1. Constraints on Polarization Efficiency in the Vela C Molecular Cloud: First Results from BLASTPol 2012
Laura Fissel1
P. Ade2, P. Ashton1, F.E. Angile3, S. Benton4, M. Devlin3, B. Dober 3, Y. Fukui 5, N., Galitzki3, N. Gandilo4, J. Klein3, A. Korotkov6, Z. Li7 ,L. Moncelsi8, T. Matthews1, F. Nakamura8, C. B. Netterfield4, G. Novak1, E. Pascale2, F. Poidevin9,10,P. Martin10, G. Savini11, D.Scott12, F. Santos1, J. Shariff4, J. D. Soler13, N. Thomas14, C. Tucker2, G. Tucker6, D. Ward-Thompson15
1CIERA and Northwestern University, 2Cardiff University, 3University of Pennsylvania, 4University of Toronto, 5Nagoya University, 6Brown University, 7University of Virginia, 8California Institute of Technology, 8NAOJ, 9Inst. de Astrofisica de Canarias, 10CITA, 11University College London, 12University of British Columbia, 13CNRS-IAS, 14NASA Goddard, 15University of Central Lancashire
Detailed Maps of Magnetic Field Morphology with BLASTPol
Motivation
BLASTPol:The Balloon-borne Large Aperture Sub-mm Telescope for Polarimetry
BLASTPol Magnetic Field Map of the Vela C GMC
Magnetic fields may play an important role in the dynamics and evolution of molecular clouds. In order to constrain the role of magnetic fields in star formation, we require detailed observations of magnetic fields for a large sample of molecular clouds.
Vela C: An early stage GMC
d = 700 pc
M = 5 x 104 Msun, (Yamaguchi et al. 1999)
Polarized dust emission can be used to trace magnetic field morphology. Dust grains tend to align perpendicular to the local magnetic field, possibly due radiative alignment torques (Cho & Lazarian 2005), and thus we observe polarized dust emission that is perpendicular to the cloud local average field direction. However ground based polarimeters are generally limited to small (<0.1 deg2) maps, while Planck’s limited resolution gives it only a few independent polarization measurements across most molecular clouds.
Here we present the most detailed sub-mm polarization map ever made of a giant molecular cloud. Our goals are to:
Find an empirical model for our polarization data. This model can then be compared to predictions from numerical models of star formation.
Examine whether our measured magnetic field orientations could be preferentially sampling less dense regions where the dust grain alignment efficiency is high.
25 pc
Compact HII region RCW 36
BLASTPol team with the telescope, prior to the 16 day Dec 2012 Antarctic flight. BLASTPol utilizes a 1.8 m primary mirror and maps polarized emission at 250, 350 and 500 μm.
Resolution=2.5’ (0.5pc)
> 4400 Nyquist sampled polarization measurements
The BLASTPol polarimeter operates at an altitude of ~38km, so it can have wide frequency bands covering the spectral peak of K dust. This makes BLASTPol extremely sensitive and able to map large areas of the sky quickly.
Background: BLASTPol 500 μm intensity smoothed to 2.5’ (0.5 pc) resolution. Drapery Image: Line Integral (LIC, Cabral & Leedom (1993)) showing the inferred magnetic field orientation projected on the plane of the sky.
Modeling the BLASTPol Vela C Polarization Data
We look for correlations between:
Best fit power-law model: p(N,S) = p0 N-0.4±0.1 S-0.6±0.01
Contours: I500
Outlines: Vela C sub-regions defined by Hill et al. (2011)
Polarization trends Observed:
Polarization Fraction p
Column Density N (cm-2)
1022
1023
Angular Dispersion on 0.5 pc scales S (°) 10 1
p decreases
as S and N increase
p: fraction of 500 μm emission that is polarized
Decrease of p with increasing S: is likely caused by changes in the average magnetic field direction within the beam. This leads to cancellation of polarization components. A decrease of p with S was also seen in Planck Intermediate XX, albeit for more diffuse sightlines.
Decrease of p with increasing N: Possibly due to reduced effectiveness of radiative alignment torques for sightlines where much of the dust is highly shielded (Whittet et al., 2008), or because the magnetic field is more tangled towards high dust column sightlines (Falceta-Goncalves et al., 2008).
Lack of correlation between N and S: Implies that sharp changes in the magnetic field orientation do not tend to occur near high column density features. This is in contrast to what was found by Falceta-Goncalves et al. (2008), when they examined simulated polarization observations of an MHD cloud model. A correlation might be expected if the magnetic field direction was significantly bent by gravitational motions of gas near dense filaments.
S: Dispersion in the local magnetic field angle
(on 0.5 pc scales)
Large where there are sharp changes in the average B-field direction
2-D histogram of N vs S. Color: median p for each bin.
1.0° °
p(N,S)
p/p(N,S)
Using linear fits in logarithmic space we look for correlations between:
2e23
1e23
N: Hydrogen column density
(derived from Herschel Data)
Histogram of p (red) , our power-law model p(N,S) (blue) and p with the dependence on N and S removed (yellow). Our two power-law model is able to reproduce most of the variance in polarization fraction seen in our map.
Can BLASTPol Trace Magnetic Fields in Deeply Embedded Dust?
Conclusions
Large area detailed portraits of cloud magnetic field morphology are now becoming available (Planck for very near clouds, BLASTPol for more distant clouds). To use these maps to constrain magnetic field strength we need statistical measures of polarization data that can be also be applied to simulated observations from 3-D numerical models of magnetized clouds.
Here we have shown that our polarization fraction (p) data is well fit by a power-law model of two variables: the column density (N) and the local field angular dispersion on 0.5 pc scales (S). Our best fit model is p(N,S) = p0 N-0.4 S We see little evidence for a correlation between N and S. In future work we will look for these trends in simulated observations.
We also considered the implications of the extreme case that all of the decrease in p with N is due to reduced polarization efficiency. We appear to trace the average field direction for moderate columns (AV~10) well, though we may not be sensitive to field direction changes in deeply embedded dust for highly extincted sightlines (AV ~50).
If all of the decrease in polarization with increasing column density is caused by varying grain properties (e.g., grain alignment efficiency, less elongated grains) then our magnetic field measurements preferentially sample regions where the polarization efficiency is high.
The polarization efficiency model can be used to predict p vs AV:
Best fit
power-law model
ε(χ) α χ-0.84 χ
AV/2
Line of Sight (z) ρ
ε=ε0
We parameterize the polarization efficiency ε as a function of the depth into the cloud χ (total extinction to the nearest cloud surface).
In this model ε(χ) is constant up to a critical cloud depth (χcrit) and falls as a power-law of slope η for χ > χcrit.
Uniform polarization efficiency
ε(χ) constant up to AVcrit then ε(χ)=0
χcrit
Our Power-law Model Predictions: z
Moderate column sightline (AV=10):
Inner half of dust (χ=2.5-5) contributes 40% of the total polarized flux
Well traced by BLASTPol
Highest column sightlines (AV = 50):
Inner half of dust (χ= ) contributes only 25% to the total polarized flux
Not well traced by BLASTPol
ε=ε0(χ/χcrit)η
ε=ε0
Note: For this exercise we assume that all of the decrease with p vs N is due to grain physics (not due to field tangling). This can be considered a worst case scenario.
References:
Cabral & Leedom, 1993, 263
Yamaguchi et al.,1999, ApJ 696, 567
Cho & Lazarian, 2005, ApJ, 631,361
Lazarian, 2007, QJSTR, 106, 225
Falceta-Goncalves et al., 2008, ApJ, 679,537
Whittet et al. 2008, ApJ, 674, 304
Hill et al., 2011,A&A, 533, A94
Planck Collab.,2014, A&A, 576,105
Galitzki et al. 2014, SPIE, 9145