Sutra trains a U-Net on the union of DisPerSE and getsf skeletons to predict filament crest-likelihood maps and then filters and characterizes them with beam-scale Plummer fits.
L1495 Revisited: A PPMAP View of a Star-Forming Filament
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We have analysed the Herschel and SCUBA-2 dust continuum observations of the main filament in the Taurus L1495 star forming region, using the Bayesian fitting procedure PPMAP. (i) If we construct an average profile along the whole length of the filament, it has fwhm $\simeq 0.087\pm 0.003\,{\rm pc};\;$, but the closeness to previous estimates is coincidental. (ii) If we analyse small local sections of the filament, the column-density profile approximates well to the form predicted for hydrostatic equilibrium of an isothermal cylinder. (iii) The ability of PPMAP to distinguish dust emitting at different temperatures, and thereby to discriminate between the warm outer layers of the filament and the cold inner layers near the spine, leads to a significant reduction in the surface-density, $\varSigma$, and hence in the line-density, $\mu$. If we adopt the canonical value for the critical line-density at a gas-kinetic temperature of $10\,{\rm K}$, $\mu_{_{\rm CRIT}}\simeq 16\,{\rm M_{_\odot}\,pc^{-1}}$, the filament is on average trans-critical, with ${\bar\mu}\sim \mu_{_{\rm CRIT}};\;$ local sections where $\mu >\mu_{_{\rm CRIT}}$ tend to lie close to pre-stellar cores. (iv) The ability of PPMAP to distinguish different types of dust, i.e. dust characterised by different values of the emissivity index, $\beta$, reveals that the dust in the filament has a lower emissivity index, $\beta\leq1.5$, than the dust outside the filament, $\beta\geq 1.7$, implying that the physical conditions in the filament have effected a change in the properties of the dust.
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2026 1verdicts
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Sutra : An integrated framework for identification and characterization of filaments in the interstellar medium
Sutra trains a U-Net on the union of DisPerSE and getsf skeletons to predict filament crest-likelihood maps and then filters and characterizes them with beam-scale Plummer fits.