A systematic bias in fitting the surface-density profiles of interstellar filaments
Anthony Whitworth (1), Felix Priestley (1), Doris Arzoumanian (2) ((1), Cardiff University, (2) Aix Marseille University)

TL;DR
The paper reveals a systematic bias in fitting the surface-density profiles of interstellar filaments, showing that averaging along the filament length can underestimate the true Plummer exponent, especially for steeper profiles.
Contribution
It demonstrates how averaging observational data biases the estimated Plummer profile exponent, highlighting a significant factor in analyzing filament density profiles.
Findings
Averaging along filaments reduces the estimated Plummer exponent.
Bias increases with the true exponent and the range of scale-lengths.
The effect is minor for p_intrinsic=2 but substantial for p_intrinsic=4.
Abstract
The surface-density profiles of dense filaments, in particular those traced by dust emission, appear to be well fit with Plummer profiles, i.e. Sigma(b)=Sigma_B+Sigma_O{1+[b/w_O]^2}^{[1-p]/2}. Here Sigma_B is the background surface-density; Sigma_B+Sigma_O is the surface-density on the filament spine; b is the impact parameter of the line-of-sight relative to the filament spine; w_O is the Plummer scale-length (which for fixed p is exactly proportional to the full-width at half-maximum, w_O=FWHM/2{2^{2/[p-1]}-1}^{1/2}); and p is the Plummer exponent (which reflects the slope of the surface-density profile away from the spine).} In order to improve signal-to-noise it is standard practice to average the observed surface-densities along a section of the filament, or even along its whole length, before fitting the profile. We show that, if filaments do indeed have intrinsic Plummer profiles…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGreen IT and Sustainability · Astrophysics and Star Formation Studies
