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J1231-1411 Posterior Distributions for Masses, Inclination, and Parallax

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Zenodo2026-02-26 更新2026-05-26 收录
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This record contains two *.npz (compressed NumPy archive) files to accompany the manuscript Shapiro Delay Measurements from Fifteen Years of PSR J1231-1411 Radio Observations (arXiv:2511.10529), which was submitted to ApJ on December 2, 2025 and published on February 26, 2026 (https://doi.org/10.3847/1538-4357/ae3d95). cro+25_mcmc_linear.npz contains MCMC output chains (posterior samples) from the fixed-noise MCMC fits presented in Section 4.3. cro+25_mcmc_nonlinear.npz contains chains from the fully Bayesian fits in Section 4.4, resulting from simultaneous sampling of the (white and red) noise and timing models. Each file contains the same keys: M1PDF, M2PDF, SINIPDF, and PXPDF containing chains for the pulsar mass (M1, solar masses), companion mass (M2, solar masses), sine of inclination angle (SINI), and parallax (PX, mas). Minimal example to load and plot posteriors: import numpy as npfrom matplotlib import pyplot as plt fig, axs = plt.subplots(4, 1, figsize=(4, 6))alpha = 0.3c1, c2 = 'steelblue', 'darkorange' # lin/nonlin loaddata_lin = np.load('cro+25_mcmc_linear.npz', allow_pickle=True)data_nonlin = np.load('cro+25_mcmc_nonlinear.npz', allow_pickle=True) for i, k in enumerate(data_lin.keys()): axs[i].hist(data_lin[k], bins='auto', alpha=alpha, density=True, color=c1, label='Linear') axs[i].hist(data_nonlin[k], bins='auto', alpha=alpha, density=True, color=c2, label='Nonlinear') axs[i].set_xlabel(k) axs[i].get_yaxis().set_ticks([]) axs[0].legend()plt.tight_layout()plt.show()

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2025-11-13
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