The orthogonal fitting procedure for determination of the empirical {\Sigma} - D relations for supernova remnants: application to starburst galaxy M82

TL;DR

This paper introduces an orthogonal regression method to determine the empirical surface brightness-diameter relation for supernova remnants in galaxy M82, providing a more robust way to estimate their distances unaffected by selection biases.

Contribution

It applies orthogonal regression to derive the {} - D relation for M82 SNRs, improving accuracy over previous methods and demonstrating its robustness against selection effects.

Findings

Derived {} - D slope .9 for 31 SNRs in M82.

Monte Carlo simulations show minimal influence of sensitivity selection effects.

The relation can estimate distances for SNRs in dense interstellar environments.

Abstract

The radio surface brightness-to-diameter ({\Sigma} - D) relation for supernova remnants (SNRs) in the starburst galaxy M82 is analyzed in a statistically more robust manner than in the previous studies that mainly discussed sample quality and related selection effects. The statistics of data fits in log {\Sigma} - log D plane are analyzed by using vertical (standard) and orthogonal regressions. As the parameter values of D - {\Sigma} and {\Sigma} - D fits are invariant within the estimated uncertainties for orthogonal regressions, slopes of the empirical {\Sigma} - D relations should be determined by using the orthogonal regression fitting procedure. Thus obtained {\Sigma} - D relations for samples which are not under severe influence of the selection effects could be used for estimating SNR distances. Using the orthogonal regression fitting procedure {\Sigma} - D slope {\beta} \approx 3.9 is obtained for the sample of 31 SNRs in M82. The results of implemented Monte Carlo simulations show that the sensitivity selection effect does not significantly influence the slope of M82 relation. This relation could be used for estimation of distances to SNRs that evolve in denser interstellar environment, with number denisty up to 1000 particles per cm3 .