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REVIEW 4 major objections 5 minor 242 references

Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Low-resolution near-infrared spectra of M0–T9 dwarfs can be automatically typed to within one subtype 95.5% of the time, and to the right gravity/metallicity class 89.5% of the time, using a K-nearest-neighbor model trained on synthetic spe

desk verdict A competent ML spectral-typing pipeline with a partly circular benchmark; the headline accuracies overstate external skill, but the methods and ablations are worth refereeing. read the letter →

arxiv 2508.09370 v1 pith:6SOBRXZK submitted 2025-08-12 astro-ph.SR astro-ph.EPastro-ph.GAastro-ph.IMcs.LG

Tianxing Zhou , Christopher A. Theissen , S. Jean Feeser , William M. J. Best , Adam J. Burgasser , Kelle L. Cruz , Lexu Zhao This is my paper · ORCID
classification astro-ph.SRastro-ph.EPastro-ph.GAastro-ph.IMcs.LG
keywords browndwarfsMdwarfstarsLTstellarclassificationmachinelearningrandomforestsk-nearestneighbors
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

This paper sets out to show that machine learning on binned near-infrared fluxes alone can replace visual inspection and hand-measured indices for classifying cool dwarfs (spectral types M0–T9), including the gravity and metallicity subclasses that normally need a trained eye. Its route is to inflate 70 human-defined spectral standards into a 70,000-spectrum synthetic training set and compare three off-the-shelf classifiers on 1,300 real spectra kept out of training. The best classifier, K-nearest neighbors, puts 95.5 ± 0.6% of test sources within one spectral subtype and gets the gravity/metallicity class right 89.5 ± 0.9% of the time. If that holds, the labor-intensive typing of cool dwarfs can be automated on large spectroscopic surveys, with a recommended signal-to-noise of about 60.

What carries the argument

The load-bearing object is the synthetic training set: for each of 70 spectral standards, 1,000 spectra are drawn by adding Gaussian noise to every flux point, with the noise set to the larger of the standard's own uncertainty and a tuned 5% fractional floor. This inflates the 70 standards to 70,000 training spectra and gives a plain distance-based classifier enough within-class variation to generalize. The model itself is a multi-output KNN (k = 29, Euclidean distance) that predicts spectral type and peculiarity class in parallel from 67 mean-flux features in 0.02 µm bins; the same binned-flux representation is what makes the method independent of hand-measured spectral indices.

What would settle it

Take a blinded sample of a few hundred prism spectra spanning M0–T9, have several experienced classifiers independently assign spectral types and peculiarity classes, and compare the KNN predictions to that independent human consensus. If within-one-subtype agreement falls substantially below 95.5%, the headline accuracy is partly an artifact of sharing the same standard grid between training and ground-truth labels.

Watch

Extended reading notes

Core claim

Stated on the paper's own terms: a multi-output K-nearest-neighbor classifier (k = 29, Euclidean distance) operating on 67 mean-flux bins between 0.89 and 2.45 µm recovers adopted spectral subtypes M0–T9 within ±1 subtype for 95.5 ± 0.6% of 1,300 held-out spectra and gets the six gravity/metallicity classes right 89.5 ± 0.9% of the time. The classifier is trained on 70,000 synthetic spectra built from 70 standards by adding Gaussian noise with a tuned 5% floor. The same model keeps a 97% lower-percentile accuracy at SNR ≈ 60 and ≈90% average accuracy below SNR 40. Random-forest feature attribution puts the strongest discriminative weight in the zy-band (0.89–0.91 µm, secondary at ~1.0 µm), t

Load-bearing premise

The accuracy numbers depend on 'adopted' spectral types that were obtained by chi-square fitting to the very same 70 standards used to generate the synthetic training spectra, so the test measures consistency with that reference grid rather than agreement with an independent human classification.

Editorial extensions

If this is right

  • Large low-resolution NIR surveys can be typed automatically with no human inspection: a simple KNN on binned flux reproduces adopted M0–T9 subtypes at 95% accuracy.
  • Observers can budget exposure times to reach SNR ≈ 60; beyond that, added integration time buys little classification accuracy.
  • Because zy-band flux carries most of the discriminative signal, future instruments or reduced spectral coverage can retain most of the classification power even if H- and K-band data are limited.
  • K-band features are the main gravity diagnostic within this model: removing the K band drops int-g and vl-g classification accuracy by roughly 10–14%, while metallicity classification falls only about 3%.
  • The binned-flux-plus-synthetic-augmentation procedure carries over to other low-resolution NIR spectrographs, but the paper cautions that the model must be retrained on that instrument's standards and noise characteristics rather than applied directly.

Reading between the lines

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

  • The small independent literature checks in Sections 5.1 and 5.5 (15 low-gravity and 15 subdwarf sources) are my best estimate of how the model would fare against human classifiers; because the adopted labels share the training standards' grid, the headline 95.5% likely overstates skill against fully independent human typing by a few points.
  • The same recipe of synthetic augmentation around a small trusted standard set could be applied to other classification ladders with scarce labels, such as white dwarfs or Y dwarfs.
  • The zy-band dominance suggests that extending coverage bluer than 0.89 µm would improve the model's weakest classes—early M dwarfs, whose flux peaks shortward of the current range—more cheaply than adding more standards at existing wavelengths.
  • The concentration of confusion among very-low-gravity L dwarfs implies that the limiting factor for gravity classification is the scarcity and variability of young standards, not classifier choice.
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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 trains three supervised classifiers (Random Forest, SVM, KNN) on 70,000 synthetic near-infrared spectra generated by adding Gaussian noise to 70 spectral standards from Table 1, using binned fluxes as features. The models are evaluated on 1,300 SpeX prism spectra whose adopted labels were themselves obtained by chi-square fitting against the same Table 1 standards (Section 3.1.1, Eq. 1). The authors report a best KNN accuracy of 95.5 ± 0.6% within ±1 spectral subtype and 89.5 ± 0.9% class (gravity/metallicity) accuracy, investigate SNR dependence, and perform ablation studies removing the K-band and H-band continuum. The central methodological concern is that the test labels and the training set share the same reference grid, so the headline accuracies largely measure agreement between two estimators anchored to identical standards rather than external spectral-typing skill.

Significance. If the headline accuracies represent genuine external classification skill, the paper would provide a useful tool for automated cool-dwarf typing in large low-resolution NIR surveys, with practical guidance on binning, SNR, and feature importance. The manuscript has clear strengths: it releases models and preprocessed data, documents a reproducible workflow, compares three algorithms under a common preprocessing chain, and provides informative ablations of K-band and H-band continuum contributions. The main weakness is benchmark validity: the 'ground truth' labels are generated by chi-square matching to the same standards that seed the synthetic training set, and the only external checks are 15 low-gravity and 15 subdwarf sources. As a result, the aggregate accuracy figures overstate what is currently demonstrated about classification of unseen objects, and per-class accuracies for the peculiar classes that motivate the paper are substantially lower than the weighted headline number.

major comments (4)
  1. [§3.1.1, Eq. (1) and §3.1.4] The test labels are the adopted spectral types from a chi-square fit against the Table 1 standards, and the synthetic training set is generated from those same standards. The KNN model learns the distance structure of the standard grid, while the test labels are the best-fit positions on that same grid. The reported 95.5 ± 0.6% ±1-SpT accuracy therefore quantifies agreement between KNN and the chi-square labeler on a shared reference manifold, not external skill against human classification. The external checks in Sections 5.1 and 5.5 cover only 15 + 15 sources, too small to anchor aggregate accuracies. Please reframe the headline as 'label-reproduction accuracy' and validate against independent literature classifications for a larger sample, or use a leave-one-standard-out scheme in which test spectra are labeled without the corresponding standard.
  2. [§2.2 and §3.1] The manuscript never states that the 70 Table 1 standards are excluded from the 1,548 SPL-based validation/test spectra. Since the standards are drawn from SPL/SPLAT and the training set contains 1,000 synthetic copies of each, any standard present in the test set can be recovered essentially exactly by KNN, inflating accuracy. Please state explicitly whether standards were removed from the test and validation sets, and if not, repeat the benchmark with them excluded.
  3. [§3.1.2 and §3.1.3] The evaluation set is cleaned by removing approximately 3% of the sample as normalization outliers (Table 4) and 43 CMD outliers (Figure 4) before computing accuracies. These cuts are based on the same normalization/chi-square machinery used to define labels, so they can preferentially remove hard-to-classify objects. Report the headline accuracies with and without these cuts, and justify that the removals are not label-selective.
  4. [Abstract and §4.3/Fig. 15] The claim of 89.5 ± 0.9% class accuracy is a class-imbalance-weighted number. The confusion matrix shows per-class accuracies of only 64% for vl-g, 78% for sd, and 87% for d/sd, while field dwarfs (the majority class) are at 94%. Because the paper's stated goal is peculiar-object classification, reporting a single weighted accuracy overstates performance for the peculiar classes. Report macro-averaged or per-class metrics and qualify the abstract accordingly.
minor comments (5)
  1. [§1] Typo: 'decribes' should be 'describes'.
  2. [§3.1 vs §3.1.6] Section 3.1 says all data are constrained to a fit range of [0.87, 2.39] µm, but Section 3.1.6 and Figure 3 use a feature range of 0.89–2.45 µm. Please harmonize these numbers.
  3. [§4.2] The sentence 'the classifier classifies metallicity class ... at 82.7 ± 1.0% accuracy, shown in Figure 11' should refer to Figure 13, which is the SVM class confusion matrix; Figure 11 is the RF matrix.
  4. [§6, summary point 6] The sentence 'with replaced5 out of 98 out of 13 vl-g sources and 2 out of 2 int-g sources' is garbled. From Table 8 it should read '5 of 13 vl-g sources and 2 of 2 int-g sources'.
  5. [§4.4] The SNR analysis adds Gaussian noise to test spectra and classifies the noisy copies with the same KNN model. This probes resistance to random noise only. The text should state this limitation explicitly, since real observations at a given SNR also include systematics, telluric residuals, and calibration errors.

Circularity Check

1 steps flagged · score 6.0 of 10

Circular benchmark: adopted test labels are χ² matches to the same standards that seed the synthetic training set, so headline accuracies certify consistency with the labeler, not external spectral-typing skill.

  1. fitted input called prediction [Section 3.1.1 (Eq. 1), Section 3.1.4, Abstract/Table 6]
    "The primary labels for our sample are the spectral classifications based on the NIR data. To ensure a common set of labels, we followed the methods described in Cushing et al. (2008) and reclassified all of the sources by minimizing the chi-square residual to our spectral standards. ... We expanded our initial sample of 70 spectral standards by generating 1,000 synthetic spectra from each one. ... Our best-performing model (using KNN) classifies 95.5 ± 0.6% of sources to within ±1 SpT, and assigns surface gravity and metallicity subclasses with 89.5 ± 0.9% accuracy."

    The 'adopted spectral type' used as ground truth for the 1,300 test spectra is assigned by minimizing χ² (Eq. 1) against the 70 Table 1 standards, and the 70,000 synthetic training spectra are generated by adding Gaussian noise to those same 70 standards (Section 3.1.4). KNN therefore learns the distance structure of the standard set, and the test labels are the best-fit positions on that same grid. The reported 95.5% ±1 SpT and 89.5% class accuracies measure agreement between two estimators (KNN and χ² matching) anchored to identical references, not skill against external human classification. External checks cover only 15 low-gravity sources (Section 5.1) and 15 subdwarfs (Section 5.5), too small to validate the aggregate claims.

full rationale

The paper's headline numbers are not benchmarked against an independent ground truth: the test-set labels are derived by χ² fitting to the same 70-standard grid from which the synthetic training set is generated. This is a benchmark-validity circularity rather than a strict mathematical identity—KNN and χ² matching could in principle disagree—but the high accuracies certify that KNN reproduces the χ² labeler's mapping onto the standard grid, not that either estimator is externally accurate. The paper's own Section 1 limitation statement ('it inherently carries the subjectivity of the “ground truth” defined by the human-classified training set') and its small external checks (15+15 sources) support this reading. No load-bearing self-citation chain or imported uniqueness theorem is present; the standards are drawn from established literature. The feature-importance, SNR, and K-band/H-band ablation results are independent of this circularity and retain value. Overall, the central classification-accuracy claim is partially circular.

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

No new physical entities are introduced; the gravity and metallicity classes (vl-g, int-g, fld-g, d/sd, sd, esd) are adopted from prior literature (Allers & Liu 2013; Gizis 1997; Burgasser 2004a). The model's real contributions are the benchmark and feature-importance analysis; all discriminative content comes from pre-existing standards and classes. The headline accuracy depends on several hand-chosen settings (noise scale, bin size, outlier cut, hyperparameters) and on the assumption that the adopted chi-square labels are valid ground truth.

free parameters (8)
  • noise_scale (synthetic training noise) = 0.05 (5%)
    Section 3.1.4, Figure 5a: the Gaussian width added to each standard's flux was optimized on validation accuracy.
  • number of synthetic spectra per standard = 1000 (70,000 total)
    Section 3.1.4, Figure 5b: chosen as the accuracy-versus-training-time knee.
  • wavelength bin width = 0.02 µm
    Section 3.1.6 and Table 9: best validation performance among 0.01, 0.02, 0.03, 0.04, 0.10 µm bins.
  • outlier rejection threshold = beyond 1.5σ (≈3% of sample removed)
    Section 3.1.2 and Table 4: hand-chosen cut removing sources whose B06 vs C18 normalization types differ by >3 subtypes; affects the reported test-set accuracy.
  • KNN number of neighbors = k = 29
    Section 4.3: tuned on validation set over k = 1 to 200.
  • Random Forest hyperparameters = n_estimators = 700, min_samples_split = 5, max_features = sqrt
    Section 4.1.1: chosen by validation tuning.
  • SVM hyperparameters = C = 10, kernel = rbf, gamma = scale
    Section 4.2.1: chosen by validation tuning.
  • normalization window = 1.27-1.28 µm (J-band peak)
    Sections 3.1 and 4.6: chosen as a pseudo-continuum region; the paper notes this suppresses feature importance and H-band shape information.
assumptions (5)
  • domain assumption The 70 spectral standards in Table 1 correctly represent their subtypes and peculiar classes, and the chi-square-minimization labels (Eq. 1) adopted for all sources are correct ground truth.
    Section 3.1.1 relabels every source by best fit to these standards; all test accuracies are measured against these labels.
  • ad hoc to paper Gaussian perturbations of single-epoch standards adequately span real within-class spectral variance.
    Section 3.1.4 builds all 70,000 training spectra this way; Section 6 acknowledges it does not model time-domain variability and may fail for objects in extreme variability states.
  • domain assumption The 70 standards cover the M0-T9 and peculiar-class feature space with no unrepresented subtypes undermining the benchmark.
    Section 2.1 notes gravity standards exist only for late-M/L dwarfs and subdwarf standards only for M/early-L dwarfs; the test set lacks peculiar T dwarfs (Figure 1).
  • standard math Scikit-learn implementations of RandomForest, SVC, and KNN with MultiOutputClassifier behave as documented, and Euclidean distance in binned-flux space is a valid spectral similarity metric.
    Section 4 uses scikit-learn defaults with tuned hyperparameters; no formal verification is provided.
  • domain assumption Telluric masking (1.35-1.42, 1.80-1.95 µm) and the 0.89-2.45 µm feature range exclude no information required for subtype or class discrimination.
    Sections 3.1 and 3.1.6; the paper itself notes early-M classification suffers from the 0.89 µm blue cut (Section 4.4).

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Cite this review

Pith. "Pith review of Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning." pith.science (2026). https://pith.science/paper/6SOBRXZK

@misc{pith2026250809370,
  author       = {Pith},
  title        = {Pith review of: Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6SOBRXZK}},
  note         = {Machine review of arXiv:2508.09370}
}
abstract

Low-mass stars and brown dwarfs -- spectral types (SpTs) M0 and later -- play a significant role in studying stellar and substellar processes and demographics, reaching down to planetary-mass objects. Currently, the classification of these sources remains heavily reliant on visual inspection of spectral features, equivalent width measurements, or narrow-/wide-band spectral indices. Recent advances in machine learning (ML) methods offer automated approaches for spectral typing, which are becoming increasingly important as large spectroscopic surveys such as Gaia, SDSS, and SPHEREx generate datasets containing millions of spectra. We investigate the application of ML in spectral type classification on low-resolution (R $\sim$ 120) near-infrared spectra of M0--T9 dwarfs obtained with the SpeX instrument on the NASA Infrared Telescope Facility. We specifically aim to classify the gravity- and metallicity-dependent subclasses for late-type dwarfs. We used binned fluxes as input features and compared the efficacy of spectral type estimators built using Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) models. We tested the influence of different normalizations and analyzed the relative importance of different spectral regions for surface gravity and metallicity subclass classification. Our best-performing model (using KNN) classifies 95.5 $\pm$ 0.6% of sources to within $\pm$1 SpT, and assigns surface gravity and metallicity subclasses with 89.5 $\pm$ 0.9% accuracy. We test the dependence of signal-to-noise ratio on classification accuracy and find sources with SNR $\gtrsim$ 60 have $\gtrsim$ 95% accuracy. We also find that zy-band plays the most prominent role in the RF model, with FeH and TiO having the highest feature importance.

Figures

Figures reproduced from arXiv: 2508.09370 by the authors.

Figure 1
Figure 1. Stacked spectral type distribution of the 1548 spectra. Metallicity and gravity classes are indicated by different color bars. Despite having standards for sdM9.5, sdL3.5, sdL4, and esdM6.5, we do not have examples of these classes in the testing and validation set. 1.28 µm. This range was chosen to sample a pseudo-continuum region common across all spec￾tral types in the sample. This normalization step helps to mit… view at source ↗
Figure 2
Figure 2. Conceptual workflow diagram for the ML models, detailing data gathering, preprocessing, training, validation, testing, and prediction stages, along with spectral sources and methods. after B06) and C18. The two papers provide two nor￾malization methods: 1. Normalizing the entire NIR band into one section, standardizing the continuum for direct comparison with a reference standard. 2. Nor￾malizing the NIR band in thr… view at source ↗
Figure 3
Figure 3. Spectrum of the L3γ spectral standard 2MASSW J2208136+292121 (green), along with 100 synthetic spectra in blue drawn from the spectrum and uncertainty. The or￾ange line shows the downsampled 0.02 µm bins used as fea￾tures and discussed in Section 3.1.6. We masked out the grey telluric regions at ∼1.35 and ∼1.9 µm. We removed data at the edges, bluer than 0.89 µm and redder than 2.45 µm as indicated by the red dashed… view at source ↗
Figures from the paper (20 more)
Figure 4
Figure 4. Figure 4: Color-Magnitude Diagram showing absolute J magnitude (MJ ) versus J-K color (after outliers removal) for SpeX Prism Library objects with adopted spectral types from M0 to T9. This plot confirms the consistency of our adopted spectral typing method. Objects that deviate…
Figure 6
Figure 6. Figure 6: Distribution of SNR for our validation and testing sample. Spectral classes of M, L, and T are distinguished by blue, orange, and green bars, in bins of 20 SNR. The inset plot shows a more granular view of sources with SNR ≤ 50, in bins of 2 SNR. The majority of very l…
Figure 5
Figure 5. Figure 5: Random Forest metrics for synthetic spectra opti￾mization. Top panel: The relationship between noise scaling (fractional uncertainty on the flux) and classification accu￾racy (subtype and class), indicating an optimal noise scale of 5%. Bottom panel: The relationship b…
Figure 7
Figure 7. Figure 7: Top: All M, L, and T field Spectral Standards plotted together in order. All spectra are normalized around 1.27 µm, and different spectral types are separated by an offset. The shaded region is the telluric absorption that was masked. The normalization region (1.27–1.2…
Figure 8
Figure 8. Figure 8: Same as [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]
Figure 9
Figure 9. Figure 9: Same as [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: Random Forest (RF) model classification of spectral subtypes. All field and peculiar subtypes are included and grouped under their base type (i.e., L6γ goes under L6). This graph demonstrates the predicted label of the RF model compared to the adopted label defined in…
Figure 11
Figure 11. Figure 11: Confusion Matrix for the RF model in classify￾ing classes. All field and peculiar classes are included. The values are normalized across each row (True label), i.e., each row sums to 1. This graph demonstrates the predicted dwarf label by the RF model versus the actua…
Figure 12
Figure 12. Figure 12: Same as [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: Same as [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]
Figure 14
Figure 14. Figure 14: Same as [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]
Figure 16
Figure 16. Figure 16: SNR histogram of the KNN model’s predictions. The y-axis displays the log frequency, and the x-axis is the difference between the true label and the prediction (e.g., M0 = 0, L0 = 10; M0 − L0 = 0 − 10 would lead to a −10 classification error). Despite the expectation …
Figure 17
Figure 17. Figure 17: Mean ±1 subtype classification accuracy versus SNR for our multi-output KNN classifier. Each data point (grey dot) is 100 synthetic spectra generated with Gaussian noise from table 2. Each point shows the mean accuracy within a given SNR bin, with 5 to 95th percentile…
Figure 18
Figure 18. Figure 18: Model performance of KNN, RF, and SVM for each subtype. We show how accuracy varies for KNN (blue line and circles), RF (orange line and diamonds), and SVM (green line and triangles). The x-axis shows the subtypes from M0–T9, with the number in brackets representing t…
Figure 19
Figure 19. Figure 19: Comparison of spectral standards at fixed spectral types for M4 through L3, showing field, low-gravity (int-g, vl￾g), and low-metallicity (d/sd, sd, esd) classes. Only spectral types with at least 3 different classes were selected. Normalization and feature importance…
Figure 20
Figure 20. Figure 20: Grouped bar charts showing the performance before and after the removal of K-band. Accuracies of Spectral Type, d/sd, sd, esd, int-g, and vl-g are plotted against each ML model. Bars in blue correspond to models trained with K-band data, and those in orange correspond…
Figure 21
Figure 21. Figure 21: Same as [PITH_FULL_IMAGE:figures/full_fig_p025_21.png]
Figure 22
Figure 22. Figure 22: Confusion Matrix for the Random Forest (RF) model in classifying spectral subtypes. All field and peculiar subtypes are included and grouped under their base type (i.e., L6γ goes under L6). The values are normalized across each row (actual label), i.e., each row sums …
Figure 23
Figure 23. Figure 23: Same as [PITH_FULL_IMAGE:figures/full_fig_p028_23.png]
Figure 24
Figure 24. Figure 24: Same as [PITH_FULL_IMAGE:figures/full_fig_p029_24.png]

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Works this paper leans on

242 extracted references · 65 canonical work pages

  1. [1]

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

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    t b K >f WG_-Fn O sKt] m q_٫u.|lKt ݥb ۟ Rc >V _yۻc֋n N?ւ z+ٕ| In--e 6 םZ ] r>e|둉گ5 C/E1cc 1 ZE (C[k8l&NW =]CzWyc[ a˒sb-uw ٣ iZ] V d kOj jmr+Ђ 񣯲 ^ ?8i v Mw d(z ro )mٞ+g 9 K݊cnm@,ʽ蛇

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

    2021

  4. [4]

    J., Faherty , J

    Aganze , C., Burgasser , A. J., Faherty , J. K., et al. 2016, , 151, 46, 10.3847/0004-6256/151/2/46

    work page doi:10.3847/0004-6256/151/2/46 2016

  5. [5]

    J., Malkan , M., et al

    Aganze , C., Burgasser , A. J., Malkan , M., et al. 2022, , 924, 114, 10.3847/1538-4357/ac35ea

    doi:10.3847/1538-4357/ac35ea 2022

  6. [6]

    Alden , H. L. 1951, , 56, 34, 10.1086/106580

    work page doi:10.1086/106580 1951

  7. [7]

    R., Burgasser , A

    Allen , P. R., Burgasser , A. J., Faherty , J. K., & Kirkpatrick , J. D. 2012, , 144, 62, 10.1088/0004-6256/144/2/62

    work page doi:10.1088/0004-6256/144/2/62 2012

  8. [8]

    R., Koerner , D

    Allen , P. R., Koerner , D. W., McElwain , M. W., Cruz , K. L., & Reid , I. N. 2007, , 133, 971, 10.1086/510346

    work page doi:10.1086/510346 2007

Show all 242 references
  1. [9]

    R., & Reid , I

    Allen , P. R., & Reid , I. N. 2008, , 135, 2024, 10.1088/0004-6256/135/6/2024

    2008 doi

  2. [10]

    N., & Liu , M

    Allers , K. N., & Liu , M. C. 2013, , 772, 79, 10.1088/0004-637X/772/2/79

    2013 doi

  3. [11]

    N., Liu , M

    Allers , K. N., Liu , M. C., Dupuy , T. J., & Cushing , M. C. 2010, , 715, 561, 10.1088/0004-637X/715/1/561

    2010 doi

  4. [12]

    2022, , 657, A129, 10.1051/0004-6361/202142050

    Almendros-Abad , V., Mu z i \'c , K., Moitinho , A., Krone-Martins , A., & Kubiak , K. 2022, , 657, A129, 10.1051/0004-6361/202142050

    2022 doi

  5. [13]

    C., Manjavacas , E., et al

    Ashraf , A., Bardalez Gagliuffi , D. C., Manjavacas , E., et al. 2022, , 934, 178, 10.3847/1538-4357/ac7aab

    2022 doi

  6. [14]

    P., Tollerud , E

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

    2013 doi

  7. [15]

    M., Sip o cz , B

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

    2018 doi

  8. [16]

    M., Lim , P

    Astropy Collaboration , Price-Whelan , A. M., Lim , P. L., et al. 2022, , 935, 167, 10.3847/1538-4357/ac7c74

    2022 doi

  9. [17]

    F., Rocha, A

    Azevedo, B. F., Rocha, A. M. A. C., & Pereira, A. I. 2024, Machine Learning, 113, 4055, 10.1007/s10994-023-06467-x

    2024 doi

  10. [18]

    Ballard , S., & Johnson , J. A. 2016, , 816, 66, 10.3847/0004-637X/816/2/66

    2016 doi

  11. [19]

    S., Allard , F., & Hauschildt , P

    Baraffe , I., Chabrier , G., Barman , T. S., Allard , F., & Hauschildt , P. H. 2003, , 402, 701, 10.1051/0004-6361:20030252

    2003 doi

  12. [20]

    C., Burgasser , A

    Bardalez Gagliuffi , D. C., Burgasser , A. J., Gelino , C. R., et al. 2014, , 794, 143, 10.1088/0004-637X/794/2/143

    2014 doi

  13. [21]

    C., Burgasser , A

    Bardalez Gagliuffi , D. C., Burgasser , A. J., Schmidt , S. J., et al. 2019, , 883, 205, 10.3847/1538-4357/ab253d

    2019 doi

  14. [22]

    Best , W. M. J., Liu , M. C., Magnier , E. A., et al. 2013, , 777, 84, 10.1088/0004-637X/777/2/84

    2013 doi

  15. [23]

    2015, , 814, 118, 10.1088/0004-637X/814/2/118

    ---. 2015, , 814, 118, 10.1088/0004-637X/814/2/118

    2015 doi

  16. [24]

    R., B \'e jar , V

    Bihain , G., Rebolo , R., Zapatero Osorio , M. R., B \'e jar , V. J. S., & Caballero , J. A. 2010, , 519, A93, 10.1051/0004-6361/200913676

    2010 doi

  17. [25]

    D., Storm , J., & Schnurr , O

    Bihain , G., Scholz , R. D., Storm , J., & Schnurr , O. 2013, , 557, A43, 10.1051/0004-6361/201322141

    2013 doi

  18. [26]

    J., Hawley , S

    Bochanski , J. J., Hawley , S. L., Covey , K. R., et al. 2010, , 139, 2679, 10.1088/0004-6256/139/6/2679

    2010 doi

  19. [27]

    L., et al

    Bouy , H., Brandner , W., Mart \' n , E. L., et al. 2003, , 126, 1526, 10.1086/377343

    2003 doi

  20. [28]

    2022, , 664, A111, 10.1051/0004-6361/202243850

    Bouy , H., Tamura , M., Barrado , D., et al. 2022, , 664, A111, 10.1051/0004-6361/202243850

    2022 doi

  21. [29]

    P., Liu , M

    Bowler , B. P., Liu , M. C., & Cushing , M. C. 2009, , 706, 1114, 10.1088/0004-637X/706/2/1114

    2009 doi

  22. [30]

    P., Liu , M

    Bowler , B. P., Liu , M. C., Shkolnik , E. L., et al. 2012, , 753, 142, 10.1088/0004-637X/753/2/142

    2012 doi

  23. [32]

    2004 b , , 614, L73, 10.1086/425418

    ---. 2004 b , , 614, L73, 10.1086/425418

    2004 doi

  24. [33]

    2007 a , , 659, 655, 10.1086/511027

    ---. 2007 a , , 659, 655, 10.1086/511027

    2007 doi

  25. [34]

    2007 b , , 658, 617, 10.1086/511176

    ---. 2007 b , , 658, 617, 10.1086/511176

    2007 doi

  26. [35]

    J., Bardalez-Gagliuffi , D

    Burgasser , A. J., Bardalez-Gagliuffi , D. C., & Gizis , J. E. 2011, , 141, 70, 10.1088/0004-6256/141/3/70

    2011 doi

  27. [37]

    2006 b , , 639, 1095, 10.1086/499344

    ---. 2006 b , , 639, 1095, 10.1086/499344

    2006 doi

  28. [38]

    J., Cruz , K

    Burgasser , A. J., Cruz , K. L., Cushing , M., et al. 2010 a , , 710, 1142, 10.1088/0004-637X/710/2/1142

    2010 doi

  29. [39]

    J., Cruz , K

    Burgasser , A. J., Cruz , K. L., & Kirkpatrick , J. D. 2007 a , , 657, 494, 10.1086/510148

    2007 doi

  30. [40]

    J., Geballe , T

    Burgasser , A. J., Geballe , T. R., Leggett , S. K., Kirkpatrick , J. D., & Golimowski , D. A. 2006 c , , 637, 1067, 10.1086/498563

    2006 doi

  31. [42]

    2006 b , , 645, 1485, 10.1086/504375

    ---. 2006 b , , 645, 1485, 10.1086/504375

    2006 doi

  32. [44]

    2008 b , , 681, 579, 10.1086/588379

    ---. 2008 b , , 681, 579, 10.1086/588379

    2008 doi

  33. [45]

    J., Looper , D., & Rayner , J

    Burgasser , A. J., Looper , D., & Rayner , J. T. 2010 b , , 139, 2448, 10.1088/0004-6256/139/6/2448

    2010 doi

  34. [46]

    J., Looper , D

    Burgasser , A. J., Looper , D. L., Kirkpatrick , J. D., Cruz , K. L., & Swift , B. J. 2008 c , , 674, 451, 10.1086/524726

    2008 doi

  35. [47]

    J., Looper , D

    Burgasser , A. J., Looper , D. L., Kirkpatrick , J. D., & Liu , M. C. 2007 b , , 658, 557, 10.1086/511518

    2007 doi

  36. [48]

    J., & Mamajek , E

    Burgasser , A. J., & Mamajek , E. E. 2017, , 845, 110, 10.3847/1538-4357/aa7fea

    2017 doi

  37. [49]

    J., & McElwain , M

    Burgasser , A. J., & McElwain , M. W. 2006, , 131, 1007, 10.1086/499042

    2006 doi

  38. [50]

    J., McElwain , M

    Burgasser , A. J., McElwain , M. W., & Kirkpatrick , J. D. 2003, , 126, 2487, 10.1086/378608

    2003 doi

  39. [52]

    2004 b , , 127, 2856, 10.1086/383549

    ---. 2004 b , , 127, 2856, 10.1086/383549

    2004 doi

  40. [53]

    J., & Splat Development Team

    Burgasser , A. J., & Splat Development Team . 2017, in Astronomical Society of India Conference Series, Vol. 14, Astronomical Society of India Conference Series, 7--12, 10.48550/arXiv.1707.00062

    2017 doi arXiv

  41. [54]

    J., Witte , S., Helling , C., et al

    Burgasser , A. J., Witte , S., Helling , C., et al. 2009, , 697, 148, 10.1088/0004-637X/697/1/148

    2009 doi

  42. [55]

    J., Schneider , A

    Burgasser , A. J., Schneider , A. C., Meisner , A. M., et al. 2024 a , arXiv e-prints, arXiv:2411.01378, 10.48550/arXiv.2411.01378

    2024 doi arXiv

  43. [56]

    J., Bezanson , R., Labbe , I., et al

    Burgasser , A. J., Bezanson , R., Labbe , I., et al. 2024 b , , 962, 177, 10.3847/1538-4357/ad206f

    2024 doi

  44. [57]

    B., Lunine , J

    Burrows , A., Hubbard , W. B., Lunine , J. I., & Liebert , J. 2001, Reviews of Modern Physics, 73, 719, 10.1103/RevModPhys.73.719

    2001 doi

  45. [58]

    A., Mart \' n , E

    Caballero , J. A., Mart \' n , E. L., Dobbie , P. D., & Barrado Y Navascu \'e s , D. 2006, , 460, 635, 10.1051/0004-6361:20066162

    2006 doi

  46. [59]

    J., & Pickering , E

    Cannon , A. J., & Pickering , E. C. 1918, Annals of Harvard College Observatory, 91, 1

    1918

  47. [60]

    J., Gizis , J

    Castro , P. J., Gizis , J. E., Harris , H. C., et al. 2013, , 776, 126, 10.1088/0004-637X/776/2/126

    2013 doi

  48. [61]

    2014, , 437, 968, 10.1093/mnras/stt1961

    Cavuoti , S., Brescia , M., D'Abrusco , R., Longo , G., & Paolillo , M. 2014, , 437, 968, 10.1093/mnras/stt1961

    2014 doi

  49. [62]

    2000, , 38, 337, 10.1146/annurev.astro.38.1.337

    Chabrier , G., & Baraffe , I. 2000, , 38, 337, 10.1146/annurev.astro.38.1.337

    2000 doi

  50. [63]

    C., Magnier , E

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

    2016 doi arXiv

  51. [65]

    2006 b , , 131, 2722, 10.1086/501431

    ---. 2006 b , , 131, 2722, 10.1086/501431

    2006 doi

  52. [66]

    L., Kirkpatrick , J

    Cruz , K. L., Kirkpatrick , J. D., & Burgasser , A. J. 2009, , 137, 3345, 10.1088/0004-6256/137/2/3345

    2009 doi

  53. [67]

    L., N \'u \ n ez , A., Burgasser , A

    Cruz , K. L., N \'u \ n ez , A., Burgasser , A. J., et al. 2018, , 155, 34, 10.3847/1538-3881/aa9d8a

    2018 doi

  54. [68]

    L., Reid , I

    Cruz , K. L., Reid , I. N., Liebert , J., Kirkpatrick , J. D., & Lowrance , P. J. 2003, , 126, 2421, 10.1086/378607

    2003 doi

  55. [69]

    L., Reid , I

    Cruz , K. L., Reid , I. N., Kirkpatrick , J. D., et al. 2007, , 133, 439, 10.1086/510132

    2007 doi

  56. [70]

    C., Looper , D., Burgasser , A

    Cushing , M. C., Looper , D., Burgasser , A. J., et al. 2009, , 696, 986, 10.1088/0004-637X/696/1/986

    2009 doi

  57. [71]

    C., Rayner , J

    Cushing , M. C., Rayner , J. T., & Vacca , W. D. 2005, , 623, 1115, 10.1086/428040

    2005 doi

  58. [72]

    C., Marley , M

    Cushing , M. C., Marley , M. S., Saumon , D., et al. 2008, , 678, 1372, 10.1086/526489

    2008 doi

  59. [73]

    C., Kirkpatrick , J

    Cushing , M. C., Kirkpatrick , J. D., Gelino , C. R., et al. 2011, , 743, 50, 10.1088/0004-637X/743/1/50

    2011 doi

  60. [74]

    C., Harris , H

    Dahn , C. C., Harris , H. C., Vrba , F. J., et al. 2002, , 124, 1170, 10.1086/341646

    2002 doi

  61. [75]

    P., et al

    Dawson , P., Scholz , A., Ray , T. P., et al. 2014, , 442, 1586, 10.1093/mnras/stu973

    2014 doi

  62. [76]

    C., Marocco , F., Pinfield , D

    Day-Jones , A. C., Marocco , F., Pinfield , D. J., et al. 2013, , 430, 1171, 10.1093/mnras/sts685

    2013 doi

  63. [77]

    R., & Hambly , N

    Deacon , N. R., & Hambly , N. C. 2007, , 468, 163, 10.1051/0004-6361:20066844

    2007 doi

  64. [78]

    R., Liu , M

    Deacon , N. R., Liu , M. C., Magnier , E. A., et al. 2011, , 142, 77, 10.1088/0004-6256/142/3/77

    2011 doi

  65. [79]

    2012 a , , 755, 94, 10.1088/0004-637X/755/2/94

    ---. 2012 a , , 755, 94, 10.1088/0004-637X/755/2/94

    2012 doi

  66. [80]

    2012 b , , 757, 100, 10.1088/0004-637X/757/1/100

    ---. 2012 b , , 757, 100, 10.1088/0004-637X/757/1/100

    2012 doi

  67. [81]

    2014, , 792, 119, 10.1088/0004-637X/792/2/119

    ---. 2014, , 792, 119, 10.1088/0004-637X/792/2/119

    2014 doi

  68. [82]

    D., Dunning , C., et al

    Desai , M., Draxl Giannoni , J. D., Dunning , C., et al. 2023, Research Notes of the American Astronomical Society, 7, 13, 10.3847/2515-5172/acb54a

    2023 doi

  69. [83]

    L., Montgomery , M

    Deshpande , R., Mart \' n , E. L., Montgomery , M. M., et al. 2012, , 144, 99, 10.1088/0004-6256/144/4/99

    2012 doi

  70. [84]

    2010, , 725, L186, 10.1088/2041-8205/725/2/L186

    Dierickx , M., Klement , R., Rix , H.-W., & Liu , C. 2010, , 725, L186, 10.1088/2041-8205/725/2/L186

    2010 doi

  71. [85]

    D., & Charbonneau , D

    Dressing , C. D., & Charbonneau , D. 2015, , 807, 45, 10.1088/0004-637X/807/1/45

    2015 doi

  72. [86]

    J., & Liu , M

    Dupuy , T. J., & Liu , M. C. 2012, , 201, 19, 10.1088/0067-0049/201/2/19

    2012 doi

  73. [87]

    1999, , 351, L5, 10.48550/arXiv.astro-ph/9905162

    EROS Collaboration , Goldman , B., Delfosse , X., et al. 1999, , 351, L5, 10.48550/arXiv.astro-ph/9905162

    1999 doi arXiv

  74. [88]

    L., & Luhman , K

    Esplin , T. L., & Luhman , K. L. 2020, , 159, 282, 10.3847/1538-3881/ab8dbd

    2020 doi

  75. [89]

    L., Luhman , K

    Esplin , T. L., Luhman , K. L., & Mamajek , E. E. 2014, , 784, 126, 10.1088/0004-637X/784/2/126

    2014 doi

  76. [90]

    K., Burgasser , A

    Faherty , J. K., Burgasser , A. J., Bochanski , J. J., et al. 2011, , 141, 71, 10.1088/0004-6256/141/3/71

    2011 doi

  77. [91]

    K., Burgasser , A

    Faherty , J. K., Burgasser , A. J., Cruz , K. L., et al. 2009, , 137, 1, 10.1088/0004-6256/137/1/1

    2009 doi

  78. [92]

    K., Burgasser , A

    Faherty , J. K., Burgasser , A. J., West , A. A., et al. 2010, , 139, 176, 10.1088/0004-6256/139/1/176

    2010 doi

  79. [93]

    K., Riedel , A

    Faherty , J. K., Riedel , A. R., Cruz , K. L., et al. 2016, , 225, 10, 10.3847/0067-0049/225/1/10

    2016 doi

  80. [94]

    J., & Best , W

    Feeser , S. J., & Best , W. M. J. 2022, , 513, 516, 10.1093/mnras/stac855

    2022 doi

  81. [95]

    1998, , 298, L34, 10.1046/j.1365-8711.1998.01847.x

    Festin , L. 1998, , 298, L34, 10.1046/j.1365-8711.1998.01847.x

    1998

  82. [96]

    L., Pinfield , D

    Folkes , S. L., Pinfield , D. J., Jones , H. R. A., et al. 2012, , 427, 3280, 10.1111/j.1365-2966.2012.21132.x

    2012

  83. [97]

    K., Cruz , K., et al

    Gagn \'e , J., Faherty , J. K., Cruz , K., et al. 2014 a , , 785, L14, 10.1088/2041-8205/785/1/L14

    2014 doi

  84. [98]

    2014 b , , 792, L17, 10.1088/2041-8205/792/1/L17

    Gagn \'e , J., Lafreni \`e re , D., Doyon , R., et al. 2014 b , , 792, L17, 10.1088/2041-8205/792/1/L17

    2014 doi

  85. [99]

    2015 a , , 798, 73, 10.1088/0004-637X/798/2/73

    Gagn \'e , J., Lafreni \`e re , D., Doyon , R., Malo , L., & Artigau , \'E . 2015 a , , 798, 73, 10.1088/0004-637X/798/2/73

    2015 doi

  86. [100]

    K., Cruz , K

    Gagn \'e , J., Faherty , J. K., Cruz , K. L., et al. 2015 b , , 219, 33, 10.1088/0067-0049/219/2/33

    2015 doi

  87. [101]

    K., Mamajek , E

    Gagn \'e , J., Faherty , J. K., Mamajek , E. E., et al. 2017, , 228, 18, 10.3847/1538-4365/228/2/18

    2017 doi

  88. [102]

    2018, , 616, A10, 10.1051/0004-6361/201832843

    Gaia Collaboration , Babusiaux , C., van Leeuwen , F., et al. 2018, , 616, A10, 10.1051/0004-6361/201832843

    2018 doi

  89. [103]

    L., Sarro , L

    Gaia Collaboration , Smart , R. L., Sarro , L. M., et al. 2021, , 649, A6, 10.1051/0004-6361/202039498

    2021 doi

  90. [104]

    C., Kuznetsov , M., Clarke , J

    G \'a lvez-Ortiz , M. C., Kuznetsov , M., Clarke , J. R. A., et al. 2014, , 439, 3890, 10.1093/mnras/stu241

    2014 doi

  91. [105]

    D., Berriman , G

    Gei ler , K., Metchev , S., Kirkpatrick , J. D., Berriman , G. B., & Looper , D. 2011, , 732, 56, 10.1088/0004-637X/732/1/56

    2011 doi

  92. [106]

    M., et al

    Gillon , M., Jehin , E., Lederer , S. M., et al. 2016, , 533, 221, 10.1038/nature17448

    2016 doi

  93. [107]

    Gillon , M., Triaud , A. H. M. J., Demory , B.-O., et al. 2017, , 542, 456, 10.1038/nature21360

    2017 doi

  94. [108]

    Gizis , J. E. 1997, , 113, 806, 10.1086/118302

    1997 doi

  95. [109]

    2002, , 575, 484, 10.1086/341259

    ---. 2002, , 575, 484, 10.1086/341259

    2002 doi

  96. [110]

    E., Burgasser , A

    Gizis , J. E., Burgasser , A. J., Faherty , J. K., Castro , P. J., & Shara , M. M. 2011, , 142, 171, 10.1088/0004-6256/142/5/171

    2011 doi

  97. [111]

    E., Monet , D

    Gizis , J. E., Monet , D. G., Reid , I. N., et al. 2000, , 120, 1085, 10.1086/301456

    2000 doi

  98. [112]

    E., Reid , I

    Gizis , J. E., Reid , I. N., Knapp , G. R., et al. 2003, , 125, 3302, 10.1086/374991

    2003 doi

  99. [113]

    E., Faherty , J

    Gizis , J. E., Faherty , J. K., Liu , M. C., et al. 2012, , 144, 94, 10.1088/0004-6256/144/4/94

    2012 doi

  100. [114]

    C., Faherty , J

    Gonzales , E. C., Faherty , J. K., Gagn \'e , J., et al. 2019, , 886, 131, 10.3847/1538-4357/ab48fc

    2019 doi

  101. [115]

    I., Meyer , M

    Gorlova , N. I., Meyer , M. R., Rieke , G. H., & Liebert , J. 2003, , 593, 1074, 10.1086/376730

    2003 doi

  102. [116]

    J., Schneider , A

    Greco , J. J., Schneider , A. C., Cushing , M. C., Kirkpatrick , J. D., & Burgasser , A. J. 2019, , 158, 182, 10.3847/1538-3881/ab3ebe

    2019 doi

  103. [117]

    B., & Rutledge , R

    Haakonsen , C. B., & Rutledge , R. E. 2009, , 184, 138, 10.1088/0067-0049/184/1/138

    2009 doi

  104. [118]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, 10.1038/s41586-020-2649-2

    2020 doi

  105. [119]

    L., Covey , K

    Hawley , S. L., Covey , K. R., Knapp , G. R., et al. 2002, , 123, 3409, 10.1086/340697

    2002 doi

  106. [120]

    2022, , 927, 122, 10.3847/1538-4357/ac4e16

    Hejazi , N., L \'e pine , S., & Nordlander , T. 2022, , 927, 122, 10.3847/1538-4357/ac4e16

    2022 doi

  107. [121]

    J., Jao , W.-C., Subasavage , J

    Henry , T. J., Jao , W.-C., Subasavage , J. P., et al. 2006, , 132, 2360, 10.1086/508233

    2006 doi

  108. [122]

    J., Jao , W.-C., Winters , J

    Henry , T. J., Jao , W.-C., Winters , J. G., et al. 2018, , 155, 265, 10.3847/1538-3881/aac262

    2018 doi

  109. [123]

    Ho, T. K. 1995, in Proceedings of 3rd International Conference on Document Analysis and Recognition, Vol. 1, 278--282 vol.1, 10.1109/ICDAR.1995.598994

    1995

  110. [124]

    J., Theissen , C

    Hsu , C.-C., Burgasser , A. J., Theissen , C. A., et al. 2021, , 257, 45, 10.3847/1538-4365/ac1c7d

    2021 doi

  111. [125]

    2002, IEEE Transactions on Neural Networks, 13, 415, 10.1109/72.991427

    Hsu, C.-W., & Lin, C.-J. 2002, IEEE Transactions on Neural Networks, 13, 415, 10.1109/72.991427

    2002 doi

  112. [126]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, 10.1109/MCSE.2007.55

    2007 doi

  113. [127]

    J., et al

    Karalidi , T., Marley , M., Fortney , J. J., et al. 2021, , 923, 269, 10.3847/1538-4357/ac3140

    2021 doi

  114. [128]

    2015, , 150, 182, 10.1088/0004-6256/150/6/182

    Kellogg , K., Metchev , S., Gei ler , K., et al. 2015, , 150, 182, 10.1088/0004-6256/150/6/182

    2015 doi

  115. [129]

    A., & Tannock , M

    Kellogg , K., Metchev , S., Miles-P \'a ez , P. A., & Tannock , M. E. 2017, , 154, 112, 10.3847/1538-3881/aa83b0

    2017 doi

  116. [131]

    V., Khovritchev , M

    Khrutskaya , E. V., Khovritchev , M. Y., & Bronnikova , N. M. 2004, , 418, 357, 10.1051/0004-6361:20034232

    2004 doi

  117. [132]

    J., Angus , R., et al

    Kiman , R., Schmidt , S. J., Angus , R., et al. 2019, , 157, 231, 10.3847/1538-3881/ab1753

    2019 doi

  118. [133]

    Kirkpatrick , J. D. 2005, , 43, 195, 10.1146/annurev.astro.42.053102.134017

    2005

  119. [134]

    D., Barman , T

    Kirkpatrick , J. D., Barman , T. S., Burgasser , A. J., et al. 2006, , 639, 1120, 10.1086/499622

    2006 doi

  120. [135]

    D., Dahn , C

    Kirkpatrick , J. D., Dahn , C. C., Monet , D. G., et al. 2001, , 121, 3235, 10.1086/321085

    2001 doi

  121. [136]

    D., Henry , T

    Kirkpatrick , J. D., Henry , T. J., & McCarthy , Donald W., J. 1991, , 77, 417, 10.1086/191611

    1991 doi

  122. [137]

    D., Reid , I

    Kirkpatrick , J. D., Reid , I. N., Liebert , J., et al. 1999, , 519, 802, 10.1086/307414

    1999 doi

  123. [138]

    2000, , 120, 447, 10.1086/301427

    ---. 2000, , 120, 447, 10.1086/301427

    2000 doi

  124. [139]

    D., Cruz , K

    Kirkpatrick , J. D., Cruz , K. L., Barman , T. S., et al. 2008, , 689, 1295, 10.1086/592768

    2008 doi

  125. [140]

    D., Looper , D

    Kirkpatrick , J. D., Looper , D. L., Burgasser , A. J., et al. 2010, , 190, 100, 10.1088/0067-0049/190/1/100

    2010 doi

  126. [141]

    D., Cushing , M

    Kirkpatrick , J. D., Cushing , M. C., Gelino , C. R., et al. 2011, , 197, 19, 10.1088/0067-0049/197/2/19

    2011 doi

  127. [143]

    2014 b , , 783, 122, 10.1088/0004-637X/783/2/122

    ---. 2014 b , , 783, 122, 10.1088/0004-637X/783/2/122

    2014 doi

  128. [144]

    D., Kellogg , K., Schneider , A

    Kirkpatrick , J. D., Kellogg , K., Schneider , A. C., et al. 2016, , 224, 36, 10.3847/0067-0049/224/2/36

    2016 doi

  129. [145]

    D., Gelino , C

    Kirkpatrick , J. D., Gelino , C. R., Faherty , J. K., et al. 2021, , 253, 7, 10.3847/1538-4365/abd107

    2021 doi

  130. [146]

    R., Leggett , S

    Knapp , G. R., Leggett , S. K., Fan , X., et al. 2004, , 127, 3553, 10.1086/420707

    2004 doi

  131. [147]

    J., & Wilking , B

    Lada , C. J., & Wilking , B. A. 1984, , 287, 610, 10.1086/162719

    1984 doi

  132. [149]

    M., & Shara , M

    L \'e pine , S., Rich , R. M., & Shara , M. M. 2003, , 125, 1598, 10.1086/345972

    2003 doi

  133. [150]

    2007, , 669, 1235, 10.1086/521614

    ---. 2007, , 669, 1235, 10.1086/521614

    2007 doi

  134. [151]

    L \'e pine , S., & Shara , M. M. 2005, , 129, 1483, 10.1086/427854

    2005 doi

  135. [152]

    2020, , 901, 49, 10.3847/1538-4357/abaef3

    Li , L., Shao , Z., Li , Z.-Z., et al. 2020, , 901, 49, 10.3847/1538-4357/abaef3

    2020 doi

  136. [153]

    Liebert , J., & Burgasser , A. J. 2007, , 655, 522, 10.1086/509882

    2007 doi

  137. [154]

    C., Dupuy , T

    Liu , M. C., Dupuy , T. J., & Allers , K. N. 2016, , 833, 96, 10.3847/1538-4357/833/1/96

    2016 doi

  138. [155]

    R., et al

    Lodieu , N., Espinoza Contreras , M., Zapatero Osorio , M. R., et al. 2017, , 598, A92, 10.1051/0004-6361/201629410

    2017 doi

  139. [156]

    D., McCaughrean , M

    Lodieu , N., Scholz , R. D., McCaughrean , M. J., et al. 2005, , 440, 1061, 10.1051/0004-6361:20042456

    2005 doi

  140. [158]

    2007 b , , 669, L97, 10.1086/523812

    ---. 2007 b , , 669, L97, 10.1086/523812

    2007 doi

  141. [159]

    L., Kirkpatrick , J

    Looper , D. L., Kirkpatrick , J. D., & Burgasser , A. J. 2007 c , , 134, 1162, 10.1086/520645

    2007 doi

  142. [161]

    2008 b , , 686, 528, 10.1086/591025

    ---. 2008 b , , 686, 528, 10.1086/591025

    2008 doi

  143. [162]

    W., Tinney , C

    Lucas , P. W., Tinney , C. G., Burningham , B., et al. 2010, , 408, L56, 10.1111/j.1745-3933.2010.00927.x

    2010

  144. [163]

    P., et al

    Lueber , A., Heng , K., Bowler , B. P., et al. 2024, , 690, A357, 10.1051/0004-6361/202451301

    2024 doi

  145. [164]

    E., et al

    Lueber , A., Kitzmann , D., Fisher , C. E., et al. 2023, , 954, 22, 10.3847/1538-4357/ace530

    2023 doi

  146. [165]

    Luhman , K. L. 2006, , 645, 676, 10.1086/504073

    2006 doi

  147. [166]

    2014, , 781, 4, 10.1088/0004-637X/781/1/4

    ---. 2014, , 781, 4, 10.1088/0004-637X/781/1/4

    2014 doi

  148. [167]

    L., Allen , P

    Luhman , K. L., Allen , P. R., Espaillat , C., Hartmann , L., & Calvet , N. 2010, , 186, 111, 10.1088/0067-0049/186/1/111

    2010 doi

  149. [168]

    L., Esplin , T

    Luhman , K. L., Esplin , T. L., & Loutrel , N. P. 2016, , 827, 52, 10.3847/0004-637X/827/1/52

    2016 doi

  150. [169]

    L., Herrmann , K

    Luhman , K. L., Herrmann , K. A., Mamajek , E. E., Esplin , T. L., & Pecaut , M. J. 2018, , 156, 76, 10.3847/1538-3881/aacc6d

    2018 doi

  151. [170]

    L., Mamajek , E

    Luhman , K. L., Mamajek , E. E., Shukla , S. J., & Loutrel , N. P. 2017, , 153, 46, 10.3847/1538-3881/153/1/46

    2017 doi

  152. [171]

    L., & Sheppard , S

    Luhman , K. L., & Sheppard , S. S. 2014, , 787, 126, 10.1088/0004-637X/787/2/126

    2014 doi

  153. [172]

    L., Patten , B

    Luhman , K. L., Patten , B. M., Marengo , M., et al. 2007, , 654, 570, 10.1086/509073

    2007 doi

  154. [173]

    L., Loutrel , N

    Luhman , K. L., Loutrel , N. P., McCurdy , N. S., et al. 2012, , 760, 152, 10.1088/0004-637X/760/2/152

    2012 doi

  155. [174]

    C., Henry , T

    Lurie , J. C., Henry , T. J., Jao , W.-C., et al. 2014, , 148, 91, 10.1088/0004-6256/148/5/91

    2014 doi

  156. [175]

    Luyten , W. J. 1979, New Luyten catalogue of stars with proper motions larger than two tenths of an arcsecond; and first supplement; NLTT. (Minneapolis (1979)); Label 12 = short description; Label 13 = documentation by Warren; Label 14 = catalogue

    1979

  157. [176]

    N., Kirkpatrick , J

    Mace , G. N., Kirkpatrick , J. D., Cushing , M. C., et al. 2013, , 205, 6, 10.1088/0067-0049/205/1/6

    2013 doi

  158. [177]

    W., Feiden , G

    Mann , A. W., Feiden , G. A., Gaidos , E., Boyajian , T., & von Braun , K. 2016, , 819, 87, 10.3847/0004-637X/819/1/87

    2016 doi

  159. [178]

    S., Saumon , D., Visscher , C., et al

    Marley , M. S., Saumon , D., Visscher , C., et al. 2021, , 920, 85, 10.3847/1538-4357/ac141d

    2021 doi

  160. [179]

    H., Smart , R

    Marocco , F., Andrei , A. H., Smart , R. L., et al. 2013, , 146, 161, 10.1088/0004-6256/146/6/161

    2013 doi

  161. [180]

    C., Mace , G

    Martin , E. C., Mace , G. N., McLean , I. S., et al. 2017, , 838, 73, 10.3847/1538-4357/aa6338

    2017 doi

  162. [181]

    L., Basri , G., Delfosse , X., & Forveille , T

    Martin , E. L., Basri , G., Delfosse , X., & Forveille , T. 1997, , 327, L29

    1997

  163. [182]

    L., Phan-Bao , N., Bessell , M., et al

    Mart \' n , E. L., Phan-Bao , N., Bessell , M., et al. 2010, , 517, A53, 10.1051/0004-6361/201014202

    2010 doi

  164. [183]

    W., & Burgasser , A

    McElwain , M. W., & Burgasser , A. J. 2006, , 132, 2074, 10.1086/508199

    2006 doi

  165. [184]

    R., Kirkpatrick , J

    McGovern , M. R., Kirkpatrick , J. D., McLean , I. S., et al. 2004, , 600, 1020, 10.1086/379849

    2004 doi

  166. [185]

    S., McGovern , M

    McLean , I. S., McGovern , M. R., Burgasser , A. J., et al. 2003, , 596, 561, 10.1086/377636

    2003 doi

  167. [186]

    M., Faherty , J

    Meisner , A. M., Faherty , J. K., Kirkpatrick , J. D., et al. 2020, , 899, 123, 10.3847/1538-4357/aba633

    2020 doi

  168. [187]

    A., Kirkpatrick , J

    Metchev , S. A., Kirkpatrick , J. D., Berriman , G. B., & Looper , D. 2008, , 676, 1281, 10.1086/524721

    2008 doi

  169. [188]

    E., Mamajek , E

    Moolekamp , F. E., Mamajek , E. E., James , D. J., et al. 2019, , 484, 5049, 10.1093/mnras/stz183

    2019 doi

  170. [189]

    W., Keenan , P

    Morgan , W. W., Keenan , P. C., & Kellman , E. 1943, An atlas of stellar spectra, with an outline of spectral classification

    1943

  171. [190]

    V., Mukherjee , S., Marley , M

    Morley , C. V., Mukherjee , S., Marley , M. S., et al. 2024, arXiv e-prints, arXiv:2402.00758, 10.48550/arXiv.2402.00758

    2024 doi arXiv

  172. [191]

    A., Lada , C

    Muench , A. A., Lada , C. J., Luhman , K. L., Muzerolle , J., & Young , E. 2007, , 134, 411, 10.1086/518560

    2007 doi

  173. [192]

    J., Morley , C

    Mukherjee , S., Fortney , J. J., Morley , C. V., et al. 2024, , 963, 73, 10.3847/1538-4357/ad18c2

    2024 doi

  174. [193]

    R., Charbonneau , D., Irwin , J., et al

    Newton , E. R., Charbonneau , D., Irwin , J., et al. 2014, , 147, 20, 10.1088/0004-6256/147/1/20

    2014 doi

  175. [194]

    2011, Journal of Machine Learning Research, 12, 2825

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of Machine Learning Research, 12, 2825

    2011

  176. [195]

    P \'e rez Garrido , A., Lodieu , N., B \'e jar , V. J. S., et al. 2014, , 567, A6, 10.1051/0004-6361/201423615

    2014 doi

  177. [196]

    S., Mart \' n , E

    Phan-Bao , N., Bessell , M. S., Mart \' n , E. L., et al. 2008, , 383, 831, 10.1111/j.1365-2966.2007.12564.x

    2008

  178. [197]

    W., Tremblin , P., Baraffe , I., et al

    Phillips , M. W., Tremblin , P., Baraffe , I., et al. 2020, , 637, A38, 10.1051/0004-6361/201937381

    2020 doi

  179. [198]

    J., Jones , H

    Pinfield , D. J., Jones , H. R. A., Lucas , P. W., et al. 2006, , 368, 1281, 10.1111/j.1365-2966.2006.10213.x

    2006

  180. [199]

    S., Jones , H

    Pokorny , R. S., Jones , H. R. A., & Hambly , N. C. 2003, , 397, 575, 10.1051/0004-6361:20021385

    2003 doi

  181. [200]

    S., Jones , H

    Pokorny , R. S., Jones , H. R. A., Hambly , N. C., & Pinfield , D. J. 2004, , 421, 763, 10.1051/0004-6361:20047009

    2004 doi

  182. [201]

    2014, , 793, 75, 10.1088/0004-637X/793/2/75

    Radigan , J., Lafreni \`e re , D., Jayawardhana , R., & Artigau , E. 2014, , 793, 75, 10.1088/0004-637X/793/2/75

    2014 doi

  183. [202]

    2024, , 685, A6, 10.1051/0004-6361/202347954

    Ravinet , T., Reyl \'e , C., Lagarde , N., et al. 2024, , 685, A6, 10.1051/0004-6361/202347954

    2024 doi

  184. [203]

    T., Toomey , D

    Rayner , J. T., Toomey , D. W., Onaka , P. M., et al. 2003, , 115, 362, 10.1086/367745

    2003 doi

  185. [204]

    N., Cruz , K

    Reid , I. N., Cruz , K. L., & Allen , P. R. 2007, , 133, 2825, 10.1086/517914

    2007 doi

  186. [205]

    N., Cruz , K

    Reid , I. N., Cruz , K. L., Burgasser , A. J., & Liu , M. C. 2008 a , , 135, 580, 10.1088/0004-6256/135/2/580

    2008 doi

  187. [206]

    N., Cruz , K

    Reid , I. N., Cruz , K. L., Kirkpatrick , J. D., et al. 2008 b , , 136, 1290, 10.1088/0004-6256/136/3/1290

    2008 doi

  188. [207]

    N., & Gizis , J

    Reid , I. N., & Gizis , J. E. 2005, , 117, 676, 10.1086/430462

    2005 doi

  189. [208]

    N., Lewitus , E., Allen , P

    Reid , I. N., Lewitus , E., Allen , P. R., Cruz , K. L., & Burgasser , A. J. 2006 a , , 132, 891, 10.1086/505626

    2006 doi

  190. [209]

    N., Lewitus , E., Burgasser , A

    Reid , I. N., Lewitus , E., Burgasser , A. J., & Cruz , K. L. 2006 b , , 639, 1114, 10.1086/499484

    2006 doi

  191. [210]

    2018, , 619, L8, 10.1051/0004-6361/201834082

    Reyl \'e , C. 2018, , 619, L8, 10.1051/0004-6361/201834082

    2018 doi

  192. [211]

    2021, , 650, A201, 10.1051/0004-6361/202140985

    Reyl \'e , C., Jardine , K., Fouqu \'e , P., et al. 2021, , 650, A201, 10.1051/0004-6361/202140985

    2021 doi

  193. [212]

    2016, , 830, 144, 10.3847/0004-637X/830/2/144

    Robert , J., Gagn \'e , J., Artigau , \'E ., et al. 2016, , 830, 144, 10.3847/0004-637X/830/2/144

    2016 doi

  194. [213]

    R., van der Plas , G., Kastner , J

    Rodriguez , D. R., van der Plas , G., Kastner , J. H., et al. 2015, , 582, L5, 10.1051/0004-6361/201527031

    2015 doi

  195. [214]

    J., Cruz , K

    Schmidt , S. J., Cruz , K. L., Bongiorno , B. J., Liebert , J., & Reid , I. N. 2007, , 133, 2258, 10.1086/512158

    2007 doi

  196. [215]

    J., Hawley , S

    Schmidt , S. J., Hawley , S. L., West , A. A., et al. 2015, , 149, 158, 10.1088/0004-6256/149/5/158

    2015 doi

  197. [216]

    J., West , A

    Schmidt , S. J., West , A. A., Bochanski , J. J., Hawley , S. L., & Kielty , C. 2014, , 126, 642, 10.1086/677403

    2014 doi

  198. [217]

    J., West , A

    Schmidt , S. J., West , A. A., Hawley , S. L., & Pineda , J. S. 2010, , 139, 1808, 10.1088/0004-6256/139/5/1808

    2010 doi

  199. [218]

    C., Cushing , M

    Schneider , A. C., Cushing , M. C., Kirkpatrick , J. D., et al. 2014, , 147, 34, 10.1088/0004-6256/147/2/34

    2014 doi

  200. [219]

    C., Greco , J., Cushing , M

    Schneider , A. C., Greco , J., Cushing , M. C., et al. 2016, , 817, 112, 10.3847/0004-637X/817/2/112

    2016 doi

  201. [220]

    C., Windsor , J., Cushing , M

    Schneider , A. C., Windsor , J., Cushing , M. C., Kirkpatrick , J. D., & Shkolnik , E. L. 2017, , 153, 196, 10.3847/1538-3881/aa6624

    2017 doi

  202. [221]

    C., Burgasser , A

    Schneider , A. C., Burgasser , A. J., Gerasimov , R., et al. 2020, , 898, 77, 10.3847/1538-4357/ab9a40

    2020 doi

  203. [222]

    Scholz , R. D. 2010, , 515, A92, 10.1051/0004-6361/201014264

    2010 doi

  204. [223]

    D., Lehmann , I., Matute , I., & Zinnecker , H

    Scholz , R. D., Lehmann , I., Matute , I., & Zinnecker , H. 2004 a , , 425, 519, 10.1051/0004-6361:20041059

    2004 doi

  205. [224]

    D., Lodieu , N., & McCaughrean , M

    Scholz , R. D., Lodieu , N., & McCaughrean , M. J. 2004 b , , 428, L25, 10.1051/0004-6361:200400098

    2004 doi

  206. [225]

    2021, , 645, A100, 10.1051/0004-6361/202038827

    Sebastian , D., Gillon , M., Ducrot , E., et al. 2021, , 645, A100, 10.1051/0004-6361/202038827

    2021 doi

  207. [226]

    S., & Cushing , M

    Sheppard , S. S., & Cushing , M. C. 2009, , 137, 304, 10.1088/0004-6256/137/1/304

    2009 doi

  208. [227]

    C., & Reid , I

    Shkolnik , E., Liu , M. C., & Reid , I. N. 2009, , 699, 649, 10.1088/0004-637X/699/1/649

    2009 doi

  209. [228]

    M., Burgasser , A

    Siegler , N., Close , L. M., Burgasser , A. J., et al. 2007, , 133, 2320, 10.1086/513273

    2007 doi

  210. [229]

    2023, , 135, 044502, 10.1088/1538-3873/acc974

    Sithajan , S., & Meethong , S. 2023, , 135, 044502, 10.1088/1538-3873/acc974

    2023 doi

  211. [230]

    F., Cutri , R

    Skrutskie , M. F., Cutri , R. M., Stiening , R., et al. 2006, , 131, 1163, 10.1086/498708

    2006 doi

  212. [231]

    J., & Faherty , J

    Skrzypek , N., Warren , S. J., & Faherty , J. K. 2016, , 589, A49, 10.1051/0004-6361/201527359

    2016 doi

  213. [232]

    L., Hillenbrand , L

    Slesnick , C. L., Hillenbrand , L. A., & Carpenter , J. M. 2004, , 610, 1045, 10.1086/421898

    2004 doi

  214. [233]

    W., Burningham , B., et al

    Smith , L., Lucas , P. W., Burningham , B., et al. 2014, , 437, 3603, 10.1093/mnras/stt2156

    2014 doi

  215. [234]

    R., Barrado y Navascu \'e s , D., Bouvier , J., et al

    Stauffer , J. R., Barrado y Navascu \'e s , D., Bouvier , J., et al. 1999, , 527, 219, 10.1086/308069

    1999 doi

  216. [235]

    2012, , 537, A94, 10.1051/0004-6361/201118097

    Stelzer , B., Alcal \'a , J., Biazzo , K., et al. 2012, , 537, A94, 10.1051/0004-6361/201118097

    2012 doi

  217. [236]

    B., Brandner , W., Henning , T., et al

    Stumpf , M. B., Brandner , W., Henning , T., et al. 2008, arXiv e-prints, arXiv:0811.0556, 10.48550/arXiv.0811.0556

    2008 doi arXiv

  218. [237]

    2009, , 503, 639, 10.1051/0004-6361/200810699

    Testi , L. 2009, , 503, 639, 10.1051/0004-6361/200810699

    2009 doi

  219. [238]

    A., Burgasser , A

    Theissen , C. A., Burgasser , A. J., Bardalez Gagliuffi , D. C., et al. 2018, , 853, 75, 10.3847/1538-4357/aaa0cf

    2018 doi

  220. [239]

    A., Kirkpatrick , J

    Thompson , M. A., Kirkpatrick , J. D., Mace , G. N., et al. 2013, , 125, 809, 10.1086/671426

    2013 doi

  221. [240]

    Veilleux , S., & Osterbrock , D. E. 1987, , 63, 295, 10.1086/191166

    1987 doi

  222. [241]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, 10.1038/s41592-019-0686-2

    2020 doi

  223. [242]

    N., & Mateer , B

    Vyssotsky , A. N., & Mateer , B. A. 1952, , 116, 117, 10.1086/145597

    1952 doi

  224. [243]

    A., Hawley , S

    West , A. A., Hawley , S. L., Bochanski , J. J., et al. 2008, , 135, 785, 10.1088/0004-6256/135/3/785

    2008 doi

  225. [244]

    A., Hawley , S

    West , A. A., Hawley , S. L., Walkowicz , L. M., et al. 2004, , 128, 426, 10.1086/421364

    2004 doi

  226. [245]

    A., Morgan , D

    West , A. A., Morgan , D. P., Bochanski , J. J., et al. 2011, , 141, 97, 10.1088/0004-6256/141/3/97

    2011 doi

  227. [246]

    C., Miller , N

    Wilson , J. C., Miller , N. A., Gizis , J. E., et al. 2003, in IAU Symposium, Vol. 211, Brown Dwarfs, ed. E. Mart \' n , 197

    2003

  228. [247]

    L., Eisenhardt , P

    Wright , E. L., Eisenhardt , P. R. M., Mainzer , A. K., et al. 2010, , 140, 1868, 10.1088/0004-6256/140/6/1868

    2010 doi

  229. [248]

    J., Newton , E

    Wright , N. J., Newton , E. R., Williams , P. K. G., Drake , J. J., & Yadav , R. K. 2018, , 479, 2351, 10.1093/mnras/sty1670

    2018 doi

  230. [249]

    G., Adelman , J., Anderson , John E., J., et al

    York , D. G., Adelman , J., Anderson , John E., J., et al. 2000, , 120, 1579, 10.1086/301513

    2000 doi

  231. [250]

    H., Burgasser , A

    Zhang , Z. H., Burgasser , A. J., G \'a lvez-Ortiz , M. C., et al. 2019, , 486, 1260, 10.1093/mnras/stz777

    2019 doi

  232. [251]

    H., Pokorny , R

    Zhang , Z. H., Pokorny , R. S., Jones , H. R. A., et al. 2009, , 497, 619, 10.1051/0004-6361/200810314

    2009 doi

  233. [252]

    H., Pinfield , D

    Zhang , Z. H., Pinfield , D. J., Day-Jones , A. C., et al. 2010, , 404, 1817, 10.1111/j.1365-2966.2010.16394.x

    2010

  234. [253]

    H., Pinfield , D

    Zhang , Z. H., Pinfield , D. J., Burningham , B., et al. 2013, , 434, 1005, 10.1093/mnras/stt1030

    2013 doi

Pith tools

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