtual reality environment.
Physics-informed multi-view spectral representations for unsupervised stellar anomaly detections.
Scripts for analyzing NEID Sun-as-a-star observations using RVSpectML
SDO Data Analysis for CLV
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