Mapping synthetic observations to pre-stellar core models: An interpretable machine learning approach

Open Access

Fig. 8

Kernel density estimation of the peak value of the spectrum for each transition (one per panel, see x-axis label) and the ζ¹^e⁴ of the corresponding model (see y-axis label). We note the scaling factor for the y-axis (1e-17), and the varying range of the x-axis. The parameters of the KDE algorithm are the same as in Fig. 6.

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