2020ApJ...890...64X

Query : 2020ApJ...890...64X

2020ApJ...890...64X - Astrophys. J., 890, 64-64 (2020/February-2)

Application of Convolutional neural networks to identify stellar feedback bubbles in CO emission.

XU D., OFFNER S.S.R., GUTERMUTH R. and VAN OORT C.

Abstract (from CDS):

We adopt the deep learning method called the Convolutional Approach to Shell Identification (CASI) and extend it to 3D (CASI-3D) to identify signatures of stellar feedback in molecular line spectra. We use magnetohydrodynamics simulations modeling the impact of stellar winds in a turbulent molecular cloud to generate synthetic 13CO (J = 1 - 0) observations. We train two CASI-3D models: ME1 predicts only the position of feedback, while MF predicts the fraction of the mass coming from feedback in each voxel. We adopt 75% of the synthetic observations as the training set and assess the accuracy of the two models with the remaining data. Both models identify bubbles in simulated data within 5% error. We use bubbles previously visually identified in Taurus in 13CO to validate the models and show that both perform well on the highest confidence bubbles. Models ME1 and MF predict total feedback gas mass of 2894 M☉ and 302 M☉, respectively. After correcting for missing energy due to the limited velocity range, model ME1 predicts feedback kinetic energies of 4.0 x 1046 erg and 1.5 x 1047 erg with and without subtracting the cloud velocity gradient. Model MF predicts feedback kinetic energies of 9.6 x 1045 erg and 2.8 x 1046 erg with and without subtracting the cloud velocity gradient. Model ME1 predicts bubble locations and properties consistent with previous visual identifications. However, model MF demonstrates that feedback properties computed using visual identifications significantly overestimate feedback impact, due to line-of-sight confusion and contamination from background and foreground gas.

Abstract Copyright: © 2020. The American Astronomical Society. All rights reserved.

Journal keyword(s): Stellar wind bubbles - Interstellar medium - Molecular clouds - Interstellar clouds - Astronomy data analysis - Astronomical methods - Convolutional neural networks - Neural networks - Astrostatistics - Interdisciplinary astronomy - Stellar feedback - Star formation

Simbad objects: 41

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Number of rows : 41
N Identifier Otype ICRS (J2000)
RA 		ICRS (J2000)
DEC 		Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2026 		#notes
1 NGC 1333 OpC 03 29 09.2 +31 20 59           ~ 1575 1
2 NAME Perseus Cloud SFR 03 35.0 +31 13           ~ 1553 0
3 [LLQ2015] TMB 1 bub 04 12 08.0 +24 53 33           ~ 2 0
4 [LLQ2015] TMB 2 Y*O 04 14 28.0 +27 45 53           ~ 2 0
5 [LLQ2015] TMB 3 Y*O 04 16 20.0 +28 28 53           ~ 2 0
6 [LLQ2015] TMB 4 Y*O 04 19 05.0 +27 33 33           ~ 2 0
7 [LLQ2015] TMB 5 Y*O 04 21 12.0 +26 55 33           ~ 2 0
8 [LLQ2015] TMB 6 bub 04 25 17.0 +25 32 13           ~ 2 0
9 [LLQ2015] TMB 7 bub 04 25 29.0 +26 10 13           ~ 2 0
10 [LLQ2015] TMB 8 bub 04 27 07.0 +24 20 13           ~ 2 0
11 [LLQ2015] TMB 9 bub 04 27 31.0 +26 16 53           ~ 2 0
12 [LLQ2015] TMB 10 Y*O 04 28 52.0 +24 14 33           ~ 2 0
13 [LLQ2015] TMB 11 Y*O 04 29 32.0 +26 32 33           ~ 2 0
14 [LLQ2015] TMB 12 Y*O 04 29 44.0 +26 32 53           ~ 2 0
15 [LLQ2015] TMB 13 Y*O 04 30 31.0 +24 26 13           ~ 2 0
16 [LLQ2015] TMB 14 Y*O 04 31 14.0 +29 25 53           ~ 2 0
17 [LLQ2015] TMB 15 Y*O 04 31 30.0 +24 14 33           ~ 2 0
18 [LLQ2015] TMB 16 bub 04 31 32.0 +24 09 53           ~ 2 0
19 [LLQ2015] TMB 17 bub 04 31 35.0 +23 35 13           ~ 2 0
20 [LLQ2015] TMB 18 bub 04 31 50.0 +24 22 13           ~ 2 0
21 [LLQ2015] TMB 19 Y*O 04 31 59.0 +25 43 13           ~ 2 0
22 [LLQ2015] TMB 20 bub 04 32 03.0 +25 36 53           ~ 2 0
23 [LLQ2015] TMB 21 bub 04 32 37.0 +29 29 13           ~ 2 0
24 [LLQ2015] TMB 22 Y*O 04 32 39.0 +24 46 13           ~ 2 0
25 [LLQ2015] TMB 23 bub 04 33 10.0 +26 08 53           ~ 2 0
26 [LLQ2015] TMB 24 bub 04 33 13.0 +25 24 53           ~ 2 0
27 [LLQ2015] TMB 25 Y*O 04 33 34.0 +24 20 53           ~ 2 0
28 [LLQ2015] TMB 26 bub 04 34 47.0 +29 37 13           ~ 2 0
29 [LLQ2015] TMB 27 Y*O 04 36 02.0 +28 23 13           ~ 2 0
30 [LLQ2015] TMB 28 Y*O 04 36 23.0 +25 36 33           ~ 2 0
31 [LLQ2015] TMB 29 Y*O 04 37 04.0 +25 46 33           ~ 2 0
32 [LLQ2015] TMB 30 Y*O 04 38 11.0 +26 05 53           ~ 2 0
33 [LLQ2015] TMB 31 bub 04 39 11.0 +29 05 13           ~ 2 0
34 [LLQ2015] TMB 32 bub 04 39 48.0 +28 35 33           ~ 2 0
35 NAME Taurus Complex SFR 04 41.0 +25 52           ~ 4971 0
36 [LLQ2015] TMB 33 bub 04 41 10.0 +25 31 13           ~ 2 0
37 [LLQ2015] TMB 34 bub 04 44 20.0 +28 36 53           ~ 2 0
38 [LLQ2015] TMB 35 bub 04 46 12.0 +25 07 33           ~ 2 0
39 [LLQ2015] TMB 36 Y*O 04 46 43.0 +24 59 13           ~ 2 0
40 [LLQ2015] TMB 37 bub 04 48 12.0 +24 50 33           ~ 2 0
41 NAME Ophiuchus Molecular Cloud SFR 16 28 06 -24 32.5           ~ 3969 1

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