Multicomponent, multiwavelength benchmarks for source- and filament-extraction methods

TL;DR

This paper benchmarks and compares the performance of the new getsf method against previous methods for extracting sources and filaments from multiwavelength observations of star-forming regions, demonstrating getsf's superior qualities.

Contribution

It introduces a detailed benchmarking framework for source and filament extraction methods using realistic multiwavelength simulations, highlighting getsf's improved detection and measurement capabilities.

Findings

getsf outperforms getold in detection and measurement quality

High-resolution surface density is optimal for source detection

Benchmarking formalism enables quantitative comparison of extraction methods

Abstract

Modern multiwavelength observations of star-forming regions that reveal highly structured molecular clouds require adequate extraction methods that provide both detection of the structures and their accurate measurements. The omnipresence of filamentary structures and their physical connection to prestellar cores demand methods that are able to disentangle and extract both sources and filaments. It is fundamentally important to test all extraction methods to compare their detection and measurement qualities and fully understand their capabilities before their scientific applications. A recent publication described getsf, the new method for source and filament extraction that employs the separation of the structural components, a successor to getsources, getfilaments, and getimages (collectively referred to as getold). This new paper describes a detailed benchmarking of both getsf and getold using two multicomponent, multiwavelength benchmarks resembling the Herschel observations of the nearby star-forming regions. Each benchmark consists of simulated images at six Herschel wavelengths and one additional surface density image with a 13 arcsec resolution. The structural components of the benchmarks include a background cloud, a dense filament, hundreds of starless and protostellar cores, and instrumental noise. Five variants of benchmark images of different complexity are used to perform the source and filament extractions with getsf and getold. A formalism for evaluating source detection and measurement qualities is presented, allowing quantitative comparisons of extraction methods in terms of their completeness, reliability, and goodness, as well as the detection and measurement accuracies and the overall quality. A detailed analysis shows that getsf has better qualities than getold and that the best choice for source detection is the high-resolution surface density.