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A New Approach to Identifying Red Supergiant Stars in Metal-poor Galaxies: A Case Study of NGC 6822

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper argues that combining Gaia astrometry with a metallicity-calibrated color–color locus recovers a far more complete census of red supergiants in metal-poor galaxies, demonstrated in NGC 6822 with 1,184 candidate RSGs (843 in a…

desk verdict A method paper whose own §5.1.2 undermines its headline RSG counts. read the letter →

arxiv 2412.15763 v1 pith:YATZ4JQR submitted 2024-12-20 astro-ph.GA

Zhiwen Li , Ming Yang , Biwei Jiang , Yi Ren This is my paper · ORCID
classification astro-ph.GA
keywords redsupergiantstarsstellarclassificationcolor-colordiagramsGaiaastrometryNGC6822metal-poorgalaxiesasymptoticgiantbranch
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

Red supergiants are massive evolved stars, but in metal-poor galaxies their faintest members land on the same color–color tracks as foreground Galactic dwarfs, so the usual two-color selection throws them away. This paper combines the color–color method with Gaia astrometry and, crucially, a metallicity-dependent empirical red supergiant locus calibrated on four galaxies, so that candidates previously lost inside the dwarf branch are retained and then cleaned astrometrically. Applied to NGC 6822, the approach returns 1,184 red supergiant candidates in a complete sample and 843 in a deliberately pure sample, more than doubling the earlier census and adding about 600 genuinely new candidates in the complete sample. A more complete red supergiant census across metallicities matters because these stars are major dust producers and supernova progenitors in environments like the early universe.

What carries the argument

The load-bearing object is an empirical, metallicity-dependent selection region for red supergiants in two color–color diagrams, $(r-z)_0$ versus $(z-H)_0$ and $(J-H)_0$ versus $(H-K)_0$, where low-gravity evolved stars separate from high-gravity dwarfs because of the H-band flux bump. Starting from the contour of SMC red supergiants, the region is shifted by the linear color–metallicity relations in Eq. (1) and rotated by the exponential inclination–metallicity relation in Eq. (2), both fitted to RSG samples in the SMC, LMC, M33, and M31. This shifted-and-rotated region is what lets the procedure retain faint candidate RSGs that fall inside the dwarf branch, with Gaia proper motion and parallax serving as an independent, metallicity-free screen for the remaining foreground dwarfs.

What would settle it

Spectroscopically measure the metallicities and surface gravities of a few dozen of the newly identified faint RSG candidates, especially those that fall inside the foreground dwarf branch; if most turn out to be foreground dwarfs or O-AGBs rather than RSGs, or if the confirmed RSGs are systematically more metal-rich than the assumed value for NGC 6822 in a way that shifts their colors away from the predicted region, the completeness and contamination claims would not hold.

Watch

Extended reading notes

Core claim

The central claim is that the incompleteness of red supergiant samples in metal-poor galaxies is avoidable: the RSG locus in the $(r-z)_0$ versus $(z-H)_0$ and $(J-H)_0$ versus $(H-K)_0$ diagrams can be defined empirically from the SMC and then shifted and rotated with metallicity using Eqs. (1)–(2), so that faint candidate RSGs overlapping the foreground dwarf branch are kept rather than rejected. Gaia parallax and proper motion then remove foreground dwarfs that survive the color cuts. For NGC 6822 this yields 1,184 RSG candidates in the complete sample (about 600 newly identified compared with the previous census) with an estimated foreground contamination of 20.5%, and 843 candidates in the pure sample with 6.5% contamination; the same workflow also classifies 1,559 oxygen-rich AGB, 1,075 carbon-rich AGB, and 140 extreme AGB candidates in the complete sample.

Load-bearing premise

The selection region for NGC 6822 is obtained by assuming that the red supergiant locus shifts and rotates smoothly with the galaxy's average metallicity according to fits to only four galaxies, even though the red supergiants' own metallicity is higher than the galaxy average and model atmospheres do not reproduce the observed colors.

Editorial extensions

If this is right

  • The complete sample of 1,184 RSG candidates in NGC 6822 more than doubles the previous census of 465, adding about 600 newly identified candidates.
  • The pure sample of 843 candidates, with foreground contamination reduced to 6.5%, gives follow-up spectroscopy a cleaner target list for measuring RSG metallicities, masses, and mass-loss rates.
  • The same empirical calibration can be applied to other metal-poor Local Group galaxies, where faint RSGs would otherwise be lost inside the dwarf branch.
  • Combining the optical and near-infrared color–color diagrams with Gaia astrometry removes foreground dwarfs more completely than any single method, with the $(r-z)_0$ versus $(z-H)_0$ diagram doing most of the work.
  • The companion catalog of 1,559 O-AGB, 1,075 C-AGB, and 140 x-AGB candidates extends the evolved-star census of NGC 6822 and yields a carbon-to-oxygen ratio consistent with earlier work.

Reading between the lines

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

  • If the Eqs. (1)–(2) calibration is transferable, the same shifted-region logic could recover faint RSGs in more distant, more metal-poor dwarfs where Gaia astrometry is too shallow to help, using only the color–color regions plus careful extinction correction.
  • The authors' own note that RSG metallicities run higher than galaxy-average metallicities implies their color–metallicity fit may absorb a systematic offset; refitting the relation to RSG-specific metallicities could shift the region and change the candidate counts.
  • The estimated 35–45% contamination of NIR-selected RSG candidates by O-AGBs suggests the faint end of any photometric RSG census is intrinsically ambiguous; combining the NIR CMD with optical colors could become a standard part of the selection rather than a post-hoc diagnostic.
  • A direct stress test would be to apply the calibration to another metal-poor galaxy with an independent RSG catalog and compare the recovered number and sky distribution; agreement would support the metallicity-scaling assumption, disagreement would localize where it fails.
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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. The paper proposes a new approach to identifying red supergiant stars (RSGs) in metal-poor galaxies by combining optical/near-infrared color-color diagrams (CCDs) with Gaia astrometry. The RSG regions in the (r-z)/(z-H) and (J-H)/(H-K) diagrams are calibrated empirically as functions of metallicity using RSG samples in the SMC, LMC, M33, and M31 (Eqs. 1-3), and the method is applied to NGC 6822. The authors report 1,184 RSG candidates in a 'complete' sample and 843 in a 'pure' sample, with foreground-dwarf contamination rates of 20.5% and 6.5%, respectively, and claim about 600 and 450 newly identified RSGs compared to Ren et al. (2021b).

Significance. If validated, the method would address a real limitation of CCD-based RSG searches in metal-poor galaxies, where faint RSGs overlap the foreground dwarf sequence, and Gaia astrometry provides an independent means to remove foreground stars. The paper includes a case study with cross-matching to previous samples and a JWST image for a small patch, which are useful sanity checks. However, the central claims rest on the treatment of contamination, and the paper's own optical-CMD diagnostic in §5.1.2 indicates that 35.7% of the complete-sample and 45.0% of the pure-sample RSG candidates are likely oxygen-rich AGB stars. Since these objects are retained in the final counts, the headline numbers and the 'newly identified' estimates are not supported as stated.

major comments (3)
  1. [§5.1.2, Table 1] The O-AGB contamination rates derived in §5.1.2 are not applied to the final RSG counts. The authors report that among RSG candidates with optical data, 320/897 in the complete sample and 299/665 in the pure sample are classified as O-AGBs in the optical (r-z) vs. z diagram. Yet Table 1 and the abstract list 1,184 and 843 RSG candidates respectively, with no removal or reclassification of these objects. Taking the optical classification at face value, the number of genuine RSGs is at most 864 in the complete sample and 544 in the pure sample (assuming all sources without optical data are RSGs). The 'pure' sample is therefore more O-AGB-contaminated than the complete sample, directly contradicting its advertised purity. The headline counts must be corrected or the optical classification must be shown to be unreliable.
  2. [§5.3] The estimate of 'about 600 new RSG candidates' is calculated by subtracting only the foreground-dwarf contamination (20.5%) and explicitly ignoring the O-AGB contamination described in §5.1.2 as 'intrinsic and inevitable.' This is not a valid basis for comparing with Ren et al. (2021b), because the new sample's larger size may be partly an artifact of including the O-AGB-contaminated objects, and previous samples could have different O-AGB contamination. The comparison must account for O-AGB contamination on both sides, or the 'newly identified' claim should be withdrawn.
  3. [§3.1.2, Eqs. (1)-(2)] The metallicity calibration of the RSG region is built from only four galaxies, with no uncertainties quoted for the fitted coefficients in Eqs. (1) and (2). The paper itself notes that galaxy-average [Fe/H] differs from RSG [Fe/H] and that stellar atmosphere models fail to reproduce the observed trend. This makes the extrapolation to NGC 6822 at [Fe/H] ≈ -1.0 fragile, and a systematic error in the adopted locus could either exclude genuine faint RSGs or include additional foreground dwarfs. The authors should report the fit uncertainties and perform a sensitivity test (e.g., varying the slopes and intercepts within their uncertainties and recomputing the sample sizes and contamination rates) to demonstrate that the central conclusions are robust.
minor comments (5)
  1. [Abstract, §4.1, §5.2, §6] There are several typographical errors: 'extragalatic' (Abstract), 'metellicity' (§4.1), 'diveded' (§5.2), and 'metalicity-limited' (§6).
  2. [§3.1.2, Fig. 3] The 5% marginal-density contour and the enlargement factor of 1.3 for the RSG region are stated without justification. Please quantify how the final sample size and contamination rates change when these thresholds are varied within reasonable bounds.
  3. [§4.1, Eq. (4)] The boundary lines k1, k2, and k3 are 'manually shifted by eye' to match the expected morphology. This introduces subjective choices that may affect the RSG/AGB classification; please state the criteria used for the shift and whether the results are sensitive to the exact placement.
  4. [§5.1.2, Fig. 13] The optical CMD classification that separates RSGs from O-AGBs is described only by reference to Figure 13, without the actual boundary equation. Please specify the division line used in the (r-z) vs. z diagram so that the reader can reproduce the 35.7% and 45.0% rates.
  5. [§3.3, Fig. 9] The Gaia proper-motion ellipse is fitted to the distribution of sources that satisfy the CCD criteria, which include foreground dwarfs. Please explain how the ellipse parameters are determined robustly and how the 'members with error' selection affects the final sample.

Circularity Check

2 steps flagged · score 4.0 of 10

The CCD selection region and CMD dividing lines are calibrated on the same group's earlier RSG catalogs and eye-tuned to NGC 6822 itself, so the counts are partly constructed; Gaia astrometry and external cross-matches provide partial independent content.

  1. self citation load bearing [Section 3.1.2 (empirical RSG region, Eqs. 1-3) and Section 4.1 (adoption of Ren et al. 2022 boundaries)]
    "the location of RSGs in the CCD has to be determined empirically, for which the RSG regions at different metallicities are entirely derived based on the RSGs in the SMC, and subsequently shifted and rotated to fit RSG populations in other galaxies (e.g., the LMC, M31, and M33). ... Here we adopt the recent results from Ren et al. (2022), which are based on the largest sample of RSGs and AGBs in fourteen Local Group galaxies with various metellicity."

    The classifier used to 'identify' RSGs in NGC 6822 is the contour of RSGs already selected in Ren et al. (2021b) by the same authors with the same CCD method, shifted/rotated by fits to RSG samples from Ren et al. (2021a,b); the CMD dividing lines are also taken from Ren et al. (2022), another paper by this group. NGC 6822 is placed at the same [Fe/H] as the SMC anchor, so the 'modified' region is effectively the earlier SMC region. The method's core selection is therefore a re-application of the same group's prior color-color classifications rather than an independent first-principles derivation, although the NGC 6822 data and Gaia astrometry are new.

  2. fitted input called prediction [Section 4.1 (Eq. 4, k-lines) and Section 4.2 (Eq. 5, l-lines and shifts)]
    "The three main borderlines of k1, k2 and k3 are manually shifted by eye to match the expected morphological distribution of the stellar populations in the CMD, and specifically listed below ... Besides, δ(BP − RP) and δRP are the color and magnitude shift to account for the difference caused by metallicity, photometry uncertainty, extinction correction, distance, and so on in an individual galaxy, which is 0.18 mag redder and 4.28 mag fainter respectively, to match with the distribution of RSGs and AGBs in NGC 6822."

    The CMD boundaries are tuned to the same NGC 6822 data they are then used to classify. The k-lines are shifted by eye until the expected RSG/O-AGB/C-AGB morphology appears, and the Eq. (5) shifts are chosen to match the RSG/AGB distribution in NGC 6822 before 62 Gaia-only RSGs are counted. Thus the final numbers (1,184 and 843) are not independent measurements; they are outputs of a classifier whose parameters were adjusted on the target sample. The RSG counts are partly constructed by the fitting procedure, although the Gaia proper-motion cut and external comparisons add non-circular information.

full rationale

This is an empirical catalog paper, not a first-principles derivation, so most steps are calibrations and definitions rather than predictions. The clearest circularity is the calibration chain: the CCD RSG region is contoured from SMC RSGs in Ren et al. (2021b), the metallicity relations are fit to RSGs from Ren et al. (2021a,b), and the CMD boundaries come from Ren et al. (2022), all with overlapping authorship. Because NGC 6822 is assigned the same [Fe/H] as the SMC, the applied region is essentially the SMC region. In addition, the CMD borderlines are eye-tuned or shift-fitted to the NGC 6822 distribution before the final counts are made, so the headline 1,184/843 totals are partly constructed by the fitting procedure. However, the Gaia astrometric filter is metallicity-free and independent, the paper cross-matches with external catalogs (Hirschauer et al. 2020; Tantalo et al. 2022; Dimitrova et al. 2022), and a JWST image provides a spot check. No equation sets the final count equal to an input by construction, so the paper is not a pure tautology. A separate, non-circular but serious limitation is reported in Sec. 5.1.2: the paper's own optical CMD classifies 35.7% (complete) and 45.0% (pure) of the RSG candidates as O-AGBs, yet these objects remain in Table 1 and the 'new identification' estimate corrects only foreground-dwarf contamination; this internal inconsistency weakens the claimed counts independently of the circularity assessment. On balance, the central claim has partial independent content, so a moderate score of 4 is appropriate.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The central method rests on several fitted or hand-adjusted parameters: the metallicity-dependent RSG region (Eqs. 1-3, fitted to four galaxies), the by-eye CMD boundaries, the fitted Gaia PM ellipse, and fitted shifts in the Gaia-only CMD. No new physical entities are introduced. The key domain assumption is that the SMC-derived empirical region, after shift and rotation, describes RSGs in NGC 6822, which is an extrapolation from a calibration sample.

free parameters (8)
  • RSG region contour threshold (5% of maximum marginal density) and enlargement factor 1.3 = 5% density, 1.3x
    The empirical red supergiant region in the CCD is defined by contouring SMC red supergiants at 5% of maximum density and enlarging by 1.3; these values are chosen by hand to compensate for extinction and photometric error (Sect. 3.1.2).
  • Linear fit coefficients in Eq. (1) = (r-z)0 = 0.400[Fe/H]+1.053; (z-H)0 = 0.387[Fe/H]+2.271
    The shift of the RSG region with metallicity is determined by a linear fit to the mean colors of RSG samples in SMC, LMC, M33, and M31. These coefficients are fitted to data and carry no quoted uncertainties.
  • Exponential fit coefficients in Eq. (2) = theta = 0.397 exp(-0.500[Fe/H])
    The rotation angle of the RSG branch in the r-z/z-H CCD is fitted as an exponential function of [Fe/H] using four galaxies. This fit controls the rotation applied to the empirical region for the target galaxy.
  • Linear fit coefficients in Eq. (3) = (J-H)0 = 0.113[Fe/H]+0.730; (H-K)0 = 0.078[Fe/H]+0.235
    The shift of the RSG region in the J-H/H-K diagram is fitted to the same four-galaxy sample.
  • k1, k2, k3 boundary lines in the NIR CMD = Given in Eq. (4), with slopes -15.366, -11.618, -9.268 and intercepts 22.048, 21.917, 22.481
    These boundaries are taken from Ren et al. (2022) and then 'manually shifted by eye to match the expected morphological distribution' (Sect. 4.1), so the final values are hand-adjusted.
  • Color and magnitude shifts in the Gaia-only CMD = delta(BP-RP) = 0.18 mag; deltaRP = 4.28 mag
    These shifts are added to the SMC-derived boundaries to match the distribution of stars in NGC 6822 (Sect. 4.2), i.e., fitted to the target data.
  • Gaia proper motion ellipse parameters = Center PM_RA=-0.05, PM_Dec=-0.11 mas/yr; semimajor 1.80, semiminor 1.26 mas/yr; PA 37 deg
    An ellipse is fitted to the proper motion distribution of the CCD-selected sources to define likely members (Sect. 3.3).
  • Uniform foreground extinction E(B-V)=0.169 mag = 0.169 mag
    A single foreground extinction value from Schlegel et al. (1998) is applied to all NGC 6822 sources, rather than per-source extinction, which affects the intrinsic colors and hence the selection regions.
assumptions (5)
  • domain assumption The RSG locus in the color-color diagrams varies smoothly and predictably with galaxy-average metallicity over the range [Fe/H] = -1.0 to +0.3 (Eqs. 1-2).
    This assumption is the basis for shifting and rotating the SMC region to NGC 6822. The paper notes stellar atmosphere models are inconsistent with observations, so the empirical fit is used instead (Sect. 3.1.2).
  • domain assumption The reference region (blue square in Fig. 1) contains no RSGs or AGBs, so the number of objects selected there equals the foreground contamination.
    The contamination rates in Table 1 are computed by applying the same selection to the reference region. The authors acknowledge the reference region may include some NGC 6822 members, which would overestimate contamination (Sect. 5.1.1).
  • domain assumption Sources detected by Gaia but lacking astrometric data are likely member stars of the distant galaxy.
    In Sect. 3.3, 'All the sources observed by Gaia but without astrometric data are kept, because they are likely to be the member stars of the distant target galaxy.' This is not tested against an external benchmark.
  • domain assumption The TRGB marks the faint end of the RSG branch, and the Sobel-filter-derived K-TRGB = 17.41 is correct.
    The classification of RSGs and AGBs in the CMD relies on this assumed boundary. The value agrees with two prior works, which supports it.
  • ad hoc to paper The boundaries k1-k3 from Ren et al. (2022), after manual by-eye adjustment, correctly separate RSGs, O-AGBs, C-AGBs, and x-AGBs in NGC 6822.
    Sect. 4.1 states the boundaries are 'manually shifted by eye to match the expected morphological distribution'. This is a subjective calibration.

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Pith. "Pith review of A New Approach to Identifying Red Supergiant Stars in Metal-poor Galaxies: A Case Study of NGC 6822." pith.science (2026). https://pith.science/paper/YATZ4JQR

@misc{pith2026241215763,
  author       = {Pith},
  title        = {Pith review of: A New Approach to Identifying Red Supergiant Stars in Metal-poor Galaxies: A Case Study of NGC 6822},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YATZ4JQR}},
  note         = {Machine review of arXiv:2412.15763}
}
abstract

A complete sample of red supergiant stars (RSGs) is important for studying their properties. Identifying RSGs in extragalatic field first requires removing the Galactic foreground dwarfs. The color-color diagram (CCD) method, specifically using $r-z/z-H$ and $J-H/H-K$, has proven successful in several studies. However, in metal-poor galaxies, faint RSGs will mix into the dwarf branch in the CCD and would be removed, leading to an incomplete RSG sample. This work attempts to improve the CCD method in combination with the Gaia astrometric measurement to remove foreground contamination in order to construct a complete RSG sample in metal-poor galaxies. The empirical regions of RSGs in both CCDs are defined and modified by fitting the locations of RSGs in galaxies with a range of metallicity. The metal-poor galaxy NGC 6822 is taken as a case study for its low metallicity ([Fe/H] $\approx$ -1.0) and moderate distance (about 500 kpc). In the complete sample, we identify 1,184 RSG, 1,559 oxygen-rich AGB (O-AGBs), 1,075 carbon-rich AGB (C-AGBs), and 140 extreme AGB (x-AGBs) candidates, with a contamination rate of approximately 20.5%, 9.7%, 6.8%, and 5.0%, respectively. We also present a pure sample, containing only the sources away from the dwarf branch, which includes 843 RSG, 1,519 O-AGB, 1,059 C-AGB, and 140 x-AGB candidates, with a contamination rate of approximately 6.5%, 8.8%, 6.1%, and 5.0%, respectively. About 600 and 450 RSG candidates are newly identified in the complete and pure sample, respectively, compared to the previous RSG sample in NGC 6822.

Figures

Figures reproduced from arXiv: 2412.15763 by the authors.

Figure 1
Figure 1. Field of view of NGC 6822. The black dots denote point sources from UKIRT. The red and blue square represents the sample and reference region, respectively [PITH_FULL_IMAGE:figures/full_fig_p013_1.png] view at source ↗
Figure 2
Figure 2. [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. The (r − z)0)/(z − H)0 diagram from the reference region of NGC 6822. The gray dots represent the foreground Galactic dwarfs within the reference region. The red and green dots represent the RSGs and the brightest 0.7 magnitude RGBs in SMC, with the purple and blue dashed circle represent their boundaries, respectively. The curved black dashed line represents the borderline of the dwarf branch, and the vertical dash… view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: The variation of the RSG region with [Fe/H] in the (r − z)0)/(z − H)0 diagram. The red dots represent RSGs in the SMC, LMC, M31, and M33 (Ren et al. 2021a,b), respectively, and the gray dots represent foreground Galactic dwarfs. The purple circle marks the empirical re…
Figure 5
Figure 5. Figure 5: The variation of the intrinsic colors of RSGs with [Fe/H]. The error bars represent the 1σ dispersion of the color, and the solid line is the result of a linear fitting. 2.0 1.5 1.0 0.5 0.0 0.5 [Fe/H] 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 (ra dia n) M33 M31 LMC SMC [PIT…
Figure 6
Figure 6. Figure 6: An exponential fitting of the inclination angle of the RSG branch in the (r − z)0/(z − H)0 diagram with [Fe/H] [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Selection of RSGs in the (r −z)0/(z −H)0 diagram. These are for NGC 6822, but with the SMC regions highlighted. The purple circle represents the empirical region of RSG, the blue dashed circle represents the upper-RGB region, and the black dashed line denotes the borde…
Figure 8
Figure 8. Figure 8: The same as [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Left panel: Distribution of the PMs for the Gaia sources that satisfy the CCD criteria. The ellipse represents the constraints of the PM. The red dots represent sources that fall within the PM ellipse, the blue dots represent sources that fall within the PM ellipse aft…
Figure 10
Figure 10. Figure 10: Identified RSG and AGB candidates of the complete sample in the NIR and optical CMDs. The left, upper right, and lower right panels show the sources from the CCD-Gaia, CCD-only, and Gaia-only sample, respectively. The orange, blue, green, and purple dots represent RSG…
Figure 11
Figure 11. Figure 11: A JWST image of a patch of sky in NGC 6822. A fraction of RSG candidates in the complete sample is denoted by orange circles. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 (J K)0 11 12 13 14 15 16 17 18 19 20 K0 k1 k2 k3 TRGB RSGs O-AGBs C-AGBs x-AGBs Member Stars 0.0 0.5 1.0 1…
Figure 12
Figure 12. Figure 12: Identified RSG and AGB candidates in the pure sample. The color convention is the same as in [PITH_FULL_IMAGE:figures/full_fig_p019_12.png]
Figure 13
Figure 13. Figure 13: The optical (r − z)0 vs. z0 diagram for RSGs (orange dots) and O-AGBs (blue dots) identified in the NIR CMD. 296.5 296.4 296.3 296.2 296.1 296.0 R.A. (deg) 15.0 14.9 14.8 14.7 14.6 Decl. (deg) This Work 296.5296.4296.3296.2296.1296.0 R.A. (deg) 15.0 14.9 14.8 14.7 14.…
Figure 14
Figure 14. Figure 14: The spatial distribution of the RSG candidates in this work, Yang et al. (2021b), Ren et al. (2021b), Dimitrova et al. (2022), and Hirschauer et al. (2020) [PITH_FULL_IMAGE:figures/full_fig_p020_14.png]

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Reference graph

Works this paper leans on

75 extracted references · 16 canonical work pages · cited by 1 Pith paper

  1. [1]

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  2. [2]

    write newline

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  3. [3]

    S)Uz @ݵje)y w _?o? ?_ Y??jNTn.F ? #wч;.W.kS1:ɿc , W< C WNjo#R ˟ ݟ O <Tq-ǚJSڻ\ ??# z9 ]u않 \_G ȿX tgTzE 5 &7[ 1৺' z9X``,Bؼ\ q W Ϊ/ ] > cdPr /O 88 DU|/vp -

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 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 E...

    2021

  4. [4]

    P., Tollerud , E

    Astropy Collaboration , Robitaille , T. P., Tollerud , E. J., et al. 2013, , 558, A33, 10.1051/0004-6361/201322068

    doi:10.1051/0004-6361/201322068 2013

  5. [5]

    M., Sip o cz , B

    Astropy Collaboration , Price-Whelan , A. M., Sip o cz , B. M., et al. 2018, , 156, 123, 10.3847/1538-3881/aabc4f

    doi:10.3847/1538-3881/aabc4f 2018

  6. [6]

    R., & Davies , B

    Beasor , E. R., & Davies , B. 2016, , 463, 1269, 10.1093/mnras/stw2054

    doi:10.1093/mnras/stw2054 2016

  7. [7]

    L., Srinivasan , S., van Loon , J

    Boyer , M. L., Srinivasan , S., van Loon , J. T., et al. 2011, in Astronomical Society of the Pacific Conference Series, Vol. 445, Why Galaxies Care about AGB Stars II: Shining Examples and Common Inhabitants, ed. F. Kerschbaum , T. Lebzelter , & R. F. Wing , 473

    work page 2011

  8. [8]

    Castelli , F., & Kurucz , R. L. 2003, in Modelling of Stellar Atmospheres, ed. N. Piskunov , W. W. Weiss , & D. F. Gray , Vol. 210, A20, 10.48550/arXiv.astro-ph/0405087

    arXiv doi:10.48550/arxiv.astro-ph/0405087 2003

Show all 75 references
  1. [9]

    C., Magnier , E

    Chambers , K. C., Magnier , E. A., Metcalfe , N., et al. 2016, arXiv e-prints, arXiv:1612.05560, 10.48550/arXiv.1612.05560

    2016 doi arXiv

  2. [10]

    W., Bedding , T

    Chatys , F. W., Bedding , T. R., Murphy , S. J., et al. 2019, , 487, 4832, 10.1093/mnras/stz1584

    2019 doi

  3. [11]

    A., & Sohn , Y

    Choudhury , S., Subramaniam , A., Cole , A. A., & Sohn , Y. J. 2018, , 475, 4279, 10.1093/mnras/sty087

    2018 doi

  4. [12]

    Cioni , M. R. L., Girardi , L., Marigo , P., & Habing , H. J. 2006, , 448, 77, 10.1051/0004-6361:20053933

    2006 doi

  5. [13]

    Davidge , T. J. 2003, , 115, 635, 10.1086/375389

    2003 doi

  6. [14]

    2015, , 806, 21, 10.1088/0004-637X/806/1/21

    Davies , B., Kudritzki , R.-P., Gazak , Z., et al. 2015, , 806, 21, 10.1088/0004-637X/806/1/21

    2015 doi

  7. [15]

    2017, , 847, 112, 10.3847/1538-4357/aa89ed

    Davies , B., Kudritzki , R.-P., Lardo , C., et al. 2017, , 847, 112, 10.3847/1538-4357/aa89ed

    2017 doi

  8. [16]

    A., Neugent , K

    Dimitrova , T. A., Neugent , K. F., Massey , P., & Levesque , E. M. 2022, , 163, 70, 10.3847/1538-3881/ac410e

    2022 doi

  9. [17]

    D., Cole , A

    Dobbie , P. D., Cole , A. A., Subramaniam , A., & Keller , S. 2014, , 442, 1680, 10.1093/mnras/stu926

    2014 doi

  10. [18]

    2018, , 478, 5379, 10.1093/mnras/sty1381

    Dong , H., Olsen , K., Lauer , T., et al. 2018, , 478, 5379, 10.1093/mnras/sty1381

    2018 doi

  11. [19]

    2013, in EAS Publications Series, Vol

    Ekstr \"o m , S., Georgy , C., Meynet , G., Groh , J., & Granada , A. 2013, in EAS Publications Series, Vol. 60, EAS Publications Series, ed. P. Kervella , T. Le Bertre , & G. Perrin , 31--41, 10.1051/eas/1360003

    2013

  12. [20]

    1995, in Revista Mexicana de Astronomia y Astrofisica Conference Series, Vol

    Esteban , C., & Peimbert , M. 1995, in Revista Mexicana de Astronomia y Astrofisica Conference Series, Vol. 3, Revista Mexicana de Astronomia y Astrofisica Conference Series, ed. M. Pena & S. Kurtz , 133

    1995

  13. [21]

    W., Whitelock , P

    Feast , M. W., Whitelock , P. A., Menzies , J. W., & Matsunaga , N. 2012, , 421, 2998, 10.1111/j.1365-2966.2012.20525.x

    2012

  14. [22]

    Gaia Collaboration , Vallenari , A., Brown , A. G. A., et al. 2023, , 674, A1, 10.1051/0004-6361/202243940

    2023 doi

  15. [23]

    R., Shields , G

    Garnett , D. R., Shields , G. A., Skillman , E. D., Sagan , S. P., & Dufour , R. J. 1997, , 489, 63, 10.1086/304775

    1997 doi

  16. [24]

    2018, , 156, 278, 10.3847/1538-3881/aaeacb

    G \'o rski , M., Pietrzy \'n ski , G., Gieren , W., et al. 2018, , 156, 278, 10.3847/1538-3881/aaeacb

    2018 doi

  17. [25]

    Green , G. M. 2018, The Journal of Open Source Software, 3, 695, 10.21105/joss.00695

    2018 doi

  18. [26]

    M., Schlafly , E., Zucker , C., Speagle , J

    Green , G. M., Schlafly , E., Zucker , C., Speagle , J. S., & Finkbeiner , D. 2019, , 887, 93, 10.3847/1538-4357/ab5362

    2019 doi

  19. [27]

    2008, , 486, 951, 10.1051/0004-6361:200809724

    Gustafsson , B., Edvardsson , B., Eriksson , K., et al. 2008, , 486, 951, 10.1051/0004-6361:200809724

    2008 doi

  20. [28]

    M., Brown , A., & Lim , J

    Harper , G. M., Brown , A., & Lim , J. 2001, , 551, 1073, 10.1086/320215

    2001 doi

  21. [29]

    S., Gray , L., Meixner , M., et al

    Hirschauer , A. S., Gray , L., Meixner , M., et al. 2020, , 892, 91, 10.3847/1538-4357/ab7b60

    2020 doi

  22. [30]

    J., Freedman , W

    Hoyt , T. J., Freedman , W. L., Madore , B. F., et al. 2018, , 858, 12, 10.3847/1538-4357/aab7ed

    2018 doi

  23. [31]

    M., & Davidson , K

    Humphreys , R. M., & Davidson , K. 1979, , 232, 409, 10.1086/157301

    1979 doi

  24. [32]

    Irwin , M. J. 2013, in Astrophysics and Space Science Proceedings, Vol. 37, Thirty Years of Astronomical Discovery with UKIRT, 229, 10.1007/978-94-007-7432-2_21

    2013 doi

  25. [33]

    2024, in IAU Symposium, Vol

    Jiang , B., Ren , Y., & Yang , M. 2024, in IAU Symposium, Vol. 376, IAU Symposium, ed. R. de Grijs , P. A. Whitelock , & M. Catelan , 292--305, 10.1017/S1743921323003356

    2024 doi

  26. [34]

    L., Szab \'o , G

    Kiss , L. L., Szab \'o , G. M., & Bedding , T. R. 2006, , 372, 1721, 10.1111/j.1365-2966.2006.10973.x

    2006

  27. [35]

    2000, , 10, 1, 10.1007/s001590000005

    Kunth , D., & \"O stlin , G. 2000, , 10, 1, 10.1007/s001590000005

    2000 doi

  28. [36]

    G., Freedman , W

    Lee , M. G., Freedman , W. L., & Madore , B. F. 1993, , 417, 553, 10.1086/173334

    1993 doi

  29. [37]

    Levesque , E. M. 2010, , 54, 1, 10.1016/j.newar.2009.10.002

    2010 doi

  30. [38]

    M., Massey , P., Olsen , K

    Levesque , E. M., Massey , P., Olsen , K. A. G., et al. 2005, , 628, 973, 10.1086/430901

    2005 doi

  31. [39]

    2024, , 167, 123, 10.3847/1538-3881/ad23e8

    Li , Y., Jiang , B., & Ren , Y. 2024, , 167, 123, 10.3847/1538-3881/ad23e8

    2024 doi

  32. [40]

    1998, , 501, 153, 10.1086/305818

    Massey , P. 1998, , 501, 153, 10.1086/305818

    1998 doi

  33. [41]

    2013, , 57, 14, 10.1016/j.newar.2013.05.002

    ---. 2013, , 57, 14, 10.1016/j.newar.2013.05.002

    2013 doi

  34. [42]

    Massey , P., & Evans , K. A. 2016, , 826, 224, 10.3847/0004-637X/826/2/224

    2016 doi

  35. [43]

    F., Levesque , E

    Massey , P., Neugent , K. F., Levesque , E. M., Drout , M. R., & Courteau , S. 2021, , 161, 79, 10.3847/1538-3881/abd01f

    2021 doi

  36. [44]

    Massey , P., Olsen , K. A. G., Hodge , P. W., et al. 2007, , 133, 2393, 10.1086/513319

    2007 doi

  37. [45]

    M., et al

    Massey , P., Plez , B., Levesque , E. M., et al. 2005, , 634, 1286, 10.1086/497065

    2005 doi

  38. [46]

    2011, , 526, A156, 10.1051/0004-6361/201013993

    Mauron , N., & Josselin , E. 2011, , 526, A156, 10.1051/0004-6361/201013993

    2011 doi

  39. [47]

    McConnachie , A. W. 2012, , 144, 4, 10.1088/0004-6256/144/1/4

    2012 doi

  40. [48]

    F., Levesque , E

    Neugent , K. F., Levesque , E. M., Massey , P., Morrell , N. I., & Drout , M. R. 2020 a , , 900, 118, 10.3847/1538-4357/ababaa

    2020 doi

  41. [49]

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

    Neugent , K. F., Massey , P., Georgy , C., et al. 2020 b , , 889, 44, 10.3847/1538-4357/ab5ba0

    2020 doi

  42. [50]

    A., Wolf , C., Bessell , M

    Onken , C. A., Wolf , C., Bessell , M. S., et al. 2019, , 36, e033, 10.1017/pasa.2019.27

    2019 doi

  43. [51]

    R., Evans , C

    Patrick , L. R., Evans , C. J., Davies , B., et al. 2015, , 803, 14, 10.1088/0004-637X/803/1/14

    2015 doi

  44. [52]

    2022, Universe, 8, 465, 10.3390/universe8090465

    Ren , T., Jiang , B., Ren , Y., & Yang , M. 2022, Universe, 8, 465, 10.3390/universe8090465

    2022 doi

  45. [53]

    2021 a , , 907, 18, 10.3847/1538-4357/abcda5

    Ren , Y., Jiang , B., Yang , M., et al. 2021 a , , 907, 18, 10.3847/1538-4357/abcda5

    2021 doi

  46. [54]

    2021 b , , 923, 232, 10.3847/1538-4357/ac307b

    Ren , Y., Jiang , B., Yang , M., Wang , T., & Ren , T. 2021 b , , 923, 232, 10.3847/1538-4357/ac307b

    2021 doi

  47. [55]

    2020, , 898, 24, 10.3847/1538-4357/ab9c17

    Ren , Y., & Jiang , B.-W. 2020, , 898, 24, 10.3847/1538-4357/ab9c17

    2020 doi

  48. [56]

    2019, , 241, 35, 10.3847/1538-4365/ab0825

    Ren , Y., Jiang , B.-W., Yang , M., & Gao , J. 2019, , 241, 35, 10.3847/1538-4365/ab0825

    2019 doi

  49. [57]

    F., & Freedman , W

    Sakai , S., Madore , B. F., & Freedman , W. L. 1996, , 461, 713, 10.1086/177096

    1996 doi

  50. [58]

    F., & Finkbeiner , D

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

    2011 doi

  51. [59]

    J., Finkbeiner , D

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

    1998 doi

  52. [60]

    2023, , 523, 6048, 10.1093/mnras/stad1681

    Shahbandeh , M., Sarangi , A., Temim , T., et al. 2023, , 523, 6048, 10.1093/mnras/stad1681

    2023 doi

  53. [61]

    F., Cioni , M

    Sibbons , L. F., Cioni , M. R. L., Irwin , M., & Rejkuba , M. 2011, in Astronomical Society of the Pacific Conference Series, Vol. 445, Why Galaxies Care about AGB Stars II: Shining Examples and Common Inhabitants, ed. F. Kerschbaum , T. Lebzelter , & R. F. Wing , 409, 10.4855...

    2011 doi arXiv

  54. [62]

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

    Skowron , D. M., Skowron , J., Udalski , A., et al. 2021, , 252, 23, 10.3847/1538-4365/abcb81

    2021 doi

  55. [63]

    D., Bildsten , L., Drout , M

    Soraisam , M. D., Bildsten , L., Drout , M. R., et al. 2018, , 859, 73, 10.3847/1538-4357/aabc59

    2018 doi

  56. [64]

    2022, , 933, 197, 10.3847/1538-4357/ac7468

    Tantalo , M., Dall'Ora , M., Bono , G., et al. 2022, , 933, 197, 10.3847/1538-4357/ac7468

    2022 doi

  57. [65]

    Taylor , M. B. 2005, in Astronomical Society of the Pacific Conference Series, Vol. 347, Astronomical Data Analysis Software and Systems XIV, ed. P. Shopbell , M. Britton , & R. Ebert , 29

    2005

  58. [66]

    2019, , 877, 116, 10.3847/1538-4357/ab1c61

    Wang , S., & Chen , X. 2019, , 877, 116, 10.3847/1538-4357/ab1c61

    2019 doi

  59. [67]

    G., et al

    Wu , J., Scolnic , D., Riess , A. G., et al. 2023, , 954, 87, 10.3847/1538-4357/acdd7b

    2023 doi

  60. [68]

    Yang , M., & Jiang , B. W. 2011, , 727, 53, 10.1088/0004-637X/727/1/53

    2011 doi

  61. [69]

    2012, , 754, 35, 10.1088/0004-637X/754/1/35

    ---. 2012, , 754, 35, 10.1088/0004-637X/754/1/35

    2012 doi

  62. [70]

    Z., Jiang , B.-W., et al

    Yang , M., Bonanos , A. Z., Jiang , B.-W., et al. 2019, , 629, A91, 10.1051/0004-6361/201935916

    2019 doi

  63. [71]

    Z., Jiang , B., et al

    Yang , M., Bonanos , A. Z., Jiang , B., et al. 2021 a , , 646, A141, 10.1051/0004-6361/202039475

    2021 doi

  64. [72]

    2021 b , , 647, A167, 10.1051/0004-6361/202039596

    ---. 2021 b , , 647, A167, 10.1051/0004-6361/202039596

    2021 doi

  65. [73]

    2023, , 676, A84, 10.1051/0004-6361/202244770

    ---. 2023, , 676, A84, 10.1051/0004-6361/202244770

    2023 doi

  66. [74]

    2024, , 965, 106, 10.3847/1538-4357/ad28c4

    Yang , M., Zhang , B., Jiang , B., et al. 2024, , 965, 106, 10.3847/1538-4357/ad28c4

    2024 doi

  67. [75]

    2010, , 717, L62, 10.1088/2041-8205/717/1/L62

    Yoon , S.-C., & Cantiello , M. 2010, , 717, L62, 10.1088/2041-8205/717/1/L62

    2010 doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.