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EWOCS-VI: Probing the hidden intermediate-mass population of Westerlund 1

T0 review · 0 major / 3 minor · reviewed 2026-06-29 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Near-infrared data and clustering reveal 1286 candidate members of Westerlund 1 spanning 1.5 to 20 solar masses.

desk verdict This paper gives a practical membership catalog for the intermediate-mass stars in Westerlund 1 using clustering on survey photometry. read the letter →

arxiv 2606.27623 v1 pith:3PXS72ZC submitted 2026-06-26 astro-ph.SR astro-ph.GA

C. Ordenes-Huanca , A. Bayo , M. Guarcello , M. Zoccali , A. Rojas-Arriagada , V. Almendros-Abad , M. Andersen , R. Bonito
show 6 more authors
V. Guzmán E. Moraux K. Muzic L. Prisinzano S. Sciortino N.J. Wright
This is my paper
classification astro-ph.SRastro-ph.GA
keywords Westerlund1starclustermembershipnear-infraredphotometrystellarvariabilityHDBSCANclusteringintermediate-massstarsVVVXsurvey
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 seeks to map the intermediate-mass stars in Westerlund 1 that have been hidden by extinction, bridging the gap between Gaia-traced high-mass stars and earlier low-mass surveys. It applies the HDBSCAN algorithm to six-dimensional data from VVVX NIR photometry and astrometry to isolate probable cluster members from field stars, then assigns masses using PARSEC isochrones at 5-6 Myr. This produces a catalog of 1286 candidates where 34 percent exhibit significant Ks-band flux changes, with the first parametric description of those variability patterns. The resulting membership list is intended to support future kinematic work on the cluster.

What carries the argument

HDBSCAN density-based clustering performed in a six-dimensional parameter space of sky position, near-infrared magnitudes and proper motions, with Monte Carlo simulations used to test separation of cluster members from field contaminants.

What would settle it

A radial-velocity or proper-motion survey of the 1286 candidates that finds most have velocities or motions inconsistent with membership at 4.23 kpc and 5-6 Myr age would falsify the membership list.

Watch

Extended reading notes

Core claim

HDBSCAN clustering in a 6D space of position, photometry and astrometry, applied to VIRAC2 Ks-band data from the VVVX survey, isolates 1286 high-probability members with 12 < J < 18 that correspond to 1.5-20 solar masses under PARSEC 5 and 6 Myr isochrones at 4.23 kpc and A_Ks = 0.6 mag; 34 percent of these sources display statistically significant variability whose modes are analyzed parametrically for the first time.

Load-bearing premise

The HDBSCAN run in the selected 6D space correctly separates true cluster members from field stars with low contamination and incompleteness, and the chosen PARSEC isochrones with fixed age, distance and extinction accurately convert the observed magnitudes into the stated mass range.

Editorial extensions

If this is right

  • The catalog supplies a ready sample for kinematic studies of the cluster's dynamics and extent.
  • The 34 percent variability rate and its parametric description supply the first systematic view of Ks-band behavior among these intermediate-mass candidates.
  • The spatial coverage extends membership information from the dense core into the cluster outskirts.
  • The mass range closes the observational gap between Gaia high-mass stars and prior very-low-mass surveys of the same region.

Reading between the lines

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

  • If the candidates prove to be genuine members, the spatial distribution could be used to test whether the cluster formed with a single burst or shows evidence of sequential star formation.
  • The variability fraction may correlate with stellar rotation or accretion signatures that could be checked with multi-epoch spectroscopy.
  • The same 6D clustering approach could be tested on other highly extincted young clusters where optical data are unavailable.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 3 minor

Summary. The manuscript uses VIRAC2 NIR PSF photometry and astrometry from the VVVX survey to apply the HDBSCAN algorithm in 6D parameter space (position, photometry, astrometry) to Westerlund 1, identifying 1286 high-probability candidate members with 12 < J < 18 mag. These are mapped to a mass range of ~1.5--20 M_⊙ via PARSEC isochrones at 5 and 6 Myr with A_Ks=0.6 mag and d=4.23 kpc. Monte Carlo simulations assess robustness; 34% of candidates show statistically significant Ks-band variability, for which a parametric analysis of variability modes is presented, along with a membership catalog for future kinematic work.

Significance. If the clustering selection holds, the work fills the intermediate-mass gap between Gaia-traced high-mass stars and prior EWOCS low-mass studies, providing a more complete census of Wd1 in a starburst environment. Explicit strengths include reliance on public VIRAC2 catalogs, Monte Carlo robustness tests for the unsupervised membership assignment, and the first parametric Ks-band variability characterization for these candidates.

minor comments (3)
  1. [Abstract] Abstract and §3: the precise six features comprising the 6D input space to HDBSCAN (e.g., exact combination of RA/Dec, J/H/Ks magnitudes or colors, proper motions, and any parallax or other astrometric quantities) are not enumerated; an explicit list would aid reproducibility.
  2. Results section: the procedure for combining or selecting between the 5 Myr and 6 Myr PARSEC isochrones when assigning individual stellar masses is not described; it is unclear whether masses are taken from one isochrone, averaged, or assigned via a joint probability.
  3. Variability analysis: the exact statistical threshold and time-sampling criteria used to declare 'statistically significant flux variations' (and the subsequent parametric mode classification) should be stated with reference to the relevant equation or table.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript, including the recognition of its strengths in using public VIRAC2 data, Monte Carlo robustness tests, and providing the first parametric Ks-band variability analysis for Wd1 candidates. The recommendation for minor revision is noted. No specific major comments were raised in the report.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity identified

full rationale

The paper applies the unsupervised HDBSCAN algorithm to public VIRAC2 photometry and astrometry in a 6D space (position, photometry, astrometry), validates robustness with Monte Carlo simulations, and adopts fixed PARSEC isochrone parameters (5/6 Myr, A_Ks=0.6 mag, d=4.23 kpc) solely to convert observed J magnitudes into a mass range. The reported 1286 high-probability members and 34% variability fraction are computed directly from the clustering output and flux measurements without any fitted parameter being redefined as a prediction, without self-citation chains supporting the core claims, and without equations that reduce the results to quantities defined by the same inputs. The derivation is therefore self-contained against external data and algorithms.

Assumptions & free parameters 3 free parameters · 2 assumptions · 0 invented entities

The mass assignments rest on adopted cluster parameters and standard isochrones; the membership selection rests on the assumption that the 6D clustering cleanly isolates the cluster population.

free parameters (3)
  • Isochrone age = 5-6 Myr
    Adopted 5 and 6 Myr values from PARSEC models to span the observed population.
  • Extinction A_Ks = 0.6 mag
    Fixed value used to deredden photometry before mass assignment.
  • Distance = 4.23 kpc
    Fixed value used with isochrones to convert apparent to absolute magnitudes.
assumptions (2)
  • domain assumption PARSEC isochrones accurately predict the mass-luminosity relation for stars in the 1.5-20 solar mass range at the adopted age and metallicity.
    Invoked to convert J and Ks magnitudes into masses for the candidate members.
  • domain assumption The 6D parameter space constructed from VIRAC2 astrometry and photometry permits reliable statistical separation of cluster members from field stars via HDBSCAN.
    Central premise of the membership selection step.

how reviews work

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Cite this review

Pith. "Pith review of EWOCS-VI: Probing the hidden intermediate-mass population of Westerlund 1." pith.science (2026). https://pith.science/paper/3PXS72ZC

@misc{pith2026260627623,
  author       = {Pith},
  title        = {Pith review of: EWOCS-VI: Probing the hidden intermediate-mass population of Westerlund 1},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3PXS72ZC}},
  note         = {Machine review of arXiv:2606.27623}
}
abstract

Context: Westerlund 1 (Wd1), the most massive young star cluster in the Milky Way, is an excellent laboratory for studying star formation and early stellar evolution in a starburst-like environment. However, high extinction restricts studies of its stellar content, and focus on high-mass stars limits our knowledge of the full spatial extent of the cluster. Aims: We characterize the near-infrared (NIR) variability of the stellar population of Wd1, filling the mass gap between massive stars traced by Gaia and very low-mass stars from previous Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) studies, to provide a more complete view of cluster membership across solar and super-solar masses.} Methods: We exploited data from the VISTA Variables of the V\'ia L\'actea survey and its extension (VVVX), using NIR point spread function (PSF) photometry and astrometric solutions from its latest data release, namely the VIRAC2 catalogs, mainly in the Ks band. Their large spatial coverage enables study of both the central regions and outskirts of the cluster. We applied HDBSCAN clustering algorithm in a 6D parameter space to differentiate cluster members from field contaminants, assessing robustness through Monte Carlo simulations. Variable sources along the line of sight were also identified and characterized. Results: We identify 1286 high-probability candidate members (12 < J < 18 mag) spanning $\sim 1.5$--$20\,M_{\odot}$, adopting both PARSEC 5 and 6 Myr isochrones ($A_{K_{\rm{s}}}=0.6\,\rm{mag}$, $d=4.23\,\rm{kpc}$). A considerable fraction (34%) shows statistically significant flux variations. We present, for the first time, a parametric analysis of variability modes of Wd1 candidate members in the Ks band, providing a membership catalog suitable for future kinematic studies.

Figures

Figures reproduced from arXiv: 2606.27623 by the authors.

Figure 2
Figure 2. Spatial distribution of all stars in Wd1 region (in gray) and can￾didate members (in black) for Rs = 11 arcmin, which was considered to compute the best wedge located in PA 225°–360° associated to a low extinction sector (encircled in green) in Wd1 region. Green stars are within this best wedge and considered to compute the fraction of clus￾ter members and their PM dispersion. the 5 or 6 Myr isochrone. Stars inside … view at source ↗
Figure 1
Figure 1. J vs. J − Ks CMD for the refinement selection process. Core and high confidence stars redder than the 5 Myr isochrone (black line) are shown in dark orange open circles. The isochrone is located at a distance of 4.23 kpc and using an extinction value of AV = 9 mag, which is the lowest value expected for Wd1 members. Stars bluer than the isochrone, even considering their photometric errors, were discarded and are sho… view at source ↗
Figure 3
Figure 3. Upper panel: Cluster member fraction against the search radius. The number of cluster members, NC, and the total number of stars, Ntotal, are computed only from the best wedge region described in the text. The vertical dashed line is located at Rs = 8 arcmin, where the fraction stops decreasing. Lower panel: As in the upper panel, but now showing the PM dispersion of identified cluster members for different values o… view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Final list of our Wd1 census (blue dots) compared to all field stars (gray) in Rs = 9 arcmin. Left panel: Spatial distribution of the candidate members. Scale length, according to the distance of the cluster, is on the bottom left. Central panel: J vs. J − Ks CMD. The …
Figure 5
Figure 5. Figure 5: VVV Ks image of the 9 arcmin × 9 arcmin region around Wd1. The cyan star shows the position of the center of the cluster considered in this study. The red circle depicts the exclusion region for the surface density fit of 2 arcmin radius. Central massive stars are clea…
Figure 6
Figure 6. Figure 6: Surface density profile and their Poisson errors of 292 of our proposed members located 2 arcmin or more away from the center of Wd1 and within the best wedge. The green dashed line indicates the computed core radius, the orange is related to the tidal radius and gray …
Figure 8
Figure 8. Figure 8: Fraction of variable stars in our proposed list of members (blue) compared to field stars (dark gray) against different threshold values to find variable behaviors. Binomial errors are also shown for each com￾putation. least 50 epochs and mean photometric errors less t…
Figure 7
Figure 7. Figure 7: Excess variance σ 2 XS,Ks against the mean Ks magnitude of stars, Ks . Panel b) is for randomly selected field stars (in gray), whereas panel b) is for our 1286 VIRAC2 candidate members of Wd1 (in blue). In both, objects showing real variations are highlighted in red o…
Figure 9
Figure 9. Figure 9: Q vs. M plane for all candidates with good quality light curves. Each region of the plot defines a variability class. The ones described in the text are highlighted using different colors and markers. for variability or spot modulation (Bonito et al. 2023). We set the …
Figure 13
Figure 13. Figure 13: Both sources have already been selected as Wd1 [PITH_FULL_IMAGE:figures/full_fig_p009_13.png]
Figure 10
Figure 10. Figure 10: Examples of dipper Ks phase folded light curves found among member candidates. VIRAC2 source IDs are indicated in each panel along with their periods in days. The one in the upper right panel was already classified as an eclipsing binary EA/EB by Molnar et al. (2022) …
Figure 12
Figure 12. Figure 12: Examples of symmetric phase folded Ks light curves found in our proposed member candidates. VIRAC2 source IDs are indicated in each panel along with their periods. are a powerful tool to identify members, particularly on the Main Sequence. In our case, since we are us…
Figure 13
Figure 13. Figure 13: Examples of long timescale variables Ks light curves found in our proposed member candidates. VIRAC2 source IDs are indicated in each panel. Both have also been pointed as members of Wd1 by Gen￾naro et al. (2017) [PITH_FULL_IMAGE:figures/full_fig_p011_13.png]
Figure 14
Figure 14. Figure 14: CMD of stars the region of Wd1 (in gray) against our variable candidates. The three tentative classes discussed in the text are high￾lighted with dippers in red circles, symmetric in orange triangles and long timescale sources as purple stars. fraction of the evolved …

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Works this paper leans on

49 extracted references

  1. [1]

    G., Drake, J

    Anastasopoulou, K., Guarcello, M. G., Drake, J. J., et al. 2025, A&A, 701, A138

    2025

  2. [2]

    2017, A&A, 602, A22

    Andersen, M., Gennaro, M., Brandner, W., et al. 2017, A&A, 602, A22

    2017

  3. [3]

    Anderson, R. I. & Hunt, E. L. 2025, A&A, 700, L13

    2025

  4. [4]

    H., et al

    Balog, Z., Muzerolle, J., Rieke, G. H., et al. 2007, ApJ, 660, 1532

    2007

  5. [5]

    R., Davies, B., Smith, N., Gehrz, R

    Beasor, E. R., Davies, B., Smith, N., Gehrz, R. D., & Figer, D. F. 2021, ApJ, 912, 16

    2021

  6. [6]

    Bessell, M. S. & Brett, J. M. 1988, PASP, 100, 1134

    1988

  7. [7]

    Bonanos, A. Z. 2007, AJ, 133, 2696

    2007

  8. [8]

    2023, ApJS, 265, 27

    Bonito, R., Venuti, L., Ustamujic, S., et al. 2023, ApJS, 265, 27

    2023

Show all 49 references
  1. [9]

    Campello, R. J. G. B., Moulavi, D., & Sander, J. 2013, in Advances in Knowl- edge Discovery and Data Mining, ed. J. Pei, V . S. Tseng, L. Cao, H. Motoda, & G. Xu (Berlin, Heidelberg: Springer Berlin Heidelberg), 160–172

    2013

  2. [10]

    & Anders, F

    Cantat-Gaudin, T. & Anders, F. 2020, A&A, 633, A99

    2020

  3. [11]

    M., Hillenbrand, L

    Carpenter, J. M., Hillenbrand, L. A., & Skrutskie, M. F. 2001, AJ, 121, 3160

    2001

  4. [12]

    2026, A&A, 708, A209

    Castellanos, R., Najarro, F., Garcia, M., et al. 2026, A&A, 708, A209

    2026

  5. [13]

    S., Negueruela, I., Crowther, P

    Clark, J. S., Negueruela, I., Crowther, P. A., & Goodwin, S. P. 2005, A&A, 434, 949

    2005

  6. [14]

    S., Ritchie, B

    Clark, J. S., Ritchie, B. W., & Negueruela, I. 2010, A&A, 514, A87

    2010

  7. [15]

    S., Ritchie, B

    Clark, J. S., Ritchie, B. W., & Negueruela, I. 2020, A&A, 635, A187

    2020

  8. [16]

    M., Stauffer, J., Baglin, A., et al

    Cody, A. M., Stauffer, J., Baglin, A., et al. 2014, AJ, 147, 82

    2014

  9. [17]

    R., Andersen, M., & Espinoza, P

    Cottaar, M., Meyer, M. R., Andersen, M., & Espinoza, P. 2012, A&A, 539, A5

    2012

  10. [18]

    A., Blum, R

    Damineli, A., Almeida, L. A., Blum, R. D., et al. 2016, MNRAS, 463, 2653

    2016

  11. [19]

    S., Alessi, B

    Dias, W. S., Alessi, B. S., Moitinho, A., & Lépine, J. R. D. 2002, A&A, 389, 871 Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1 Gaia Collaboration, Vallenari, A., Brown, A. G. A., et al. 2023, A&A, 674, A1

    2002

  12. [20]

    2011, MNRAS, 412, 2469

    Gennaro, M., Brandner, W., Stolte, A., & Henning, T. 2011, MNRAS, 412, 2469

    2011

  13. [21]

    P., Parker, R

    Gennaro, M., Goodwin, S. P., Parker, R. J., Allison, R. J., & Brandner, W. 2017, MNRAS, 472, 1760

    2017

  14. [22]

    G., Almendros-Abad, V ., Lovell, J

    Guarcello, M. G., Almendros-Abad, V ., Lovell, J. B., et al. 2025, A&A, 693, A120

    2025

  15. [23]

    G., Flaccomio, E., Albacete-Colombo, J

    Guarcello, M. G., Flaccomio, E., Albacete-Colombo, J. F., et al. 2024, A&A, 682, A49

    2024

  16. [24]

    W., Smith, L

    Guo, Z., Lucas, P. W., Smith, L. C., et al. 2022, MNRAS, 513, 1015

    2022

  17. [25]

    & Bonanos, A

    Koumpia, E. & Bonanos, A. Z. 2012, A&A, 547, A30

    2012

  18. [26]

    2012, ApJ, 750, L44

    Kudryavtseva, N., Brandner, W., Gennaro, M., et al. 2012, ApJ, 750, L44

    2012

  19. [27]

    2013, AJ, 145, 46

    Lim, B., Chun, M.-Y ., Sung, H., et al. 2013, AJ, 145, 46

    2013

  20. [28]

    Lomb, N. R. 1976, Ap&SS, 39, 447

    1976

  21. [29]

    A., Whitworth, A

    Marsh, K. A., Whitworth, A. P., Lomax, O., et al. 2017, MNRAS, 471, 2730

    2017

  22. [30]

    2017, The Journal of Open Source Software, 2, 205

    McInnes, L., Healy, J., & Astels, S. 2017, The Journal of Open Source Software, 2, 205

    2017

  23. [31]

    R., Calvet, N., & Hillenbrand, L

    Meyer, M. R., Calvet, N., & Hillenbrand, L. A. 1997, AJ, 114, 288

    1997

  24. [32]

    Michel, A., van der Marel, N., & Matthews, B. C. 2021, ApJ, 921, 72

    2021

  25. [33]

    W., Emerson, J

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

    2010

  26. [34]

    A., Sanders, J

    Molnar, T. A., Sanders, J. L., Smith, L. C., et al. 2022, MNRAS, 509, 2566

    2022

  27. [35]

    E., Rocha, D

    Navarete, F., Damineli, A., Ramirez, A. E., Rocha, D. F., & Almeida, L. A. 2022, MNRAS, 516, 1289

    2022

  28. [36]

    J., Dorda, R., et al

    Negueruela, I., Alfaro, E. J., Dorda, R., et al. 2022, A&A, 664, A146

    2022

  29. [37]

    S., & Ritchie, B

    Negueruela, I., Clark, J. S., & Ritchie, B. W. 2010, A&A, 516, A78

    2010

  30. [38]

    2022, MNRAS, 517, 6191

    Ordenes-Huanca, C., Zoccali, M., Bayo, A., et al. 2022, MNRAS, 517, 6191

    2022

  31. [39]

    2024, MNRAS, 533, 841

    Ordenes-Huanca, C., Zoccali, M., Bayo, A., et al. 2024, MNRAS, 533, 841

    2024

  32. [40]

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, J. Mach. Learn. Res., 12, 2825

    2011

  33. [41]

    W., Clark, J

    Ritchie, B. W., Clark, J. S., Negueruela, I., & Najarro, F. 2022, A&A, 660, A89

    2022

  34. [42]

    K., Hempel, M., Alonso-García, J., et al

    Saito, R. K., Hempel, M., Alonso-García, J., et al. 2024, A&A, 689, A148 Sánchez, N., Alfaro, E. J., & López-Martínez, F. 2020, MNRAS, 495, 2882 Sánchez, N., Vicente, B., & Alfaro, E. J. 2010, A&A, 510, A78

    2024

  35. [43]

    & Gregorio-Hetem, J

    Santos-Silva, T. & Gregorio-Hetem, J. 2012, A&A, 547, A107

    2012

  36. [44]

    Scargle, J. D. 1982, ApJ, 263, 835

    1982

  37. [45]

    C., Lucas, P

    Smith, L. C., Lucas, P. W., Koposov, S. E., et al. 2025, MNRAS, 536, 3707

    2025

  38. [46]

    M., Rebull, L., et al

    Stauffer, J., Cody, A. M., Rebull, L., et al. 2016, AJ, 151, 60

    2016

  39. [47]

    2022, A&A, 659, A59

    Tarricq, Y ., Soubiran, C., Casamiquela, L., et al. 2022, A&A, 659, A59

    2022

  40. [48]

    C., Lu, J

    Wei, L., Boyle, P. C., Lu, J. R., et al. 2025, ApJ, 992, 213

    2025

  41. [49]

    Yuk, H., Dai, X., Jayasinghe, T., et al. 2022, ApJ, 930, 110 Article number, page 12 of 17 Ordenes-Huanca et al.: EWOCS-VI Appendix A: Dipper variables Variability parameters of the members classifies as dippers and confirmed as this class by visual inspection. Table A.1 lists...

    2022

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