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REVIEW 5 major objections 4 minor 119 references

The SEDs of very long-period cepheids in the Milky Way, the Magellanic Clouds, M31 and M33

T0 review · 5 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Pith's one-line read Among 35 very long-period Cepheids in the Magellanic Clouds, M31, and M33, only one shows a significant infrared excess — dust is unlikely to bias the Cepheid distance scale.

desk verdict A careful multi-galaxy census that confirms IR excess is rare in external-gala xy Cepheids, though the null result is less airtight than the abstract suggests. read the letter →

arxiv 2507.04757 v1 pith:ETKDNMQS submitted 2025-07-07 astro-ph.GA astro-ph.SR

classification astro-ph.GAastro-ph.SR PACS 97.30.Gj
keywords spectralenergydistributionsclassicalCepheidsinfraredexcesscircumstellardustMagellanicCloudsM31M33distancescale
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

The paper asks whether the very longest-period classical Cepheids — stars with pulsation periods above 50 days that are used as standard candles — are commonly surrounded by dust that adds extra infrared light to their spectra. It builds spectral energy distributions (their light spread across wavelengths) for 55 such stars in the Milky Way, the Large and Small Magellanic Clouds, M31, and M33, then fits each with model stellar atmospheres, adding a dust shell only when the data require it. Across the 35 stars in the external galaxies, exactly one clear infrared excess survives (LMC-CEP-0619), with none in the SMC, M31, or M33, and one further excess in the Milky Way (II Car) whose status as a classical Cepheid is unclear. A sympathetic reader would care because infrared excess can contaminate the near- and mid-infrared photometry that feeds period-luminosity relations; the paper concludes that for these long-period Cepheids the impact on the distance scale should be low at best.

What carries the argument

The central tool is the spectral energy distribution assembled from literature photometry spanning ultraviolet to mid-infrared wavelengths (GALEX, Gaia, 2MASS, VMC, WISE, IRAC, MIPS, and others). Each SED is fitted with MARCS model atmospheres at a fixed adopted distance and reddening, yielding the best luminosity and effective temperature; for stars where long-wavelength flux remains unexplained, a spherical dust shell is added with the dust optical depth and inner-edge temperature as free parameters, and the Bayesian information criterion decides whether the dust model is genuinely better. The decisive step is image inspection: WISE W1/W3 and IRAC channels 1 and 4 cut-outs are used to verify that claimed excess emission actually coincides with the star, which removes the M31 and M33 candidates whose infrared flux turned out to be diffuse or blended background instead.

What would settle it

Point new mid- or far-infrared observations at the 19 Cepheids in M31 and M33 — most directly the four that currently lack data beyond the near-infrared (M31-PSO009.76, M33-013331, M33-V00021, M33-013405) — for example with JWST/MIRI imaging or spectroscopy; if several of these stars show excess emission above the best-fitting photosphere, or silicate features appear in the spectra, the conclusion that long-period Cepheids in external galaxies are dust-free would be overturned.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that very long-period classical Cepheids in external galaxies do not commonly present an infrared excess in their spectral energy distributions. Fitting MARCS model atmospheres with the MoD radiative-transfer code to photometry from the ultraviolet to 24 micrometers, and checking candidate excesses against WISE and IRAC images to discard blended or diffuse background emission, the author finds one significant excess in the LMC (LMC-CEP-0619) and none among the seven SMC, twelve M31, and seven M33 objects; in the Milky Way, II Car shows an excess but it is unclear whether it is a classical Cepheid or a Type II Cepheid. This runs contrary to earlier Galactic work that hinted infrared excess might be more prominent in Milky Way Cepheids than in the Magellanic Clouds. The same fits locate nearly all objects inside the classical Cepheid instability strip in the Hertzsprung-Russell diagram, with masses near 10-15 solar masses, while the outliers are largely the objects already re-classified as Type II Cepheids; for Milky Way objects, changing the adopted distance or reddening can move stars in and out of the strip, so those positions carry larger uncertainty.

Load-bearing premise

The finding that no infrared excess exists in most of these stars rests on the available mid-infrared photometry being sensitive enough to reveal one — the paper itself states that the absence of proof of infrared excess is not proof of absence, and four of the M31 and M33 objects have no photometry beyond the near-infrared.

Editorial extensions

If this is right

  • If the near-null holds, mid-infrared photometry of long-period Cepheids in the LMC, SMC, M31, and M33 is not contaminated by circumstellar dust, so period-luminosity relations built from those bands carry no dust-excess bias for these objects.
  • The earlier hint that infrared excess is more prominent among Milky Way Cepheids than in the Magellanic Clouds is not confirmed for the longest-period stars, weakening the case that metallicity drives the fraction of dusty Cepheids.
  • The HRD placement inside the classical Cepheid instability strip for most objects supports treating the >50-day variables as classical Cepheids with masses near 10-15 solar masses, while the confirmed outliers clarify which catalog entries are actually Type II Cepheids.
  • For the Milky Way subsample, distance and reddening uncertainties move stars substantially in the HRD, so improved parallaxes (such as Gaia DR4) are needed before those objects can pin down luminosities or effective temperatures tightly.

Reading between the lines

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

  • A direct test the paper leaves implicit: longer-wavelength photometry for the four M31 and M33 objects that currently stop at the near-infrared could still reveal warm dust, so the zero-detection count in those galaxies is an upper limit until such data exist.
  • If the genuine excesses (LMC-CEP-0619 and II Car) turn out to come from free-free emission of ionized gas rather than dust — an option the paper itself discusses — then the interesting comparison across galaxies is not dust formation but the prevalence of circumstellar ionized gas around the most luminous Cepheids.
  • Extending the same fitting pipeline to the shorter-period Cepheids in M31 and M33 (left unstudied here) would test whether the roughly 5 percent excess fraction seen in Milky Way stars is universal; a null there would make the Milky Way the anomaly rather than the rule.
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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

5 major / 4 minor

Summary. The paper constructs and fits SEDs for 55 long-period (P > 50 d) classical Cepheid candidates in the Milky Way, LMC, SMC, M31, and M33, using literature photometry and MARCS model atmospheres, optionally adding a dust shell. Distances and reddenings are adopted from the literature, and WISE/IRAC images are inspected to verify candidate infrared excesses. The principal result is that only LMC-CEP-0619 shows a confirmed IR excess, with II Car in the MW being an additional but possibly Type II Cepheid case; no IR excess is found in the SMC, M31, or M33. The paper also places the stars in the HRD and derives PL and PR classifications for the MW/Bulge objects, concluding that the impact of IR excess on the Cepheid distance scale is likely small.

Significance. If the null result is robust, the paper provides an important constraint: warm circumstellar dust around long-period Cepheids is not common in external galaxies, and the Cepheid distance scale is unlikely to be significantly biased by IR excess. The analysis is careful and homogeneous across five galaxies, and the two confirmed excesses (LMC-CEP-0619 and II Car) are supported by independent WISE/IRAC image inspection. The public release of the SED fits and the use of established fitting tools are strengths. However, the weight of the central null claim depends on the sensitivity of the available MIR data, which is never quantified; the paper itself acknowledges the relevant caveat in Sec. 4.3. As it stands, the abstract overstates the contrast with the Milky Way for the same period range.

major comments (5)
  1. [Sec. 4.3, Table 4] The central null claim is not accompanied by a quantified detection threshold. The paper states in Sec. 4.3 that four objects have no photometry beyond the NIR and that 'the absence of proof for infrared excess is not the proof of absence,' but this caveat applies more broadly: for the remaining M31 and M33 stars the MIR data are often single-epoch and shallow, and the SED fits show large reduced chi-squared values (Table A.2). No test is made of whether an excess like that of LMC-CEP-0619 (T_d ~ 925 K, tau_V ~ 0.08; Table 2) would be statistically preferred given the actual photometric error bars. I request an injection/recovery test or, at minimum, upper limits on tau_V for each non-detection, and a corresponding softening of the abstract and conclusion.
  2. [Abstract, Table 4] The claimed contrast with the Milky Way is overstated. For P > 50 d, Table 4 gives MW 0/5-1/8, LMC 1/9, SMC 0/7, and M31+M33 0/19; these rates are statistically indistinguishable from one another. The 'contrary to earlier work' phrasing in the abstract implicitly compares with the shorter-period Galactic sample (16/350 for P < 50 d) rather than with the long-period sample studied here. The abstract and summary should be rephrased to report that no strong excess is seen in the long-period external-galaxy sample, and that a comparison with MW long-period Cepheids is not yet statistically meaningful.
  3. [Sec. 2 / Sec. 1] The Introduction calls this 'a complete sample of long-period Cepheids,' but Sec. 2 states that known CCs in M31 and M33 with P > 50 d are not included if they are absent from the Gaia vari_cepheid table. The sample is therefore not a complete census of long-period Cepheids in those galaxies, and the null rates in Table 4 are rates for a Gaia-selected subsample. The completeness statement should be corrected and the selection function should be stated explicitly as a limitation on the external-galaxy null result.
  4. [Sec. 4.3, Table 2] The BIC comparison is not made on equal footing. The 'no dust' model in Table A.2 excludes clipped outliers, while the dust model appears to use a different treatment of the photometric points; for LMC-CEP-0619 the reduced chi-squared is actually larger for the dust model (54.8) than for the no-dust model (51.3), yet the dust model is preferred by BIC. Because six of the eight BIC-preferred candidates are later rejected on the basis of image inspection, the statistical selection step is not the decisive evidence. I recommend reporting BIC or likelihood values computed with identical outlier handling and identical data sets, and specifying the criterion used to call an image detection 'associated' with the central star.
  5. [Table A.2, Sec. 4.4] Distance errors are deliberately not propagated into the quoted luminosity errors (Table A.2 note). For MW stars with fractional distance uncertainties of 10-30% (e.g., ATO093m31 at 15.78 +/- 2.58 kpc), this affects the HRD, PL, and PR classifications in Table 3, and hence the denominator of the MW >50 d excess rate in Table 4. The alternative-distance runs in Sec. 4.4 address this partially, but the standard-model classifications in Table 3 are used without this uncertainty, so the MW comparison rate should carry a corresponding caveat.
minor comments (4)
  1. [Sec. 3.3] The description of how Teff errors are derived ('models within a certain range above the minimum') should specify the adopted Delta-chi-squared threshold and how the 125 K interpolation step enters the quoted uncertainties.
  2. [Table B.1] The W3/W4 quality cuts are given in magnitude-error terms, but the number of epochs and the pulsation phase of single-epoch MIR measurements are not documented; phase-dependent photometry could masquerade as scatter or as a false excess.
  3. [Figs. C.2 and C.3] The visual classification of 'not clearly associated' would be more reproducible with a quantitative measure, such as PSF-matched aperture photometry at the expected stellar position versus the local background.
  4. [Title / Sec. 2] The phrase 'very long-period' is not defined; since the sample starts at 50 d and includes many objects below the usual 80 d ultra-long-period threshold, 'long-period' may be more accurate.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the IR-excess result is derived from independent SED fitting, literature distances/reddenings, and image inspection, with the author's own PL relations used only as auxiliary classification aids.

full rationale

The central claim — that only one long-period Cepheid in the LMC and none in the SMC, M31, or M33 show a significant infrared excess — is obtained from SED fits using MARCS model atmospheres and the MoD/DUSTY radiative-transfer code, with distances and reddenings adopted from external literature and the excess assessed via BIC comparison and direct WISE/IRAC image inspection. No parameter entering the SED fitting is calibrated to the IR-excess outcome itself, and no relation derived from the same data is used to define the excess. The author's earlier period-luminosity relations (Groenewegen & Lub 2023; Groenewegen & Jurkovic 2017) appear only as auxiliary tools for classifying CC versus T2C candidates and as comparison lines in the PL/PR diagrams; they do not enter the SED fits or the IR-excess determination. The paper explicitly acknowledges the sensitivity limitation for objects without mid-infrared photometry ('the absence of proof for infrared excess is not the proof of absence'), which is an honest data-completeness caveat rather than a circular step. Self-citations to G20 and Groenewegen & Lub (2023) are legitimate continuations of prior work with external distance anchors and do not carry the load of the present null result. Therefore no circular step is exhibited, and the derivation is self-contained with respect to its central claim.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The central claims rest on literature distances and reddenings, model atmospheres, and the completeness of the constructed sample. No new physical entities are introduced; the fitted dust parameters are standard model outputs, not independent inputs.

free parameters (4)
  • Bolometric luminosity L = per star, e.g., 16,668 L_sun for M31-PSO009.76 (Table A.2)
    Best-fitting luminosity from SED fit for each star; the main derived quantity.
  • Effective temperature T_eff = per star from a 125 K grid, e.g., 4625 K for M31-PSO009.76
    Grid search over MARCS models to minimize chi-squared.
  • Dust temperature at inner radius T_d = 330-1858 K for the 9 dust-model candidates (Table 2)
    Fitted free parameter in the dust radiative-transfer models.
  • Dust optical depth tau_V = 0.082-1.161 for the 9 dust-model candidates (Table 2)
    Fitted free parameter controlling the amount of dust.
assumptions (6)
  • domain assumption MARCS model atmospheres at log g=1.5 represent Cepheid photospheres
    Used for all SED fits (Sect. 3.3); log g is fixed, not fitted.
  • domain assumption DUSTY radiative transfer with spherical symmetry describes any dust shell
    Dust models assume a spherically symmetric shell (Sect. 4.3).
  • domain assumption Adopted distances from the literature are correct
    Distance is an input to the SED fitting; errors are not propagated (Table A.2 note).
  • domain assumption Adopted reddening values from 3D maps are correct
    E(V-I) or A_V taken from Lallement et al. (2022), Vergely et al. (2022), Skowron et al. (2021).
  • ad hoc to paper The Gaia vari_cepheid and OGLE samples are representative of long-period Cepheids in each galaxy
    The sample is built from these catalogs; incompleteness is acknowledged in Sect. 2 and 4.3.
  • domain assumption Theoretical instability strips and evolutionary tracks (De Somma 2021; Anderson 2016; MIST) are accurate
    Used for HRD classification (Sect. 4.2).

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Cite this review

Pith. "Pith review of The SEDs of very long-period cepheids in the Milky Way, the Magellanic Clouds, M31 and M33." pith.science (2026). https://pith.science/paper/ETKDNMQS

@misc{pith2026250704757,
  author       = {Pith},
  title        = {Pith review of: The SEDs of very long-period cepheids in the Milky Way, the Magellanic Clouds, M31 and M33},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ETKDNMQS}},
  note         = {Machine review of arXiv:2507.04757}
}
read the original abstract

The spectral energy distributions (SEDs) of 20 Milky Way (MW), 9 Large Magellanic Cloud (LMC), 7 Small Magellanic Cloud (SMC), 12 M31, and 7 M33 (classical) Cepheids with periods longer than 50 days were constructed using photometric data from the literature and fitted with model atmospheres with the aim of identifying objects with an infrared excess. The SEDs were fitted with stellar photosphere models to derive the best-fitting luminosity and effective temperature; a dust component was added when required. The distance and reddening values were taken from the literature. WISE and IRAC images were inspected to verify whether potential excess emission was related to the central objects. Only one star with a significant infrared (IR) excess was found in the LMC and none in the SMC, M31, and M33, contrary to earlier work on the MW suggesting that IR excess may be more prominent in MW Cepheids than in the Magellanic Clouds. One additional object in the MW was found to have an IR excess, but it is unclear whether it is a classical Cepheid or a type-{\sc ii} Cepheid. The stars were plotted in a Hertzsprung--Russell diagram (HRD) and compared to evolutionary tracks for CCs and to theoretical instability strips. For the large majority of stars, the position in the HRD is consistent with the instability strip. For stars in the MW uncertainties in the distance and reddening can significantly change their position in the HRD.

Figures

Figures reproduced from arXiv: 2507.04757 by the authors.

Figure 1
Figure 1. Examples of best-fitting models assuming no dust. The upper panels show the observations (with error bars) and the model. The lower panel shows the residuals. Outliers that have been clipped are plotted with an (arbitrary) error bar of 3.0 mag. ically symmetric. Models with different initial guesses were run (Td starting from 250, 400, 600, 800, 1000, and 1500 K; τd start￾ing from 0.1, 0.3, 0.6, and 1). The Bayesian… view at source ↗
Figure 2
Figure 2. Hertzsprung–Russell diagram. The left panel presents an overview while the red panel focusses on the T2Cs. The symbols are follows: filled squares (SMC), open squares (LMC), open triangles (M31), filled triangles (M33), filled circles (Galactic Bulge), and open circles (MW). Stars located outside the bulk of objects are identified. The blue and red lines indicate the blue and red edge of the IS of CCs. The results f… view at source ↗
Figure 3
Figure 3. Examples of best-fitting models assuming dust (right) compared to no dust (left panels). We note the difference in the range of the ordinate in the left and right bottom panels. The other six objects are shown in Fig. C.1. In the models without dust some photometric points are considered outliers and are plotted with a large error bar, instead of omitting them. lematic (how 1000 K dust can form around a 6000 K centr… view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Period-Mbol and PR relations. The error bars in Mbol are plotted but are typically smaller than the symbol size. The symbols are follows: filled squares (SMC), open squares (LMC), open triangles (M31), filled triangles (M33), filled circles (Galactic Bulge), and open c…

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Works this paper leans on

119 extracted references · 63 canonical work pages

  1. [1]

    Anders , F., Khalatyan , A., Queiroz , A. B. A., et al. 2022, , 658, A91

  2. [2]

    I., Saio , H., Ekstr \"o m , S., Georgy , C., & Meynet , G

    Anderson , R. I., Saio , H., Ekstr \"o m , S., Georgy , C., & Meynet , G. 2016, , 591, A8

  3. [3]

    Bailer-Jones , C. A. L., Rybizki , J., Fouesneau , M., Demleitner , M., & Andrae , R. 2021, , 161, 147

  4. [4]

    Beichmann , C. A. 1985, Infrared Astronomical Satellite (IRAS) catalogs and atlases. Explanatory supplement

  5. [5]

    Berdnikov , L. N. 2008, VizieR Online Data Catalog, 2285

  6. [6]

    N., Kniazev , A

    Berdnikov , L. N., Kniazev , A. Y., Sefako , R., et al. 2015, VizieR Online Data Catalog, J/PAZh/41/27

  7. [7]

    2017, , 230, 24

    Bianchi , L., Shiao , B., & Thilker , D. 2017, , 230, 24

  8. [8]

    C., Stanek , K

    Bird , J. C., Stanek , K. Z., & Prieto , J. L. 2009, , 695, 874

Show all 119 references
  1. [9]

    & Kiss , L

    B \'o di , A. & Kiss , L. L. 2019, , 872, 60

  2. [10]

    2016, , 587, A117

    Breitfelder , J., M \'e rand , A., Kervella , P., et al. 2016, , 587, A117

  3. [11]

    G., Macri , L

    Breuval , L., Riess , A. G., Macri , L. M., et al. 2023, , 951, 118

  4. [12]

    C., Magnier , E

    Chambers , K. C., Magnier , E. A., Metcalfe , N., et al. 2016, arXiv e-prints, arXiv:1612.05560

  5. [13]

    2020, , 249, 18

    Chen , X., Wang , S., Deng , L., et al. 2020, , 249, 18

  6. [14]

    2016, , 823, 102

    Choi , J., Dotter , A., Conroy , C., et al. 2016, , 823, 102

  7. [15]

    H., Scowcroft , V., & Wuyts , S

    Chown , A. H., Scowcroft , V., & Wuyts , S. 2021, , 500, 817

  8. [16]

    L., Clementini , G., Girardi , L., et al

    Cioni , M.-R. L., Clementini , G., Girardi , L., et al. 2011, , 527, A116

  9. [17]

    & Anderson , R

    Cruz Reyes , M. & Anderson , R. I. 2023, , 672, A85

  10. [18]

    Cutri , R. M. & et al. 2014, VizieR Online Data Catalog, 2328, 0

  11. [19]

    M., Skrutskie , M

    Cutri , R. M., Skrutskie , M. F., van Dyk , S., et al. 2003, VizieR Online Data Catalog, II/246

  12. [20]

    M., Skrutskie , M

    Cutri , R. M., Skrutskie , M. F., van Dyk , S., et al. 2012, VizieR Online Data Catalog, II/281

  13. [21]

    J., Williams , B

    Dalcanton , J. J., Williams , B. F., Lang , D., et al. 2012, , 200, 18

  14. [22]

    2021, , 508, 1473

    De Somma , G., Marconi , M., Cassisi , S., et al. 2021, , 508, 1473

  15. [23]

    2005, VizieR Online Data Catalog, B/denis

    Denis , C. 2005, VizieR Online Data Catalog, B/denis

  16. [24]

    2016, , 222, 8

    Dotter , A. 2016, , 222, 8

  17. [25]

    E., Gonzales-Solares , E., Greimel , R., et al

    Drew , J. E., Gonzales-Solares , E., Greimel , R., et al. 2016, VizieR Online Data Catalog, II/341

  18. [26]

    E., Gonzalez-Solares , E., Greimel , R., et al

    Drew , J. E., Gonzalez-Solares , E., Greimel , R., et al. 2014, , 440, 2036

  19. [27]

    P., Price , S

    Egan , M. P., Price , S. D., Kraemer , K. E., et al. 2003, VizieR Online Data Catalog, V/114

  20. [28]

    Eggen , O. J. 1977, , 34, 33

  21. [29]

    Gaia Collaboration , Prusti , T., de Bruijne , J. H. J., et al. 2016, , 595, A1

  22. [30]

    , Brown, A.G.A

    Gaia Collaboration , Vallenari, A. , Brown, A.G.A. , Prusti, T. , & et al. 2022, A&A

  23. [31]

    2012, , 538, A24

    Gallenne , A., Kervella , P., & M \'e rand , A. 2012, , 538, A24

  24. [32]

    2017, , 608, A18

    Gallenne , A., Kervella , P., M \'e rand , A., et al. 2017, , 608, A18

  25. [33]

    2013, , 558, A140

    Gallenne , A., M \'e rand , A., Kervella , P., et al. 2013, , 558, A140

  26. [34]

    B., et al

    Graczyk , D., Pietrzy \'n ski , G., Thompson , I. B., et al. 2020, , 904, 13

  27. [35]

    2022, , 657, L12

    GRAVITY Collaboration , Abuter , R., Aimar , N., et al. 2022, , 657, L12

  28. [36]

    2021, , 647, A59

    GRAVITY Collaboration , Abuter , R., Amorim , A., et al. 2021, , 647, A59

  29. [37]

    J., Sarajedini , A., Olsen , K

    Grocholski , A. J., Sarajedini , A., Olsen , K. A. G., Tiede , G. P., & Mancone , C. L. 2007, , 134, 680

  30. [38]

    Groenewegen , M. A. T. 2012, , 543, A36

  31. [39]

    Groenewegen , M. A. T. 2020 a , , 635, A33, (G20)

  32. [40]

    Groenewegen , M. A. T. 2020 b , , 640, A113

  33. [41]

    Groenewegen , M. A. T. & Jurkovic , M. I. 2017, , 604, A29

  34. [42]

    Groenewegen , M. A. T. & Lub , J. 2023, , 676, A136

  35. [43]

    2008, , 486, 951

    Gustafsson , B., Edvardsson , B., Eriksson , K., et al. 2008, , 486, 951

  36. [44]

    Gutermuth , R. A. & Heyer , M. 2015, , 149, 64

  37. [45]

    A., Levine , S., Terrell , D., & Welch , D

    Henden , A. A., Levine , S., Terrell , D., & Welch , D. L. 2015, in American Astronomical Society Meeting Abstracts, Vol. 225, American Astronomical Society Meeting Abstracts \#225, 336.16

  38. [46]

    2020, VizieR Online Data Catalog, VIII/106

    Herschel PSC Working Group , Marton , G., Calzoletti , L., et al. 2020, VizieR Online Data Catalog, VIII/106

  39. [47]

    2025 a , , 694, L15

    Hocd \'e , V., Kami \'n ski , T., Lewis , M., et al. 2025 a , , 694, L15

  40. [48]

    2025 b , , 694, A101

    Hocd \'e , V., Matter , A., Nardetto , N., et al. 2025 b , , 694, A101

  41. [49]

    2020 a , , 633, A47

    Hocd \'e , V., Nardetto , N., Lagadec , E., et al. 2020 a , , 633, A47

  42. [50]

    2020 b , , 641, A74

    Hocd \'e , V., Nardetto , N., z Borgniet , S., et al. 2020 b , , 641, A74

  43. [51]

    2010, , 514, A1

    Ishihara , D., Onaka , T., Kataza , H., et al. 2010, , 514, A1

  44. [52]

    2010, , 62, 273

    Ita , Y., Onaka , T., Tanab \'e , T., et al. 2010, , 62, 273

  45. [53]

    1999, DUSTY: Radiation transport in a dusty environment , Astrophysics Source Code Library

    Ivezi \'c , Z ., Nenkova , M., & Elitzur , M. 1999, DUSTY: Radiation transport in a dusty environment , Astrophysics Source Code Library

  46. [54]

    T., & Mirtorabi , M

    Javadi , A., van Loon , J. T., & Mirtorabi , M. T. 2011, , 411, 263

  47. [55]

    R., Van Winckel , H., & Nie , J

    Kamath , D., Wood , P. R., Van Winckel , H., & Nie , J. D. 2016, , 586, L5

  48. [56]

    2012, , 144, 179

    Kato , D., Ita , Y., Onaka , T., et al. 2012, , 144, 179

  49. [57]

    2006, , 448, 623

    Kervella , P., M \'e rand , A., Perrin , G., & Coud \'e du Foresto , V. 2006, , 448, 623

  50. [58]

    2017, , 228, 5

    Khan , R. 2017, , 228, 5

  51. [59]

    Z., Kochanek , C

    Khan , R., Stanek , K. Z., Kochanek , C. S., & Sonneborn , G. 2015, , 219, 42

  52. [60]

    M., Graziani , R., et al

    Kourkchi , E., Courtois , H. M., Graziani , R., et al. 2020, , 159, 67

  53. [61]

    L., Babusiaux , C., & Cox , N

    Lallement , R., Vergely , J. L., Babusiaux , C., & Cox , N. L. J. 2022, , 661, A147

  54. [62]

    Laney , C. D. & Stobie , R. S. 1992, , 93, 93

  55. [63]

    G., Busch , M

    Li , S., Riess , A. G., Busch , M. P., et al. 2021, , 920, 84

  56. [64]

    2025, arXiv e-prints, arXiv:2504.18779

    Li , Y., Jiang , B., & Ren , Y. 2025, arXiv e-prints, arXiv:2504.18779

  57. [65]

    2021, , 649, A4

    Lindegren , L., Bastian , U., Biermann , M., et al. 2021, , 649, A4

  58. [66]

    2018, , 479, 111

    Ma , B., Shang , Z., Hu , Y., et al. 2018, , 479, 111

  59. [67]

    Madore , B. F. 1975, , 29, 219

  60. [68]

    Madore , B. F. 1982, , 253, 575

  61. [69]

    2018, , 618, A21

    Manick , R., Van Winckel , H., Kamath , D., Sekaran , S., & Kolenberg , K. 2018, , 618, A21

  62. [70]

    Marocco , F., Eisenhardt , P. R. M., Fowler , J. W., et al. 2021, , 253, 8

  63. [71]

    Martin , W. L. & Warren , P. R. 1979, South African Astronomical Observatory Circular, 1, 98

  64. [72]

    F., & Smart , B

    Massey , P., Neugent , K. F., & Smart , B. M. 2016, , 152, 62

  65. [73]

    G., Banerji , M., Gonzalez , E., et al

    McMahon , R. G., Banerji , M., Gonzalez , E., et al. 2013, The Messenger, 154, 35

  66. [74]

    2006, , 453, 155

    M \'e rand , A., Kervella , P., Coud \'e du Foresto , V., et al. 2006, , 453, 155

  67. [75]

    W., Emerson , J

    Minniti , D., Lucas , P. W., Emerson , J. P., et al. 2010, , 15, 433

  68. [76]

    Monson , A. J. & Pierce , M. J. 2011, , 193, 12

  69. [77]

    S., Riess , A

    Murakami , Y. S., Riess , A. G., Stahl , B. E., et al. 2023, , 2023, 046

  70. [78]

    2022, Universe, 8, 335

    Musella , I. 2022, Universe, 8, 335

  71. [79]

    2021, , 501, 866

    Musella , I., Marconi , M., Molinaro , R., et al. 2021, , 501, 866

  72. [80]

    2016, , 593, A45

    Nardetto , N., M \'e rand , A., Mourard , D., et al. 2016, , 593, A45

  73. [81]

    F., Massey , P., Georgy , C., et al

    Neugent , K. F., Massey , P., Georgy , C., et al. 2020, , 889, 44

  74. [82]

    L., Olsen , K., Choi , Y., et al

    Nidever , D. L., Olsen , K., Choi , Y., et al. 2021, , 161, 74

  75. [83]

    2011, , 192, 3

    Paxton , B., Bildsten , L., Dotter , A., et al. 2011, , 192, 3

  76. [84]

    2013, , 208, 4

    Paxton , B., Cantiello , M., Arras , P., et al. 2013, , 208, 4

  77. [85]

    2015, , 220, 15

    Paxton , B., Marchant , P., Schwab , J., et al. 2015, , 220, 15

  78. [86]

    Pel , J. W. 1976, , 24, 413

  79. [87]

    & Macri , L

    Pellerin , A. & Macri , L. M. 2011, , 193, 26

  80. [88]

    2024, , 110, 123518

    Perivolaropoulos , L. 2024, , 110, 123518

  81. [89]

    2021, , 71, 205

    Pietrukowicz , P., Soszy \'n ski , I., & Udalski , A. 2021, , 71, 205

  82. [90]

    2019, , 567, 200

    Pietrzy \'n ski , G., Graczyk , D., Gallenne , A., et al. 2019, , 567, 200

  83. [91]

    Queiroz , A. B. A., Anders , F., Santiago , B. X., et al. 2018, , 476, 2556

  84. [92]

    G., Casertano , S., Yuan , W., et al

    Riess , A. G., Casertano , S., Yuan , W., et al. 2021, , 908, L6

  85. [93]

    G., Casertano , S., Yuan , W., Macri , L

    Riess , A. G., Casertano , S., Yuan , W., Macri , L. M., & Scolnic , D. 2019, , 876, 85

  86. [94]

    G., Yuan , W., Macri , L

    Riess , A. G., Yuan , W., Macri , L. M., et al. 2022, , 934, L7

  87. [95]

    2022, , 512, 563

    Ripepi , V., Chemin , L., Molinaro , R., et al. 2022, , 512, 563

  88. [96]

    L., Moretti , M

    Ripepi , V., Cioni , M.-R. L., Moretti , M. I., et al. 2017, , 472, 808

  89. [97]

    2023, , 674, A17

    Ripepi , V., Clementini , G., Molinaro , R., et al. 2023, , 674, A17

  90. [98]

    I., et al

    Ripepi , V., Marconi , M., Moretti , M. I., et al. 2016, , 224, 21

  91. [99]

    1978, Ann

    Schwarz, G. 1978, Ann. Stat., 6, 461

  92. [100]

    M., Skowron , J., Udalski , A., et al

    Skowron , D. M., Skowron , J., Udalski , A., et al. 2021, , 252, 23

  93. [101]

    K., et al

    Soszy \'n ski , I., Udalski , A., Szyma \'n ski , M. K., et al. 2019, , 69, 87

  94. [102]

    K., et al

    Soszy \'n ski , I., Udalski , A., Szyma \'n ski , M. K., et al. 2020, , 70, 101

  95. [103]

    K., et al

    Soszy \'n ski , I., Udalski , A., Szyma \'n ski , M. K., et al. 2017, , 67, 103

  96. [104]

    2009, VizieR Online Data Catalog, II/293

    Spitzer Science , C. 2009, VizieR Online Data Catalog, II/293

  97. [105]

    1977, Mitt

    Szabados , L. 1977, Mitt. Sternw. Ungarisch. Akad. Wiss, 70

  98. [106]

    1980, Communications of the Konkoly Obs

    Szabados , L. 1980, Communications of the Konkoly Obs. Hungary, 76, 1

  99. [107]

    1981, Communications of the Konkoly Obs

    Szabados , L. 1981, Communications of the Konkoly Obs. Hungary, 77, 1

  100. [108]

    1991, Communications of the Konkoly Obs

    Szabados , L. 1991, Communications of the Konkoly Obs. Hungary, 96, 123

  101. [109]

    2019, PhD thesis, L'Universit\'e PSL, l'Observatoire de Paris

    Trahin, B. 2019, PhD thesis, L'Universit\'e PSL, l'Observatoire de Paris

  102. [110]

    2021, , 656, A102

    Trahin , B., Breuval , L., Kervella , P., et al. 2021, , 656, A102

  103. [111]

    2018, , 68, 315

    Udalski , A., Soszy \'n ski , I., Pietrukowicz , P., et al. 2018, , 68, 315

  104. [112]

    K., Udalski , A., et al

    Ulaczyk , K., Szyma \'n ski , M. K., Udalski , A., et al. 2012, , 62, 247

  105. [113]

    K., Udalski , A., et al

    Ulaczyk , K., Szyma \'n ski , M. K., Udalski , A., et al. 2013, , 63, 159

  106. [114]

    & Wood , P

    Vassiliadis , E. & Wood , P. R. 1994, , 92, 125

  107. [115]

    L., Lallement , R., & Cox , N

    Vergely , J. L., Lallement , R., & Cox , N. L. J. 2022, , 664, A174

  108. [116]

    F., Durbin , M

    Williams , B. F., Durbin , M. J., Dalcanton , J. J., et al. 2021, , 253, 53

  109. [117]

    F., Lang , D., Dalcanton , J

    Williams , B. F., Lang , D., Dalcanton , J. J., et al. 2014, , 215, 9

  110. [118]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sent...

  111. [119]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.