| Author(s) and Co-Author(s) with Affiliation: Anumanchi Agastya Sai Ram Likhit(School of Arts & Sciences, Ahmedabad University, Ahmedabad—380009, India), Samyaday Choudhury(School of Arts & Sciences, Ahmedabad University, Ahmedabad—380009, India; International Centre for Space and Cosmology,Ahmedabad), Shivam Kumaran(Space Sciences Division, SESG, EPSA, Space Applications Centre, ISRO, Ahmedabad-380015, India), Vipin Kumar(Space Sciences Division, SESG, EPSA, Space Applications Centre, ISRO, Ahmedabad-380015, India), Mehul R. Pandya(Space Sciences Division, SESG, EPSA, Space Applications Centre, ISRO, Ahmedabad-380015, India) |
| Abstract: Filamentary structures in the interstellar medium (ISM) play an essential role in the star-formation process, as they act as the primary sites for the formation of dense cores. The detection and characterisation of their physical properties, such as filament widths, density profiles, and critical line masses, are essential for connecting cloud-scale structure to the physics of star formation. Although various algorithms exist to detect and estimate filament properties (e.g., FilFinder, DisPerSE), a systematic and qualitative framework to validate filament properties using a single algorithm and quantify the differences between various algorithms is missing. To address this problem, we have developed a statistical framework. We analyse filaments in nearby (100 - 500 pc) molecular clouds using column-density maps derived from Herschel Gould Belt Survey observations covering wavelengths from 70 µm to 500 µm. We estimate mean radial column-density profiles of ISM beam elements, which are fitted with Plummer profiles. We estimate a goodness-of-fit (R²) and contrast parameter (C0) to define a quantitative score that characterises the overall reliability of the filament skeleton. This score is used to tune the input parameters of filament-detection algorithms (FilFinder and DisPerSE) and estimate optimal values that provide physically consistent and robust filament representations. The framework aims to provide a physically motivated and statistical approach for validating filament skeletons and characterising their properties. |
