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While intensively studied, it remains unclear how the star formation (SF) in infrared dark clouds (IRDCs) compares to that of nearby clouds. We study G351.77-0.53 (henceforth G351), a cluster-forming filamentary IRDC. We begin by characterizing its young stellar object (YSO) content. Based on the average parallax of likely members, we obtain a Gaia distance of $\sim \, 2.0\pm 0.14$ kpc, resolving the literature distance ambiguity. Using our Herschel-derived N(H2) map, we measure a total gas mass of 10 200 M⊙ (within 11 pc2) and the average line-mass profile of the entire filament, which we model as $\lambda =~1660 (w/\rm pc)^{0.62}\, \, {\rm M}_{\odot }\, \rm {pc}^{-1}$. At w < 0.63 pc, our λ profile is higher and has a steeper power-law index than λ profiles extracted in Orion A and most of its substructures. Based on the YSOs inside the filament area, we estimate the SF efficiency (SFE) and SF rate (SFR). We calculate a factor of 5 incompleteness correction for our YSO catalogue relative to Spitzer surveys of Orion A. The G351 SFE is ∼1.8 times lower than that of Orion A and lower than the median value for local clouds. We measure SFR and gas masses to estimate the efficiency per free-fall time, ϵff. We find that ϵff is ∼1.1 dex below the previously proposed mean local relation, and $\sim \, 4.7\times$ below Orion A. These observations indicate that local SF-relations do not capture variations present in the Galaxy. We speculate that cloud youth and/or magnetic fields might account for the G351 inefficiency.

 

We present the first data release of the ALMA-IMF Large Program, which covers the 12m-array continuum calibration and imaging. The ALMA-IMF Large Program is a survey of fifteen dense molecular cloud regions spanning a range of evolutionary stages that aims to measure the core mass function. We describe the data acquisition and calibration done by the Atacama Large Millimeter/submillimeter Array (ALMA) observatory and the subsequent calibration and imaging we performed. The image products are combinations of multiple 12 m array configurations created from a selection of the observed bandwidth using multi-term, multi-frequency synthesis imaging and deconvolution. The data products are self-calibrated and exhibit substantial noise improvements over the images produced from the delivered data. We compare different choices of continuum selection, calibration parameters, and image weighting parameters, demonstrating the utility and necessity of our additional processing work. Two variants of continuum selection are used and will be distributed: the “best-sensitivity” ( bsens ) data, which include the full bandwidth, including bright emission lines that contaminate the continuum, and “cleanest” ( cleanest ), which select portions of the spectrum that are unaffected by line emission. We present a preliminary analysis of the spectral indices of the continuum data, showing that the ALMA products are able to clearly distinguish free-free emission from dust emission, and that in some cases we are able to identify optically thick emission sources. The data products are made public with this release. 

Aims. Thanks to the high angular resolution, sensitivity, image fidelity, and frequency coverage of ALMA, we aim to improve our understanding of star formation. One of the breakthroughs expected from ALMA, which is the basis of our Cycle 5 ALMA-IMF Large Program, is the question of the origin of the initial mass function (IMF) of stars. Here we present the ALMA-IMF protocluster selection, first results, and scientific prospects. Methods. ALMA-IMF imaged a total noncontiguous area of ~53 pc 2 , covering extreme, nearby protoclusters of the Milky Way. We observed 15 massive (2.5 −33 × 10 3 M ⊙ ), nearby (2−5.5 kpc) protoclusters that were selected to span relevant early protocluster evolutionary stages. Our 1.3 and 3 mm observations provide continuum images that are homogeneously sensitive to point-like cores with masses of ~0.2 M ⊙ and ~0.6 M ⊙ , respectively, with a matched spatial resolution of ~2000 au across the sample at both wavelengths. Moreover, with the broad spectral coverage provided by ALMA, we detect lines that probe the ionized and molecular gas, as well as complex molecules. Taken together, these data probe the protocluster structure, kinematics, chemistry, and feedback over scales from clouds to filaments to cores. Results. We classify ALMA-IMF protoclusters as Young (six protoclusters), Intermediate (five protoclusters), or Evolved (four proto-clusters) based on the amount of dense gas in the cloud that has potentially been impacted by H  II region(s). The ALMA-IMF catalog contains ~700 cores that span a mass range of ~0.15 M ⊙ to ~250 M ⊙ at a typical size of ~2100 au. We show that this core sample has no significant distance bias and can be used to build core mass functions (CMFs) at similar physical scales. Significant gas motions, which we highlight here in the G353.41 region, are traced down to core scales and can be used to look for inflowing gas streamers and to quantify the impact of the possible associated core mass growth on the shape of the CMF with time. Our first analysis does not reveal any significant evolution of the matter concentration from clouds to cores (i.e., from 1 pc to 0.01 pc scales) or from the youngest to more evolved protoclusters, indicating that cloud dynamical evolution and stellar feedback have for the moment only had a slight effect on the structure of high-density gas in our sample. Furthermore, the first-look analysis of the line richness toward bright cores indicates that the survey encompasses several tens of hot cores, of which we highlight the most massive in the G351.77 cloud. Their homogeneous characterization can be used to constrain the emerging molecular complexity in protostars of high to intermediate masses. Conclusions. The ALMA-IMF Large Program is uniquely designed to transform our understanding of the IMF origin, taking the effects of cloud characteristics and evolution into account. It will provide the community with an unprecedented database with a high legacy value for protocluster clouds, filaments, cores, hot cores, outflows, inflows, and stellar clusters studies.