Summary of the three calibration methods tested on the five mock datasets and the lab dataset. Here we plot the fractional difference between the calculated noise of the calibrated validation source solution and the analytic expected noise of the validation source. It can be seen that while the methods perform equally for the mock datasets, we see the methods presented in this paper outperform the existing conjugate priors method on a lab dataset.
New paper on the arXiv today! 😎
In this paper we show the importance of accounting for noise and singularities in your calibrator data when calibrating global 21-cm cosmology experiments with Bayesian methods!
arxiv.org/abs/2412.14023
19.12.2024 09:40 — 👍 3 🔁 0 💬 0 📌 0
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A community effort to develop a common core package for Astronomy in Python and foster an ecosystem of interoperable astronomy packages.
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