Updated Radio Sigma-D Relation and Distances to the Shell-Like Galactic Supernova Remnants -- IV

TL;DR

The study tackles how to reliably estimate distances to Galactic SNRs using the radio Σ-D relation by updating the calibrator set with 69 shell-type SNRs that have robust independent distances and applying two calibration approaches: orthogonal-offset fitting and a kernel-density (median) method in the Σ-D plane. The core methodological advance is the use of Gaussian kernel density in (Σ,D) space with cross-validated kernel widths and median-based distance inference, together with standard radio-derived quantities Σ1GHz and D from flux and angular sizes: $\Sigma_{1\mathrm{GHz}} = \frac{S_{1\mathrm{GHz}}}{\Omega}$ with $\Omega = \pi \theta_1 \theta_2$, and $D = \sqrt{\theta_1 \theta_2}$, $\log \Sigma = \log A + \beta \log D$. The main contributions are (i) the updated calibrator sample and distances, (ii) the derived Σ-D calibration applied to 164 SNRs lacking reliable distances and 27 new SNR candidates, and (iii) a demonstration that the kernel-density approach yields larger fidelity in distance estimates than the orthogonal fit for most, but not all, cases, especially highlighting its sensitivity to higher-density regions of the Σ-D plane. This work improves distance estimates for a substantial population of SNRs and informs methodological choices for future population studies and distance determinations in the presence of ISM environmental diversity and observational biases.

Abstract

We present a new selected sample of 69 Galactic supernova remnants (SNRs) for calibration of radio $Σ-D$ relation at 1 GHz. Calibrators with the most reliable distances were selected through an extensive literature search. The calibration is performed using kernel smoothing of the selected sample of calibrators in $Σ-D$ plane and an orthogonal offsets fitting procedure. We use the obtained calibration to derive the distances to 164 Galactic SNRs and 27 new detected SNRs/SNR candidates with none or poor distance estimates. The analysis given in this paper confirms the expected predictions from our previous papers that the kernel smoothing method is more reliable for SNR distance calibration than the orthogonal offset fitting method, except for the distance determinations of the very low brightness SNRs.

Figures (1)

• Figure 1: Calibration SNR sample consisting of $86$ calibrators with reliable distances. Black thick line presents the orthogonal offsets fit while the thin gray line designates median values of diameter distributions for the corresponding surface brightness values for the data normalized probability density matrix (contours). Contour levels are at 0.01, 0.05, 0.1, 0.2, 0.3, 0.4 and 0.5. The orthogonal fit parameters, optimal kernel widths $h$ for data probability density calculation and fractional distance errors $f$ for both, orthogonal fit and median values of diameter distributions at corresponding surface brightness values are designated on the plot. Cygnus Loop SNR is designated with a purple dot. Thin dashed line is the orthogonal fit translated along horizontal axis to the position of Cygnus Loop. Calibrators that are within $0.02$ on $\log D$ axis from the dashed line are additionally designated with green crosses.