Determining thermal dust emission from <i>Planck</i> HFI data using a sparse, parametric technique

Irfan, Melis O.; Bobin, J.; Miville-Deschênes, M.-A.; Grenier, I. A.

doi:10.1051/0004-6361/201834394

Cited by 26 publications

(23 citation statements)

References 34 publications

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“…These challenges have motivated modelling approaches where spatially uniform spectral parameters are assumed over a set of defined sky regions. These have included regions defined as superpixels on low NSIDE HEALPIX maps, and regions defined according to similarities in their spectral properties (Górski et al 2005;Stompor et al 2009Stompor et al , 2016Errard et al 2011;Alonso et al 2017;Irfan et al 2019;Khatri 2019;Thorne et al 2019). A detailed discussion of the modelling approaches that can be adopted for spectral parameters in these sky regions is given in Sections 5.3 and 5.4.…”

Section: E a N S H I F T C L U S T E R I N G O F S K Y R E G I O N Smentioning

confidence: 99%

“…Previous CMB component separation analyses have considered the extent to which foreground spectral parameters should be allowed to vary, such that a balance can be struck between model realism and simplicity. These studies have considered various approaches to defining regions on the sky, over which foreground spectral parameters are typically assumed to be constant (Stompor et al 2009;Errard, Stivoli & Stompor 2011;Stompor, Errard & Poletti 2016;Alonso et al 2017;Irfan et al 2019;Khatri 2019; Thorne et al 2019). In this paper, we use the mean shift algorithm to define regions on the sky, clustering according to naively estimated synchrotron and dust spectral parameters.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Bayesian CMB component separation with the No-U-Turn Sampler

Grumitt

Jew

Dickinson

2020

Monthly Notices of the Royal Astronomical Society

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In this paper, we present a novel implementation of Bayesian cosmic microwave background (CMB) component separation. We sample from the full posterior distribution using the No-U-Turn Sampler (NUTS), a gradient-based sampling algorithm. Alongside this, we introduce new foreground modelling approaches. We use the mean shift algorithm to define regions on the sky, clustering according to naively estimated foreground spectral parameters. Over these regions we adopt a complete pooling model, where we assume constant spectral parameters, and a hierarchical model, where we model individual pixel spectral parameters as being drawn from underlying hyperdistributions. We validate the algorithm against simulations of the LiteBIRD and C-Band All-Sky Survey (C-BASS) experiments, with an input tensor-to-scalar ratio of r = 5 × 10−3. Considering multipoles 30 ≤ ℓ < 180, we are able to recover estimates for r. With LiteBIRD-only observations, and using the complete pooling model, we recover r = (12.9 ± 1.4) × 10−3. For C-BASS and LiteBIRD observations we find r = (9.0 ± 1.1) × 10−3 using the complete pooling model, and r = (5.2 ± 1.0) × 10−3 using the hierarchical model. Unlike the complete pooling model, the hierarchical model captures pixel-scale spatial variations in the foreground spectral parameters, and therefore produces cosmological parameter estimates with reduced bias, without inflating their uncertainties. Measured by the rate of effective sample generation, NUTS offers performance improvements of ∼103 over using Metropolis–Hastings to fit the complete pooling model. The efficiency of NUTS allows us to fit the more sophisticated hierarchical foreground model that would likely be intractable with non-gradient-based sampling algorithms.

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Section: E a N S H I F T C L U S T E R I N G O F S K Y R E G I O N Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian CMB component separation with the No-U-Turn Sampler

Grumitt

Jew

Dickinson

2020

Monthly Notices of the Royal Astronomical Society

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“…pelgrims@physics.uoc.gr One of the most challenging aspect of modeling dust foregrounds is that the spectral energy distribution (SED) of dust emission is not uniform across the sky. Variations in dust temperature and opacity law are now well-attested across the Galaxy (e.g., Finkbeiner et al 1999;Planck Collaboration XI 2014;Meisner & Finkbeiner 2015;Planck Collaboration IV 2020;Irfan et al 2019), with evidence for correlations with gas velocity (Planck Collaboration XXIV 2011;Planck Collaboration XI 2014), strength of the ambient radiation field (Planck Collaboration XXIX 2016; Fanciullo et al 2015), and location in the Galactic disk (Schlafly et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Evidence for Line-of-Sight Frequency Decorrelation of Polarized Dust Emission in $Planck$ Data

Pelgrims,

Clark,

Hensley

et al. 2021

Preprint

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If a single line of sight (LOS) intercepts multiple dust clouds having different spectral energy distributions (SEDs) and magnetic field orientations, then the frequency scaling of each of the Stokes Q and U parameters of the thermal dust emission may be different, a phenomenon we refer to as LOS frequency decorrelation. We present first evidence for LOS frequency decorrelation in Planck data by using independent measurements of neutral hydrogen (Hi) emission to probe the 3D structure of the magnetized ISM. We use Hibased measurements of the number of clouds per LOS and the magnetic field orientation in each cloud to select two sets of sightlines: (i) a target sample of pixels that are likely to exhibit LOS frequency decorrelation and (ii) a control sample of pixels that lack complex LOS structure. We test the null hypothesis that LOS frequency decorrelation is not detectable in Planck 353 and 217 GHz polarization data at high Galactic latitudes. We find that the data reject the null hypothesis at high significance, showing that the combined effect of polarization angle variation with frequency and depolarization are detected in the target sample. This detection is robust against choice of CMB map and map-making pipeline. The observed change in polarization angle due to LOS frequency decorrelation is detectable above the Planck noise level. The probability that the detected effect is due to noise alone ranges from 5 × 10 −2 to 4 × 10 −7 , depending on the CMB subtraction algorithm and treatment of residual systematics; correcting for residual systematics consistently increases the significance of the effect. Within the target sample, the LOS decorrelation effect is stronger for sightlines with more misaligned magnetic fields, as expected. With our sample, we estimate that an intrinsic variation of ∼ 15% in the ratio of 353 to 217 GHz polarized emission between clouds is sufficient to reproduce the measured effect. Our finding underlines the importance of ongoing studies to map the three-dimensional structure of the magnetized and dusty ISM that could ultimately help component separation methods to account for frequency decorrelation effects in CMB polarization studies.

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“…One of the most challenging aspect of modeling dust foregrounds is that the spectral energy distribution (SED) of dust emission is not uniform across the sky. Variations in dust temperature and opacity law are now well attested across the Galaxy (e.g., Finkbeiner et al 1999;Planck Collaboration XI 2014;Meisner & Finkbeiner 2015;Planck Collaboration IV 2020;Irfan et al 2019), with evidence for correlations with gas velocity (Planck Collaboration XXIV 2011; Planck Collaboration XI 2014), strength of the ambient radiation field (Planck Collaboration XXIX 2016;Fanciullo et al 2015), and location in the Galactic disk (Schlafly et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Evidence for line-of-sight frequency decorrelation of polarized dust emission in Planck data

Pelgrims

Clark

Hensley

et al. 2021

A&A

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If a single line of sight (LOS) intercepts multiple dust clouds with different spectral energy distributions and magnetic field orientations, then the frequency scaling of each of the Stokes Q and U parameters of the thermal dust emission may be different, a phenomenon we refer to as LOS frequency decorrelation. We present first evidence for LOS frequency decorrelation in Planck data using independent measurements of neutral-hydrogen (HI) emission to probe the 3D structure of the magnetized interstellar medium (ISM). We use HI-based measurements of the number of clouds per LOS and the magnetic field orientation in each cloud to select two sets of sightlines: (i) a target sample of pixels that are likely to exhibit LOS frequency decorrelation and (ii) a control sample of pixels that lack complex LOS structure. We test the null hypothesis that LOS frequency decorrelation is not detectable in Planck 353 and 217 GHz polarization data at high Galactic latitudes. We reject the null hypothesis at high significance based on data that show that the combined effect of polarization angle variation with frequency and depolarization are detected in the target sample. This detection is robust against the choice of cosmic microwave background (CMB) map and map-making pipeline. The observed change in polarization angle due to LOS frequency decorrelation is detectable above the Planck noise level. The probability that the detected effect is due to noise alone ranges from 5 × 10−2 to 4 × 10−7, depending on the CMB subtraction algorithm and treatment of residual systematic errors; correcting for residual systematic errors consistently increases the significance of the effect. Within the target sample, the LOS decorrelation effect is stronger for sightlines with more misaligned magnetic fields, as expected. With our sample, we estimate that an intrinsic variation of ~15% in the ratio of 353 to 217 GHz polarized emission between clouds is sufficient to reproduce the measured effect. Our finding underlines the importance of ongoing studies to map the three-dimensional structure of the magnetized and dusty ISM that could ultimately help component separation methods to account for frequency decorrelation effects in CMB polarization studies.

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Determining thermal dust emission from Planck HFI data using a sparse, parametric technique

Cited by 26 publications

References 34 publications

Hierarchical Bayesian CMB component separation with the No-U-Turn Sampler

Hierarchical Bayesian CMB component separation with the No-U-Turn Sampler

Evidence for Line-of-Sight Frequency Decorrelation of Polarized Dust Emission in $Planck$ Data

Evidence for line-of-sight frequency decorrelation of polarized dust emission in Planck data

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