Post on 06-Aug-2020
Background Starlight Polarimetry in the Age of SOFIA and Gaia
Dan Clemens (Boston University)
NSF/AST 14‐12269 & 18‐14531 , USRA SOF_4‐0026, & NASA NNX15AE51G gratefully acknowledged
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Findings First, Talk Second1. The combination of Gaia DR2 with near‐infrared (NIR) Background Starlight
Polarimetry (BSP) is transformative– Distances to dark clouds to <3% uncertainty– Can classify stars as foreground, embedded, background, and deep background– Better star cluster stellar memberships
• Proper motions are better than distances
2. The HAWC+ far‐infrared (FIR) imaging polarimeter on SOFIA is fabulous– Sensitive tool for performing FIR thermal emission polarimetry (TEP)– Combined with NIR BSP ‐> B‐field characterized over wide range of scale sizes
3. B‐field strengths from Davis‐Chandrasekhar‐Fermi (DCF) method ‐> too low– Forward modeling of cluster star Position Angle dispersions (PA) reveals BSP noise bias– Bayesian posteriors for (B) also favor lower PA, higher B strengths– Foregrounds and deep backgrounds both contribute PA noise, masking target cloud value
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BSP Review
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Backgrou
nd Stars
(Unp
olarize
d Light*)
Molecular (or atomic) cloud or zone with B‐field
aligned dust grainsBPlane of Sky
Starlight with impressedLinear polarization signal
(P || BPOS)
Observer Sees:
Andersson+’13
Estimate B‐field Strength as 4 ∆∆
(Davis, Chandrasekhar & Fermi)
We use Mimir – NIR Imaging Polarimeter for BSP1. Galactic Plane survey (GPIPS) of 3,237 10x10’ FOVs like this one2. Individual Dark Clouds located off the Galactic plane, like L1544
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• FOV = 10x10 arcmin at 1024x1024 pixels at 0.6”/pixel on the 1.8m Perkins telescope in Flagstaff, AZ
• H (1.6m) and K (2.2m) stellar polarimetry.
3. Outer Galaxy fields towards Perseus Spiral Arm with open stellar clusters
Cluster Stars = Ideal, Same‐Distance, Multiple BSP Probes
• Must isolate cluster stars from field (fore + background) in the FOV• Past techniques: spatial over‐density +/‐ Color‐Magnitude Diagram
placements– Select “members” based on probability of being in the over‐density and
falling into the Giant or Main Sequence branches (Mercer+’05)– Fit colors for age, metallicity, distance, reddening (Hoq & Clemens ’15)– Not very robust, nor accurate if Giant branch is poorly populated, esp
when using NIR colors5
Observer Foreground Stars
Target Cloud
Star Cluster BackgroundStars
(J‐K)
mH
CMD of NGC663 in JHK with best‐fit Isochrone (H&C ’15)
Then came Gaia DR2 in April 2018…• Parallax, proper motions, optical g‐band magnitudes
– to > 10 kpc, mg < 20 mag
• We fetched Gaia data for 30+ open cluster fields in Perseus– Test B‐field changes from Perseus Spiral arm to interarm regions– Clusters selected to be in front of, within, and behind Perseus Arm
• Matched Gaia stars to Mimir‐observed NIR polarimetry stars (simple 1.5” cone)– Incredible match rate
• On average, ~925 Mimir polarization‐measured stars per FOV 900 (97%) matched to Gaia stars• More than half of the cluster FOVs had < 10 stars that did not match.• Reiterate: Nearly every star with measured polarization now has a distance measure, also
• Created Bayesian Markov Chain Monte Carlo (MCMC) code to separate cluster members from field stars
– Each star has likelihood of being drawn from the cluster or the field; greatest one kept– Cluster likelihood based on gaussians in 3‐D of parallax, proper motion in RA, pm in Dec 6
Cluster Parameters Were Recovered Stunningly Well
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S/N ~ 80‐380For FOV containing cluster NGC 1245:(left) Stars across FOV(right) parallax vs proper motion
Field Stars
100 pc
1 kpc
10 kpc
100 kpc
Cluster Parameters Were Recovered Stunningly Well
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S/N ~ 80‐380For FOV containing cluster NGC 1245:(left) Stars across FOV(right) parallax vs proper motion
Field Stars
100 pc
1 kpc
10 kpc
100 kpc
Cluster Member Stars
MCMC Cluster Separation Recovers the Color‐Mag. Diagram
• Color (J‐K) – magnitude (H) diagram for one cluster– Cluster member stars populate Giant
and Main Sequence Branches well– Field stars look like, well, field stars– Polarization fraction uncertainty P
below 5˚ to H=15.5
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Gaia Parallaxes + NIR BSP – Cloud, Cluster Distances• Classically, cloud distance methods plot
E(B‐V) or AV vs stellar distance – Looking for a jump associated with the cloud– Difficult, if AV varies across the face of the
cloud (which it does)– Polarization percentage, P, often has the
same problem
• Try using Pol. PA as the changing property– Versus Gaia DR2 distances (w/ uncertainties)
• Better, use Stokes U and Q simultaneously – Look for jumps at the same distance(s) in
both10
GF9‐2 distance of 27010 pc from PA jump with parallax.
Fit using MCMC (Clemens+’18)
Gaia Parallaxes + NIR BSP – Cloud, Cluster Distances• Classically, cloud distance methods plot
E(B‐V) or AV vs stellar distance – Looking for a jump associated with the cloud– Difficult, if AV varies across the face of the
cloud (which it does)– Polarization percentage, P, often has the
same problem
• Try using Pol. PA as the changing property– Versus Gaia DR2 distances (w/ uncertainties)
• Better, use Stokes U and Q simultaneously – Look for jumps at the same distance(s) in
both11
GF9‐2 distance of 27010 pc from PA jump with parallax.
Fit using MCMC (Clemens+’18)
Gaia, NIR Polarimetry, and Stokes U, Q steps• Used MCMC to find
number(s), location(s) of jumps in Stokes U, Q in open cluster FOVs
• Field stars in blue• Cluster members in red• NGC 2262 located at
about 4.740.1 kpc– Behind two distinct
foreground layers– One with no P, one with
significant P
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Gaia, NIR Polarimetry, and Stokes U, Q steps• Used MCMC to find
number(s), location(s) of jumps in Stokes U, Q in open cluster FOVs
• Field stars in blue• Cluster members in red• NGC 2262 located at
about 4.740.1 kpc– Behind two distinct
foreground layers– One with no P, one with
significant P
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B‐field strengths – Davis‐Chandrasekhar‐Fermi method
• 4 ∆∆
; where V is gas velocity dispersion, is gas mass
density, and PA is BSP PA dispersion.• PA can be found many ways
– Unweighted frequentist dispersion– Weighted frequentist dispersion– Boostrap with resampling– Gaussian fitting of PA distributions
• PA can be corrected for observational uncertainty, if uniform in the sample
– Works for TEP; ∆ ∆ and is the same for each pixel
• Not so for BSP because of the wide range of stellar brightnesses ‐> range of – Forward‐model from observations to uncover ∆ for BSP
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Forward‐Modeling : Cluster Fields ‐> PA vs mH; Cluster N(mH)
• Thousand(s) of stars per Mimir FOV– 16 HWP angles ‐> 4 U, Q sets and robust PA
– Fit run of PA vs mH for each FOV• Predictive noise function for model stars
• Each cluster ‐> apparent magnitude function– Binned by magnitude– Can fit, but that misses Giant vs Main
Sequence numbers too much– Keep averages in bins for modeling
• Poisson draws, using these means
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Forward Modeling Flow of Computation
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1. Establish ClusterMembership withGaia + MCMC
2. Create apparent magnitude bins 3. For the FOV, characterize polarization
uncertainty as function of apparent brightness
4. Draw Poisson‐based numbers of stars for each mag bin to create mock cluster with same magnitude distribution
5. Form model Stokes U, Q for each mock star. Use <U>, <Q> from real cluster plus PA(mH) from fit.
• Compute PA for model cluster stars– “model output PA” (I use bootstrap)– Find it is < PA(observed) for the real cluster
• Whoops! Forgot to include PA(true)– The “astrophysical” dispersion ‐> DCF Method– Include a synthetic version of this dispersion
• Implemented as draws from a gaussian distribution of PAs with G = PA (true)
• Future ‐> based on 2nd Order Structure Function?• Sweep of PA(true) steps from 0 to 40˚ by 0.5˚
• (Re)Compute model output PA
• Do for 50,000 mock clusters for each PA(true)– Generates distribution functions of model
output PA
• Each cluster shows different relation between PA (true) and PA (model output)– Reflects S/N of observations; stellar brightnesses
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PA(true
)
(model output)
Compare Cluster PA(obs.) to Infer Mock Cluster PA(true)• PA(obs.) values select a range
of likely PA (true) values– True values < observed values
• Convolve PA (true) distributions with gaussianmodeled PA (obs.)
• Result is a posterior distribution (PA(true))– Very gaussian looking
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NGC 869
7˚
4˚
Orange = Convolved ValuesBlue = Gaussian Fit
(true)
Modeling findings1. BSP PA values are noise‐biased
– Like Ricean, but more complex2. Forward modeling recovers PA (true) distributions
– PA(true) < PA (obs) ‐> B(true) > B(obs+DCF)
3. ∝ ∆ + ∆ ~gaussian → – Favors lower PA, higher B again
4. Realization – PA noise bias, both observational and astrophysical, will be added by foreground and deep background ISM – Increasing PA (obs) and decreasing the DCF B‐strength we would have estimated– Either eliminate them, or must forward model their effects, too
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1/PA
Foreground adds noise
Deep background adds noise
It’s not hopeless – GF9‐2 Mimir + SOFIA/HAWC+
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GF9‐2 YSO
• Quick progress can be made by choosing a clean astrophysical laboratory– No foreground ISM– No deep background ISM
• GF9 Filamentary Dark Cloud– With GF9‐2 YSO – Distance = 27010 pc (our MCMC)
• Near‐Quiescent low‐mass early SF– ~Lowest luminosity Class 0 YSO – 0.3 L– Minimal outflow found
• Is its B‐field still intact, or was it modified?• B‐field Probes:
– Near‐IR BSP w/ Mimir– SOFIA HAWC+ E‐Band (216 m) Polarimetry– Optical I‐band BSP (Poidevin & Bastien ’06)– Submm 850m Planck Polarization (XIX ’15)
Follow the FIR Flux…
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• Select synthetic beam placements that:1. Are centered on the brightest Stokes I
emission (thin contours in the figure)2. Are offset from each other so as to be
independent samples (the circles don’t cross)
• Select synthetic beam sizes so that:1. Maximize the number of synthetic
beams with good PSNR2. Don’t give away too much angular
resolution• Tried a range of gaussian sizes, found best
at 4 HAWC+ pixels FWHM• 6 beams with PSNR > 1.6 (PA < 18˚);
11 not detected
Blue = Mimir H‐band; Green = K‐band; Orange = HAWC+ Mapped Region; Black contours = 216m Intensity; Circles = synthetic beams – Black non‐detections and Magenta detections; Red = HAWC+ BPA (from Clemens+’18)
Combining all B‐field probes – Plane of Sky Magnetic Field Orientation vs Offset from YSO
• B‐field orientation is unchanged from ~3pc to ~6000 AU; No B‐field disruption near the YSO• No strong evidence of B‐field orientation change with 22
Comparisons with Models
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• Hull+’17• Simulations with
synthetic observations• Multi‐scale
• GF9‐2 observations are most consistent with strong B‐field case
• BPA uniform from 3pc to 6000AU
• Last column, top two rows
• Seems GF9‐2 YSO formed in a fairly strong B‐field region
Findings (the final time…)1. The combination of Gaia DR2 with near‐infrared (NIR) Background Starlight
Polarimetry (BSP) is transformative– Distances to dark clouds to <3% uncertainty –MCMC is a powerful tool here– Can classify stars as foreground, embedded, background, and deep background– Better star cluster stellar memberships – again, MCMC is powerful
• Proper motions are better than distances
2. The HAWC+ far‐infrared (FIR) imaging polarimeter on SOFIA is fabulous– Sensitive FIR thermal emission polarimetry (TEP) –more so with smart smoothing– Combined with NIR BSP ‐> B‐field characterized over wide range of scale sizes
3. B‐field strengths from Davis‐Chandrasekhar‐Fermi (DCF) method ‐> too low– Forward modeling of cluster star Position Angle dispersions (PA) reveals BSP noise bias– Bayesian posteriors for (B) also favor lower PA, higher B strengths– Foregrounds and deep backgrounds both contribute PA noise, masking target cloud value
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Boston University Team• From left:
– Catherine Cerny• Joined in 2018 Summer – Gaia expert
– Genevieve Schroeder (U. Rochester)• Summer 2017 REU intern• Flew on SOFIA tomorrow in September for
HAWC+ observations of L1448– Jordan Montgomery, now Dr. J. Montgomery
• At MIT Lincoln Labs, along with Sadia Hoq, John Vaillancourt, Mike Pavel…)
• Flew on SOFIA 2016 December– Sofia Kressy
• BU Senior Undergrad – lead on Mimir Orion project (HRO, Gaia, …)
– Adham El‐Batal• 3rd year grad student• Just back from GAIA workshop in Heidelberg• Flew on SOFIA 2016 December
– Dr. Thushara Pillai• BU Senior Research Scientist
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