Bayesian ages of local young stellar associations I. Through the expansion rate method

H. Bouy; J. Olivares; N. Miret-Roig; P.A.B. Galli

REVIEW 4 major objections 6 minor 92 references

Bayesian ages of local young stellar associations I. Through the expansion rate method

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read By inverting the measured expansion of nearby young stellar associations, this paper derives Bayesian ages for 18 systems and argues that most kinematic-versus-nuclear age disagreements are artifacts of mixing distinct stellar populations.

desk verdict Homogeneous expansion-age catalogue plus several new nearby associations, but the age inversion defers to an unavailable companion paper and the oldest ages press against the method's own 40 Myr limit. read the letter →

arxiv 2506.05130 v1 pith:6KUKQA27 submitted 2025-06-05 astro-ph.SR astro-ph.GA

J. Olivares , N. Miret-Roig , P.A.B. Galli , H. Bouy This is my paper

classification astro-ph.SRastro-ph.GA

keywords youngstellarassociationsexpansionagesBayesianhierarchicalmodellingGaiaastrometryphase-spacesubstructureskinematicsnearbystarformationGalacticpotential

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

Using literature membership lists, Gaia DR3 astrometry, and compiled radial velocities, the paper applies a Bayesian expansion-rate dating method to the ten classical local young stellar associations and reports 20 expansion ages for 18 systems. The central claim is that expansion ages are valid and compatible with independent nuclear ages, and that most previously reported tensions between kinematic and nuclear dating disappear once hidden populations and substructures inside the classical member lists are separated. The paper rediscovers three known groups and identifies several new nearby associations, so the nearby star-formation history is more phase-space complex than earlier censuses indicated. A reader should care because these ages and the disentangled samples anchor calibrations of pre-main-sequence evolution and exoplanet-host properties.

What carries the argument

The load-bearing machinery is the Bayesian expansion-rate age estimator: an association's phase space is modelled with a linear velocity field whose diagonal entries are expansion-rate components, and those components are drawn from a single expansion rate that is sampled hierarchically and inverted into an age through a weakly informative age prior. Before the age is estimated, a Gaussian-mixture decontamination step and a two-component Gaussian substructure search remove field contaminants and separate mutually exclusive populations, so the age is not biased by blended member lists. The inputs are Gaia DR3 astrometry and radial velocities compiled from public catalogues, with unresolved-binary and outlier filters applied first.

What would settle it

A full-Galactic-potential traceback of an association older than about 40 Myr, for instance Carina, that shows the expansion rate at birth differs from the present rate beyond uncertainties, or a two-epoch measurement of the same association's expansion rate that changes over time, would falsify the constant-expansion assumption on which every quoted age rests.

Watch

Extended reading notes

Core claim

The paper's core discovery is that the Bayesian expansion-rate method yields a homogeneous set of ages for local young stellar associations—for example TW Hydrae A at 10.2±1.0 Myr, beta Pictoris at 23.4±4.8 Myr, and Carina at 45.4±9.2 Myr—and that these ages agree with literature estimates once the input lists are cleaned. Within the classical membership lists it independently rediscovers Musca-Foreground, HSC 2597, and Platais 8, and it presents Háap, Balaam, OMAU, Nal, and Chem as newly characterised associations, with OMAU located at only about 36 pc, making it the closest known association to the Sun. The implication is that apparent conflicts between kinematic and nuclear ages were, in most cases, artifacts of mixing distinct stellar populations rather than evidence that one dating technique is wrong.

Load-bearing premise

The ages assume each association began expanding outward at the moment its stars were born and has kept the same expansion rate ever since; if the expansion was imprinted later, or was accelerated or slowed by the Galaxy's gravity, gas removal, or tides, the quoted ages are biased.

Editorial extensions

If this is right

Age calibrations for young stars, exoplanet hosts, and debris disks will have to use the decontaminated, population-separated samples rather than the classical blended lists.
The reconciliation of expansion ages with nuclear ages means older 'age tensions' in the literature should be re-examined for hidden populations before invoking systematic dating errors.
The newly identified associations, especially OMAU at about 36 pc, become prime targets for brown-dwarf and exoplanet searches and for tracing recent star formation in the solar neighbourhood.
The homogeneous map of expansion, contraction, and rotation patterns across 18 systems provides a dynamical baseline for testing models of gas expulsion, tidal interaction, and association dissolution.

Reading between the lines

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

Editorial: the constant-expansion assumption is least secure for the oldest systems; since the paper itself notes the Galactic potential becomes important after about 40 Myr, the Carina age of 45.4±9.2 Myr should be read as pending a Galactic-potential correction rather than as final.
Editorial: applying the same decontamination and Bayesian structure search to traceback and lithium-depletion-boundary datasets would test whether population mixing explains the tensions in those dating techniques too.
Editorial note: the abstract says four newly discovered associations, while the conclusion and Table 1 list five (Háap, Balaam, OMAU, Nal, and Chem); the age values are unaffected but the counts need reconciliation.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Read this if you want a uniform kinematic age scale for the local young associations. The paper does something useful and concrete: it applies the group's own Bayesian expansion-rate estimator (Olivares et al. 2025a) to ten LYSAs, producing the largest homogeneous expansion-age catalogue to date. The methodological novelty is not the estimator itself, which is from their earlier work, but the systematic application and the new groups it exposes—Háap inside 32 Ori, Balaam inside beta Pic, the omega Aurigae association at 36 pc, and Nal and Chem beyond 150 pc. The age tables and kinematic-pattern compilation are practical outputs for anyone working on disk evolution, exoplanet system ages, or the local star-formation history, and the code is open-source with data tables promised at CDS. The authors are also candid: they flag CAR's posterior as resembling the prior and therefore a lower limit, note ETCHA's marginal shrinkage, and state the method's applicability domain as ~10-40 Myr and ~150 pc.

Now the soft spots, in proportion. The largest is procedural: the central age inversion is taken from a companion paper that is still listed as submitted, so the load-bearing calibration and validation are not inspectable. That severely limits what a referee can verify. The circularity worry is real but modest: most priors are weakly informative and wider than the specific literature ages they are compared with, so agreement is not trivially manufactured; however, in weak-data cases (CAR, Nal, ETCHA) the posterior is close enough to the prior that the quantitative ages in Table 1 overstate the constraint. The 40 Myr boundary is the most concrete scientific issue: CAR at 45.4, OMAU at 40.1, and HSC2597 at 39.6 sit exactly where the authors themselves say the constant-expansion assumption breaks down, and Nal/Chem exceed the distance bound. The abstract's conclusion that previous age tensions are 'in most cases' explained by substructures is well-supported for the well-constrained young groups (TW Hya, beta Pic, 32 Ori) but not for these older entries. A final small irritation: the conclusions list five newly discovered associations while the abstract says four, and the table's beta Pic age is chosen as the smallest-uncertainty list result rather than from an explicit selection criterion, so some post hoc selection is present without a systematic uncertainty on it.

This paper is a serious contribution, not a desk reject. People working on young-association ages will want it as a reference. The right referee will need access to the method paper and will want the authors to flag which ages lie outside the stated domain and to provide a systematic comparison across membership-list choices. I would send it to review with major-revision expectations, and I'd cite it once the companion paper is out.

Referee Report

4 major / 6 minor

Summary. This paper applies a Bayesian expansion-rate dating method to local young stellar associations (LYSAs) within ~10-40 Myr and ~150 pc. The authors compile literature membership lists, Gaia DR3 astrometry, and radial velocities, then use the Kalkayotl code with Gaussian-mixture decontamination and substructure identification to infer expansion ages. They report ages for 18 systems, including classical associations (EPCHA, ETCHA, TW A, THOR, 118TAU, BPIC, OCT, COL, CAR, THA) and newly identified or rediscovered populations (MuscaFG, HSC2597, Platais 8, Háap, Balaam, OMAU, Nal, Chem). The central claim is that these are valid expansion ages and that previous tensions between kinematic and nuclear ages can be explained, in most cases, by unidentified populations or substructures.

Significance. If the reported ages are valid, this is a valuable and homogeneous dataset for studies of star and planet formation in the solar neighborhood. The paper's strengths are its use of open-source software, careful compilation of public data, transparent decontamination procedures, and the independent rediscovery of known structures plus the identification of new ones. However, the central age inversion rests on assumptions that are not valid for all reported entries, and several posterior distributions are close to their priors, so the compatibility claims are weaker than presented. The method itself is deferred to a submitted companion paper, which limits the verifiability of the results.

major comments (4)

[§3.5, §5.7, and Table 1] The paper's central age conversion, age = 1/expansion rate, relies on Assumptions 1-3 of Olivares et al. (2025a): the association expanded ballistically, the expansion started at birth, and the expansion rate has remained constant. The paper itself restricts the applicability domain to ~10-40 Myr and ~150 pc (§2.1) and states in §5.7 that Galactic-potential effects become important after ~40 Myr. Yet Table 1 reports CAR at 45.4±9.2 Myr (112 pc), OMAU at 40.1±5.9 Myr, HSC2597 at 39.6±6.9 Myr, and Nal and Chem at 183 and 168 pc. For these entries the constant-expansion assumption is not satisfied under the paper's own criteria, so the quoted numbers are not valid expansion ages as defined. Moreover, for CAR, §5.13 explicitly says the posterior resembles the prior and 'must be taken as a lower limit', but Table 1 presents 45.4±9.2 as a measurement without this caveat. This affects the abstract's claim because the oldest systems are part of the evidence for resolving previous age tensions. Please either flag these entries as lower limits or exploratory values, or recompute them with a model that accounts for the Galactic potential.
[§5.13, §5.17.2, and Table B.1] Several posterior distributions are close to the prior. CAR's posterior (45.4±9.2 Myr) is essentially the prior of 45±11 Myr; ETCHA's posterior has 'only mildly shrank' (§5.2); Nal's posterior relies on only four radial velocities and broad uncertainty. In these cases the reported age is mostly prior-driven rather than data-driven. The paper acknowledges this for CAR in prose but does not carry the caveat into Table 1 or the abstract. Please quantify the information gain relative to the prior (e.g., prior-to-posterior divergence or effective sample size) and either exclude prior-dominated entries from the compatibility claims or mark them as lower limits. Without this, the claim that expansion ages are 'compatible with literature age estimates' is not a meaningful test for those systems.
[§3.5 and Appendix A] The age priors are centered on the same literature isochrone and LDB ages later used to claim compatibility (e.g., EPCHA/ETCHA on Ratzenböck et al. 2023a, TW A on Luhman 2023, CAR on Bell et al. 2015, THA on Galli et al. 2023). When the posterior is close to the prior (CAR, ETCHA, Nal), the statement that the expansion ages are compatible with literature ages is largely a restatement of the prior, not an independent validation. This is a circularity concern in the weak-data cases. Please report prior-robustness checks (e.g., reruns with substantially wider priors) or restrict the compatibility discussion to cases where the posterior is demonstrably narrower than the prior.
[§3.5 and References] The central method, including the expansion-rate to age inversion and its validation on synthetic datasets, is only available in Olivares et al. (2025a), listed as 'submitted to A&A'. That paper is not available for inspection, so the claims of high credibility, <10% error, and <20% uncertainty cannot be verified. For a journal publication, the method description should be self-contained enough for the reader to assess the inversion, or the companion paper should be accepted and accessible before the results are published. This is a load-bearing issue because the entire paper's results depend on that unverified method.

minor comments (6)

[§3.5] There is a typo: 'hte Bayesian method' should be 'the Bayesian method'.
[§4.5] The word 'arsing' should be 'arising' in the discussion of the bimodal age posterior.
[§5.17.1] The phrase 'One one hand' is a typo and should read 'On the one hand'.
[§6] The conclusions list the new association as 'Nel', but it is named 'Nal' throughout the rest of the paper; please harmonize the spelling.
[§2.1] The text says OCT is located at ~150 pc, but Table B.1 gives distances of 115 pc for the LACEwING list and 150 pc for UNION-A; please clarify that the 150 pc value refers to a specific component.
[Figures 1-18] The violin plots compare prior and posterior distributions but do not show the 95% HDI bounds in the figure itself; adding the HDI would make the figures more informative.

Circularity Check

3 steps flagged · score 6.0 of 10

CAR and ETCHA 'expansion ages' reproduce their literature-based priors, so the claimed agreement with literature is partly built in for these entries.

self definitional [Sect. 5.13, Appendix A (CAR prior), Table 1]
"It is important to notice that our posterior age distribution resembles that of our prior. Thus indicating that the dataset lacks constraining information to update our prior. For this reason, our CAR age estimate must be taken as a lower limit of the true age of the system (see Sect. 5.17). ... In CAR, we choose an age prior of 45 ± 11 Myr, which peaks at the isochrone fitting age estimate reported by Bell et al. (2015)."

The reported CAR expansion age, 45.4 ± 9.2 Myr in Table 1, is essentially the literature-based prior (45 ± 11 Myr) relabeled as a measurement, because the paper states that the posterior resembles the prior and that the data lack constraining information. Section 5.13 then counts agreement with Bell et al. (2015) and Wood et al. (2023) as support, but that agreement is guaranteed by the prior choice, not by the expansion-rate data. Table 1 lists the value as a measured expansion age without the lower-limit caveat given in Sect. 5.13.
self definitional [Sect. 5.2 and Appendix A (EPCHA/ETCHA prior), Sect. 4.2, Table 1]
"ETCHA’s expansion age is consistent, at the 1σ level, with all the literature ages. This overall agreement is explained by the posterior similarity with the prior due to the scarcity of members and RV measurements. ... In EPCHA and ETCHA ... we set its mode to the 9 Myr, similar to the 8.8+2.0−0.4 Myr found by Ratzenböck et al. (2023a)."

The age prior for ETCHA is centered on the literature isochrone age of ~9 Myr, and the paper explicitly says the posterior is similar to the prior because of sparse members and radial velocities. The reported expansion age (7.6 ± 3.4 Myr) and the claimed 1σ consistency with all literature ages are therefore a restatement of the input prior, not an independent expansion-rate determination.

1 more flagged steps

fitted input called prediction [Sect. 3.5, Abstract, Conclusions]
"Finally, we establish age prior distribution using the available information in the literature and following the recommendations given in Sect. 6.2 of the method paper. ... The expansion ages we report here are compatible with literature age estimates."

For every association, Appendix A centers the age prior on a literature age estimate. The global abstract claim that the reported ages are 'compatible with literature age estimates' is therefore partly constructed: any association whose data do not move the posterior (CAR and ETCHA are explicitly admitted) will automatically satisfy the compatibility claim. The summary conclusion overstates independent confirmation for the unconstrained subset, even though many associations do have data-driven posteriors.

full rationale

The expansion-rate-to-age inversion itself (age = 1/expansion rate) is an external physical assumption and is not circular within this paper; the method is implemented in open-source code and most associations show posteriors that are narrower than, or shifted from, their priors (e.g., TW A, EPCHA, 118TAU, THOR, Chem). However, the paper contains explicit cases where the reported 'expansion age' is just the literature-based prior because the data are uninformative: CAR (Sect. 5.13: posterior 'resembles that of our prior') and ETCHA (Sect. 5.2: 'overall agreement is explained by the posterior similarity with the prior'). In those cases, the age is set by the isochrone/LDB literature values used as priors, and the subsequent claim of consistency with literature is a tautology rather than a validation. The abstract's blanket statement that 'previous age tensions can be explained ... by unidentified populations or substructures' also leans on this prior-built compatibility for the weakest-data systems. This is partial circularity affecting some headline entries, not a complete collapse of the method: the majority of the 18 reported systems have significant expansion detections and data-informed posteriors, so the central derivation retains independent content. Score 6 reflects the explicit admission that at least two reported ages reduce by construction to their priors.

Assumptions & free parameters 10 free parameters · 6 assumptions · 5 invented entities

The central age values depend on literature-based age priors, hand-set filtering thresholds, and a submitted method paper. The new associations depend on a 2-sigma Gaussian-separation criterion. These inputs are listed above so readers can see what the quoted ages and discoveries actually rest on.

free parameters (10)

Age prior mean and dispersion for EPCHA/ETCHA = 9 ± 5 Myr
Set from Ratzenböck et al. (2023a) isochrone age; affects ETCHA strongly because its posterior is only mildly narrower than the prior.
Age prior mean and dispersion for TW Hya = 10 ± 5 Myr
Set from Luhman (2023) with increased dispersion; weakly informative but still centered on a literature age.
Age prior mean and dispersion for THOR/118 Tau = 20 ± 10 Myr
Set from Lee & Song (2019a); affects the reported ages for the entangled 32 Orionis region.
Age prior mean and dispersion for beta Pic = 23 ± 8 Myr
Set from Lee et al. (2024) isochrone and LDB estimate.
Age prior mean and dispersion for Octans and Columba = 34 ± 10 Myr
Chosen from Galli et al. (2024) and Luhman (2024) with widened dispersion.
Age prior mean and dispersion for Carina and Platais 8 = 45 ± 11 Myr
Based on Bell et al. (2015); for Carina the posterior resembles the prior, so the prior is a significant input to the quoted age.
Age prior mean and dispersion for Nal and Chem = 10 ± 10 Myr
Updated after discovery from Hunt & Reffert (2023) HSC 2139 isochrone age; Chem's posterior moves to 22.2 ± 2.1 Myr, but the prior still enters the analysis.
Age prior mean and dispersion for Tucana-Horologium = 38 ± 15 Myr
Taken from Galli et al. (2023) and widened to cover the literature range.
Field component dispersions in FGMM = 20 pc, 5 km/s
Set to upper-end values for LYSAs; controls how many sources are classified as field contaminants in Section 3.3.
Substructure separation thresholds = weights > 5%, Mahalanobis M > 2
The 2-sigma mutual-exclusivity criterion in Section 3.4 determines which GMM components are called populations, and therefore which new associations are claimed.

assumptions (6)

standard math Bayesian inference via MCMC sampling of the posterior is valid and the sampler has converged where results are reported.
Kalkayotl relies on Hamiltonian Monte Carlo; the paper reports several convergence failures but still quotes ages from the converged runs.
domain assumption Expansion started at birth and has remained constant, so age equals the inverse of the present-day expansion rate.
Stated in Section 1 and Section 3.5 as Assumptions 1-3 of Olivares et al. (2025a); this is the physical foundation of the dating method.
domain assumption The phase-space distribution of each association is Gaussian or a mixture of two Gaussians.
Used throughout Sections 3.3 and 3.4 for decontamination and substructure identification; also Assumption 4 of the method paper.
domain assumption The linear velocity field model adequately captures the internal kinematics of the associations.
Invoked in Section 3.1 and discussed as a caveat in Section 5.17.2; convergence failures in beta Pic, Octans, and Tucana-Horologium show this can be violated.
domain assumption Gaia DR3 astrometry is unbiased after the Lindegren et al. (2021) recipe corrections, and the SIMBAD RV global shift of 0.1 km/s is negligible.
Section 5.17.1 relies on Gaia systematics corrections and treats the SIMBAD shift as statistically insignificant.
domain assumption Missing radial velocities are missing in a way that does not bias the inferred expansion rates.
The method allows missing RVs, but if RVs are preferentially missing for certain kinematic subgroups, the age estimate could be biased; this is not quantitatively tested.

invented entities (5)

Háap
purpose: New stellar association identified within the 32 Orionis region, component C of UNIONS+L22.
No prior literature identification; the group is defined from the same Gaia and RV data used in this paper, so independent confirmation is not yet available.
Balaam
purpose: New stellar population formerly entangled with beta Pictoris members.
All members were previously classified as beta Pic members; the separation is internal to this analysis.
omega Aurigae association (OMAU)
purpose: Proposed association found inside Columba membership lists, claimed to be the nearest association to the Sun at 36 pc.
The identification relies on the GMM separation and cross-matches in this paper; no independent prior census exists.
Nal
purpose: New association identified within the Carina-Extended population.
Separated from Luhman (2024) Carina members using this paper's GMM criteria; no independent confirmation yet.
Chem
purpose: New association identified within the Carina-Extended population.
Same internal phase-space analysis; the quoted age is precise but only from this paper's data and assumptions.

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Cite this review

Pith. "Pith review of Bayesian ages of local young stellar associations I. Through the expansion rate method." pith.science (2026). https://pith.science/paper/6KUKQA27

@misc{pith2026250605130,
  author       = {Pith},
  title        = {Pith review of: Bayesian ages of local young stellar associations I. Through the expansion rate method},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6KUKQA27}},
  note         = {Machine review of arXiv:2506.05130}
}

read the original abstract

Context. Local young stellar associations (LYSAs <50 Myr and <150 pc) are important laboratories to test predictions from star-formation theories. Estimating their ages through various dating techniques with minimal biases is thus of paramount importance. Aims. We aim at determining the ages of LYSAs with the expansion rate dating technique. Methods. We estimate the ages of the LYSAs using literature membership lists, publicly available data (astrometry and radial velocities), and a recent open-source Bayesian code that implements the expansion rate method. This code in combination with simple statistical assumptions allow us to decontaminate, identify possible substructures or populations, and estimate expansion ages. Results. We derive the largest and most methodological homogeneous set of ages of LYSAs. We rediscover three and discover four associations hidden within the literature membership lists of the classical ones. Conclusions. The expansion ages we report here are compatible with literature age estimates. Moreover, our analysis shows that previous age tensions can be explained, in most cases, by the presence of unidentified populations or substructures.

Figures

Figures reproduced from arXiv: 2506.05130 by the authors.

**Figure 1.** Age distribution for EPCHA. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison [PITH_FULL_IMAGE:figures/full_fig_p011_1.png] view at source ↗

**Figure 2.** Age distribution for ETCHA. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison. The two age estimates of Murphy et al. (2013) correspond to different isochrone models. 5.2. ϵ− and η− Chamaeleontis EPCHA and ETCHA have been the subject of various literature age estimates, most of… view at source ↗

**Figure 3.** Age distribution for MuscaFG. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison. The two age estimates by Kerr et al. (2021) correspond to those of its SC-27A and SC-27B groups. case of Hunt & Reffert (2024), our membership list has four and six members in common with their HSC2… view at source ↗

Figures from the paper (13 more)

**Figure 6.** Figure 6: Age distribution for 118TAU. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison. The two age estimates by Krolikowski et al. (2021…

**Figure 5.** Figure 5: Age distribution for THOR. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison. The age of THOR has been determined in the literat…

**Figure 7.** Figure 7: Age distribution for Háap. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates for 32 Orionis are included for comparison. Given that Háap has been proposed for…

**Figure 8.** Figure 8: Age distribution for BPIC. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison. the expansion method assumes that the members expan…

**Figure 10.** Figure 10: Age distribution for OCT. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison. The two age estimates by Hunt & Reffert (2023) cor…

**Figure 9.** Figure 9: Age distribution for Balaam. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Balaam is a new stellar association whose members were previously entangled with those of BPIC in the literature …

**Figure 11.** Figure 11: Age distribution for HSC 2597. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. The isochrone age by Hunt & Reffert (2023) is included for comparison [PITH_FULL_IMAGE:figures/full_fig_p016_…

**Figure 12.** Figure 12: Age distribution for COL. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison. The ages by Hunt & Reffert (2023) correspond, in as…

**Figure 14.** Figure 14: Age distribution for CAR. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison [PITH_FULL_IMAGE:figures/full_fig_p016_14.png]

**Figure 17.** Figure 17: Age distribution for Chem. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. The previous literature age estimate by Hunt & Reffert (2023) for HSC 2139 is included for comparison. The Nal an…

**Figure 15.** Figure 15: Age distribution for Platais 8. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison. Platais 8 was found within the Car-Ext list by…

**Figure 16.** Figure 16: Age distribution for Nal. The violin plot shows kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. The previous literature age estimate by Hunt & Reffert (2023) for HSC 2139 is included for comparison [PITH_FULL_…

**Figure 18.** Figure 18: Age distribution for THA A and B. The violin plots show kernel density estimates obtained from samples of the prior (bottom) and posterior (top) distributions. Previous literature age estimates are included for comparison. but still compatible, with that of substruct…

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Works this paper leans on

92 extracted references · 45 canonical work pages

[1]

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

ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....
[3]

2022, , 259, 35

Abdurro'uf , Accetta , K., Aerts , C., et al. 2022, , 259, 35

2022
[4]

Ambartsumian , V. A. 1954, in Liege International Astrophysical Colloquia, Vol. 5, Liege International Astrophysical Colloquia, 293

1954
[5]

Ambartsumian , V. A. 1955, The Observatory, 75, 72

1955
[6]

R., & Patten , B

Barrado y Navascu \'e s , D., Stauffer , J. R., & Patten , B. M. 1999, , 522, L53

1999
[7]

Bell , C. P. M., Mamajek , E. E., & Naylor , T. 2015, , 454, 593

2015
[8]

Bell , C. P. M., Murphy , S. J., & Mamajek , E. E. 2017, , 468, 1198

2017

Show all 92 references

[9]

1952, , 11, 414

Blaauw , A. 1952, , 11, 414

1952
[10]

1964, , 2, 213

Blaauw , A. 1964, , 2, 213

1964
[11]

2023, , 674, A7

Blomme , R., Fr \'e mat , Y., Sartoretti , P., et al. 2023, , 674, A7

2023
[12]

Bobylev , V. V. & Bajkova , A. T. 2024, Research in Astronomy and Astrophysics, 24, 055004

2024
[13]

Booth , M., del Burgo , C., & Hambaryan , V. V. 2021, , 500, 5552

2021
[14]

& Alves , J

Bouy , H. & Alves , J. 2015, , 584, A26

2015
[15]

Brown , A. G. A., Dekker , G., & de Zeeuw , P. T. 1997, , 285, 479

1997
[16]

J., Lopez , M

Burgasser , A. J., Lopez , M. A., Mamajek , E. E., et al. 2016, , 820, 32

2016
[17]

2023, , 946, 6

Couture , D., Gagn \'e , J., & Doyon , R. 2023, , 946, 6

2023
[18]

D., Ireland , M

Crundall , T. D., Ireland , M. J., Krumholz , M. R., et al. 2019, , 489, 3625

2019
[19]

2012, Research in Astronomy and Astrophysics, 12, 1197

Cui , X.-Q., Zhao , Y.-H., Chu , Y.-Q., et al. 2012, Research in Astronomy and Astrophysics, 12, 1197

2012
[20]

J., Mamajek , E

David , T. J., Mamajek , E. E., Vanderburg , A., et al. 2018, , 156, 302

2018
[21]

K., Weinberger , A

Donaldson , J. K., Weinberger , A. J., Gagn \'e , J., et al. 2016, , 833, 95

2016
[22]

A., Loinard , L., Rodr \' guez , L

Dzib , S. A., Loinard , L., Rodr \' guez , L. F., et al. 2017, , 834, 139

2017
[23]

& Faherty , J

Gagn \'e , J. & Faherty , J. K. 2018, , 862, 138

2018
[24]

E., Malo , L., et al

Gagn \'e , J., Mamajek , E. E., Malo , L., et al. 2018 a , , 856, 23

2018
[25]

K., Doyon , R., & Malo , L

Gagn \'e , J., Roy-Loubier , O., Faherty , J. K., Doyon , R., & Malo , L. 2018 b , , 860, 43

2018
[26]

J., Barrado , D., Bouy , H., et al

Galindo-Guil , F. J., Barrado , D., Bouy , H., et al. 2022, , 664, A70

2022
[27]

Galli , P. A. B., Bouy , H., Olivares , J., et al. 2021, , 646, A46

2021
[28]

Galli , P. A. B., Loinard , L., Bouy , H., et al. 2019, , 630, A137

2019
[29]

Galli , P. A. B., Miret-Roig , N., Bouy , H., Olivares , J., & Barrado , D. 2023, , 520, 6245

2023
[30]

Galli , P. A. B., Miret-Roig , N., Bouy , H., Olivares , J., & Barrado , D. 2024, , 689, A11

2024
[31]

2023, , 673, A128

Guilherme-Garcia , P., Krone-Martins , A., & Moitinho , A. 2023, , 673, A128

2023
[32]

Hills , J. G. 1980, , 235, 986

1980
[33]

Hunt , E. L. & Reffert , S. 2023, , 673, A114

2023
[34]

Hunt , E. L. & Reffert , S. 2024, , 686, A42

2024
[35]

V., Kroupa , P., Wu , W., Pflamm-Altenburg , J., & Thies , I

Jadhav , V. V., Kroupa , P., Wu , W., Pflamm-Altenburg , J., & Thies , I. 2024, , 687, A89

2024
[36]

D., Jackson , R

Jeffries , R. D., Jackson , R. J., Wright , N. J., et al. 2023, , 523, 802

2023
[37]

G., & de la Reza , R

Jilinski , E., Ortega , V. G., & de la Reza , R. 2005, , 619, 945

2005
[38]

2023, , 674, A5

Katz , D., Sartoretti , P., Guerrier , A., et al. 2023, , 674, A5

2023
[39]

Kerr , R. M. P., Rizzuto , A. C., Kraus , A. L., & Offner , S. S. R. 2021, , 917, 23

2021
[40]

L., Herczeg , G

Kraus , A. L., Herczeg , G. J., Rizzuto , A. C., et al. 2017, , 838, 150

2017
[41]

M., Kraus , A

Krolikowski , D. M., Kraus , A. L., & Rizzuto , A. C. 2021, , 162, 110

2021
[42]

Kruijssen , J. M. D., Maschberger , T., Moeckel , N., et al. 2012, , 419, 841

2012
[43]

R., McKee , C

Krumholz , M. R., McKee , C. F., & Bland-Hawthorn , J. 2019, , 57, 227

2019
[44]

2021, , 650, A48

Kubiak , K., Mu z i \'c , K., Sousa , I., et al. 2021, , 650, A48

2021
[45]

A., Feigelson , E

Kuhn , M. A., Feigelson , E. D., Getman , K. V., et al. 2015, , 812, 131

2015
[46]

A., Hillenbrand , L

Kuhn , M. A., Hillenbrand , L. A., Sills , A., Feigelson , E. D., & Getman , K. V. 2019, , 870, 32

2019
[47]

& Song , I

Lee , J. & Song , I. 2019 a , , 486, 3434

2019
[48]

& Song , I

Lee , J. & Song , I. 2019 b , , 489, 2189

2019
[49]

A., Gaidos , E., van Saders , J., Feiden , G

Lee , R. A., Gaidos , E., van Saders , J., Feiden , G. A., & Gagn \'e , J. 2024, [ [arXiv] 2312.15792 ]

2024 arXiv
[50]

A., Hern \'a ndez , J., et al

Lindegren , L., Klioner , S. A., Hern \'a ndez , J., et al. 2021, , 649, A2

2021
[51]

2000, , 356, 1119

Lindegren , L., Madsen , S., & Dravins , D. 2000, , 356, 1119

2000
[52]

Luhman , K. L. 2018, , 156, 271

2018
[53]

Luhman , K. L. 2022, , 164, 151

2022
[54]

Luhman , K. L. 2023, , 165, 269

2023
[55]

Luhman , K. L. 2024, , 168, 159

2024
[56]

V., Gaume , R

Makarov , V. V., Gaume , R. A., & Andrievsky , S. M. 2005, , 362, 1109

2005
[57]

Mamajek , E. E. 2007, in Triggered Star Formation in a Turbulent ISM, ed. B. G. Elmegreen & J. Palous , Vol. 237, 442--442

2007
[58]

Mamajek , E. E. & Bell , C. P. M. 2014, , 445, 2169

2014
[59]

2024, Nature Astronomy, 8, 216

Miret-Roig , N., Alves , J., Barrado , D., et al. 2024, Nature Astronomy, 8, 216

2024
[60]

2025, , 694, A60

Miret-Roig , N., Alves , J., Ratzenb \"o ck , S., et al. 2025, , 694, A60

2025
[61]

Miret-Roig , N., Galli , P. A. B., Brandner , W., et al. 2020, , 642, A179

2020
[62]

Miret-Roig , N., Galli , P. A. B., Olivares , J., et al. 2022, , 667, A163

2022
[63]

Moranta , L., Gagn \'e , J., Couture , D., & Faherty , J. K. 2022, , 939, 94

2022
[64]

Murphy , S. J. & Lawson , W. A. 2015, , 447, 1267

2015
[65]

J., Lawson , W

Murphy , S. J., Lawson , W. A., & Bessell , M. S. 2013, , 435, 1325

2013
[66]

2025 a , submitted to

Olivares , J., Berihuete , A., & Bouy , H. 2025 a , submitted to

2025
[67]

Z., & Berihuete , A

Olivares , J., Bouy , H., Dorn-Wallenstein , T. Z., & Berihuete , A. 2025 b , , 693, A12

2025
[68]

2023 a , , 671, A1

Olivares , J., Bouy , H., Miret-Roig , N., et al. 2023 a , , 671, A1

2023
[69]

Olivares , J., Lodieu , N., B \'e jar , V. J. S., et al. 2023 b , , 675, A28

2023
[70]

J., Ruelas-Mayorga , A., et al

Olivares , J., S \'a nchez , L. J., Ruelas-Mayorga , A., et al. 2013, , 146, 106

2013
[71]

M., Bouy , H., et al

Olivares , J., Sarro , L. M., Bouy , H., et al. 2020, , 644, A7

2020
[72]

K., Curtis , J

Popinchalk , M., Faherty , J. K., Curtis , J. L., et al. 2023, , 945, 114

2023
[73]

2005, , 627, L61

Poveda , A., Allen , C., & Hern \'a ndez-Alc \'a ntara , A. 2005, , 627, L61

2005
[74]

2023, , 265, 12

Qin , S., Zhong , J., Tang , T., & Chen , L. 2023, , 265, 12

2023
[75]

E., Alves , J., et al

Ratzenb \"o ck , S., Gro schedl , J. E., Alves , J., et al. 2023 a , , 678, A71

2023
[76]

o ck , S., Gro schedl , J. E., M \

Ratzenb \"o ck , S., Gro schedl , J. E., M \"o ller , T., et al. 2023 b , , 677, A59

2023
[77]

R., Blunt , S

Riedel , A. R., Blunt , S. C., Lambrides , E. L., et al. 2017, , 153, 95

2017
[78]

Rousseeuw, P. J. & Driessen, K. V. 1999, Technometrics, 41, 212

1999
[79]

R., Liebert , J., & Giampapa , M

Stauffer , J. R., Liebert , J., & Giampapa , M. 1995, , 109, 298

1995
[80]

Torres , C. A. O., Quast , G. R., Melo , C. H. F., & Sterzik , M. F. 2008, in Handbook of Star Forming Regions, Volume II, ed. B. Reipurth , Vol. 5, 757

2008
[81]

2022, , 659, A95

Tsantaki , M., Pancino , E., Marrese , P., et al. 2022, , 659, A95

2022
[82]

Tutukov , A. V. 1978, , 70, 57

1978
[83]

S., Mathew , B., Manoj , P., & Narang , M

Ujjwal , K., Kartha , S. S., Mathew , B., Manoj , P., & Narang , M. 2020, , 159, 166

2020
[84]

H., Scott , J., et al

von Hippel , T., Jefferys , W. H., Scott , J., et al. 2006, , 645, 1436

2006
[85]

2023, , 678, A75

Z erjal , M., Lodieu , N., P \'e rez-Garrido , A., et al. 2023, , 678, A75

2023
[86]

2000, , 143, 9

Wenger , M., Ochsenbein , F., Egret , D., et al. 2000, , 143, 9

2000
[87]

L., Mann , A

Wood , M. L., Mann , A. W., Barber , M. G., et al. 2023, , 166, 247

2023
[88]

J., Bouy , H., Drew , J

Wright , N. J., Bouy , H., Drew , J. E., et al. 2016, , 460, 2593

2016
[89]

J., Jeffries , R

Wright , N. J., Jeffries , R. D., Jackson , R. J., et al. 2024, , 533, 705

2024
[90]

X., Reffert , S., Schilbach , E., et al

Yen , S. X., Reffert , S., Schilbach , E., et al. 2018, , 615, A12

2018
[91]

2012, Research in Astronomy and Astrophysics, 12, 723

Zhao , G., Zhao , Y.-H., Chu , Y.-Q., Jing , Y.-P., & Deng , L.-C. 2012, Research in Astronomy and Astrophysics, 12, 723

2012
[92]

M., Wolf, A., Ch \'e reau, F., & Ch \'e reau, G

Zotti, G., Hoffmann, S. M., Wolf, A., Ch \'e reau, F., & Ch \'e reau, G. 2021, Journal of Skyscape Archaeology, 6, 221–258

2021

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

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

ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

[2] [2]

write newline

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

[3] [3]

2022, , 259, 35

Abdurro'uf , Accetta , K., Aerts , C., et al. 2022, , 259, 35

2022

[4] [4]

Ambartsumian , V. A. 1954, in Liege International Astrophysical Colloquia, Vol. 5, Liege International Astrophysical Colloquia, 293

1954

[5] [5]

Ambartsumian , V. A. 1955, The Observatory, 75, 72

1955

[6] [6]

R., & Patten , B

Barrado y Navascu \'e s , D., Stauffer , J. R., & Patten , B. M. 1999, , 522, L53

1999

[7] [7]

Bell , C. P. M., Mamajek , E. E., & Naylor , T. 2015, , 454, 593

2015

[8] [8]

Bell , C. P. M., Murphy , S. J., & Mamajek , E. E. 2017, , 468, 1198

2017