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Global Attenuation in Spiral Galaxies in Optical and Infrared Bands

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read This paper claims that the degree to which a normal spiral galaxy's light is obscured by its own dust is predictable from a single score built from three distance-independent observables plus the galaxy's inclination, and it derives an…

desk verdict A practical, mostly credible empirical attenuation model for spirals from a large uniform sample; the W2 reference assumption is the main uncertainty and the attenuation curve is not an independent prediction. read the letter →

arxiv 1909.01572 v1 pith:4NLZZQXD submitted 2019-09-04 astro-ph.GA astro-ph.IM

Ehsan Kourkchi , R. Brent Tully , J. Don Neill , Mark Seibert , Helene M. Courtois , Alexandra Dupuy This is my paper · ORCID
classification astro-ph.GAastro-ph.IM
keywords dustattenuationspiralgalaxiesgalaxyinclinationprincipalcomponentanalysisTully-FisherrelationSDSSugrizphotometryWISEneutralhydrogen
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

Spiral galaxies dim and redden themselves through their own dust, and how strongly depends on how tilted they are to our line of sight and on what kind of galaxy they are. This paper claims that for a normal, non-pathological spiral the amount of internal attenuation is predictable from two ingredients: the galaxy's inclination and a single score $P_1$ built from three distance-independent measurements (21 cm linewidth as a mass proxy, an H I-to-infrared flux color, and infrared surface brightness). Analyzing 2,239 local spirals with SDSS $u,g,r,i,z$ and WISE $W1,W2$ photometry, it delivers a parametric model and a Gaussian-process model for attenuation, plus an average attenuation curve from 0.36 to 4.5 $\mu$m. If true, observed spiral magnitudes can be corrected for internal dust in a way that matters directly for Tully-Fisher distance measurements and for any multi-band census of galaxy light.

What carries the argument

The load-bearing object is the first principal component $P_1$ (Eq. 7), a standardized linear combination in roughly equal parts of inclination-corrected H I linewidth $\log W^i_{mx}$, the H I-to-infrared pseudo-color $C_{21Wj}=m_{21}-W_j$, and inclination-corrected infrared effective surface brightness. It carries about 70% of the scatter in the feature space and is the strongest single correlate of face-on optical-infrared color. The attenuation itself is carried by the separable product $A^{(i)}_{\lambda J}=\gamma_{\lambda J}(P_1)F_\lambda(i)$ with $F_\lambda(i)=\log[\cos^2 i+q_\lambda^2\sin^2 i]^{-1/2}$, where $q_\lambda$ tunes how steeply the line-of-sight path length grows with inclination in each band. This machinery compresses a matrix of correlated observables into one number per galaxy plus an inclination term.

What would settle it

Measure whether W2-band (or longer-wavelength) surface brightness or colors change systematically with inclination in a sample of edge-on spirals matched in $P_1$, or compare model predictions with attenuation estimates from Balmer decrements. A detected W2 decline toward edge-on, or a systematic offset between model and Balmer-based attenuation beyond the quoted scatter, would falsify the W2-transparency premise and recalibrate $\gamma_{\lambda J}$.

Watch

Extended reading notes

Core claim

The paper's central claim is that the inclination-dependent attenuation $A^{(i)}_{\lambda J}$ obeys a separable model $A^{(i)}_{\lambda J}=\gamma_{\lambda J}F_\lambda(i)$, where $F_\lambda(i)$ is a wavelength-tuned function of inclination and $\gamma_{\lambda J}$ is a third-degree polynomial in the first principal component $P_{1,J}$ of $\log W^i_{mx}$, $C_{21Wj}$, and infrared surface brightness. Attenuation grows with $P_1$ until it peaks near $P_1\simeq 1$: the most obscured spirals are relatively massive and gas-rich, while dwarf galaxies and gas-depleted, old-star-dominated systems are nearly transparent. Fitting this model to the full sample yields an average relative attenuation curve $\gamma_\lambda/\gamma_g = 1.097(\lambda_g/\lambda - 1) + 1$ from SDSS $u$ through WISE $W1$, slightly shallower at long wavelengths than the Milky Way reddening law. The paper also shows the principal component construction is transferable between WISE bands through the linear relation $[P_{1,W2}]=1.021P_{1,W1}-0.094$, and that a Gaussian-process version reaches nearly the same predictions in well-populated regions.

Load-bearing premise

The model assumes WISE W2 (4.6 $\mu$m) emission is essentially unattenuated by dust, so the colors $m_\lambda-W_2$ and all inferred attenuation values measure only optical attenuation; if W2 is itself dimmed by dust, every amplitude and the final attenuation curve would be systematically underestimated.

Editorial extensions

If this is right

  • Spiral magnitudes in $u,g,r,i,z$ can be corrected for internal dust using $P_1$ and inclination, which should reduce scatter in Tully-Fisher distance measurements.
  • Galaxies lacking W2 photometry can still be corrected by converting $P_{1,W1}$ into $[P_{1,W2}]$, as long as W1 and H I data exist.
  • The average attenuation curve of Eq. 18 supplies correction factors for any band between 0.36 and 4.5 $\mu$m and is close to, though not identical with, the Galactic extinction law.
  • The model predicts a turnover: the heaviest obscuration occurs near $P_1\simeq 1$, so corrections must depend on galaxy type, not inclination alone.
  • When H I data are missing, the surface-brightness-only approximation of Eq. B6 extends the correction at lower precision.

Reading between the lines

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

  • If W2 carries even a small amount of attenuation, all $A^{(i)}$ values and the normalized curve come out low; testing with a longer-wavelength or independent dust tracer would set the size of that calibration shift.
  • The same principal-component construction could be rebuilt using SED-inferred stellar mass and gas fraction instead of 21 cm linewidth, which would extend the correction to galaxies without H I observations if the physical driver is the dust-to-gas ratio.
  • The model is trained on galaxies with measured inclinations above $45^\circ$, so its behavior for face-on systems and for clumpy or interacting galaxies is an extrapolation that better inclinations or larger samples could test.
  • The parametric and Gaussian-process models diverge most in sparsely populated parts of the $P_1$--inclination plane, so future samples at extreme inclinations would reveal which functional form is physically preferred.
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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 / 4 minor

Summary. The paper presents an empirical study of inclination-dependent dust attenuation in spiral galaxies using a sample of 2,239 local spirals with SDSS ugriz and WISE W1/W2 photometry, HI line widths and fluxes, and carefully measured inclinations. The authors define distance-independent observables, construct a principal component P1 from linewidth, HI-to-infrared pseudo-color, and infrared surface brightness, and fit a parametric model (Eqs. 8-12) of attenuation as a function of P1 and inclination via MCMC, cross-checking with a Gaussian process model in Appendix A. They derive an average attenuation curve from 0.36 to 4.5 microns and compare it with Milky Way and SMC extinction laws. The central claim is that the degree of obscuration of a spiral galaxy is predictable from a practical suite of observables encoded in P1 and the inclination.

Significance. If the central claim holds, the model provides a practical empirical tool for correcting Tully-Fisher luminosities and for quantifying dust attenuation in spiral galaxies over a wide wavelength range. The paper's strengths include a large, well-characterized sample; a novel citizen-science approach to inclination measurements; a careful cross-check between parametric and non-parametric models; and publicly available data-reduction code. The agreement between the parametric and Gaussian process models in well-populated regions supports the robustness of the fits. However, the attenuation curve is not an independent measurement but a re-parameterization of the fitted model, and the assumption that the W2 band is unattenuated is untested and load-bearing.

major comments (3)
  1. [§3, Appendix C] The model assumes W2-band attenuation is negligible without direct test, while Appendix C corrects only the W1-band bias. Since all colors are defined as mλ − W2 and P1 is constructed from C21W2 and W2-band surface brightness, any non-negligible A_W2 would systematically lower all inferred A(i) values in Fig. 6, contaminate the principal component with inclination-dependent signal, and bias the long-wavelength end of the attenuation curve in Eq. 18 and Fig. 12. The authors should either place an observational upper limit on A_W2 (for example, from the W1−W2 color versus inclination relation with a careful treatment of stellar population trends) or include A_W2 as a free parameter in the fit to assess the resulting systematic uncertainty.
  2. [§5, Eq. 18] The derived 'average dust attenuation curve' is not an independent empirical measurement: the γλ values used to compute (γλ/γg)av come from Eqs. 10-12 with parameters taken from the same MCMC fit in Table 4, so the comparison with Milky Way and SMC extinction laws is a comparison of a model output with other models, not a validation of the model. The paper should state explicitly that the curve is a summary of the parametric model and ideally validate it against external attenuation estimates (e.g., Balmer decrements or SED-based attenuations) for the same galaxies.
  3. [§3.1, §4.2] The fiducial face-on relation is motivated using only 225 nearly face-on galaxies with arbitrarily assigned inclinations of 40±5°, and these galaxies are then excluded from the MCMC fit, which uses only galaxies with i > 45°. The model therefore extrapolates to face-on geometry without a direct check. The authors should compare the model's face-on predictions (A(i)=0) with the actual colors of the 225 face-on galaxies or hold out a validation subset to demonstrate that the linear fiducial relation extrapolates correctly.
minor comments (4)
  1. [Abstract, §2.3] The abstract lists the infrared bands as 'WISE W1, W1'; this should read 'W1, W2'. A similar typo appears in the abstract's band list.
  2. [§2] The sentence beginning 'This catalog would be presented in a following paper' appears corrupted ('2his catalog'); it should be cleaned up.
  3. [§2.5] The text repeatedly renders 'ALFALFA' with a line break as 'ALF ALF A', which is distracting and should be fixed to a single token.
  4. [§4.2, Eq. 13] The likelihood expression in Eq. 13 is missing the factor 1/2 in the exponent and the exponent is written as the square of the ratio without parentheses; the correct form appears in Eq. 14. Please correct Eq. 13 to match the standard Gaussian likelihood.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the attenuation model and curve are empirical fits with an explicit W2 reference-band anchor, not a reduction of the result to its own inputs.

full rationale

The central derivation is an empirical, in-sample description rather than a circular reduction. Attenuation is defined through Eq. 8 and Eq. 15 as the residual of m_lambda - W2 from a linear fiducial relation in P1, while P1 is constructed from log(Wi_mx), C21W2 = m21 - W2, and W2 surface brightness (Eqs. 5-7). This does place W2 on both sides of the regression, and the paper assumes W2 is negligibly attenuated when interpreting colors as dust diagnostics: 'The optical-infrared colors (m_lambda - W_j), characterizes the attenuation of optical fluxes through the known property that the dust obscuration diminishes as wavelengths increases until it is ultimately very tiny or negligible at infrared bands.' Appendix C explicitly corrects only W1 contamination ('Although the effect of dust obscuration is very small on W1-band fluxes, it is not negligible'), leaving W2 as an untested anchor. That is a real systematic assumption and a correctness risk, but it is not circular: the optical-band A values and the g-relative attenuation ratios in Figs. 10-12 are free parameters fitted to 2,239 galaxies and are not algebraically forced by the definition of A. The average curve in Eq. 18 is similarly presented as a fit to the measured relative attenuation ('We find the following linear relationship for the average of our measured relative dust attenuation by fitting a straight line...'), not as an independent out-of-sample prediction, and it is compared against external Milky Way, SMC, and Salim et al. curves. The inclination function F(i) in Eq. 11 is adopted transparently from the standard empirical Tully et al. (1998) formalism, and the self-citations provide data, photometry pipelines, or an explicitly stated functional ansatz; none functions as a uniqueness theorem that forces the target result. No step reduces by construction to its own input, so the correct finding is no significant circularity.

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

The central model rests on a substantial set of fitted parameters (per-band slopes, intercepts, polynomial coefficients, and geometry parameters) plus several domain assumptions. No new physical entities are introduced; the principal component P1 is a constructed observable, not an invented physical quantity.

free parameters (7)
  • alpha_lambda, beta_lambda (per-band fiducial color relation) = Table 4; e.g., g-band alpha=0.287, beta=-0.424
    Fitted via MCMC to all galaxies with inclination >45 degrees; define the face-on color relation used to isolate attenuation.
  • C_lambda^(0..3) (per-band polynomial coefficients for gamma_lambda(P1)) = Table 4; e.g., r-band C3=-0.012, C2=-0.054, C1=0.069, C0=0.693
    Third-degree polynomial parameterization of the attenuation amplitude as a function of P1, adopted after exploring functional forms.
  • q_lambda (per-band inclination geometry parameter) = Table 4 via theta_lambda = -2 log(q_lambda); e.g., r-band theta=3.237
    Sets the peak of F(i) at edge-on and the sensitivity to inclination for each waveband; fitted per band.
  • rho_lambda (Appendix B wavelength-dependent factors) = 1.45 (u), 0.77 (r), 0.63 (i), 0.52 (z), 0.06 (W1)
    Used in the surface-brightness-only attenuation relation; fitted simultaneously across bands with a third-degree polynomial.
  • GP hyperparameters (sigma_e, sigma_f, l0, l1) = Table 5; e.g., r-band log(l0)=3.08, log(l1)=5.43, log(sigma_f^2)=-0.88, sigma_e^2=0.097
    For the alternative Gaussian process model; not used in the central parametric model but reported as a cross-check.
  • Slope of attenuation curve (Eq. 18) = 1.097 +/- 0.060
    Best-fit linear relation for gamma_lambda/gamma_g versus (lambda_g/lambda - 1), forced through the g-band point.
  • P1 standardization factors (u_i, sigma_i) = W2: u=(2.47,1.63,23.35), sigma=(0.18,1.15,1.38); W1 similar in Table 3
    Mean and standard deviation of the three input features used to standardize before PCA; derived from the inclined-galaxy sample and needed to define P1.
assumptions (7)
  • domain assumption The face-on optical-infrared color of a spiral is a linear function of the principal component P1 (Eq. 9).
    Observed as a strong correlation in Fig. 1 and 3 but assumed linear for all galaxies; if nonlinear, the fitted alpha and beta would be biased, and the attenuation residuals would absorb the nonlinearity.
  • ad hoc to paper The inclination-dependent attenuation is separable: A(i) = gamma_lambda(P1) * F(i) (Eq. 10).
    Adopted following Tully et al. (1998); no physical derivation. The true coupling between galaxy properties and geometry could be non-separable.
  • ad hoc to paper The geometry function F(i) = log[cos^2 i + q_lambda^2 sin^2 i]^{-1/2} (Eq. 11).
    Phenomenological form related to the axial-ratio model; q_lambda is a fitted hyperparameter with no direct geometric meaning, as the authors acknowledge.
  • ad hoc to paper gamma_lambda is a third-degree polynomial in P1 (Eq. 12).
    Chosen after exploring functional forms; the polynomial shape is not derived and may not generalize outside the fitted P1 range, approximately -2 to 3.
  • domain assumption W2-band emission is negligibly attenuated by dust, so W2 is a dust-free reference.
    Central to defining the color excess; only W1 bias is corrected in Appendix C, and W2 attenuation is not directly measured or modeled.
  • domain assumption Visual inclination estimates are statistically accurate to +/-4 degrees rms with no systematic bias.
    Stated in Section 2.5; the full model uses these inclinations to compute inclination-corrected linewidths and surface brightness corrections.
  • domain assumption The sample of 2,239 spirals is representative of the local spiral population for deriving a global attenuation curve.
    Selection required high-quality HI detections, full SDSS and WISE photometry, and an inclination cut at 45 degrees, which may bias toward gas-rich, moderately inclined systems.

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Pith. "Pith review of Global Attenuation in Spiral Galaxies in Optical and Infrared Bands." pith.science (2026). https://pith.science/paper/4NLZZQXD

@misc{pith2026190901572,
  author       = {Pith},
  title        = {Pith review of: Global Attenuation in Spiral Galaxies in Optical and Infrared Bands},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4NLZZQXD}},
  note         = {Machine review of arXiv:1909.01572}
}
read the original abstract

The emerging light from a galaxy is under the influence of its own interstellar medium, as well as its spatial orientation. Considering a sample of 2,239 local spiral galaxies in optical (SDSS u, g, r, i, and z) and infrared bands (WISE W1, W1), we study the dependency of the global intrinsic attenuation in spiral galaxies on their morphologies, sizes, and spatial inclinations. Reddening is minimal at the extremes of low mass and gas depletion and maximal in galaxies that are relatively massive and metal-rich and still retain substantial gas reserves. A principal component constructed from observables that monitor galaxy mass, relative HI content to old stars, and infrared surface brightness is strongly correlated with the amplitude of obscuration. We determine both a parametric model for dust obscuration and a non-parametric model based on the Gaussian process formalism. An average dust attenuation curve is derived for wavelengths between 0.36 and 4.5 microns.

Figures

Figures reproduced from arXiv: 1909.01572 by the authors.

Figure 2
Figure 2. Deviation of r − W2 colors from the best fitted line in panel (d) of [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 1
Figure 1. r−W2 vs. distance independent observ￾ables for galaxies more face-on than 45◦ . Dashed lines represent the best linear fit with minimizing residuals along the horizontal axis. Each point rep￾resents a galaxy with blue, green and red colors corresponding to C21W2 < 1, 1 < C21W2 < 3 and C21W2 > 3 respectively. In each panel, Corr. is the correlation factor for plotted parameters [PITH_FULL_IMAGE:figures/full_fig_p011… view at source ↗
Figure 3
Figure 3. Correlation matrices for distance independent observables for spirals more face-on than 45◦ where the absolute value of correlation coefficients are reported. the same analysis with and without the concen￾tration parameter does not significantly change the outcomes. As such, we decided to remove the concentration parameter from our analysis due to its small correlations and to avoid dis￾tracting our model by incorpo… view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: The posterior distribution of the optimized parameters to estimate r − W2 = γ (o) r,W2 + A (i) r,W2 . Contours represent σ/2, σ, 3σ/2 and 2σ levels of the 2-dimensional distributions and they enclose 12%, 39%, 68% and 86% of the distributed points respectively. To faci…
Figure 5
Figure 5. Figure 5: First principal component constructed based on the observable features (i.e. log(Wi mx), C21W j and hµj i (i) e ) versus the inclination corrected color terms, ∆m (o) λJ = (mλ − W2) − A (i) λ,W2 = αλP1,W2 + βλ. Each point represents a galaxy whose inclination is betwee…
Figure 6
Figure 6. Figure 6: Deviation of galaxies from the fiducial relation, Eq. 9 due to their inclination, A(i) λ,W2, versus their inclination for different intervals of P1,W2. Each gray dot represents an individual galaxy and color points are the average of data points in 5◦ inclination bins,…
Figure 7
Figure 7. Figure 7 [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]
Figure 8
Figure 8. Figure 8: The r-band inclination dependent dust obscuration in spiral galaxies, A (i) r,W2 , as a function of their spatial inclination relative to the observer line-of-sight and their main principal component, P1 (Eq 5). Left: The best fitted parametric model formulated by Eq. …
Figure 9
Figure 9. Figure 9: The inclination dependent component of dust attenuation, Fλ(i), as defined in Eq. 11, with separate qλ values taken from [PITH_FULL_IMAGE:figures/full_fig_p022_9.png]
Figure 10
Figure 10. Figure 10: Similar to [PITH_FULL_IMAGE:figures/full_fig_p023_10.png]
Figure 11
Figure 11. Figure 11: Similar to [PITH_FULL_IMAGE:figures/full_fig_p024_11.png]
Figure 12
Figure 12. Figure 12: Relative attenuation in different bands. attenuation values are normalized with respect to those in g-band. Magenta dashed curve shows the Milky Way dust extinction (Cardelli et al. 1989). Blue dashed dotted curve display the relative reddening in the Small Magellanic…
Figure 13
Figure 13. Figure 13: Similar to [PITH_FULL_IMAGE:figures/full_fig_p029_13.png]
Figure 14
Figure 14. Figure 14: Top row: The discrepancy between the inclination dependent attenuation in spirals calculated based on W1 and W2-band main principal components, A (i) W2 − A (i) W1 , versus inclination in different optical bands. Bottom row: The same as the top row but for the differe…
Figure 15
Figure 15. Figure 15: First principal component calculated using W2-band photometric data, P1,W2 versus that obtained from the W1-band images, P1,W1. Each black dot represent a galaxy with inclination > 45◦ . The red line shows the best fitted line with the slope m and intercept b. The RMS…

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Works this paper leans on

55 extracted references · 15 canonical work pages · cited by 1 Pith paper

  1. [1]

    r 7D|5梅 LKDGG gm۶W utt o ovMD S^ 7D 9_

    thebibliography [1] 20pt to REFERENCES 6pt =0pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command Each re...

    2017

  2. [2]

    W., & O'Neil , M

    Ambikasaran , S., Foreman-Mackey , D., Greengard , L., Hogg , D. W., & O'Neil , M. 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 10.1109/TPAMI.2015.2448083

    arXiv 2015

  3. [3]

    1996, , 117, 393, 10.1051/aas:1996164

    Bertin , E., & Arnouts , S. 1996, , 117, 393, 10.1051/aas:1996164

    doi:10.1051/aas:1996164 1996

  4. [4]

    S., Maiolino , R., Peng , Y., et al

    Bothwell , M. S., Maiolino , R., Peng , Y., et al. 2016, , 455, 1156, 10.1093/mnras/stv2121

    work page doi:10.1093/mnras/stv2121 2016

  5. [5]

    1995, , 296, 64

    Bottinelli , L., Gouguenheim , L., Paturel , G., & Teerikorpi , P. 1995, , 296, 64

    work page 1995

  6. [6]

    A., Clayton , G

    Cardelli , J. A., Clayton , G. C., & Mathis , J. S. 1989, , 345, 245, 10.1086/167900

    doi:10.1086/167900 1989

  7. [8]

    2009, , 693, 1045, 10.1088/0004-637X/693/2/1045

    Cho , J., & Park , C. 2009, , 693, 1045, 10.1088/0004-637X/693/2/1045

    work page doi:10.1088/0004-637x/693/2/1045 2009

  8. [9]

    M., & Tully , R

    Courtois , H. M., & Tully , R. B. 2012, , 749, 174, 10.1088/0004-637X/749/2/174

    doi:10.1088/0004-637x/749/2/174 2012

Show all 55 references
  1. [10]

    M., Tully , R

    Courtois , H. M., Tully , R. B., Fisher , J. R., et al. 2009, , 138, 1938, 10.1088/0004-6256/138/6/1938

    2009 doi

  2. [12]

    G., et al

    de Vaucouleurs , G., de Vaucouleurs , A., Corwin , Jr., H. G., et al. 1991, Third Reference Catalogue of Bright Galaxies. Volume I: Explanations and references. Volume II: Data for galaxies between 0 ^ h and 12 ^ h . Volume III: Data for galaxies between 12 ^ h and 24 ^ h

    1991

  3. [13]

    Draine , B. T. 2011, Physics of the Interstellar and Intergalactic Medium

    2011

  4. [14]

    2015, , 449, 3274, 10.1093/mnras/stv552

    Feldmann , R. 2015, , 449, 3274, 10.1093/mnras/stv552

    2015 doi

  5. [15]

    Fitzpatrick , E. L. 1999, , 111, 63, 10.1086/316293

    1999 doi

  6. [16]

    W., Lang , D., & Goodman , J

    Foreman-Mackey , D., Hogg , D. W., Lang , D., & Goodman , J. 2013, , 125, 306, 10.1086/670067

    2013 doi

  7. [18]

    P., Salzer , J

    Giovanelli , R., Haynes , M. P., Salzer , J. J., et al. 1995, , 110, 1059, 10.1086/117586

    1995 doi

  8. [19]

    D., Clayton , G

    Gordon , K. D., Clayton , G. C., Misselt , K. A., Landolt , A. U., & Wolff , M. J. 2003, , 594, 279, 10.1086/376774

    2003 doi

  9. [20]

    1992, , 391, 617, 10.1086/171375

    Han , M. 1992, , 391, 617, 10.1086/171375

    1992 doi

  10. [21]

    P., Giovanelli , R., Martin , A

    Haynes , M. P., Giovanelli , R., Martin , A. M., et al. 2011, , 142, 170, 10.1088/0004-6256/142/5/170

    2011 doi

  11. [22]

    P., Giovanelli , R., Kent , B

    Haynes , M. P., Giovanelli , R., Kent , B. R., et al. 2018, , 861, 49, 10.3847/1538-4357/aac956

    2018 doi

  12. [23]

    Huang , J.-S., Ashby , M. L. N., Barmby , P., et al. 2007, , 664, 840, 10.1086/519241

    2007 doi

  13. [24]

    Inoue , A. K. 2011, Earth, Planets, and Space, 63, 1027, 10.5047/eps.2011.02.013

    2011 doi

  14. [25]

    C., Katz , D

    Jacob , J. C., Katz , D. S., Berriman , G. B., et al. 2010. 1010.036

    2010

  15. [26]

    Kent , S. M. 1985, , 59, 115, 10.1086/191066

    1985 doi

  16. [27]

    2015, , 450, 342, 10.1093/mnras/stv522

    Kudritzki , R.-P., Ho , I.-T., Schruba , A., et al. 2015, , 450, 342, 10.1093/mnras/stv522

    2015 doi

  17. [28]

    D., & Misselt , K

    Law , K.-H., Gordon , K. D., & Misselt , K. A. 2018, , 236, 32, 10.3847/1538-4365/aabf41

    2018 doi

  18. [29]

    K., Bolatto , A., Gordon , K., et al

    Leroy , A. K., Bolatto , A., Gordon , K., et al. 2011, , 737, 12, 10.1088/0004-637X/737/1/12

    2011 doi

  19. [30]

    2019, , 484, 2587, 10.1093/mnras/sty3323

    Marassi , S., Schneider , R., Limongi , M., et al. 2019, , 484, 2587, 10.1093/mnras/sty3323

    2019 doi

  20. [31]

    Masci , F. 2013. 1302.010

    2013

  21. [32]

    J., & Fowler , J

    Masci , F. J., & Fowler , J. W. 2009, in Astronomical Society of the Pacific Conference Series, Vol. 411, Astronomical Data Analysis Software and Systems XVIII, ed. D. A. Bohlender , D. Durand , & P. Dowler , 67

    2009

  22. [33]

    L., Giovanelli , R., & Haynes , M

    Masters , K. L., Giovanelli , R., & Haynes , M. P. 2003, , 126, 158, 10.1086/375758

    2003 doi

  23. [34]

    L., Nichol , R., Bamford , S., et al

    Masters , K. L., Nichol , R., Bamford , S., et al. 2010, , 404, 792, 10.1111/j.1365-2966.2010.16335.x

    2010

  24. [35]

    D., Seibert , M., Tully , R

    Neill , J. D., Seibert , M., Tully , R. B., et al. 2014, , 792, 129, 10.1088/0004-637X/792/2/129

    2014 doi

  25. [36]

    B., & Sandage , A

    Oke , J. B., & Sandage , A. 1968, , 154, 21, 10.1086/149737

    1968 doi

  26. [37]

    2003, , 412, 45, 10.1051/0004-6361:20031411

    Paturel , G., Petit , C., Prugniel , P., et al. 2003, , 412, 45, 10.1051/0004-6361:20031411

    2003 doi

  27. [39]

    E., & Williams, C

    Rasmussen, C. E., & Williams, C. K. I. 2006, Gaussian Processes for Machine Learning (MIT Press)

    2006

  28. [40]

    , S., Bailer-Jones , C

    Rezaei Kh. , S., Bailer-Jones , C. A. L., Hanson , R. J., & Fouesneau , M. 2017, , 598, A125, 10.1051/0004-6361/201628885

    2017 doi

  29. [41]

    Salim , S., Boquien , M., & Lee , J. C. 2018, , 859, 11, 10.3847/1538-4357/aabf3c

    2018 doi

  30. [42]

    2015, , 219, 4, 10.1088/0067-0049/219/1/4

    Salo , H., Laurikainen , E., Laine , J., et al. 2015, , 219, 4, 10.1088/0067-0049/219/1/4

    2015 doi

  31. [43]

    F., & Finkbeiner , D

    Schlafly , E. F., & Finkbeiner , D. P. 2011, , 737, 103, 10.1088/0004-637X/737/2/103

    2011 doi

  32. [44]

    J., Finkbeiner , D

    Schlegel , D. J., Finkbeiner , D. P., & Davis , M. 1998, , 500, 525, 10.1086/305772

    1998 doi

  33. [45]

    M., Haynes , M

    Springob , C. M., Haynes , M. P., Giovanelli , R., & Kent , B. R. 2005, , 160, 149, 10.1086/431550

    2005 doi

  34. [46]

    A., Heckman , T

    Tremonti , C. A., Heckman , T. M., Kauffmann , G., et al. 2004, , 613, 898, 10.1086/423264

    2004 doi

  35. [47]

    B., & Courtois , H

    Tully , R. B., & Courtois , H. M. 2012, , 749, 78, 10.1088/0004-637X/749/1/78

    2012 doi

  36. [48]

    B., Courtois , H

    Tully , R. B., Courtois , H. M., & Sorce , J. G. 2016, , 152, 50, 10.3847/0004-6256/152/2/50

    2016 doi

  37. [49]

    B., & Fisher , J

    Tully , R. B., & Fisher , J. R. 1977, , 54, 661

    1977

  38. [50]

    B., & Pierce , M

    Tully , R. B., & Pierce , M. J. 2000, , 533, 744, 10.1086/308700

    2000 doi

  39. [51]

    B., Pierce , M

    Tully , R. B., Pierce , M. J., Huang , J.-S., et al. 1998, , 115, 2264, 10.1086/300379

    1998 doi

  40. [52]

    B., Verheijen , M

    Tully , R. B., Verheijen , M. A. W., Pierce , M. J., Huang , J.-S., & Wainscoat , R. J. 1996, , 112, 2471, 10.1086/118196

    1996 doi

  41. [53]

    T., & Ryden , B

    Unterborn , C. T., & Ryden , B. S. 2008, , 687, 976, 10.1086/591898

    2008 doi

  42. [54]

    L., Eisenhardt , P

    Wright , E. L., Eisenhardt , P. R. M., Mainzer , A. K., et al. 2010, , 140, 1868, 10.1088/0004-6256/140/6/1868

    2010 doi

  43. [55]

    2012, , 421, 486, 10.1111/j.1365-2966.2011.20327.x

    Xiao , T., Wang , T., Wang , H., et al. 2012, , 421, 486, 10.1111/j.1365-2966.2011.20327.x

    2012

  44. [56]

    M., Kauffmann , G., & Guo , Q

    Yates , R. M., Kauffmann , G., & Guo , Q. 2012, , 422, 215, 10.1111/j.1365-2966.2012.20595.x

    2012

  45. [57]

    S., Wyse , R

    Yip , C.-W., Szalay , A. S., Wyse , R. F. G., et al. 2010, , 709, 780, 10.1088/0004-637X/709/2/780

    2010 doi

  46. [58]

    G., Adelman , J., Anderson , Jr., J

    York , D. G., Adelman , J., Anderson , Jr., J. E., et al. 2000, , 120, 1579, 10.1086/301513

    2000 doi

  47. [59]

    J., Dima , G

    Zahid , H. J., Dima , G. I., Kudritzki , R.-P., et al. 2014, , 791, 130, 10.1088/0004-637X/791/2/130

    2014 doi

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.