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RR Lyrae Stars in Intermediate-age Magellanic Clusters: Membership Probabilities and Delay Time Distribution

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read 23 RR Lyrae stars are probable members of 10 intermediate-age (1–8 Gyr) Magellanic Cloud clusters, and the inferred delay time distribution puts RR Lyrae production in young populations about an order of magnitude below the old-population…

desk verdict Careful, honest search for intermediate-age RR Lyrae; old-age DTD is a strong result, but the young/intermediate signal leans on a control-field subtraction of unproven representativeness. read the letter →

arxiv 2411.12741 v2 pith:UJXYQ5ZX submitted 2024-11-19 astro-ph.GA

Bolivia Cuevas-Otahola , Cecilia Mateu , Ivan Cabrera-Ziri , Gustavo Bruzual , Fabiola Hernández-Pérez , Gladis Magris , Holger Baumgardt This is my paper · ORCID
classification astro-ph.GA
keywords RRLyraestarsintermediate-agestellarpopulationsMagellanicCloudsstarclustersdelaytimedistributionGaiaDR3OGLEIVbinaryevolution
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

RR Lyrae stars are the canonical tracers of the oldest stellar populations, but this paper argues they can also form in populations only 1–8 Gyr old. Because no individual RR Lyrae can be dated directly, the authors searched for them inside intermediate-age clusters of the Magellanic Clouds, whose ages are reliably known, and identified 23 RR Lyrae as probable members of 10 such clusters using Gaia DR3 proper motions and photometric distances in a probabilistic membership model. From the ensemble they infer a delay time distribution — the number of RR Lyrae produced per unit stellar mass as a function of time since star formation — of $0.34^{+0.17}_{-0.12}$, $0.071^{+0.073}_{-0.041}$, and $2.5^{+0.4}_{-0.3}$ RR Lyrae per $10^5\,M_\odot$ for populations aged 1–2, 2–8, and >8 Gyr, after subtracting contamination estimated from off-cluster control fields. If the memberships survive radial-velocity confirmation, this would be the first direct proof that intermediate-age RR Lyrae exist, supporting binary mass transfer as their formation channel and lowering the age floor usually attached to this star class.

What carries the argument

The argument runs on three pieces of machinery. First, a two-component mixture model assigns each RR Lyrae a membership probability: the cluster component is a Gaussian in photometric parallax and proper motion centred on the cluster's values, and the background component is a Gaussian mixture fitted to an annular control region around the cluster; because RR Lyrae photometric distances from the $M_G$–[Fe/H] relation carry errors below 6%, the distance term separates cluster members from the Magellanic field far more cleanly than Gaia's own parallaxes. Second, a hierarchical Bayesian model converts per-cluster expected member counts into the delay time distribution: the expected number of RR Lyrae in a cluster is $\lambda = M \times \mathrm{DTD}$, modelled with a Gamma likelihood, a generalisation of the Poisson distribution suited to the real-valued expected counts, that marginalises over the uncertainty in each cluster's initial mass under uniform priors on $\log \mathrm{DTD}$ and $\log M$. Third, a control-field contamination test reruns the identical membership model on eight off-cluster fields centred at 23 half-light radii, and the median number of false-positive members found there is subtracted from each cluster's expected count before the DTD is inferred.

What would settle it

Measure radial velocities of the 23 candidates at about 1 km/s precision: true cluster members must match each cluster's systemic velocity within its few-km/s dispersion, and a systematic offset would overturn the membership claim. A companion calculation would recompute the DTD with clusters whose control-field contaminant counts rival their member counts — NGC 1806 (1.7 members, 1.7 contaminants) in the young bin, and NGC 2121 (3.5 members, 4.0 contaminants) in the intermediate bin — removed; if the young or intermediate signal disappears, the decontamination subtraction is carrying the result.

Watch

Extended reading notes

Core claim

RR Lyrae stars are not exclusively old: 23 RR Lyrae with membership probability $p>0.5$ are probable members of 10 Magellanic Cloud clusters with ages between 1 and 8 Gyr — three in the Small Magellanic Cloud (NGC 339, NGC 361, NGC 419) and seven in the Large Magellanic Cloud (Hodge 14, NGC 1718, NGC 1806, NGC 1846, NGC 2121, NGC 2153, NGC 2213). Modelling each cluster's expected RR Lyrae count against its initial mass, the authors infer the delay time distribution of RR Lyrae production and, after subtracting control-field contamination, obtain $0.34^{+0.17}_{-0.12}$ RR Lyrae per $10^5\,M_\odot$ at 1–2 Gyr, $0.071^{+0.073}_{-0.041}$ at 2–8 Gyr, and $2.5^{+0.4}_{-0.3}$ at >8 Gyr. The old-population rate agrees with the delay time distribution previously inferred from the LMC field population, while the young and intermediate rates sit roughly an order of magnitude lower than the field estimates; the paper presents them as lower limits because the RR Lyrae catalogues are incomplete in crowded cluster centres. Together the three bins assert that RR Lyrae do form in populations far younger than the canonical 10 Gyr, at rates so low that only clusters of order $10^5\,M_\odot$ or more are expected to host even one — which is why intermediate-age RR Lyrae have remained undetected until now.

Load-bearing premise

The whole result hinges on the assumption that the eight control fields placed at 23 half-light radii from each cluster centre contain the same background population of RR Lyrae stars — in density, distance, and proper motion — as the line of sight to the cluster itself, so that subtracting their false-member counts removes contamination without removing real members.

Editorial extensions

If this is right

  • The 23 candidates — especially the three in NGC 1846, which an earlier variable-star search had assumed to be field stars — are concrete targets for radial-velocity and metallicity follow-up that could deliver the first direct confirmation of intermediate-age RR Lyrae.
  • A $10^5\,M_\odot$ cluster of age 1–8 Gyr is expected to contain fewer than one RR Lyrae, which explains why the much less massive intermediate-age clusters of the Milky Way have never been found to host any.
  • The old-population DTD of $2.5$ RR Lyrae per $10^5\,M_\odot$, equivalent to about one RR Lyrae per $10^4\,M_\odot$ at the present day, matches the rate observed in Galactic globular clusters and validates the inference method.
  • The cluster DTD sits below the field DTD at young and intermediate ages, implying either that the field estimate was inflated by Milky Way foreground contamination or that dense cluster environments suppress the binaries that produce young RR Lyrae.
  • The inferred present-time frequencies predict at most 0.2 RR Lyrae in clusters younger than 8 Gyr with present masses below $10^4\,M_\odot$, consistent with the absence of RR Lyrae in all known Milky Way intermediate-age clusters.

Reading between the lines

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

  • A decisive check could be made with data already in hand: if the 23 candidates differ systematically from old-population RR Lyrae of the same galaxies in their period–amplitude distribution, that would independently support a distinct formation route before any spectroscopy is obtained.
  • The decontaminated values are lower limits by the authors' own account; future Gaia releases that recover RR Lyrae in crowded cluster cores could push the 1–2 Gyr and 2–8 Gyr rates upward, possibly closing the gap with the field DTD and removing the need for an environmental explanation.
  • If binary evolution is the channel, the DTD's shape — higher at 1–2 Gyr than at 2–8 Gyr — is a quantitative prediction for binary population synthesis codes, which so far report no DTD for their binary-evolution RR Lyrae.
  • The same membership machinery transfers to Andromeda's massive clusters once deep RR Lyrae catalogues exist there, provided the cluster ages come from turn-off photometry rather than integrated light, which is known to misjudge ages.
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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

4 major / 5 minor

Summary. The paper searches for RR Lyrae (RRL) stars in intermediate-age Magellanic Cloud clusters as a direct test of the existence of RRL populations younger than the canonical \gtrsim 10 Gyr. The authors combine the Gaia DR3 Specific Object Study and OGLE-IV RRL catalogs with a catalog of LMC/SMC cluster parameters, and build a probabilistic mixture model that assigns membership probabilities on the basis of proper motions and photometric parallaxes derived from the Muraveva et al. (2018) G-band relation. They identify 23 RRL candidates with p>0.5 in 10 clusters with ages 1\,---\,8 Gyr, and infer the RRL delay time distribution (DTD) in three age bins with a hierarchical Bayesian model. After subtracting median control-field counts as a contamination correction, they report decontaminated DTD values of 0.34\,RRL/10^5\,M_\odot in the 1\,---\,2 Gyr bin, 0.071\,RRL/10^5\,M_\odot in the 2\,---\,8 Gyr bin, and 2.5\,RRL/10^5\,M_\odot in the >8 Gyr bin. The old-age value agrees with the field DTD of Sarbadhicary et al. (2021), while the young and intermediate-age values are substantially lower, which the authors interpret as evidence for an order-of-magnitude lower RRL production rate at intermediate ages, possibly tied to binary evolution.

Significance. If the cluster associations are correct, this is the first direct evidence linking RRL stars to intermediate-age simple stellar populations, and it would provide a strong constraint on binary-evolution formation channels for RRLs. The paper is methodologically transparent: the membership model, the hierarchical DTD inference, and the control-field construction are described in enough detail to be reproduced from public catalogs, and the agreement of the old-age DTD with an independent field measurement by S21 is a nontrivial consistency check that supports the overall approach. The candidate list with light curves and membership probabilities is a useful resource for spectroscopic follow-up. The main weakness is that the young/intermediate-age DTD values rest on a small number of candidates after a control-field subtraction whose statistical uncertainty and systematic assumptions are not fully quantified; the quantitative rate measurements should be treated with caution until that treatment is strengthened.

major comments (4)
  1. [Section 4.1, Table 3] The decontaminated DTD\u2014the central result\u2014is obtained by subtracting the median control-field count N_cont from each cluster\u2019s expectation value <N_RRLs> and then feeding the difference into the hierarchical Gamma/Poisson likelihood of Eqs.\ (7)\u2013(8). However, N_cont is the median of only 8 control fields, is reported without an uncertainty, and for several clusters the Poisson sampling noise on N_cont is comparable to the signal itself (e.g., NGC\ 2121 has <N_RRLs>=3.5 and N_cont=4.0; NGC\ 1806 has 1.7 and 1.7; NGC\ 1718 has 1 and 1.3). Treating N_cont as a known constant and not propagating its sampling distribution into the DTD posterior will understate the uncertainties on the 1\u20132 and 2\u20138 Gyr bins. I recommend including N_cont in the hierarchical model with its own prior (e.g., Gamma or Poisson) and marginalizing over it, or at minimum performing a sensitivity analysis in which N_cont is drawn from its sampling distribution and the DTD fit is repeated.
  2. [Section 4.1] The control fields are placed at 23 r_h with all cluster parameters kept unchanged, which assumes that the field RRL population in proper motion, photometric distance, and density is statistically identical between the control sightlines and the cluster sightlines. The paper itself notes in this same section that the LMC/SMC background has strong spatial variations, yet no test of this assumption is provided. Because several intermediate-age clusters have N_cont comparable to or larger than <N_RRLs>, the residual young/intermediate-age signal depends directly on this assumption. A concrete test would be to measure N_cont at multiple radii (e.g., 10, 23, and 40 r_h) and to compare with maps of the field RRL density; if the scatter is substantial, the DTD inference should include a systematic component for the contamination level.
  3. [Abstract, Section 5.1, Conclusions] The paper states that the decontaminated results 'should be taken as lower limits' because incompleteness of the RRL catalogs in crowded cluster centers is not accounted for, yet the abstract and Table 4 present 0.34 and 0.071 RRL/10^5 M_\odot as the inferred rates without this caveat. Since contamination and incompleteness act in opposite directions, the quoted numbers are not unbiased estimates of the cluster DTD; they are lower limits under the stated assumptions. The abstract should be reworded either to present these as lower limits or to provide a quantitative completeness correction, so that readers do not interpret the point estimates as the full answer.
  4. [Sections 3 and 4, Eqs. (7)-(8)] The hierarchical DTD inference uses the expectation value <N_RRLs> = sum of membership probabilities as the observable N_obs in the Gamma-distribution likelihood of Eq. (8), discarding the posterior distribution P(N_RRLs|D) that the model in Section 3 explicitly computes for each cluster. For candidates with membership probability near 0.5, the difference between the expectation value and the actual count is not negligible, and the uncertainty in membership probabilities is not propagated into the DTD posterior. I suggest marginalizing over the full P(N_RRLs) using posterior samples in the hierarchical fit, at least for the clusters that drive the young/intermediate-age bins, rather than using the point estimate <N_RRLs>.
minor comments (5)
  1. [Section 4.1, Table 3] The text refers to 'the mean number of RRL members identified in control fields' but Table 3 lists the 'Median number of contaminants estimated in 8 control fields.' Please make the statistic used consistent.
  2. [Figure 1 caption] The statement 'Star clusters with smaller symbol sizes were not considered for the analysis' is unclear, since Table 1 appears to include clusters with small symbols; please clarify which clusters were excluded and why.
  3. [Introduction, paragraph 6] The sentence 'I21 and S21 thus reach a similar conclusion as S21 that intermediate-age RRL stars exist' is confusing; it should read that both studies reach a similar conclusion, or the wording should be revised.
  4. [Section 5.2] The sentence 'all of these works predate Gaia DR2 and, not having any kinematic membership criteria, are likely to include back/foreground MC contaminant RRLs' is imprecise for the Kuehn et al. works; the kinematic criterion refers to the present analysis rather than to the earlier searches, so please clarify.
  5. [Table 2] The column header uses \Delta/R_lim for the angular separation ratio, but the column description in the table notes does not define R_lim; a footnote stating that R_lim is R_t or 3 R_h as described in Section 3 would be helpful.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the DTD is an empirical fit to public catalogue data, externally compared with S21; the only self-citation is a minor methodological choice.

full rationale

The central quantities are inferred, not defined by their inputs. Membership probabilities come from a generative mixture model (Eqs. 1-4) fit to Gaia DR3 and OGLE-IV catalogues, with cluster parameters taken from an independent structural-parameter database; the expected RRL counts in Table 3 are posterior sums, not fitted values renamed as predictions. The DTD inference (Eqs. 5-8) is a hierarchical Poisson/Gamma fit to those counts and initial cluster masses, and its old-age result agrees with the independent S21 field-based estimate, providing external anchoring. The control-field contamination subtraction (Section 4.1) applies the same membership machinery to separate off-cluster sight lines at 23 r_h; this rests on a representativeness assumption about LMC/SMC field populations, which the paper itself flags as a caveat, and the decontaminated results are explicitly described as lower limits. This is a statistical limitation, not a circular reduction. The one self-citation (Cuevas-Otahola et al. 2021) sets the background annulus inner radius at 3 r_h; it is a conventional parameter choice and is not load-bearing for the DTD result. No equation in the paper is equivalent to its own input by construction, and no prediction is forced by a fitted parameter or by a self-citation chain.

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

No new physical entities are introduced. The central measurement relies on public catalog values, a calibrated RRL distance relation, a probabilistic membership model, and an uncorrected completeness term. The main non-trivial modeling choices are membership thresholds, age-bin definition, contamination subtraction, and Bayesian priors.

free parameters (7)
  • Membership probability threshold p > 0.5 = 0.5
    Adopted cut to define probable members; DTD uses summed probabilities, so the cut mainly shapes the public candidate list.
  • Angular membership limit = 3 R_h, or R_t for low R_h clusters
    Sets the outer radius for member selection; for compact clusters the tidal radius is used instead.
  • Background annulus geometry = inner 3 R_h, outer adaptive to 10,000 stars
    Control-field definition used to build the background mixture model and contamination estimates.
  • Assumed color for extinction conversion = BP-RP = 0.7
    Applied to all RRL when converting V-band extinction to G-band; affects photometric distances.
  • Age bin edges and NGC 361 placement = 1-2, 2-8, 8-15 Gyr; NGC 361 (8.1 Gyr) placed in 2-8 bin
    Binning choice changes which clusters contribute to the young and intermediate DTD bins.
  • Control-field contamination counts N_cont = Median false positives in 8 control fields per cluster (Table 3)
    Subtracted from expected cluster RRL counts before inferring the DTD; for NGC 2121 N_cont exceeds the cluster expectation.
  • Decontaminated DTD values = 0.34, 0.071, 2.5 RRL per 10^5 Msun in 1-2, 2-8, >8 Gyr bins
    Central inferred quantities; listed because they are fitted to cluster counts rather than derived from an independent theory.
assumptions (7)
  • domain assumption The Muraveva et al. (2018) M_G-[Fe/H] relation applies to LMC and SMC RRL with cluster metallicities.
    Used to compute photometric distances; errors estimated at about 6% for 0.1 dex metallicity errors. Section 2.
  • domain assumption Photometric parallax can be approximated as the inverse of photometric distance.
    Invoked for RRL because Gaia DR3 parallax errors exceed 100%; valid for distance errors below 20%. Section 3.
  • domain assumption Cluster ages, masses, metallicities, distances, proper motions, and extinctions from Table 1 are accurate.
    All membership and DTD calculations inherit these catalog values. Section 2 and Table 1.
  • domain assumption Each cluster is a simple stellar population with an initial mass estimated from PARSEC tracks and the Baumgardt et al. IMF, neglecting dynamical mass loss.
    The DTD uses initial masses; the paper explicitly ignores dynamical mass loss. Sections 2 and 4.
  • domain assumption Control fields at 23 r_h are representative of the cluster line-of-sight background.
    Basis for the contamination correction; field variations in the LMC and SMC could bias the decontaminated DTD. Section 4.1.
  • ad hoc to paper RRL catalog incompleteness in crowded cluster centers does not affect the inferred DTD point estimates.
    The authors state completeness is not accounted for and that results are lower limits; the DTD inference still treats observed counts as the expectation. Section 5.1.
  • domain assumption Uniform priors on log DTD in [-7,-3] and log initial mass in [3,7] are appropriate.
    These bounds influence posterior medians and credible intervals. Section 4.

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Cite this review

Pith. "Pith review of RR Lyrae Stars in Intermediate-age Magellanic Clusters: Membership Probabilities and Delay Time Distribution." pith.science (2026). https://pith.science/paper/UJXYQ5ZX

@misc{pith2026241112741,
  author       = {Pith},
  title        = {Pith review of: RR Lyrae Stars in Intermediate-age Magellanic Clusters: Membership Probabilities and Delay Time Distribution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UJXYQ5ZX}},
  note         = {Machine review of arXiv:2411.12741}
}
abstract

Recent works have challenged our canonical view of RR Lyrae (RRL) stars as tracers of exclusively old populations ($\gtrsim10$~Gyr) by proposing a fraction of these stars to be of intermediate ages ($\sim$2-5~Gyr). Since it is currently not possible to infer stellar ages directly for individual RRL stars, our goal in this work is to search for these in association to intermediate-age clusters whose reliable ages can then be safely be attributed to the RRL. We used the Gaia DR3 Specific Object Study and OGLE IV public catalogues to search for RRL stars around stellar clusters older than 1~Gyr in the Large and Small Magellanic Clouds. Modelling membership probabilities based on proper motion and photometric distance we obtained a list of 259 RRL stars associated with Magellanic clusters. Of these, 23 RRL are likely members of 10 intermediate-age clusters: 3 and 7 in the Small and Large Magellanic Clouds, respectively. By modelling the inferred expectation values of the number of RRL stars per cluster, we inferred the delay time distribution of the RRL in three age ranges. For the old population ($>8$~Gyr) we find $2.5^{+0.4}_{-0.3}$ RRL$/10^5 M_\odot$. For the young (1-2 Gyr) and intermediate age (2-8 Gyr) populations we find rates of $0.34^{+0.17}_{-0.12}$ and $0.071^{+0.073}_{-0.041}$ RRL$/10^5 M_\odot$, respectively, after further decontamination from control field tests. While radial velocities are necessary for definitively confirming cluster memberships, the high-probability list of intermediate-age RRL stars presented here offers a promising opportunity for the first direct confirmation of these enigmatic stars.

Figures

Figures reproduced from arXiv: 2411.12741 by the authors.

Figure 1
Figure 1. Spatial distribution of the stellar clusters in the LMC (left) and SMC (right) listed in Tab. 1. The colour scale shows the extinction map from Chen et al. (2022). Star clusters with smaller symbol sizes were not considered for the analysis. 2 4 6 8 10 12 14 Age (Gyr) 0 2 4 6 8 10 12 14 Number of clusters 1-2 Gyr 2-8 Gyr 8-15 Gyr 2 1 0 [Fe/H] 0 2 4 6 8 10 3.5 4.0 4.5 5.0 5.5 log(Mass/M ) 0 1 2 3 4 5 6 [PITH_FULL_IM… view at source ↗
Figure 2
Figure 2. Distributions of age (left), metallicity (centre) and logMass (right) for the LMC and SMC clusters from [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Posterior distribution of the inferred number of RR Lyrae in the clusters NGC 1846 (left) and NGC 1978 (right). The solid (black) line represents the histogram for all stars (generic and RRL), the dark red histograms correspond to all RRL within 3𝑟ℎ and probable cluster RRL members (𝑝 > 0.5 and < 3𝑅ℎ), whereas the light red ones correspond to RRL with low membership probability RRLs (𝑝 < 0.5) bilities and general as… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: 𝐺 vs 𝐵𝑃 − 𝑅𝑃 color magnitude diagrams for stars in the fields around NGC 1846 (left) and NGC 1978 (right). The background contours show the number counts of all Gaia DR3 stars in each field. Black dots correspond to generic stars with high probabilities (𝑝 > 0.5) that …
Figure 5
Figure 5. Figure 5: Graphical representation of the relations between parameters in the probabilistic hierarchical inference model used for the DTD. Observed data are represented with grey ovals. The rectangle encloses the part of the model specific to each cluster, while population-wide …
Figure 6
Figure 6. Figure 6: Posterior PDF for the Delay Time Distribution (DTD top) and for the Present Time Frequency (PTF, bottom) of RRL stars, for clusters in three age ranges: 1 to 2 Gyr, 2 to 8 Gyr and 8 to 15 Gyr. The solid and dashed lines correspond to the posterior PDFs before (solid) a…

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Works this paper leans on

108 extracted references · 28 canonical work pages

  1. [1]

    V., Vega-Neme L

    Ahumada A. V., Vega-Neme L. R., Clariá J. J., Minniti J. H., 2019, Publications of the Astronomical Society of the Pacific, 131, 1

    2019

  2. [2]

    Astropy Collaboration et al., 2018, @doi [ ] 10.3847/1538-3881/aabc4f , https://ui.adsabs.harvard.edu/#abs/2018AJ....156..123A 156, 123

    doi:10.3847/1538-3881/aabc4f 2018

  3. [3]

    Bailer-Jones C. A. L., Rybizki J., Fouesneau M., Mantelet G., Andrae R., 2018, @doi [ ] 10.3847/1538-3881/aacb21 , https://ui.adsabs.harvard.edu/abs/2018AJ....156...58B 156, 58

    doi:10.3847/1538-3881/aacb21 2018

  4. [4]

    I., 1902, Annals of Harvard College Observatory, https://ui.adsabs.harvard.edu/abs/1902AnHar..38....1B 38, 1

    Bailey S. I., 1902, Annals of Harvard College Observatory, https://ui.adsabs.harvard.edu/abs/1902AnHar..38....1B 38, 1

    1902

  5. [5]

    Baumgardt H., H \'e nault-Brunet V., Dickson N., Sollima A., 2023, @doi [ ] 10.1093/mnras/stad631 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.521.3991B 521, 3991

    doi:10.1093/mnras/stad631 2023

  6. [6]

    A., Hoyle F., Sharples R

    Beasley M. A., Hoyle F., Sharples R. M., 2002, @doi [ ] 10.1046/j.1365-8711.2002.05714.x , https://ui.adsabs.harvard.edu/abs/2002MNRAS.336..168B 336, 168

    arXiv 2002

  7. [7]

    L., et al., 2018, @doi [ ] 10.1007/s11214-018-0542-1 , https://ui.adsabs.harvard.edu/abs/2018SSRv..214..113B 214, 113

    Beaton R. L., et al., 2018, @doi [ ] 10.1007/s11214-018-0542-1 , https://ui.adsabs.harvard.edu/abs/2018SSRv..214..113B 214, 113

    doi:10.1007/s11214-018-0542-1 2018

  8. [8]

    Bingham E., et al., 2019, J. Mach. Learn. Res., 20, 28:1

    2019

Show all 108 references
  1. [9]

    arXiv:2208.04332

    Bobrick A., Iorio G., Belokurov V., Vos J., Vuckovic M., Giacobbo N., 2022, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2022arXiv220804332B p. arXiv:2208.04332

    2022 arXiv

  2. [10]

    Bono G., Caputo F., Cassisi S., Castellani V., Marconi M., 1997a, @doi [ ] 10.1086/303872 , https://ui.adsabs.harvard.edu/abs/1997ApJ...479..279B 479, 279

    doi

  3. [11]

    Bono G., Caputo F., Cassisi S., Incerpi R., Marconi M., 1997b, @doi [ ] 10.1086/304284 , https://ui.adsabs.harvard.edu/abs/1997ApJ...483..811B 483, 811

    doi

  4. [12]

    Bressan A., Marigo P., Girardi L., Salasnich B., Dal Cero C., Rubele S., Nanni A., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21948.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427..127B 427, 127

    2012

  5. [13]

    A., Harding P., 2011, @doi [ ] 10.1088/0004-6256/141/2/61 , https://ui.adsabs.harvard.edu/abs/2011AJ....141...61C 141, 61

    Caldwell N., Schiavon R., Morrison H., Rose J. A., Harding P., 2011, @doi [ ] 10.1088/0004-6256/141/2/61 , https://ui.adsabs.harvard.edu/abs/2011AJ....141...61C 141, 61

    2011 doi

  6. [14]

    A., 2015, Pulsating Stars , 1

    Catelan M., Smith H. A., 2015, Pulsating Stars , 1. auflage edn. Wiley-VCH, Weinheim, Bergstr

    2015

  7. [15]

    Chantereau W., Salaris M., Bastian N., Martocchia S., 2019, @doi [ ] 10.1093/mnras/stz378 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.484.5236C 484, 5236

    2019 doi

  8. [16]

    Chen Y., Girardi L., Bressan A., Marigo P., Barbieri M., Kong X., 2014, @doi [ ] 10.1093/mnras/stu1605 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.444.2525C 444, 2525

    2014 doi

  9. [17]

    Chen Y., Bressan A., Girardi L., Marigo P., Kong X., Lanza A., 2015, @doi [ ] 10.1093/mnras/stv1281 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452.1068C 452, 1068

    2015 doi

  10. [18]

    Q., Guo H

    Chen B. Q., Guo H. L., Gao J., Yang M., Liu Y. L., Jiang B. W., 2022, @doi [ ] 10.1093/mnras/stac072 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.1317C 511, 1317

    2022 doi

  11. [19]

    Clementini G., et al., 2019, @doi [ ] 10.1051/0004-6361/201833374 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A..60C 622, A60

    2019 doi

  12. [20]

    Clementini G., et al., 2023, @doi [ ] 10.1051/0004-6361/202243964 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A..18C 674, A18

    2023 doi

  13. [21]

    I., Johansson L., Netzel H., Medaric Z., 2024, @doi [ ] 10.1051/0004-6361/202348961 , https://ui.adsabs.harvard.edu/abs/2024A&A...684A.173C 684, A173

    Cruz Reyes M., Anderson R. I., Johansson L., Netzel H., Medaric Z., 2024, @doi [ ] 10.1051/0004-6361/202348961 , https://ui.adsabs.harvard.edu/abs/2024A&A...684A.173C 684, A173

    2024 doi

  14. [22]

    D., Puerari I., Rosa-Gonz \'a lez D., 2021, @doi [ ] 10.1093/mnras/staa3513 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.4422C 500, 4422

    Cuevas-Otahola B., Mayya Y. D., Puerari I., Rosa-Gonz \'a lez D., 2021, @doi [ ] 10.1093/mnras/staa3513 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500.4422C 500, 4422

    2021 doi

  15. [23]

    Duch \^e ne G., Kraus A., 2013, @doi [ ] 10.1146/annurev-astro-081710-102602 , https://ui.adsabs.harvard.edu/abs/2013ARA&A..51..269D 51, 269

    2013 doi

  16. [24]

    Foreman-Mackey D., 2014, @doi [Zenodo] doi:10.5281/zenodo.15856

    2014 doi

  17. [25]

    Foreman-Mackey D., et al., 2013a, Astrophysics Source Code Library, record ascl:1303.002

  18. [26]

    W., Lang D., Goodman J., 2013b, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    Foreman-Mackey D., Hogg D. W., Lang D., Goodman J., 2013b, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    doi

  19. [27]

    Gaia Collaboration et al., 2018, @doi [ ] 10.1051/0004-6361/201833051 , https://ui.adsabs.harvard.edu/abs/2018A&A...616A...1G 616, A1

    2018 doi

  20. [28]

    Gaia Collaboration et al., 2023a, @doi [ ] 10.1051/0004-6361/202243940 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A...1G 674, A1

    doi

  21. [29]

    Gaia Collaboration et al., 2023b, @doi [ ] 10.1051/0004-6361/202347203 , https://ui.adsabs.harvard.edu/abs/2023A&A...680A..35G 680, A35

    doi

  22. [30]

    P., G \'o mez M., Storm J., Moffett T

    Gieren W. P., G \'o mez M., Storm J., Moffett T. J., Infante L., Barnes Thomas G. I., Geisler D., Fouqu \'e P., 2000, @doi [ ] 10.1086/313418 , https://ui.adsabs.harvard.edu/abs/2000ApJS..129..111G 129, 111

    2000 doi

  23. [31]

    Giusti C., et al., 2024, @doi [ ] 10.1051/0004-6361/202449438 , https://ui.adsabs.harvard.edu/abs/2024A&A...686A...6G 686, A6

    2024 doi

  24. [32]

    K., 1998, arXiv, astro-ph/9812443v1

    Grebel E. K., 1998, arXiv, astro-ph/9812443v1

    1998 arXiv

  25. [33]

    K., 2000, eprint arXiv, p

    Grebel E. K., 2000, eprint arXiv, p. 11048

    2000

  26. [34]

    K., Chu Y.-H., 2000, @doi [ ] 10.1086/301218 , https://ui.adsabs.harvard.edu/abs/2000AJ....119..787G 119, 787

    Grebel E. K., Chu Y.-H., 2000, @doi [ ] 10.1086/301218 , https://ui.adsabs.harvard.edu/abs/2000AJ....119..787G 119, 787

    2000 doi

  27. [35]

    Hajdu G., et al., 2021, @doi [ ] 10.3847/1538-4357/abff4b , https://ui.adsabs.harvard.edu/abs/2021ApJ...915...50H 915, 50

    2021 doi

  28. [36]

    E., 1996, @doi [ ] 10.1086/118116 , https://ui.adsabs.harvard.edu/abs/1996AJ....112.1487H 112, 1487

    Harris W. E., 1996, @doi [ ] 10.1086/118116 , https://ui.adsabs.harvard.edu/abs/1996AJ....112.1487H 112, 1487

    1996 doi

  29. [37]

    Harris J., Zaritsky D., 2009, @doi [ ] 10.1088/0004-6256/138/5/1243 , https://ui.adsabs.harvard.edu/abs/2009AJ....138.1243H 138, 1243

    2009 doi

  30. [38]

    D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , http://adsabs.harvard.edu/abs/2007CSE.....9...90H 9, 90

    Hunter J. D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , http://adsabs.harvard.edu/abs/2007CSE.....9...90H 9, 90

    2007 doi

  31. [39]

    Iorio G., Belokurov V., 2021, @doi ["MNRAS"] 10.1093/mnras/stab005 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.5686I 502, 5686

    2021 doi

  32. [40]

    Jayasinghe T., et al., 2019, @doi [ ] 10.1093/mnras/stz444 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485..961J 485, 961

    2019 doi

  33. [41]

    Jones E., Oliphant T., Peterson P., et al., 2001, SciPy : Open source scientific tools for Python , @doi 10.5281/zenodo.376244 , http://www.scipy.org/

    2001 doi

  34. [42]

    Karczmarek P., Wiktorowicz G., I kiewicz K., Smolec R., St e pie \'n K., Pietrzy \'n ski G., Gieren W., Belczynski K., 2017, @doi [ ] 10.1093/mnras/stw3286 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.466.2842K 466, 2842

    2017 doi

  35. [43]

    O., Nardiello D., Ortolani S., Barbuy B., Bica E., Cassisi S., Libralato M., Vieira R

    Kerber L. O., Nardiello D., Ortolani S., Barbuy B., Bica E., Cassisi S., Libralato M., Vieira R. G., 2018, @doi [ ] 10.3847/1538-4357/aaa3fc , https://ui.adsabs.harvard.edu/abs/2018ApJ...853...15K 853, 15

    2018 doi

  36. [44]

    Kervella P., et al., 2019a, @doi [ ] 10.1051/0004-6361/201834210 , https://ui.adsabs.harvard.edu/abs/2019A&A...623A.116K 623, A116

    doi

  37. [45]

    Kervella P., et al., 2019b, @doi [ ] 10.1051/0004-6361/201834211 , https://ui.adsabs.harvard.edu/abs/2019A&A...623A.117K 623, A117

    doi

  38. [46]

    A., Wo \'z niak P

    Kinemuchi K., Smith H. A., Wo \'z niak P. R., McKay T. A., 2006, @doi [The Astronomical Journal] 10.1086/506198 , 132, 1202

    2006 doi

  39. [47]

    pp 87 -- 90

    Kluyver T., et al., 2016, in Loizides F., Schmidt B., eds, Positioning and Power in Academic Publishing: Players, Agents and Agendas. pp 87 -- 90

    2016

  40. [48]

    E., et al., 2019, @doi [ ] 10.1093/mnras/stz457 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.4726K 485, 4726

    Koposov S. E., et al., 2019, @doi [ ] 10.1093/mnras/stz457 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.4726K 485, 4726

    2019 doi

  41. [49]

    A., Smith H

    Kuehn C. A., Smith H. A., Catelan M., Pritzl B. J., Lee N. D., Borissova J., 2011, @doi [The Astronomical Journal] 10.1088/0004-6256/142/4/107 , 142, 107

    2011 doi

  42. [50]

    A., Smith H

    Kuehn C. A., Smith H. A., Catelan M., Pritzl B. J., De Lee N., Borissova J., 2012, @doi [ ] 10.1088/0004-6256/144/6/186 , https://ui.adsabs.harvard.edu/abs/2012AJ....144..186K 144, 186

    2012 doi

  43. [51]

    A., et al., 2013, @doi [The Astronomical Journal] 10.1088/0004-6256/145/6/160 , 145, 160

    Kuehn C. A., et al., 2013, @doi [The Astronomical Journal] 10.1088/0004-6256/145/6/160 , 145, 160

    2013 doi

  44. [52]

    M., 2022, @doi [Universe] 10.3390/universe8040206 , https://ui.adsabs.harvard.edu/abs/2022Univ....8..206K 8, 206

    Kunder A. M., 2022, @doi [Universe] 10.3390/universe8040206 , https://ui.adsabs.harvard.edu/abs/2022Univ....8..206K 8, 206

    2022 doi

  45. [53]

    Kunder A., Chaboyer B., 2008, @doi [The Astronomical Journal] 10.1088/0004-6256/136/6/2441 , 136, 2441

    2008 doi

  46. [54]

    Lanzoni B., et al., 2019, @doi [The Astrophysical Journal] 10.3847/1538-4357/ab54c2 , 887, 176

    2019 doi

  47. [55]

    C., 1995a, @doi [ ] 10.1086/117690 , https://ui.adsabs.harvard.edu/abs/1995AJ....110.2288L 110, 2288

    Layden A. C., 1995a, @doi [ ] 10.1086/117690 , https://ui.adsabs.harvard.edu/abs/1995AJ....110.2288L 110, 2288

    doi

  48. [56]

    C., 1995b, @doi [ ] 10.1086/117691 , https://ui.adsabs.harvard.edu/abs/1995AJ....110.2312L 110, 2312

    Layden A. C., 1995b, @doi [ ] 10.1086/117691 , https://ui.adsabs.harvard.edu/abs/1995AJ....110.2312L 110, 2312

    doi

  49. [57]

    C., Zhang H.-W., 2023, @doi [ ] 10.3847/1538-4357/acadd5 , https://ui.adsabs.harvard.edu/abs/2023ApJ...944...88L 944, 88

    Li X.-Y., Huang Y., Liu G.-C., Beers T. C., Zhang H.-W., 2023, @doi [ ] 10.3847/1538-4357/acadd5 , https://ui.adsabs.harvard.edu/abs/2023ApJ...944...88L 944, 88

    2023 doi

  50. [58]

    Lindegren L., 2018, R e-normalising the astrometric chi-square in G aia D R 2, GAIA-C3-TN-LU-LL-124, http://www.rssd.esa.int/doc_fetch.php?id=3757412

    2018

  51. [59]

    N., 2016a, @doi [ ] 10.1093/mnras/stw851 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.459.4360L 459, 4360

    Li s ka J., Skarka M., Zejda M., Mikul \'a s ek Z., de Villiers S. N., 2016a, @doi [ ] 10.1093/mnras/stw851 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.459.4360L 459, 4360

    doi

  52. [60]

    Li s ka J., Skarka M., Mikul \'a s ek Z., Zejda M., Chrastina M., 2016b, @doi [ ] 10.1051/0004-6361/201525870 , https://ui.adsabs.harvard.edu/abs/2016A&A...589A..94L 589, A94

    doi

  53. [61]

    A., Gozha M

    Marsakov V. A., Gozha M. L., Koval' V. V., 2019, @doi [Astrophysics] 10.1007/s10511-019-09596-1 , https://ui.adsabs.harvard.edu/abs/2019Ap.....62..467M 62, 467

    2019 doi

  54. [62]

    K., 2018, @doi [ ] 10.1093/mnras/sty1373 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479..211M 479, 211

    Mateu C., Vivas A. K., 2018, @doi [ ] 10.1093/mnras/sty1373 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.479..211M 479, 211

    2018 doi

  55. [63]

    E., van der Marel R

    McLaughlin D. E., van der Marel R. P., 2005, @doi [ ] 10.1086/497429 , https://ui.adsabs.harvard.edu/abs/2005ApJS..161..304M 161, 304

    2005 doi

  56. [64]

    E., Mu \ n oz R

    Medina G. E., Mu \ n oz R. R., Carlin J. L., Vivas A. K., Grebel E. K., Mart \' nez-V \'a zquez C. E., Hansen C. J., 2024, @doi [ ] 10.1093/mnras/stae1137 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.531.4762M 531, 4762

    2024 doi

  57. [65]

    Miceli A., et al., 2008, @doi [ ] 10.1086/533484 , 678, 865

    2008 doi

  58. [66]

    P., et al., 2021, @doi [ ] 10.3847/1538-4357/abefd4 , https://ui.adsabs.harvard.edu/abs/2021ApJ...912..144M 912, 144

    Mullen J. P., et al., 2021, @doi [ ] 10.3847/1538-4357/abefd4 , https://ui.adsabs.harvard.edu/abs/2021ApJ...912..144M 912, 144

    2021 doi

  59. [67]

    E., Clementini G., Sarro L

    Muraveva T., Delgado H. E., Clementini G., Sarro L. M., Garofalo A., 2018, @doi [ ] 10.1093/mnras/sty2241 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.1195M 481, 1195

    2018 doi

  60. [68]

    M., Walker A., Jeon Y.-B., 2009, @doi [ ] 10.1088/0004-6256/138/5/1310 , https://ui.adsabs.harvard.edu/abs/2009AJ....138.1310N 138, 1310

    Nemec J. M., Walker A., Jeon Y.-B., 2009, @doi [ ] 10.1088/0004-6256/138/5/1310 , https://ui.adsabs.harvard.edu/abs/2009AJ....138.1310N 138, 1310

    2009 doi

  61. [69]

    M., Goudfrooij P., Puzia T

    Pessev P. M., Goudfrooij P., Puzia T. H., Chandar R., 2006, @doi [ ] 10.1086/505625 , https://ui.adsabs.harvard.edu/abs/2006AJ....132..781P 132, 781

    2006 doi

  62. [70]

    Phan D., Pradhan N., Jankowiak M., 2019, arXiv preprint arXiv:1912.11554

    2019 arXiv

  63. [71]

    E., Santos J

    Piatti A. E., Santos J. F. C. J., Clari \'a J. J., Bica E., Ahumada A. V., Parisi M. C., 2005, @doi [ ] 10.1051/0004-6361:20052982 , https://ui.adsabs.harvard.edu/abs/2005A&A...440..111P 440, 111

    2005 doi

  64. [72]

    E., Geisler D., Sarajedini A., Gallart C., Wischnjewsky M., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13593.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.389..429P 389, 429

    Piatti A. E., Geisler D., Sarajedini A., Gallart C., Wischnjewsky M., 2008, @doi [ ] 10.1111/j.1365-2966.2008.13593.x , https://ui.adsabs.harvard.edu/abs/2008MNRAS.389..429P 389, 429

    2008

  65. [73]

    Pieres A., et al., 2016, @doi [ ] 10.1093/mnras/stw1260 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.461..519P 461, 519

    2016 doi

  66. [74]

    Pietrzy \'n ski G., et al., 2012, @doi [ ] 10.1038/nature10966 , https://ui.adsabs.harvard.edu/abs/2012Natur.484...75P 484, 75

    2012 doi

  67. [75]

    M., Mateu C., Iorio G., Pearson S., Bonaca A., Belokurov V., 2019, @doi [ ] 10.3847/1538-3881/ab4cef , https://ui.adsabs.harvard.edu/abs/2019AJ....158..223P 158, 223

    Price-Whelan A. M., Mateu C., Iorio G., Pearson S., Bonaca A., Belokurov V., 2019, @doi [ ] 10.3847/1538-3881/ab4cef , https://ui.adsabs.harvard.edu/abs/2019AJ....158..223P 158, 223

    2019 doi

  68. [76]

    K., Lee C

    Prudil Z., Skarka M., Li s ka J., Grebel E. K., Lee C. U., 2019, @doi [ ] 10.1093/mnrasl/slz069 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487L...1P 487, L1

    2019 doi

  69. [77]

    K., Kunder A., 2020, @doi [ ] 10.1093/mnras/staa046 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.3408P 492, 3408

    Prudil Z., D \'e k \'a ny I., Grebel E. K., Kunder A., 2020, @doi [ ] 10.1093/mnras/staa046 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.3408P 492, 3408

    2020 doi

  70. [78]

    M., 2020, @doi [ ] 10.1051/0004-6361/202037819 , https://ui.adsabs.harvard.edu/abs/2020A&A...638A.104R 638, A104

    Ramos P., Mateu C., Antoja T., Helmi A., Castro-Ginard A., Balbinot E., Carrasco J. M., 2020, @doi [ ] 10.1051/0004-6361/202037819 , https://ui.adsabs.harvard.edu/abs/2020A&A...638A.104R 638, A104

    2020 doi

  71. [79]

    W., 2011, J

    Richmond M. W., 2011, J. Am. Assoc. Var. Star Obs., https://app.aavso.org/media/jaavso/2734.pdf 39.2, 201

    2011

  72. [80]

    Saha A., 1985, @doi [Astrophysical Journal] 10.1086/162890 , 289, 310

    1985 doi

  73. [81]

    A., Vivas A

    Salinas R., Pajkos M. A., Vivas A. K., Strader J., Contreras Ramos R., 2018, @doi [ ] 10.3847/1538-3881/aab551 , https://ui.adsabs.harvard.edu/abs/2018AJ....155..183S 155, 183

    2018 doi

  74. [82]

    L., Das P., 2018, @doi [ ] 10.1093/mnras/sty2490 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.4093S 481, 4093

    Sanders J. L., Das P., 2018, @doi [ ] 10.1093/mnras/sty2490 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481.4093S 481, 4093

    2018 doi

  75. [83]

    Santos J. F. C. J., Piatti A. E., 2004, @doi [ ] 10.1051/0004-6361:20041560 , https://ui.adsabs.harvard.edu/abs/2004A&A...428...79S 428, 79

    2004 doi

  76. [84]

    K., et al., 2021, @doi [The Astrophysical Journal] 10.3847/1538-4357/abca86 , 912, 140

    Sarbadhicary S. K., et al., 2021, @doi [The Astrophysical Journal] 10.3847/1538-4357/abca86 , 912, 140

    2021 doi

  77. [85]

    J., Finkbeiner D

    Schlegel D. J., Finkbeiner D. P., Davis M., 1998, @doi [ ] 10.1086/305772 , https://ui.adsabs.harvard.edu/abs/1998ApJ...500..525S 500, 525

    1998 doi

  78. [86]

    M., Wood P

    Sebo K. M., Wood P. R., 1994, @doi [ ] 10.1086/117123 , https://ui.adsabs.harvard.edu/abs/1994AJ....108..932S 108, 932

    1994 doi

  79. [87]

    Sesar B., et al., 2017, @doi [ ] 10.3847/1538-3881/aa661b , http://adsabs.harvard.edu/abs/2017AJ....153..204S 153, 204

    2017 doi

  80. [88]

    G., Kolenberg K., 2018, @doi [ ] 10.1093/mnras/stx2737 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474..824S 474, 824

    Skarka M., Li s ka J., D r ev e n \'y R., Guggenberger E., S \'o dor \'A ., Barnes T. G., Kolenberg K., 2018, @doi [ ] 10.1093/mnras/stx2737 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474..824S 474, 824

    2018 doi

  81. [89]

    A., 1995, RR Lyrae Stars, Cambridge Astrophysics Series

    Smith H. A., 1995, RR Lyrae Stars, Cambridge Astrophysics Series

    1995

  82. [90]

    Soszy \'n ski I., et al., 2016, , https://ui.adsabs.harvard.edu/abs/2016AcA....66..131S 66, 131

    2016

  83. [91]

    A., 1986, @doi [ ] 10.1093/mnras/220.2.413 , https://ui.adsabs.harvard.edu/abs/1986MNRAS.220..413S 220, 413

    Strugnell P., Reid N., Murray C. A., 1986, @doi [ ] 10.1093/mnras/220.2.413 , https://ui.adsabs.harvard.edu/abs/1986MNRAS.220..413S 220, 413

    1986 doi

  84. [92]

    Szeidl B., Olah K., Mizser A., 1986, Commmunications of the Konkoly Observatory Hungary, https://ui.adsabs.harvard.edu/abs/1986CoKon..89...57S 89, 57

    1986

  85. [93]

    Tang J., Bressan A., Rosenfield P., Slemer A., Marigo P., Girardi L., Bianchi L., 2014, @doi [ ] 10.1093/mnras/stu2029 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445.4287T 445, 4287

    2014 doi

  86. [94]

    B., 2005, in Shopbell P., Britton M., Ebert R., eds, Astronomical Society of the Pacific Conference Series Vol

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

    2005

  87. [95]

    B., 2006, in Gabriel C., Arviset C., Ponz D., Enrique S., eds, Astronomical Society of the Pacific Conference Series Vol

    Taylor M. B., 2006, in Gabriel C., Arviset C., Ponz D., Enrique S., eds, Astronomical Society of the Pacific Conference Series Vol. 351, Astronomical Data Analysis Software and Systems XV. p. 666

    2006

  88. [96]

    Tolstoy E., Hill V., Tosi M., 2009, @doi [ ] 10.1146/annurev-astro-082708-101650 , https://ui.adsabs.harvard.edu/abs/2009ARA&A..47..371T 47, 371

    2009 doi

  89. [97]

    K., Zinn R., 2006, @doi [The Astronomical Journal] 10.1086/505200 , 132, 714

    Vivas A. K., Zinn R., 2006, @doi [The Astronomical Journal] 10.1086/505200 , 132, 714

    2006 doi

  90. [98]

    K., et al., 2004, @doi [ ] 10.1086/380929 , http://adsabs.harvard.edu/abs/2004AJ....127.1158V 127, 1158

    Vivas A. K., et al., 2004, @doi [ ] 10.1086/380929 , http://adsabs.harvard.edu/abs/2004AJ....127.1158V 127, 1158

    2004 doi

  91. [99]

    R., 1989, @doi [ ] 10.1086/132470 , https://ui.adsabs.harvard.edu/abs/1989PASP..101..570W 101, 570

    Walker A. R., 1989, @doi [ ] 10.1086/132470 , https://ui.adsabs.harvard.edu/abs/1989PASP..101..570W 101, 570

    1989 doi

  92. [100]

    Walt S. v. d., Colbert S. C., Varoquaux G., 2011, @doi [Computing in Science and Engg.] 10.1109/MCSE.2011.37 , 13, 22

    2011 doi

  93. [101]

    Wegg C., Gerhard O., Bieth M., 2019, @doi [ ] 10.1093/mnras/stz572 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.3296W 485, 3296

    2019 doi

  94. [102]

    R., Dolphin A

    Weisz D. R., Dolphin A. E., Skillman E. D., Holtzman J., Gilbert K. M., Dalcanton J. J., Williams B. F., 2014, @doi [ ] 10.1088/0004-637X/789/2/148 , https://ui.adsabs.harvard.edu/abs/2014ApJ...789..148W 789, 148

    2014 doi

  95. [103]

    L., Mateo M., Olszewski E

    Welch D. L., Mateo M., Olszewski E. W., Fischer P., Takamiya M., 1993, @doi [ ] 10.1086/116415 , https://ui.adsabs.harvard.edu/abs/1993AJ....105..146W 105, 146

    1993 doi

  96. [104]

    J., McGraw J

    Wetterer C. J., McGraw J. T., 1996, @doi [Astronomical Journal v.112] 10.1086/118076 , 112, 1046

    1996 doi

  97. [105]

    Worthey G., 1994, @doi [ ] 10.1086/192096 , https://ui.adsabs.harvard.edu/abs/1994ApJS...95..107W 95, 107

    1994 doi

  98. [106]

    C., Casetti-Dinescu D

    Zinn R., Chen X., Layden A. C., Casetti-Dinescu D. I., 2020, @doi [ ] 10.1093/mnras/stz3580 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.492.2161Z 492, 2161

    2020 doi

  99. [107]

    A., Barbuy B., 1995, , https://ui.adsabs.harvard.edu/abs/1995A&A...302..718D 302, 718

    de Freitas Pacheco J. A., Barbuy B., 1995, , https://ui.adsabs.harvard.edu/abs/1995A&A...302..718D 302, 718

    1995

  100. [108]

    del Pino A., et al., 2022, @doi [ ] 10.3847/1538-4357/ac70cf , https://ui.adsabs.harvard.edu/abs/2022ApJ...933...76D 933, 76

    2022 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.