Temperature structure and kinematics of the IRDC G035.39–00.33

Abstract

Aims. Infrared dark clouds represent the earliest stages of high-mass star formation. Detailed observations of their physical conditions on all physical scales are required to improve our understanding of their role in fueling star formation. Methods. We investigate the large-scale structure of the IRDC G035.39-00.33, probing the dense gas with the classical ammonia thermometer. This allows us to put reliable constraints on the temperature of the extended, pc-scale dense gas reservoir and to probe the magnitude of its non-thermal motions. Available far-infrared observations can be used in tandem with the observed ammonia emission to estimate the total gas mass contained in G035.39-00.33. Results. We identify a main velocity component as a prominent filament, manifested as an ammonia emission intensity ridge spanning more than 6 pc, consistent with the previous studies on the Northern part of the cloud. A number of additional line-of-sight components are found, and a large scale, linear velocity gradient of ∼0.2 km s −1 pc −1 is found along the ridge of the IRDC. In contrast to the dust temperature map, an ammonia-derived kinetic temperature map, presented for the entirety of the cloud, reveals local temperature enhancements towards the massive protostellar cores. We show that without properly accounting for the line of sight contamination, the dust temperature is 2-3 K larger than the gas temperature measured with NH 3 . Conclusions. While both the large scale kinematics and temperature structure are consistent with that of starless dark filaments, the kinetic gas temperature profile on smaller scales is suggestive of tracing the heating mechanism coincident with the locations of massive protostellar cores.

Multicomponent Kinematics in a Massive Filamentary Infrared Dark Cloud

“…The parsec-scale ∼0.2 km s −1 pc −1 quasi-linear velocity gradient, found in the GBT-only data (Sokolov et al 2017) is present in our combined data: the IRDC kinematics reveal a gradual change into blue-shifted velocity regime as the filament is followed southwards. Superimposed on this kinematics feature, additional velocity components are present.…”

“…To recover the extended ammonia emission, we fill in the missing flux from the Green Bank Telescope (GBT) data. A detailed description of the GBT data reduction can be found in Sokolov et al (2017). Before merging the data, the GBT images were converted to spectral flux density units and convolved to the VLA channel separation of 0.2 km s −1 from the original channel separation of 0.0386 km s −1 .…”

“…As the VLA spectral cubes consist of about a dozen thousand spectra with S/N ratio higher than three, the common practice of fitting multiple components by eye would require an excessive amount of human interaction resulting in low level of reproducibility, while methods for global non-linear regression are prohibitively computationally expensive for such a high volume of data. Therefore, to fit multiple line-ofsight line components, we employ the same fitting strategy as in Sokolov et al (2017), where an open-source Python package 1 is used to assist the Levenberg-Marquardt fitter to find an optimal starting point for exploring the parameter space of the spectral model. The free parameters for the ammonia model (same formalism as in, e.g., Friesen et al 2017), implemented within pyspeckit package (Ginsburg & Mirocha 2011), are split into a multi-dimensional parameter grid, and a synthetic ammonia model is then computed for each.…”

The Serpens filament at the onset of slightly supercritical collapse

“…Compared with other regions in the Serpens cloud (Burleigh et al 2013;Dhabal et al 2018), the Serpens filament has lower non-thermal velocity dispersions, which makes it one of the most quiescent regions in the Serpens cloud. The kinematic properties of this filament are analogous to velocity-coherent fibers in L1495/B213, NGC 1333, and the Orion integral filament (Hacar et al 2013(Hacar et al , 2017(Hacar et al , 2018, the Musca cloud (Hacar et al 2016b), filaments in Barnard 5 (Pineda et al 2011), dense filaments in OMC1 (Monsch et al 2018), filaments with H 2 column densities 8 × 10 21 cm −2 in nearby molecular clouds (Arzoumanian et al 2013), and more distant filaments in IRDCs (Sokolov et al 2017;Wang 2018). Meanwhile, the Serpens filament, similar to the Musca cloud, appears to have much less substructure than the other aforementioned filaments, but this needs to be confirmed with higher angular resolution observations.…”

Velocity-coherent Filaments in NGC 1333: Evidence for Accretion Flow?

“…Many previous multi-component fitting methods selected their best fit models for each component via an SNR threshold (e.g., Sokolov et al 2017), a velocity separation threshold, or both (e.g., Hacar et al 2013, Hacar et al 2017. While these criteria are effective at avoiding overfitting, they are not necessarily good at picking up all the spectral components present along a given line of sight.…”