Comparison height width

Alyssa Goodman

and 2 more

Let's use Authorea to keep track of B5 materials...fits cubes: ready for Glue volume rendering.YT moviesThe blue/green is \(C^{18}O\) (2-1), and red/orange is \(NH_3\) (1, 1).keynote scienceThe three slides have been uploaded.Alvaro-style 3D movies (clustering by the FoF method)The clustering is done using a code I wrote following explanation in Alvaro's paper. The friends-of-friends threshold in Alvaro's paper is 3 km s\(^{-1}\) pc\(^{-1}\) (~ 1 \(c_s\)/half beam). Using the same threshold, the (extended) B5 would be clustered/grouped into one single component. The clustering in the movies below is done using a threshold of 1 km s\(^{-1}\) pc\(^{-1}\) (one third of Alvaro's threshold), in order to cluster the data points into multiple components (which isn't too bad a choice, since Alvaro was using CO (1-0), with a broader line width). In the movies below, the clustering is run on the combined Gaussian fit, where we have one peak from one-component Gaussian fit if the residual of one-component fit is smaller, and two peaks from the two-component fit if the residual of two-component fit is smaller.movie0_opaque_linewidth: the movie made from 3D visualization of Gaussian fitted peaks without friends-in-velocity (FIVE) clustering. Brighter/white circles are where the (Gaussian fitted) emission is higher. The size is scaled with the (Gaussian fitted) line width.movie0_transparent_linewidth: the same movie, with alpha.movie_opaque: the movie made from 3D visualization of Gaussian fitted peaks with friends-in-velocity (FIVE) clustering. The size is NOT scaled with the line width.movie_transparent: the same movie, with alpha.movie_opaque_linewidth: the movie made from 3D visualization of Gaussian fitted peaks with friends-in-velocity (FIVE) clustering. The size is scaled with the line width.movie_transparent_linewidth: the same movie, with alpha.Clustering by the DBScan methodThe clustering is done using the DBScan (density-based scanning) method in scikit-learn. The DBScan method should perform better than the FoF method(, which is similar to the K-Means). In practice, the mean silhouette coefficient, measuring how the clustering performs (ranging from -1 to 1 for each data point, with -1 meaning that the clustering is not appropriate for that data point, and 1 meaning the clustering is good), shows that the result of the DBScan (mean silhouette score ~ 0.06) is better than the FoF method (mean silhouette score ~ -0.26; the score for the FoF method is calculated on the same standardized dataset used in the DBScan analysis, to be fair). The DBScan method also identifies a number of data points which cannot be clustered (the "noisy samples"). See the scikit-learn clustering page for an overview of various clustering algorithms.To implement the DBScan method, the ppv positions of fitted Gaussian peaks (of C\(^{18^{ }}\)O 2-1) are first standardized. No other scaling is applied. The best parameters for setting up DBScan are found by measuring the mean silhouette coefficient, within a reasonable range. DBScan (set up with the best parameters) finds 12 components, compared to 10 components found by FoF.movie_DBScan and movie_DBScan_linewidth in the Google drive folder show the result in the original RA-Dec-velocity space. The smaller, black data points indicate those categorized by DBScan as the "noisy samples".
All1

Hope How-Huan Chen

and 1 more

ABSTRACT. ρ Ophiuchii is a group of five B-stars, embedded in a nearby molecular cloud: Ophiuchus, at a distance of ∼ 119 pc. A “bubble”-like structure is found in dust thermal emission around ρ Oph. The circular structure on the Hα map further indicates that this bubble is physically connected to the source at the center. The goal of this paper is to estimate the impact of feedback from these embedded B-stars on the molecular cloud, by comparing the energy associated with the material entrained in the bubble to the total turbulent energy of the cloud. In this paper, we combine data from the COMPLETE Survey, which includes ¹²CO (1-0) and ¹³CO (1-0) molecular line emission from FCRAO, an extinction map derived from 2MASS near-infrared data using the NICER algorithm, and far-infrared data from IRIS (60/100 μm) with data from the Herschel Science Archive (PACS 100/160 μm and SPIRE 250/350/500 μm). With the wealth of data tracing different components of the cloud, we try to determine the best strategy to derive physical properties and to estimate the energy budget in the shell and in the cloud. We also experiment with the hierarchical Bayesian-fitting technique introduced by in an effort to eliminate the bias in the derived column densities and/or temperatures induced by noise in the far-IR data. We find that the energy entrained in the bubble is ∼ 12 % of the total turbulent energy of the Ophiuchus molecular cloud. This fraction is similar to the number give for the Perseus molecular cloud, and it suggests the non-negligible role of B-stars in driving the turbulence in clouds. We expect that a complete survey of “bubbles” in the Ophiuchus cloud will reveal the importance of B-star winds in molecular clouds.
Pof1

Alyssa Goodman

and 10 more

L1688

Hope How-Huan Chen

and 5 more