scholarly journals Giant molecular cloud catalogues for PHANGS-ALMA: methods and initial results

2021 ◽  
Vol 502 (1) ◽  
pp. 1218-1245
Author(s):  
Erik Rosolowsky ◽  
Annie Hughes ◽  
Adam K Leroy ◽  
Jiayi Sun ◽  
Miguel Querejeta ◽  
...  
Keyword(s):  
Mass Scale ◽  
Spiral Arms ◽  
Mass Distributions ◽  
Cloud Properties ◽  
Segmentation Approach ◽  
Energy Equipartition ◽  
Improved Methods ◽  
Initial Results ◽  
Python Package ◽  
Co Emission

ABSTRACT We present improved methods for segmenting CO emission from galaxies into individual molecular clouds, providing an update to the cprops algorithms presented by Rosolowsky & Leroy. The new code enables both homogenization of the noise and spatial resolution among data, which allows for rigorous comparative analysis. The code also models the completeness of the data via false source injection and includes an updated segmentation approach to better deal with blended emission. These improved algorithms are implemented in a publicly available Python package, pycprops. We apply these methods to 10 of the nearest galaxies in the PHANGS-ALMA survey, cataloguing CO emission at a common 90 pc resolution and a matched noise level. We measure the properties of 4986 individual clouds identified in these targets. We investigate the scaling relations among cloud properties and the cloud mass distributions in each galaxy. The physical properties of clouds vary among galaxies, both as a function of galactocentric radius and as a function of dynamical environment. Overall, the clouds in our target galaxies are well-described by approximate energy equipartition, although clouds in stellar bars and galaxy centres show elevated line widths and virial parameters. The mass distribution of clouds in spiral arms has a typical mass scale that is 2.5× larger than interarm clouds and spiral arms clouds show slightly lower median virial parameters compared to interarm clouds (1.2 versus 1.4).

scholarly journals Autonomous Gaussian decomposition of the Galactic Ring Survey

2020 ◽  
Vol 640 ◽  
pp. A72
Author(s):  
M. Riener ◽  
J. Kainulainen ◽  
J. D. Henshaw ◽  
H. Beuther
Keyword(s):  
Milky Way ◽  
Velocity Dispersion ◽  
Galactic Plane ◽  
Gas Emission ◽  
Spiral Arms ◽  
Precise Knowledge ◽  
Significant Difference ◽  
The Impact ◽  
Co Emission ◽  
Spiral Arm

Knowledge about the distribution of CO emission in the Milky Way is essential to understanding the impact of the Galactic environment on the formation and evolution of structures in the interstellar medium. However, our current insight as to the fraction of CO in the spiral arm and interarm regions is still limited by large uncertainties in assumed rotation curve models or distance determination techniques. In this work we use the Bayesian approach from Reid et al. (2016, ApJ, 823, 77; 2019, ApJ, 885, 131), which is based on our most precise knowledge at present about the structure and kinematics of the Milky Way, to obtain the current best assessment of the Galactic distribution of 13CO from the Galactic Ring Survey. We performed two different distance estimates that either included (Run A) or excluded (Run B) a model for Galactic features, such as spiral arms or spurs. We also included a prior for the solution of the kinematic distance ambiguity that was determined from a compilation of literature distances and an assumed size-linewidth relationship. Even though the two distance runs show strong differences due to the prior for Galactic features for Run A and larger uncertainties due to kinematic distances in Run B, the majority of their distance results are consistent with each other within the uncertainties. We find that the fraction of 13CO emission associated with spiral arm features ranges from 76 to 84% between the two distance runs. The vertical distribution of the gas is concentrated around the Galactic midplane, showing full-width at half-maximum values of ~75 pc. We do not find any significant difference between gas emission properties associated with spiral arm and interarm features. In particular, the distribution of velocity dispersion values of gas emission in spurs and spiral arms is very similar. We detect a trend of higher velocity dispersion values with increasing heliocentric distance, which we, however, attribute to beam averaging effects caused by differences in spatial resolution. We argue that the true distribution of the gas emission is likely more similar to a combination of the two distance results discussed, and we highlight the importance of using complementary distance estimations to safeguard against the pitfalls of any single approach. We conclude that the methodology presented in this work is a promising way to determine distances to gas emission features in Galactic plane surveys.

scholarly journals DREAMTools: a Python package for scoring collaborative challenges

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1030 ◽  
Author(s):  
Thomas Cokelaer ◽  
Mukesh Bansal ◽  
Christopher Bare ◽  
Erhan Bilal ◽  
Brian M. Bot ◽  
...  
Keyword(s):  
Computational Methods ◽  
Training Data ◽  
Model Parameters ◽  
Automated Scoring ◽  
Statistical Machine Learning ◽  
Science And Engineering ◽  
New Methods ◽  
Wide Range ◽  
Improved Methods ◽  
Python Package

DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of September 2015, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform https://www.synapse.org.Availability: DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools.

scholarly journals DREAMTools: a Python package for scoring collaborative challenges

F1000Research ◽  
2016 ◽  
Vol 4 ◽  
pp. 1030 ◽  
Author(s):  
Thomas Cokelaer ◽  
Mukesh Bansal ◽  
Christopher Bare ◽  
Erhan Bilal ◽  
Brian M. Bot ◽  
...  
Keyword(s):  
Computational Methods ◽  
Training Data ◽  
Model Parameters ◽  
Automated Scoring ◽  
Statistical Machine Learning ◽  
Science And Engineering ◽  
Link Type ◽  
Wide Range ◽  
Improved Methods ◽  
Python Package

DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform at https://www.synapse.org.Availability: DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools/dreamtools.

scholarly journals A diversity of starburst-triggering mechanisms in interacting galaxies and their signatures in CO emission

2019 ◽  
Vol 625 ◽  
pp. A65 ◽  
Author(s):  
F. Renaud ◽  
F. Bournaud ◽  
O. Agertz ◽  
K. Kraljic ◽  
E. Schinnerer ◽  
...  
Keyword(s):  
Star Formation ◽  
Galaxy Evolution ◽  
High Redshift ◽  
Formation Rate ◽  
Temporal Variations ◽  
Interacting Galaxies ◽  
Dense Gas ◽  
Star Forming ◽  
Cloud Properties ◽  
Co Emission

The physical origin of enhanced star formation activity in interacting galaxies remains an open question. Knowing whether starbursts are triggered by an increase in the quantity of dense gas or an increase in the star formation efficiency therein would improve our understanding of galaxy evolution and make it possible to transfer the results obtained in the local Universe to high-redshift galaxies. In this paper, we analyze a parsec-resolution simulation of a model of interacting galaxies similar to the Antennae Galaxies. We find that the interplay of physical processes such as tides, shear, and turbulence shows complex and important variations in time and space, but that different combinations of these processes can produce similar signatures in observable quantities such as the depletion time and CO emission. Some clouds within the interacting galaxies exhibit an excess of dense gas (> 104 cm−3), while others only attain similarly high densities in the tail of their density distribution. The clouds with an excess of dense gas are found across all regions of the galaxies, but their number density varies between regions due to different cloud assembly mechanisms. This translates into variations in the scale dependence of quantities related to cloud properties and star formation. The super-linearity of the relationship between the star formation rate and gas density implies that the dense gas excess corresponds to a decrease in the depletion time, and thus leads to a deviation from the classical star formation regime that is visible up to galactic scales. We find that the αCO conversion factor between the CO luminosity and molecular gas mass exhibits stronger spatial than temporal variations in a system like the Antennae. Our results raise several caveats for the interpretation of observations of unresolved star-forming regions, but also predict that the diversity of environments for star formation will be better captured by the future generations of instruments.

scholarly journals SPIRAL ARMS IN THE DISK OF HD 142527 FROM CO EMISSION LINES WITH ALMA

2014 ◽  
Vol 785 (1) ◽  
pp. L12 ◽  
Author(s):  
V. Christiaens ◽  
S. Casassus ◽  
S. Perez ◽  
G. van der Plas ◽  
F. Ménard
Keyword(s):  
Emission Lines ◽  
Spiral Arms ◽  
Co Emission

Polarization Lidar at Summit, Greenland, for the Detection of Cloud Phase and Particle Orientation

2013 ◽  
Vol 30 (8) ◽  
pp. 1635-1655 ◽  
Author(s):  
Ryan R. Neely ◽  
Matthew Hayman ◽  
Robert Stillwell ◽  
Jeffrey P. Thayer ◽  
R. Michael Hardesty ◽  
...  
Keyword(s):  
Full Range ◽  
Particle Orientation ◽  
Earth System ◽  
System Research ◽  
Cloud Properties ◽  
Atmospheric State ◽  
Initial Results

Abstract Accurate measurements of cloud properties are necessary to document the full range of cloud conditions and characteristics. The Cloud, Aerosol Polarization and Backscatter Lidar (CAPABL) has been developed to address this need by measuring depolarization, particle orientation, and the backscatter of clouds and aerosols. The lidar is located at Summit, Greenland (72.6°N, 38.5°W; 3200 m MSL), as part of the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit Project and NOAA's Earth System Research Laboratory's Global Monitoring Division's lidar network. Here, the instrument is described with particular emphasis placed upon the implementation of new polarization methods developed to measure particle orientation and improve the overall accuracy of lidar depolarization measurements. Initial results from the lidar are also shown to demonstrate the ability of the lidar to observe cloud properties.

scholarly journals Experiments with high velocity positive ions VI—The disintegration of carbon, nitrogen, and oxygen by deuterons

1936 ◽  
Vol 154 (881) ◽  
pp. 261-279 ◽  
Keyword(s):  
Energy Balance ◽  
Nuclear Reaction ◽  
High Velocity ◽  
Preceding Paper ◽  
Mass Scale ◽  
Positive Ions ◽  
Positron Emission ◽  
Reaction Energies ◽  
Improved Methods ◽  
Α Particles

1—The use of transmutation data to establish a “corrected mass scale” has made it important to measure with higher precision, nuclear reaction energies. Using the improved methods described in the preceding paper we have therefore reinvestigated the disintegration of carbon, nitrogen, and oxygen by deuterons. Several new groups of particles have been discovered and all the reaction energies determined to within about 0.1 million volts. The new results have been used to correct Bethe’s mass scale and to check the energy balance in the process of positron emission from radio-nitrogen. 2—The Disintegration of Carbon by Deuterons A target of Acheson graphite was bombarded by 2 μA of deuterons of 560 kv energy. The geometry of the apparatus was such that disintegration particles making an angle of 90° ± 6° with the direction of the incident deuterons could enter the counter. The particles were recorded by the double counter described in our paper V and the absorption curve of fig. 1 was obtained, showing the curve obtained by the counters biased to record α-particles and particles of proton ionization respectively.

scholarly journals ATOMIUM: A high-resolution view on the highly asymmetric wind of the AGB star π1Gruis

2020 ◽  
Vol 644 ◽  
pp. A61
Author(s):  
Ward Homan ◽  
Miguel Montargès ◽  
Bannawit Pimpanuwat ◽  
Anita M. S. Richards ◽  
Sofia H. J. Wallström ◽  
...  
Keyword(s):  
Spiral Structure ◽  
Asymptotic Giant Branch ◽  
Spiral Arms ◽  
Evolved Stars ◽  
Crossing Time ◽  
Trend Breaks ◽  
Circumstellar Environments ◽  
Circumstellar Environment ◽  
Co Emission ◽  
The Continuum

The nebular circumstellar environments of cool evolved stars are known to harbour a rich morphological complexity of gaseous structures on different length scales. A large part of these density structures are thought to be brought about by the interaction of the stellar wind with a close companion. The S-type asymptotic giant branch (AGB) star π1Gruis, which has a known companion at ∼440 au and is thought to harbour a second, closer-by (< 10 au) companion, was observed with the Atacama Large Millimeter/submillimeter Array as part of the ATOMIUM Large programme. In this work, the brightest CO, SiO, and HCN molecular line transitions are analysed. The continuum map shows two maxima, separated by 0.04″ (6 au). The CO data unambiguously reveal that π1Gru’s circumstellar environment harbours an inclined, radially outflowing, equatorial density enhancement. It contains a spiral structure at an angle of ∼38 ± 3° with the line-of-sight. The HCN emission in the inner wind reveals a clockwise spiral, with a dynamical crossing time of the spiral arms consistent with a companion at a distance of 0.04″ from the AGB star, which is in agreement with the position of the secondary continuum peak. The inner wind dynamics imply a large acceleration region, consistent with a beta-law power of ∼6. The CO emission suggests that the spiral is approximately Archimedean within 5″, beyond which this trend breaks down as the succession of the spiral arms becomes less periodic. The SiO emission at scales smaller than 0.5″ exhibits signatures of gas in rotation, which is found to fit the expected behaviour of gas in the wind-companion interaction zone. An investigation of SiO maser emission reveals what could be a stream of gas accelerating from the surface of the AGB star to the companion. Using these dynamics, we have tentatively derived an upper limit on the companion mass to be ∼1.1 M⊙.

CO Emission vs HI Self-Absorption

1983 ◽  
Vol 5 (2) ◽  
pp. 224-227 ◽  
Author(s):  
William L. Peters ◽  
Frank N. Bash
Keyword(s):  
Atomic Hydrogen ◽  
Molecular Clouds ◽  
Galactic Center ◽  
Galactic Plane ◽  
The Sun ◽  
Statistical Comparison ◽  
Initial Results ◽  
Co Emission

We present the initial results of a statistical comparison of CO emission and H I self-absorption in the galactic plane at large distances from the Sun. Evidence for self-absorption by cold atomic hydrogen (Ts < 60 K) over angular scales of 3 ´-20´ was reported by Baker and Burton (1979). They suggested that this hydrogen was associated with the molecular clouds of the ‘molecular ring’ located between 4 and 8 kpc from the galactic center (Burton and Gordon 1978). Burton, Lizst, and Baker (1978) did find a correspondence between CO emission and H I self-absorption; however, their observations were not extensive enough to prove that the correspondence was statistically significant or to test their prediction that all instances of H I self-absorption are accompanied by CO emission and thus associated with molecular clouds.

scholarly journals On the value of using 3D-shape and electrostatic similarities in deep generative methods

2021 ◽  
Author(s):  
Giovanni Bolcato ◽  
Jonas Boström
Keyword(s):  
State Of The Art ◽  
Molecular Shape ◽  
Scaffold Hopping ◽  
Improved Method ◽  
Generative Process ◽  
3D Shape ◽  
Partial Charges ◽  
Significant Extension ◽  
Improved Methods ◽  
Python Package

Multi-parameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compounds design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the generation of novel molecules with optimal properties. As before, the generative process outputs optimized molecules similar to the input structures, now with the improved feature of replacing parts of these molecules with fragments of similar 3D-shape and electrostatics. By performing comparisons of 3D-fragments, we can simulate 3D properties while overcoming the notoriously difficult step of accurately describing bioactive conformations. The comparison of electrostatic potential and molecular shape is performed using the new ESP-Sim python package, allowing the calculation of state-of-the-art partial charges (e.g., RESP with B3LYP/6-31G**) obtained using the quantum chemistry program Psi4. The new improved method is demonstrated with a scaffold-hopping exercise identifying CDK2 bioisosteres. All code is open-source and freely available.

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