Do dense-gas tracers fix the position-position-velocity to position-position-position mapping for fibrous molecular-cloud filaments? A synthetic-observation determination of fibre-sub-filament correspondence

Molecular-line observations of star-forming filaments reveal elongated, velocity-coherent features termed 'fibres', which are widely used to infer a filament's internal substructure. Clarke and collaborators (2018) showed, using synthetic C$^{18}$O observations of simulations of filaments growing from a turbulent medium, that fibres identified in position-position-velocity (PPV) data cubes correspond poorly to the real, continuous density structures ('sub-filaments') in the position-position-position (PPP) data: roughly half of all fibres are contaminated by, or consist of, physically unrelated gas along the line of sight. The authors explicitly deferred the decisive test to future work: synthetic observations in high-density nitrogen-bearing tracers (N$_2$H$^+$ and NH$_3$), which should be less affected by shocks and line-of-sight confusion. This problem asks for that determination: generate synthetic observations of filament-formation simulations in dense-gas tracers and quantify the fraction of PPV features that correspond to single continuous PPP structures in those tracers, in comparison with the C$^{18}$O baseline.

Background

In star-forming molecular clouds, most new stars form in elongated column-density structures called filaments. High-resolution spectral-line mapping has shown that filaments contain narrow, elongated, velocity-coherent sub-features, termed 'fibres' (Hacar et al. 2013, who decomposed C$^{18}$O data of the Taurus L1495/B213 filament into such components). A fibre is defined operationally in PPV space — the two sky coordinates plus the radial-velocity coordinate that a spectral-line data cube provides — where it appears as a structure continuous in both space and velocity. It is tempting to equate fibres with real, three-dimensional gas structures (PPP space: position-position-position), and many observational programs treat fibres as the physical building blocks of filaments. However, a PPV feature can be produced by physically separate parcels of gas that happen to lie at the same line-of-sight velocity (line-of-sight confusion), and a genuinely continuous 3D structure can be torn into several velocity components by internal shocks or velocity gradients. Moeckel & Burkert (2015) had shown, in simulations of turbulent molecular clouds, that a region which is filamentary only in projection — being made of spatially distinct features — can display the same velocity-coherent signatures as a genuinely coherent filament, and Zamora-Avilés et al. (2017) showed in more detail, for cloud-scale self-gravitating simulations, that whether a given structure registers as a 'fibre' depends on the viewing angle and that fibres often consist of density enhancements separated by more than a parsec along the line of sight. Clarke, Whitworth, Spowage, Duarte-Cabral, Suri, Jaffa, Walch & Clark (2018) made this concrete for filament-scale physics. They simulated the growth of a filament by accretion from a turbulent medium (the mechanism that generates internal 'sub-filaments' in the 3D density field), computed synthetic C$^{18}$O observations with a line radiative-transfer code, degraded them to the resolution and noise of real surveys, and fitted every spectrum with a new automated multi-component Gaussian routine called BTS ('Behind the Spectrum'). They identified fibres in the PPV cube by a friends-of-friends grouping of the fitted velocity-component centroids (following the procedure of Hacar et al. 2013) and located sub-filaments in the PPP density field with the DisPerSE filament-finding algorithm; each fibre's associated voxels were then grouped by friends-of-friends in PPP space to classify the fibre as 'complete', 'contaminated' or 'separate', while dendrograms (a dendrogram is a hierarchical tree of nested structures in the data) and JJ-moments were used to compare the projected morphologies of the two families. The result: fibres and sub-filaments are both plentiful and look statistically similar, but correspond poorly. Of the 219 fibres found across the ten simulations, only 48.9% are 'complete' in PPP space: about 50% of fibres suffer contamination from a physically separate parcel of gas, or are two or more physically distinct features superposed at the same velocity, and an observer cannot tell which ones. Both families of features originate in inhomogeneities of the turbulent accretion flow: fibres suffer line-of-sight confusion, while sub-filaments carry large velocity gradients and even velocity discontinuities that split them into multiple PPV components. The authors identified the tracer as the limitation that delimits these conclusions, and noted two caveats about their own methodology. (1) Tracer: C$^{18}$O emits over a wide, relatively low density range and is affected by shocks and by CO freeze-out onto dust grains; dense nitrogen-bearing tracers (N$_2$H$^+$ and NH$_3$) trace a smaller, denser volume and should reduce confusion — synthetic N$_2$H$^+$ and NH$_3$ observations were explicitly deferred to future work. (2) Chemistry: the instantaneous-equilibrium freeze-out approximation used probably overestimates CO depletion (it gives >50% freeze-out at gas density $n_{\mathrm{H}_2} > 3\times10^{4}\,\mathrm{cm}^{-3}$, whereas observations such as Lippok et al. 2013 indicate this level is only reached above $n_{\mathrm{H}_2} \sim 10^{5}\,\mathrm{cm}^{-3}$); this is a caveat about the C$^{18}$O post-processing, and the authors report that the identified fibres are not strongly affected by whether CO freeze-out at high densities is included. (3) Line fitting: BTS fits Gaussian, optically thin components and, as the authors note, 'ought not be used on spectra dominated by non-Gaussian components, e.g. optically thick spectra'; separately, a Markov-chain Monte Carlo routine was promised for the $\ll 1\%$ of spectra whose least-squares fit does not converge, but was never delivered. Beyond these author-stated points, the study analysed its synthetic observations along a single viewing configuration, so the viewing-angle dependence of the correspondence fraction — demonstrated at cloud scale by Zamora-Avilés et al. (2017) — was not quantified for filament-scale sub-filaments. Subsequent literature sharpened the stakes rather than the answer: ALMA observations in N$_2$H$^+$ report many fibres in Orion (Hacar et al. 2018), NGC 6334 (Shimajiri et al. 2019, where only 2–3 of 5 velocity-coherent features are even aligned with the filament), and DR21 (Cao et al. 2022; Yang et al. 2026), while new automated or Bayesian spectral-decomposition tools (GaussPy+, Riener et al. 2019; nested-sampling model selection, Sokolov et al. 2020) make multi-component fitting of large maps routine — but none of these has access to the 3D ground truth, and no simulation study has yet repeated the Clarke et al. (2018) PPV↔PPP experiment for dense tracers.

Problem Statement

Determine, by synthetic observation of filament-formation simulations with known three-dimensional structure, whether the correspondence between PPV fibres and PPP sub-filaments improves for high-density nitrogen-bearing tracers, and quantify the fraction of PPV features that correspond to single continuous PPP structures in those tracers, in comparison with the C$^{18}$O baseline. Concretely: take simulations of filaments growing from a turbulent medium (the setting of Clarke et al. 2018, or equivalent self-consistent simulations that produce sub-filaments), compute synthetic line observations in N$_2$H$^+$ and NH$_3$ (and C$^{18}$O as the baseline) with non-LTE (non-local thermodynamic equilibrium, i.e. level populations solved without assuming local equilibrium at the gas temperature) radiative transfer and a defensible treatment of the tracer abundances, including freeze-out and desorption, degrade them to realistic resolution, velocity sampling, and noise, identify fibres in the PPV cubes and sub-filaments in the PPP density fields with matched, bias-controlled identification criteria, and report the correspondence statistics — the fraction of fibres uncontaminated by line-of-sight superposition and the fraction of sub-filaments recovered as single velocity-coherent features — for the dense nitrogen-bearing tracers and for the C$^{18}$O baseline. The question is answered when these statistics are established for the dense tracers, and compared with the C$^{18}$O baseline, quantitatively enough to state whether velocity-coherent PPV features traced by the dense nitrogen-bearing molecules can be taken to represent real, continuous 3D substructures — or that they cannot.

References

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