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Mapping Galactic Dust Emission and Extinction with HI, HII, and H$_2$

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Additional gas phases leave HI-based dust maps with 20 percent residuals and no single dominant cause.

desk verdict Honest negative-result study of gas-based dust templates; the new temperature-variation limit is intriguing but is the weakest link because it comes from in-sample residuals and a mask that pre-removes high-residual regions. read the letter →

arxiv 2411.12801 v2 pith:UKBST22S submitted 2024-11-19 astro-ph.GA astro-ph.CO

Yun-Ting Cheng , Brandon S. Hensley , Tzu-Ching Chang , Olivier Doré This is my paper · ORCID
classification astro-ph.GAastro-ph.CO
keywords GalacticdustextinctionneutralhydrogenionizedgasmolecularHIclusteringtemperaturefar-infraredemission
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Neutral hydrogen emission is a standard proxy for Galactic dust, but fits of HI templates to far-infrared dust maps leave large-scale residuals of roughly 20 percent over tens of degrees. This paper tests whether the remaining mismatch can be absorbed by adding ionized and molecular gas templates and by splitting HI into data-driven clouds. It finds only modest improvement, and it shows that no single physical mechanism—variations in dust-to-gas ratio, dust temperature, opacity law, magnetic-field orientation, or extragalactic background—accounts for most of the residual pattern. Under the assumption that dust temperature is constant along each line of sight, the residuals imply an upper limit of $\sigma_T < 1.28\,\mathrm{K}$ on high-latitude temperature variation, which is tighter than earlier estimates. The result matters because gas-based extinction maps are used to correct cosmological survey data, and the paper's released reddening map offers an independent alternative to existing dust maps.

What carries the argument

The load-bearing object is the linear template equation $I_\nu = \sum_i \epsilon_{\nu,i} N_{\mathrm{HI},i} + \epsilon_{\nu,\mathrm{HII}} N_{\mathrm{HII}} + 2\epsilon_{\nu,\mathrm{H}_2} N_{\mathrm{H}_2} + b_\nu$, fitted to the far-infrared maps by weighted least squares. Each HI cloud is assigned a constant emissivity per H atom, while the HII and H2 phases each receive a single emissivity. k-means clustering on normalized angular coordinates, velocity, and HI channel density supplies the HI templates, with attribute weights and cluster number chosen to minimize chi-squared at 857 GHz. The machinery isolates what the gas templates can and cannot explain, turning leftover structure into a map of emissivity variation that the paper then compares against external tracers of dust-to-gas ratio, temperature, polarization, and extragalactic background.

What would settle it

Fit the same multi-phase templates to the far-infrared maps while including the full pixel covariance matrix, including clustering of the cosmic infrared background, instead of diagonal noise; if the large-scale residuals collapse to the noise level, the inferred emissivity variations are an artefact of ignored correlations. Separately, measure dust temperatures along high-latitude sight lines with a method independent of single-temperature modified-blackbody fitting, such as resolved multi-band photometry of background stars, and check whether the spread exceeds 1.28 K; a larger spread would falsify the paper's upper limit under its stated assumptions.

Watch

Extended reading notes

Core claim

The paper's central claim is that a linear template model in which each gas phase (HI, HII, H2) has a constant dust emissivity per hydrogen atom, with HI decomposed into a handful of clouds by k-means clustering in position-position-velocity space, still leaves residuals of less than 20 percent of the observed intensity in the 353, 545, and 857 GHz bands. Adding the HII template reduces outlier sight lines but only modestly; the H2 template built from CO is noise-dominated at high latitude and does not help. The clustering finds that a simple velocity cut already captures most of the signal, since three clusters in the north and five in the south mostly share similar emissivities, so the improvement from clustering is also modest. The residuals are coherent across frequencies and align only partially with stellar reddening residuals, dust-to-gas ratio maps, polarization-fraction fluctuations, and CMB lensing. The paper concludes that spatially varying dust emissivity not captured by discrete clouds is real, and that under a constant line-of-sight temperature assumption the implied temperature scatter is at most $\sigma_T < 1.28\,\mathrm{K}$.

Load-bearing premise

The model assumes that each HI cloud has one constant dust emissivity per hydrogen atom across its entire angular extent.

Editorial extensions

If this is right

  • Gas-based extinction and emission templates can be pushed only modestly by adding HII and clustering; users should expect roughly 10 to 20 percent large-scale residuals from any such template in the diffuse high-latitude regime.
  • Current CO-based H2 templates are not sensitive enough to improve high-latitude dust fits; higher-sensitivity CO mapping is needed before molecular gas can be incorporated into gas-based extinction maps.
  • If the constant-line-of-sight-temperature assumption holds, high-latitude dust temperature variations are below 1.28 K, implying that earlier temperature maps with roughly 1.5 to 2.5 K variation are dominated by systematics or parameter degeneracy.
  • No single mechanism explains the residual pattern, so future improvements require joint multi-tracer analyses of gas, stellar extinction, polarization, and three-dimensional distance information rather than another single tracer.
  • The released reddening map, built from multi-phase templates fitted to the SFD map, provides an independent extinction template with residual scatter of order 3 to 3.5 millimag against stellar reddening.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the constant-emissivity-per-cloud assumption is the real bottleneck, then three-dimensional extinction maps that place dust along the line of sight could break the degeneracy and produce the larger improvement that additional gas phases did not.
  • The tight 1.28 K bound should be read as much as a statement about model misspecification as about temperature; an independent temperature tracer, such as near-infrared colors of many background stars, could settle which interpretation is correct.
  • The pattern of residuals points to dust-to-gas ratio variations that are real but spatially patchy, especially toward known low-metallicity gas like the Magellanic Stream, so targeted studies of those regions could quantify the effect.
  • A natural next test is to fit the same template set to future high-latitude CO surveys or to CIB-subtracted versions of the SFD map; if residuals shrink, the missing piece is gas phase rather than dust physics.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper constructs high-latitude Galactic dust emission and reddening templates from HI4PI velocity-resolved HI, an all-sky dispersion-measure-based HII map, and a CO-based H2 map. The HI is decomposed with a k-means algorithm in position-position-velocity space, and the resulting gas templates are linearly fitted to Planck 353/545/857 GHz data and to the SFD reddening map. The authors find that adding HII improves the fit only modestly, that the H2 template has negligible impact, that the clustering-based HI decomposition yields only modest improvements over simple velocity cuts, and that large-scale residuals at the tens-of-degrees scale remain at roughly the 20% level. They investigate dust-to-gas ratio, temperature, opacity, magnetic-field orientation, and CIB contamination as explanations, concluding that no single mechanism dominates. They also derive a bound on dust temperature variation of sigma_T < 1.28 K under the assumption of a constant line-of-sight temperature, and release the resulting reddening map and templates.

Significance. If the conclusions hold, the paper is a useful contribution: it provides a public gas-based reddening map, systematically quantifies the limited gains from multi-phase tracers and data-driven HI decomposition, and documents a series of honest negative results that are relevant for the many cosmological analyses that rely on Galactic extinction corrections. The external checks against DESI stellar reddening and CMB lensing cross-correlations are valuable and partially mitigate concerns about in-sample fitting. However, the headline upper limit on dust temperature variation is not secured by the analysis as presented, because the clustering hyperparameters are selected on the same 857 GHz residuals that define sigma_T and because the adopted mask removes pixels with large residuals before the fit.

major comments (3)
  1. [Sections 5.2, 7.4, and 2.5] The sigma_T < 1.28 K limit is computed from the residuals of a model whose hyperparameters were selected in-sample on the same Planck 857 GHz map. In Section 5.2 and Figure 4, the number of clusters k and the attribute weights (w_v, w_rho) are chosen by minimizing the chi^2 against Planck 857 GHz, and in Section 7.4 these same 857 GHz residuals are converted into a temperature map via Equation (14). Any overfitting or noise exploitation in the hyperparameter search will shrink the residual variance and hence bias sigma_T downward, which is the opposite of what an 'upper limit' should do. In addition, the mask described in Section 2.5 inherits from Lenz et al. (2019) a criterion that removes pixels with residual emission found in earlier HI fits, pre-cleaning the largest outliers before the model is even built. The manuscript acknowledges both choices, but neither is revisited when the temperature limit is presented in the abstract. To support the headline claim, the authors should repeat the analysis with cross-validation or an independent calibration region, and quantify how sigma_T changes when the residual-masking criterion is relaxed.
  2. [Section 7.4, Equations (13)-(14)] Equation (14) is presented as an upper limit on dust temperature variation, but it is only a rescaling of the model residual map under a strong null hypothesis that all residuals are caused by temperature fluctuations with no variations in dust-to-gas ratio, opacity, unaccounted gas, CIB, or magnetic-field orientation. The paper itself finds evidence for several of these other contributions (Sections 7.2, 7.3, 7.6, 7.7), so the derived sigma_T is not an upper bound on the physical temperature dispersion of the interstellar dust; it is an upper bound on the temperature variation that would be inferred if the model were exact in every other respect. The abstract's statement that this limit is 'lower than the temperature variation reported in previous studies' therefore overstates the robustness of the result. The text should either explicitly frame sigma_T as a residual-based statistic under a stated model, or propagate the contributions of the other mechanisms to obtain a genuinely conservative bound.
  3. [Section 7.4] The line-of-sight constant-temperature assumption is stated, but its consequences for the claimed upper limit deserve more emphasis. If a sight line contains multiple dust components at different temperatures, the same observed intensity can be produced with a larger temperature dispersion than Equation (14) would infer. The paper notes this in passing, but the abstract and conclusions present sigma_T < 1.28 K without this caveat. At minimum, the authors should state explicitly that the limit applies only to single-temperature lines of sight and does not constrain multi-component temperature structure.
minor comments (5)
  1. [Section 2.3] The units of X_CO are written as 'cm−2 K−1 km−1 s', which appears to be missing a division by (km/s); the standard unit is cm^-2 (K km s^-1)^-1.
  2. [Table 1] The SGC HII emissivity at 545 GHz is listed as 0.0137 MJy sr^-1 (10^20 cm^-2)^-1, which is inconsistent with the 353 and 857 GHz values in the same row (0.040 and 0.374); this is likely a typographical error and should be checked against the actual fit output.
  3. [Section 8.2] The coefficients reported for the SGC are labeled with 'IVC−' and 'LVC', but the fiducial SGC templates defined in Sections 5.3 and 6 are 'LVC−' and 'LVC+'; the labels in the release description should be corrected.
  4. [Section 7.2] There is a duplicated word in the sentence 'it is difficult to explain the entirety of our fitting residuals residuals by dust-to-gas ratio variations'; the repetition should be removed.
  5. [Figure 12] The top panel is labeled 'T Model [K]', but the map is not a measured temperature map; it is a temperature inferred under the assumption that all residuals are thermal. The caption should make this construction clear in the figure label as well as in the text.

Circularity Check

2 steps flagged · score 6.0 of 10

The σ_T<1.28 K bound is an in-sample rescaling of the minimized residual map; the main null result on residual origins remains independently grounded.

  1. fitted input called prediction [Section 5.2 (cluster selection) and Section 7.4, Eq. (14)]
    "In each region, we iterate through different numbers of clusters (k). For each k, we search for the optimal weighting that minimizes the χ2 error between the Planck 857 GHz map, I d ν , and the model (Equation (8)). ... Assuming a mean temperature of T = 20 K, and using the Planck map and our model at 857 GHz, we derived the implied temperature map, shown in the top panel of Figure 12. Our derived temperature variation map has a standard deviation of σT = 1.02 K. If we consider a conservative upper limit of T < 25 K, we get σT < 1.28 K."

    The 857 GHz residual map is defined as I^d − I^m, with I^m the weighted least-squares fit to I^d (Eqs. 8–9). Equation (14) then converts that same residual map, pixel by pixel, into a temperature map under the MBB assumption; σ_T is the standard deviation of this rescaled residual. The clustering hyperparameters (k, w_v, w_rho) were themselves optimized by minimizing the χ² against this same I^d (Section 5.2), so the residual variance entering Eq. (14) is minimized in-sample. Thus σ_T < 1.28 K is not an independent bound on dust temperature variation; it is the in-sample residual scatter of the tuned model, relabeled as a temperature fluctuation. Mask pre-cleaning that removes high-residual regions can only reinforce this downward bias.

  2. self citation load bearing [Section 2.5 (Masks)]
    "We adopt the masks from Lenz et al. (2019) that incorporate a threshold NHI = 4 × 1020 cm−2, a 40% Galactic plane mask from Planck, and pixels that contain extragalactic point sources from Planck Collaboration XXVI (2016). However, this mask also removes sight lines with molecular intermediate-velocity clouds and CO-dark molecular gas from a census of these objects (Röhser et al. 2016), as well as regions with residual emission identified in their process of fitting Planck maps to H I."

    The mask used for all fits, including the one feeding Eq. (14), is taken from Lenz et al. (2019), a paper sharing author Doré with the present work. That mask was constructed by removing pixels with residual emission from an earlier fit of Planck maps to H I, i.e., it deletes exactly the kind of outliers whose variance Eq. (14) would otherwise convert into inferred temperature variation. Because σ_T is computed from the masked residual map, the headline upper limit inherits a self-citation-based pre-cleaning step. The paper asserts these regions are small and compact but does not quantify their effect on σ_T, so the advertised conservative upper limit is not secured by the presented analysis.

full rationale

The central template-building analysis is not circular: it fits a linear model of HI/HII templates to Planck maps, tests against external DESI reddening and CMB lensing, and finds that no single physical mechanism explains the residuals. Those conclusions have independent content. However, the headline σ_T<1.28 K upper limit (Section 7.4) does reduce by construction to the in-sample residual scatter of the fiducial model: Eq. (14) is a pointwise rescaling of I^d−I^m, and both the linear coefficients and the clustering hyperparameters were chosen by minimizing χ² against the same 857 GHz map (Section 5.2). Any overfitting or mask pre-cleaning shrinks the residual variance and hence lowers σ_T, opposite to the conservatism implied by calling it an upper limit. Additionally, the adopted mask comes from Lenz et al. (2019), a prior work sharing author Doré, and explicitly removes regions with residual emission from an H I-to-Planck fit, the same quantity feeding Eq. (14). These two effects make the quoted bound partially circular, while the broader null result about residual origins remains externally grounded.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central analysis assumes that dust emission is a linear combination of gas column templates with constant emissivity per cloud, that HI is optically thin, and that residuals can be interpreted under an MBB model with fixed beta and single T per LOS. The mask used is inherited from Lenz et al. (2019) and includes regions removed because of residual emission in their HI fits, which can bias the residual statistics. The clustering hyperparameters are fitted to the same 857 GHz data used in the residual analysis.

free parameters (7)
  • Dust emissivities per template per band epsilon_nu,i = NGC: IVC- 0.026/0.089/0.254; LVC 0.038/0.134/0.392; HII 0.018/0.052/0.167; SGC: LVC- 0.025/0.097/0.306; LVC+…
    Fitted via weighted linear least squares to Planck maps (Eq. 9); these carry the dust-to-gas ratio information and are the main fitted quantities.
  • Offsets b_nu per band per region = Not tabulated in the text (reported as fitted)
    Nuisance zero-point parameters in Eq. (7).
  • Number of HI clusters k = 3 (NGC), 5 (SGC)
    Chosen by minimizing 857 GHz fit chi^2 until improvement saturates (Fig. 4).
  • Clustering attribute weights w_v, w_rho = w_v = 3.56, w_rho = 0.022
    Optimized against the same Planck 857 GHz map used in the residual analysis (Appendix A).
  • Assumed mean dust temperature for residual-to-temperature mapping = 20 K (and 25 K for upper limit)
    Adopted to convert residual ratios into a temperature map via Eq. (14); the upper limit uses T=25K.
  • Dust spectral index beta = 1.5 (assumed)
    Fixed in Section 7.4 for temperature derivation; variation in beta is discussed separately.
  • CO-to-H2 conversion factor X_CO = 2 x 10^20 cm^-2 (K km/s)^-1
    Adopted from Bolatto et al. (2013) to build the H2 template; the template is ultimately dropped as noise-dominated.
assumptions (5)
  • domain assumption HI emission is optically thin (NHI conversion 1.82e18 cm^-2 per K km/s)
    Section 2.1; the HI4PI data are converted to column density assuming optically thin emission, which can fail in some clouds.
  • domain assumption Constant emissivity per identified cloud and per gas phase
    Section 3.1: 'The principal assumption of this work is that each cloud i in the gas phase x can be characterized by an emissivity that is constant across the extent of the cloud.'
  • domain assumption Modified blackbody emission with fixed beta=1.5 and a single dust temperature per line of sight
    Section 7.4: 'we assume a constant beta=1.5... constant temperature along each line of sight' to derive the sigma_T upper limit.
  • domain assumption Dispersion measure map of Hutschenreuter et al. (2024) traces HII column density
    Section 2.2: 'We adopt their DM map directly as a map of the HII column density.' The DM map has systematics at high latitude due to sparse pulsars.
  • ad hoc to paper Mask from Lenz et al. (2019) is appropriate and does not bias the residual analysis
    Section 2.5: the mask 'removes sight lines with molecular intermediate-velocity clouds... as well as regions with residual emission identified in their process of fitting Planck maps to HI', which can pre-clean the sky and reduce measured residuals.

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Pith. "Pith review of Mapping Galactic Dust Emission and Extinction with HI, HII, and H$_2$." pith.science (2026). https://pith.science/paper/UKBST22S

@misc{pith2026241112801,
  author       = {Pith},
  title        = {Pith review of: Mapping Galactic Dust Emission and Extinction with HI, HII, and H$_2$},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UKBST22S}},
  note         = {Machine review of arXiv:2411.12801}
}
abstract

Neutral hydrogen (HI) emission closely traces the dust column density at high Galactic latitudes and is thus a powerful tool for predicting dust extinction. However, the relation between HI column density $N_{\rm HI}$ and high-latitude dust emission observed by Planck has large-scale residuals at the level of $\lesssim 20\%$ on tens of degree scales. In this work, we improve HI-based dust templates in the north/south Galactic poles covering a sky fraction of $f_{\rm sky}=13.5\%/11.0\%$ (5577/4555\,deg$^2$) by incorporating data from ionized (HII) and molecular (H$_2$) gas phases. We make further improvements by employing a clustering analysis on the HI spectral data to identify discrete clouds with distinct dust properties. However, only a modest reduction in fitting residuals is achieved. We quantify the contributions to these residuals from variations in the dust-to-gas ratio, dust temperature and opacity, and magnetic field orientation using ancillary datasets. Although residuals in a few particular regions can be attributed to these factors, no single explanation accounts for the majority. Assuming a constant dust temperature along each line of sight, we derive an upper limit on the high-latitude dust temperature variation of $\sigma_T<1.28$K, lower than the temperature variation reported in previous studies. Joint analysis of multiple existing and upcoming datasets that trace Galactic gas and dust properties is needed to clarify the origins of the variation of gas and dust properties found here and to significantly improve gas-based extinction maps.

Figures

Figures reproduced from arXiv: 2411.12801 by the authors.

Figure 1
Figure 1. Column density maps of various gas components, presented in orthographic projection centered on the north/south Galactic poles. The left/right panels display the NGC/SGC, respectively, with gray regions indicating the masked areas as described in Section 2.5. Top-left panel: H I column density in the LVC component defined by |v| < 30 km s−1 . Bottom-left panel: H I column density in the IVC component defined by 30 <… view at source ↗
Figure 2
Figure 2. Residuals of a linear fit to the Planck 857 GHz map with different combinations of H templates. Top left panel: the total H I, i.e. the sum of the LVC (top-left panel in [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 4
Figure 4. Minimum χ 2 of fitting our model to the Planck 857 GHz data in the NGC (blue) and SGC (orange) as a function of number of clusters k. For each k, we optimize for the attribute weights and the linear fitting coefficients. We choose three and five clusters for NGC and SGC, respectively, marked with the star symbols. (∆Nch HI /∆v ch). For the distance metric, we use the Eu￾clidean distance in the multidimensional space… view at source ↗
Figures from the paper (15 more)
Figure 5
Figure 5. Figure 5: Top left/right: the column density of the three/five H I components in the NGC/SGC from our clustering algorithm and the H II map. We denote cluster #1 as “IVC−” and cluster #2 as “LVC” in the NGC, and cluster #2 as “LVC−” and the sum of clusters #3 and #4 as “LVC+” in…
Figure 6
Figure 6. Figure 6: Top: Planck intensity maps in the 353 (left), 545 (middle), and 857 (right) GHz bands. Bottom: the residuals of fitting our multi-phase templates to the Planck map. We define the residual to be the data minus the model map δIν = I d ν −I m ν . All residual maps are smo…
Figure 7
Figure 7. Figure 7: Locations of Regions A–G overlaid on the 857 GHz residual map. 7.2. Comparison to Stellar Extinction Maps FIR dust emission and optical reddening both depend linearly on the dust column density. Thus, if δDG varia￾tions were responsible for the majority of the residual…
Figure 8
Figure 8. Figure 8: Top: residual of fitting our multi-phase H tem￾plates to the Planck 857 GHz map. Middle: residual of fitting the same multi-phase H templates to the stellar reddening map from Zhou et al. (2024). Both maps are smoothed with a 1◦ Gaussian kernel to highlight the large-s…
Figure 9
Figure 9. Figure 9: Correlation between the 857 GHz residual map and the H I (top) and H II (bottom) column density maps in the NGC (left) and SGC (right), multiplied by ϵLVC (NGC) or ϵLVC+ (SGC). Black solid lines mark the 16th, 50th, and 84th percentiles. The Pearson correlation coeffic…
Figure 10
Figure 10. Figure 10: Correlation between the 857 GHz fit residu￾als and the H2 column density in the NGC (left) and SGC (right), multiplied by ϵLVC (NGC) or ϵLVC+ (SGC). Black solid lines mark the 16th, 50th, and 84th percentiles. Blue dashed lines are a unity slope line for reference. Re…
Figure 11
Figure 11. Figure 11: Correlation between the 857 GHz residual per H I column density and the CNM fraction fCNM in the NGC (left) and SGC (right). Black solid lines mark the 16th, 50th, and 84th percentiles. The Pearson correlation coefficient r is noted in the box. MBB model (Equation (3)…
Figure 12
Figure 12. Figure 12: The temperature variation from the Planck PR4 map over this region of sky is ∼1.5 K, slightly higher than our limit of σT < 1.28 K. Furthermore, the large-scale patterns of the temperature maps are not well matched to each other, except for a few regions, such as the …
Figure 13
Figure 13. Figure 13: Top: residual of fitting our multi-phase H templates to the Planck 857 GHz map. Middle: residual of fitting the Planck 353 GHz map to the 857 GHz map. Both maps are smoothed with a 1◦ Gaussian kernel to highlight the large-scale patterns. Bottom: the correlation betwe…
Figure 14
Figure 14. Figure 14: Top: fractional residual of fitting our multi￾phase H templates to the Planck 353 GHz map. Bottom: negative of polarization fraction fluctuations divided by 1 − ⟨pν⟩. The correlation coefficients in the two maps are 0.24 (NGC) / 0.41 (SGC). If magnetic field orientati…
Figure 15
Figure 15. Figure 15: Cross power spectrum of the CMB lensing convergence and the Planck map (black), our residual map (red), and the CIB map from Lenz et al. (2019) (blue) in the NGC (top) and the SGC (bottom). 8. A NEW DUST EXTINCTION MAP 8.1. Map Construction and Validation With our mul…
Figure 16
Figure 16. Figure 16: Top: the SFD reddening map. Bottom: our derived E(B −V ) model by fitting of fiducial template set to the SFD map. Both maps are smoothed with a 1◦ Gaussian kernel to highlight the large-scale patterns. our E(B − V ) model derived by fitting our templates to the SFD m…
Figure 19
Figure 19. Figure 19: Top: residual of fitting our multi-phase H tem￾plates to the DESI stellar reddening map. Bottom: residual of fitting the SFD map to the DESI stellar reddening map. This suggests that our templates can serve as a more accurate dust template than the SFD map. The two re…
Figure 20
Figure 20. Figure 20: Cross power spectrum of the CMB lensing con￾vergence κ and the reddening maps from SFD (gray), CSFD (yellow), and our model (red) in the NGC (left) and the SGC (right). We have conducted a high-pass filtering for ℓ < 500 modes on all three reddening maps to reduce the…
Figure 21
Figure 21. Figure 21: Examples of results from applying the clustering algorithm on the NGC with three clusters using different attribute weightings. The maps display the column densities of the three resulting H I template maps. The histogram panels are the velocity (left) and H I channel…

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