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The VMC Survey : LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A probabilistic random forest sorts 130 million Magellanic Cloud sources into stars, galaxies, and AGN, recovering 77,600 extragalactic sources including 49,500 new AGN candidates.

desk verdict A solid, honest ML classification of the VMC catalogue with useful new AGN/galaxy samples; headline accuracies are proven only for bright spectroscopic sources, so the faint-tail counts are the main caveat. read the letter →

arxiv 2501.08196 v1 pith:4YSLDXX7 submitted 2025-01-14 astro-ph.GA

Clara M. Pennock , Jacco Th. van Loon , Maria-Rosa L. Cioni , Chandreyee Maitra , Joana M. Oliveira , Jessica E. M. Craig , Valentin D. Ivanov , James Aird
show 5 more authors
Joy. O. Anih Nicholas J. G. Cross Francesca Dresbach Richard de Grijs Martin A. T. Groenewegen
This is my paper · ORCID
classification astro-ph.GA
keywords probabilisticrandomforestMagellanicCloudsactivegalacticnucleigalaxyclassificationmulti-wavelengthphotometryVMCsurveymachinelearningyoungstellarobjects
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The VMC (VISTA Survey of the Magellanic Clouds) near-infrared survey has detected more than 130 million sources toward the Small and Large Magellanic Clouds (SMC and LMC), most of which have never been individually identified. This paper claims that a probabilistic random forest — a supervised machine-learning classifier that handles missing data and returns a probability per class — trained on spectroscopically confirmed examples and fed 237 photometric features from the ultraviolet to the far-infrared, can sort that entire catalogue into ten astrophysical classes (AGN, galaxies, OB stars, red-giant-branch stars, asymptotic-giant-branch stars, red supergiants, post-AGB/post-RGB stars, planetary nebulae, HII regions/young stellar objects, and foreground high-proper-motion stars) plus an 'Unknown' class. On held-out test sources the classifier reaches average accuracies of about 79% (SMC) and 87% (LMC), improving to about 90% and 98% when only sources with class probability $P_{\rm class} > 80\%$ are kept. The high-confidence sample contains more than 77,600 extragalactic sources behind the Clouds, including over 49,500 new AGN candidates and roughly 26,500 new galaxy candidates. If these labels are right, a survey built to study stars in two nearby galaxies doubles as a working source of background-galaxy and AGN science.

What carries the argument

The load-bearing mechanism is the probabilistic random forest (PRF), a random-forest variant that handles missing data by propagating each object down both branches of a decision node, weighted by the probability that each path is correct, and that outputs a probability per class rather than a single hard label. Around it the paper builds a 237-dimensional feature space: magnitudes, photometric errors, point-spread-function sharpness values, Gaia proper motions, colours formed from all pairwise band differences, and the local far-infrared background at 250 microns, which acts as a proxy for extinction and for how close a source sits to the centre of a Cloud. A deliberately unstructured 'Unknown' class, formed by randomly drawing sources from the VMC catalogues themselves, absorbs anything the spectroscopic training set does not represent; this is what lets the pipeline report honest confidence, because the faint majority of sources land in Unknown while the headline extragalactic counts come from the subset with class probability above 80%.

What would settle it

Spectroscopically observe a random sample of the PRF's high-confidence AGN and galaxy candidates in the faint regime where the training set is thinnest (for example $G > 18.8$ mag or $K_s > 18$ mag) and measure the confirmation rate. If that rate falls far below the ~90–98% accuracy quoted for the bright test set, the headline extragalactic counts would be inflated.

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Extended reading notes

Core claim

The paper's central claim is that the probabilistic random forest assigns reliable astrophysical labels, with honest per-class probabilities, to essentially the whole VMC catalogue. The authors report average test-set accuracies of $0.79 \pm 0.01$ (SMC) and $0.87 \pm 0.01$ (LMC), rising to $0.90$ and $0.98$ when restricted to sources with $P_{\rm class} > 80\%$, and they report that 99.8–99.9% of true extragalactic test sources are placed into extragalactic classes at high confidence. After removing the Unknown class they classify 707,939 (SMC) and 397,899 (LMC) sources at high confidence, of which more than 77,600 are extragalactic: over 49,500 previously unknown AGN candidates, over 26,500 new galaxy candidates, and over 2,800 new young-stellar-object candidates. Independent checks support the physical content of the labels: the majority of X-ray sources (554/883) are classified as AGN, the majority of radio sources (1756/2694) as AGN with a further 659/2694 as galaxies, and about 86% of the spectroscopically confirmed quasars in the Quaia catalogue receive AGN labels.

Load-bearing premise

The spectroscopically confirmed training sources are taken to represent the entire 130-million-source catalogue, even though spectroscopy is systematically biased toward bright objects; the reported ~79–87% and ~90–98% accuracies are measured only on that brighter test split, so the accuracy on the faint majority of the catalogue is not directly known.

Editorial extensions

If this is right

  • More than 49,500 previously unknown AGN candidates, over 26,500 new galaxy candidates, and over 2,800 new young-stellar-object candidates become prioritized targets for spectroscopic follow-up.
  • Radio and X-ray detected sources are predominantly classified as AGN or galaxies, providing reliable multi-wavelength counterparts and tentative AGN labels for X-ray- or radio-detected Unknowns.
  • The spatial distributions match physical expectation — extragalactic sources spread uniformly, Magellanic stellar sources concentrate toward the Cloud centres — which supports the validity of the labels for population studies.
  • Stellar classes absent from the training set (Wolf-Rayet stars, R Coronae Borealis stars, supernova remnants) are classified as other stellar classes rather than as extragalactic, implying low stellar contamination of the AGN and galaxy samples.
  • The Unknown class maps where the training set is incomplete — mostly faint main-sequence and RGB stars — indicating where new spectroscopy of faint sources would most improve the classifier.

Reading between the lines

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

  • Combining the PRF probabilities with the X-ray and radio flags that were deliberately kept out of the feature space would yield a cheap, testable obscured-AGN candidate list; the paper already shows that X-ray- or radio-detected Unknowns concentrate in AGN-like regions of magnitude–flux space.
  • The brightness bias of the spectroscopic training set means the high-confidence extragalactic counts are not directly usable as population statistics; converting them into number counts or luminosity functions would require a completeness correction calibrated on the Unknown class.
  • Retraining without the 250-micron far-infrared background, which ranks as the top or near-top feature and encodes position and extinction rather than the source's own emission, would reveal how much of the extragalactic separation rests on where a source sits in the Cloud, and how portable the classifier is to fields outside the Magellanic Clouds.
  • Because the published catalogue carries a probability for every class, the Unknown probability can serve as a continuous novelty score for prioritising unusual or underrepresented sources in future spectroscopic campaigns.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper trains probabilistic random forest classifiers on spectroscopically labelled sources with 237 multi-wavelength features and applies them to the ~130 million sources of the VMC PSF catalogue, producing separate SMC and LMC classifications into ten astrophysical classes plus an Unknown class. The authors report test-set accuracies of 0.79±0.01 (SMC) and 0.87±0.01 (LMC), rising to 0.90 and 0.98 for sources with P_class > 80%. They report 707,939 (SMC) and 397,899 (LMC) high-confidence non-Unknown sources, including >77,600 extragalactic sources and >49,500 previously unknown AGN candidates, and validate the classifications using X-ray, radio, Quaia, and YSO catalogues.

Significance. If the quantitative claims hold for the full VMC population, the paper delivers a valuable public classification product for a large, deep survey and demonstrates a practical pipeline for separating extragalactic sources from Magellanic stellar populations. The use of held-out test splits, the publication of training data and feature importances, and the cross-checks with independent wavelength regimes are genuine strengths that go beyond a purely internal accuracy assessment. However, the headline counts of new AGN and galaxy candidates depend on unvalidated extrapolation to the faint, class-imbalanced catalogue population, and there is an internal inconsistency in the definition of the high-confidence sample. These issues affect the central quantitative claims and require attention before the results can be taken at face value.

major comments (3)
  1. [§3.3.4, §5.8, Table 3] The reported accuracies (79%/87% overall, 90%/98% at P_class>80%) are measured on a test split drawn from the spectroscopically labelled training set, which Section 3.3.4 admits is biased toward sources bright enough for optical spectroscopy. Section 5.8 shows that the majority of the faint catalogue population lands in the Unknown class, yet the headline counts (e.g., >49,500 new AGN candidates) are obtained by applying the classifier to the full catalogue, including the 56.7M sources with P_class>80%, of which roughly 55.6M are Unknown. The independent tests in Sections 5.4–5.6 (radio, X-ray, Quaia) are drawn from populations that are themselves brighter or extreme and do not measure the purity of the faint high-confidence extragalactic selection. The claim that the high-confidence extragalactic counts are accurate therefore rests on an assumption of robustness to covariate shift that is not tested. The authors should either restrict the quantitative claims to the magnitude range actually populated by the training set, or provide a faint-source validation (e.g., a spectroscopic or SED-based check on a random faint subset).
  2. [§4.2, Table 3, Abstract] The definition of the high-confidence catalogue is internally inconsistent. Section 4.2 states that sources with combined AGN+Galaxy probability ≥80% (56,134 for SMC and 140,212 for LMC) are 'moved to the high confidence catalogue', and that the rest of the paper refers to these high-confidence sources. However, the totals quoted in the Abstract and in Table 3—707,939 (SMC) and 397,899 (LMC) known sources, and >77,600 extragalactic sources—correspond exactly to the P_class>80% rows of Table 3 and do not include these combined-threshold sources. Moreover, the accuracy of the combined-threshold selection is not measured by the confusion matrices in Figure 2, which are restricted to P_class>80%. The paper thus mixes a validated subset (individual P_class>80%) with an unvalidated, differently defined subset in the description of the final catalogue, and the headline counts do not reflect the procedure described. Please clarify which catalogue the numbers refer to, and provide accuracy estimates for the combined-threshold selection if it is retained.
  3. [§5.1, Table 4, §6] The statement in the conclusions that 'all the AGN were classified correctly' for the dust-dominated AGN sample is an overstatement. Table 4 shows that most of the sources in this sample, including all of the SMC sources classified as AGN and many of the LMC AGN, were included in the training set (column 'T?' = Y). The held-out sources (T? = N) include several that are not confidently classified as AGN (e.g., SMCtSNE8 at P=0.45; LMCtSNE13 as Hii/YSO at P=0.21; LMCtSNE16 as Hii/YSO at P=0.41). Section 5.1 itself acknowledges the role of training-set inclusion, but the conclusion bullet in Section 6 does not carry this caveat. Please either restrict the claim to the held-out subset or report the performance separately for training and held-out sources.
minor comments (5)
  1. [§3.3.1] The description of combining the validation and test sets into a single test set is acceptable given the repeated random splits, but the wording could be tightened to clarify that parameter tuning and final evaluation are not performed on the same fixed split.
  2. [§2.2.6] The construction of the Unknown class from randomly selected VMC sources is a key modelling assumption; it is described clearly in the text, but it would be helpful to state explicitly in the conclusions that the reliability of the Unknown class as a catch-all depends on this assumption, which is not directly testable with the current data.
  3. [Table 3 caption] The caption should specify whether the columns labelled 'P_class>80%' include sources moved to the high-confidence catalogue via the combined AGN+Galaxy probability criterion of Section 4.2; the current text implies they do not, which contradicts the procedure described in the body.
  4. [Figure 2] Since Section 4.2 introduces a combined AGN+Galaxy probability threshold for the high-confidence catalogue, it would be helpful to show the confusion matrix for the resulting combined selection, or to state explicitly that the reported accuracies apply only to the individual P_class>80% selection.
  5. [Throughout] There are a number of typographical errors, e.g., 'deccreasing' in Section 5.4, 'inclde' in Section 5.5, and 'anologue' in Section 3.2; a careful proofread is recommended.

Circularity Check

1 steps flagged · score 2.0 of 10

The central PRF accuracy claims rest on held-out test splits and independent external checks; only the Section 5.1 validation of dust-dominated AGN is mildly circular because many of those sources were in the training set.

  1. fitted input called prediction [Section 5.1, Table 4]
    "Note that some of these sources were used in the training set, indicated by the ‘T?’ column, by either ‘Y’ (yes) or ‘N’ (no). ... This shows that unusual dust-dominated AGN that have often been mistaken for dusty Magellanic objects are being classified correctly by the PRF, most likely helped by the inclusion of similar sources in the training set."

    Table 4 presents the PRF classifications of the dust-dominated AGN sample from Pennock et al. (2022) and the extended LMC sample as evidence that the classifier handles these unusual AGN correctly. However, many of these sources have T? = Y, meaning they were part of the training set. For those rows, the PRF output is not an independent prediction but a reproduction of labels the model has already seen, so the agreement cannot validate generalization. The text itself concedes that the result was 'most likely helped by the inclusion of similar sources in the training set.' This is a genuine but mild circularity: it affects only this supporting validation, not the main held-out test-set accuracy claims.

full rationale

The main derivation chain is not circular. The headline accuracies (0.79/0.87 overall and 0.90/0.98 for P_class > 80%) are computed on a 25% held-out test split of the spectroscopic training set, as described in Section 3.3.5, so they measure generalization to unseen labelled examples rather than fitting. The full-catalogue classifications and the counts of >49,500 new AGN candidates and >77,600 extragalactic sources are applications of the trained model to previously unlabelled data, not fitted parameters renamed as predictions. Independent validation using X-ray sources, radio sources, Quaia spectroscopic quasars, and Kokusho et al. (2023) YSOs is external to the training labels. The bright-magnitude bias of the spectroscopic training sample is an acknowledged limitation and a potential generalisation/covariate-shift risk, but it is not circularity. The only circular element I can exhibit is in Section 5.1, where the validation sample of unusual dust-dominated AGN includes sources that were used in training; the paper discloses this with the T? column and even attributes the success partly to training-set inclusion. This is a minor, non-load-bearing circularity because the central accuracy and source-count claims do not depend on Section 5.1. Accordingly, the circularity score is 2.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper contributes a classification pipeline and catalog; it does not introduce new physical entities. The main assumptions are about the training set and survey combination.

free parameters (4)
  • Probability threshold p_th = 0.05
    Chosen based on Reis et al. (2018) to reduce computation time without losing accuracy; a hyperparameter, not fitted to the VMC data.
  • Number of trees n_trees = 100
    Chosen from plateau in accuracy vs n_trees (Section 3.3.3); hyperparameter tuned on the training set.
  • High-confidence class probability threshold = P_class > 80%
    Hand-selected threshold for reporting high-confidence classifications; affects all headline counts.
  • Combined AGN+Galaxy probability threshold = >= 80% for high-confidence, >=60% for mid-confidence
    Post-hoc adjustment in Section 4.2 to promote sources with combined extragalactic probability into higher confidence catalogues.
assumptions (4)
  • domain assumption Spectroscopic labels in the training set are correct
    Labels from literature and own SALT/SAAO observations are assumed accurate; mislabelled training examples would propagate into the classifier.
  • standard math The PRF algorithm (Reis et al. 2018) correctly handles missing data and uncertainties
    Use of the PRF implementation from Reis et al.; its behaviour is relied upon rather than re-derived.
  • ad hoc to paper The Unknown class built from random VMC sources approximates the distribution of unlabelled sources
    Randomly selected sources are assumed to represent the 'unknown' population; this is a heuristic that may not capture rare classes.
  • domain assumption A 1-arcsec cross-matching radius yields reliable multi-wavelength counterparts
    All catalogue matches use 1" radius (Section 3.1); mismatches could inject noise into features, especially in crowded fields.

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Cite this review

Pith. "Pith review of The VMC Survey : LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm." pith.science (2026). https://pith.science/paper/4YSLDXX7

@misc{pith2026250108196,
  author       = {Pith},
  title        = {Pith review of: The VMC Survey : LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4YSLDXX7}},
  note         = {Machine review of arXiv:2501.08196}
}
abstract

We used a supervised machine learning algorithm (probabilistic random forest) to classify ~130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features to be trained on, and spectra of Active Galactic Nuclei (AGN), galaxies and a range of stellar classes including from new observations with the Southern African Large Telescope (SALT) and SAAO 1.9m telescope. We also retain a label for sources that remain unknown. This yielded average classifier accuracies of ~79% (SMC) and ~87% (LMC). Restricting to the 56,696,719 sources with class probabilities (P$_{class}$) > 80% yields accuracies of ~90% (SMC) and ~98% (LMC). After removing sources classed as 'Unknown', we classify a total of 707,939 (SMC) and 397,899 (LMC) sources, including > 77,600 extragalactic sources behind the Magellanic Clouds. The extragalactic sources are distributed evenly across the field, whereas the Magellanic sources concentrate at the centres of the Clouds, and both concentrate in optical/IR colour-colour/magnitude diagrams as expected. We also test these classifications using independent datasets, finding that, as expected, the majority of X-ray sources are classified as AGN (554/883) and the majority of radio sources are classed as AGN (1756/2694) or galaxies (659/2694), where the relative AGN-galaxy proportions vary substantially with radio flux density. We have found: > 49,500 hitherto unknown AGN candidates, likely including more AGN dust dominated sources which are in a critical phase of their evolution; > 26,500 new galaxy candidates and > 2800 new Young Stellar Object (YSO) candidates.

Figures

Figures reproduced from arXiv: 2501.08196 by the authors.

Figure 1
Figure 1. Area of the sky covered by the VMC (near-IR, red), EMU ASKAP (radio, blue), SMASH (optical, green), SAGE (mid-IR, yellow) surveys of the LMC (left) and SMC (right). The approximate centres of the Clouds are marked with a black ‘X’. WISE (Wright et al. 2010) is a telescope launched in 2009 to repeatedly map the entire sky in infrared. WISE mapped the whole sky in four bands W1, W2, W3, W4 centred at 3.4 𝜇m, 4.6 𝜇m, 1… view at source ↗
Figure 2
Figure 2. Confusion matrix of final classifier trained and tested on the LMC (left) and SMC (right) datasets. The top panels, a) and b), show all sources, whilst the bottom panels, c) and d), are restricted to sources that were classified with probabilities > 80%. This dataset configuration includes extragalactic and foreground sources from both the LMC and SMC, but Magellanic stellar sources only from the respective Clouds. … view at source ↗
Figure 3
Figure 3. Simplified confusion matrix of final classifier trained and tested on the LMC (left) and SMC (right) datasets, restricted to sources that were classified with probabilities > 80%. The AGN and galaxy classes have been combined into the ‘Extragalactic’ label. The Magellanic stellar classifications have been combined into the ‘Magellanic’ label [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Top 15 important features for the SMC (left) and LMC (right) classifier. Note that, while the top 15 features are in a different order, they are almost all the same features for both the SMC and LMC classifier. The bars are colour-coded to represent the survey origins,…
Figure 5
Figure 5. Figure 5: The sky density of the combined AGN and galaxy sources with Pclass > 80% for the LMC (left) and SMC (right). The density of sources identified as extragalactic is fairly uniform over the survey areas, with a slight increase away from the centres of the Magellanic Cloud…
Figure 6
Figure 6. Figure 6: The sky density of the foreground high proper-motion (PM) sources with Pclass > 80% for the LMC (left) and SMC (right). The density of sources identified as foreground stars is fairly uniform over the survey areas, as expected. MNRAS 000, 1–31 (2024) [PITH_FULL_IMAGE:…
Figure 7
Figure 7. Figure 7: The sky density of the combined stellar Magellanic sources with Pclass > 80% for the LMC (left) and SMC (right). The distribution is dominated by intermediate-age/old RGB stars [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: The sky density of the combined stellar Magellanic sources with RGB sources removed and Pclass > 80% for the LMC (left) and SMC (right).The sources identified as Magellanic concentrate in the centres of the Clouds with fewer sources on the outskirts of the survey area.…
Figure 9
Figure 9. Figure 9: SMASH colour–magnitude diagrams of the sources classified as Magellanic sources (left), foreground stars and extragalactic (right) in the SMC and LMC fields. The contours represent a probability distribution in intervals of 0.2. The sources identified as Unknown with P…
Figure 10
Figure 10. Figure 10: VISTA colour–magnitude diagrams of the sources classified as Magellanic sources (left), foreground stars and extragalactic (right) in the SMC and LMC fields. The contours represent a probability distribution in intervals of 0.2. The sources identified as Unknown with …
Figure 11
Figure 11. Figure 11: AllWISE colour–colour diagrams of the sources classified as Magellanic sources (left), foreground stars and extragalactic (right) in the SMC and LMC fields. The contours represent a probability distribution in intervals of 0.2. The sources identified as Unknown with P…
Figure 12
Figure 12. Figure 12: AllWISE colour–colour diagram of the sources classified as OB stars (blue contours). The contours represent a probability distribution in intervals of 0.2. Over-plotted are stars that exhibit the Pleiades effect (red circles), Be stars (green circles) and B[e] stars (…
Figure 13
Figure 13. Figure 13: Radio LMC ASKAP 888 MHz (left) and SMC ASKAP 1320 MHz (right) flux density distributions and the ratio of galaxy to AGN counts (bottom panels) of the predicted AGN and galaxy sources with Pclass > 80%. A galaxy-AGN subset has also been included where the individual AG…
Figure 14
Figure 14. Figure 14: X-ray flux vs unWISE W1 (top) and SMASH g (bottom) bands for the Magellanic (left) and extragalactic and foreground sources (right). The high confidence (Pclass > 80%) Unknowns are plotted in the background of all plots. Contours and filled circles refer to sources wi…
Figure 15
Figure 15. Figure 15: The sky density distribution of the Hii/YSOs with Pclass > 80% in the LMC. by the classifier. This is not unexpected, as these are most likely the more obvious type I (broad-line) AGN, which make up the most of the AGN class in the training set. The type II (narrow-li…

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Reference graph

Works this paper leans on

162 extracted references · 33 canonical work pages · cited by 1 Pith paper

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    J., et al., 2013, @doi [ ] 10.1088/0004-637X/771/2/112 , https://ui.adsabs.harvard.edu/abs/2013ApJ...771..112A 771, 112

    Adams J. J., et al., 2013, @doi [ ] 10.1088/0004-637X/771/2/112 , https://ui.adsabs.harvard.edu/abs/2013ApJ...771..112A 771, 112

    work page doi:10.1088/0004-637x/771/2/112 2013

  3. [3]

    Ahumada R., et al., 2020, @doi [ ] 10.3847/1538-4365/ab929e , https://ui.adsabs.harvard.edu/abs/2020ApJS..249....3A 249, 3

    doi:10.3847/1538-4365/ab929e 2020

  4. [4]

    J., et al., 2013, @doi [ ] 10.1088/0004-637X/772/1/26 , https://ui.adsabs.harvard.edu/abs/2013ApJ...772...26A 772, 26

    Assef R. J., et al., 2013, @doi [ ] 10.1088/0004-637X/772/1/26 , https://ui.adsabs.harvard.edu/abs/2013ApJ...772...26A 772, 26

    doi:10.1088/0004-637x/772/1/26 2013

  5. [5]

    J., Stern D., Noirot G., Jun H

    Assef R. J., Stern D., Noirot G., Jun H. D., Cutri R. M., Eisenhardt P. R. M., 2018, @doi [ ] 10.3847/1538-4365/aaa00a , https://ui.adsabs.harvard.edu/abs/2018ApJS..234...23A 234, 23

    doi:10.3847/1538-4365/aaa00a 2018

  6. [6]

    Astropy Collaboration et al., 2013, @doi [ ] 10.1051/0004-6361/201322068 , https://ui.adsabs.harvard.edu/abs/2013A&A...558A..33A 558, A33

    doi:10.1051/0004-6361/201322068 2013

  7. [7]

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

    doi:10.3847/1538-3881/aabc4f 2018

  8. [8]

    Bell C. P. M., et al., 2019, @doi [ ] 10.1093/mnras/stz2325 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.3200B 489, 3200

    doi:10.1093/mnras/stz2325 2019

Show all 162 references
  1. [9]

    Bell C. P. M., et al., 2020, @doi [ ] 10.1093/mnras/staa2786 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.499..993B 499, 993

    2020 doi

  2. [10]

    Bell C. P. M., et al., 2022, @doi [ ] 10.1093/mnras/stac1545 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516..824B 516, 824

    2022 doi

  3. [11]

    R., et al., 2017, @doi [ ] 10.3847/1538-3881/aa7567 , https://ui.adsabs.harvard.edu/abs/2017AJ....154...28B 154, 28

    Blanton M. R., et al., 2017, @doi [ ] 10.3847/1538-3881/aa7567 , https://ui.adsabs.harvard.edu/abs/2017AJ....154...28B 154, 28

    2017 doi

  4. [12]

    Breiman L., 2001, @doi [Machine Learning] 10.1023/A:1010933404324 , 45

    2001 doi

  5. [13]

    Buckley D. A. H., Swart G. P., Meiring J. G., 2006, in Stepp L. M., ed., Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 6267, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. p. 62670Z, @doi 10.1117/12.673750

    2006 doi

  6. [14]

    B., Nordsieck K

    Burgh E. B., Nordsieck K. H., Kobulnicky H. A., Williams T. B., O'Donoghue D., Smith M. P., Percival J. W., 2003, in Iye M., Moorwood A. F. M., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 4841, Instrument Design and Performance for Opt...

    2003 doi

  7. [15]

    C., No \"e l N

    Carrera R., Conn B. C., No \"e l N. E. D., Read J. I., L \'o pez S \'a nchez \'A . R., 2017, @doi [ ] 10.1093/mnras/stx1932 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.4571C 471, 4571

    2017 doi

  8. [16]

    Choudhury S., et al., 2021, @doi [ ] 10.1093/mnras/stab2446 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.4752C 507, 4752

    2021 doi

  9. [18]

    Cioni M. R. L., et al., 2011b, @doi [ ] 10.1051/0004-6361/201016137 , https://ui.adsabs.harvard.edu/abs/2011A&A...527A.116C 527, A116

    doi

  10. [19]

    Cioni M. R. L., et al., 2013, @doi [ ] 10.1051/0004-6361/201219696 , https://ui.adsabs.harvard.edu/abs/2013A&A...549A..29C 549, A29

    2013 doi

  11. [21]

    Cioni M. R. L., et al., 2014b, @doi [ ] 10.1051/0004-6361/201322100 , https://ui.adsabs.harvard.edu/abs/2014A&A...562A..32C 562, A32

    doi

  12. [23]

    L., et al., 2016b, @doi [ ] 10.1051/0004-6361/201527004 , https://ui.adsabs.harvard.edu/abs/2016A&A...586A..77C 586, A77

    Cioni M.-R. L., et al., 2016b, @doi [ ] 10.1051/0004-6361/201527004 , https://ui.adsabs.harvard.edu/abs/2016A&A...586A..77C 586, A77

    doi

  13. [24]

    Civano F., et al., 2015, @doi [ ] 10.1088/0004-637X/808/2/185 , https://ui.adsabs.harvard.edu/abs/2015ApJ...808..185C 808, 185

    2015 doi

  14. [25]

    A., Tolstoy E., Gallagher John S

    Cole A. A., Tolstoy E., Gallagher John S. I., Smecker-Hane T. A., 2005, @doi [ ] 10.1086/428007 , https://ui.adsabs.harvard.edu/abs/2005AJ....129.1465C 129, 1465

    2005 doi

  15. [26]

    P., Crampton D., Hutchings J

    Cowley A. P., Crampton D., Hutchings J. B., Remillard R., Penfold J. E., 1983, @doi [ ] 10.1086/161267 , https://ui.adsabs.harvard.edu/abs/1983ApJ...272..118C 272, 118

    1983 doi

  16. [27]

    A., et al., 2019, @doi [Journal of Astronomical Telescopes, Instruments, and Systems] 10.1117/1.JATIS.5.2.024007 , https://ui.adsabs.harvard.edu/abs/2019JATIS...5b4007C 5, 024007

    Crause L. A., et al., 2019, @doi [Journal of Astronomical Telescopes, Instruments, and Systems] 10.1117/1.JATIS.5.2.024007 , https://ui.adsabs.harvard.edu/abs/2019JATIS...5b4007C 5, 024007

    2019 doi

  17. [28]

    M., et al., 2010, in Silva D

    Crawford S. M., et al., 2010, in Silva D. R., Peck A. B., Soifer B. T., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 7737, Observatory Operations: Strategies, Processes, and Systems III. p. 773725, @doi 10.1117/12.857000

    2010 doi

  18. [29]

    Cusano F., et al., 2021, @doi [ ] 10.1093/mnras/stab901 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504....1C 504, 1

    2021 doi

  19. [30]

    M., et al., 2013, Explanatory Supplement to the AllWISE Data Release Products , Explanatory Supplement to the AllWISE Data Release Products

    Cutri R. M., et al., 2013, Explanatory Supplement to the AllWISE Data Release Products , Explanatory Supplement to the AllWISE Data Release Products

    2013

  20. [31]

    B., et al., 2006, in McLean I

    Dalton G. B., et al., 2006, in McLean I. S., Iye M., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 6269, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. p. 62690X, @doi 10.1117/12.670018

    2006 doi

  21. [32]

    S., Ramsay S

    Dalton G., et al., 2012, in McLean I. S., Ramsay S. K., Takami H., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 8446, Ground-based and Airborne Instrumentation for Astronomy IV. p. 84460P, @doi 10.1117/12.925950

    2012 doi

  22. [33]

    J., Parisi M

    De Bortoli B. J., Parisi M. C., Bassino L. P., Geisler D., Dias B., Gimeno G., Angelo M. S., Mauro F., 2022, @doi [ ] 10.1051/0004-6361/202243762 , https://ui.adsabs.harvard.edu/abs/2022A&A...664A.168D 664, A168

    2022 doi

  23. [34]

    R., et al., 2009, @doi [IEEE Proceedings] 10.1109/JPROC.2009.2016516 , https://ui.adsabs.harvard.edu/abs/2009IEEEP..97.1507D 97, 1507

    DeBoer D. R., et al., 2009, @doi [IEEE Proceedings] 10.1109/JPROC.2009.2016516 , https://ui.adsabs.harvard.edu/abs/2009IEEEP..97.1507D 97, 1507

    2009

  24. [35]

    M., et al., 2013, @doi [ ] 10.1017/pasa.2012.003 , https://ui.adsabs.harvard.edu/abs/2013PASA...30....3D 30, e003

    Dickey J. M., et al., 2013, @doi [ ] 10.1017/pasa.2012.003 , https://ui.adsabs.harvard.edu/abs/2013PASA...30....3D 30, e003

    2013 doi

  25. [36]

    S., Paggeot K., Castro N., Moe M., 2020, @doi [ ] 10.3847/1538-4357/abbc6b , https://ui.adsabs.harvard.edu/abs/2020ApJ...903...43D 903, 43

    Dorigo Jones J., Oey M. S., Paggeot K., Castro N., Moe M., 2020, @doi [ ] 10.3847/1538-4357/abbc6b , https://ui.adsabs.harvard.edu/abs/2020ApJ...903...43D 903, 43

    2020 doi

  26. [37]

    P., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2010.18188.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.413..971D 413, 971

    Driver S. P., et al., 2011, @doi [ ] 10.1111/j.1365-2966.2010.18188.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.413..971D 413, 971

    2011

  27. [38]

    El Youssoufi D., et al., 2019, @doi [ ] 10.1093/mnras/stz2400 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1076E 490, 1076

    2019 doi

  28. [39]

    Emerson J., McPherson A., Sutherland W., 2006, The Messenger, https://ui.adsabs.harvard.edu/abs/2006Msngr.126...41E 126, 41

    2006

  29. [40]

    Esquej P., et al., 2013, @doi [ ] 10.1051/0004-6361/201218832 , https://ui.adsabs.harvard.edu/abs/2013A&A...557A.123E 557, A123

    2013 doi

  30. [41]

    J., et al., 2015a, @doi [ ] 10.1051/0004-6361/201424414 , https://ui.adsabs.harvard.edu/abs/2015A&A...574A..13E 574, A13

    Evans C. J., et al., 2015a, @doi [ ] 10.1051/0004-6361/201424414 , https://ui.adsabs.harvard.edu/abs/2015A&A...574A..13E 574, A13

    doi

  31. [42]

    J., van Loon J

    Evans C. J., van Loon J. T., Hainich R., Bailey M., 2015b, @doi [ ] 10.1051/0004-6361/201525882 , https://ui.adsabs.harvard.edu/abs/2015A&A...584A...5E 584, A5

    doi

  32. [43]

    P., et al., 2004, @doi [ ] 10.1086/425050 , https://ui.adsabs.harvard.edu/abs/2004AJ....128.2577F 128, 2577

    Finkbeiner D. P., et al., 2004, @doi [ ] 10.1086/425050 , https://ui.adsabs.harvard.edu/abs/2004AJ....128.2577F 128, 2577

    2004 doi

  33. [44]

    W., 2019a, VizieR Online Data Catalog, https://ui.adsabs.harvard.edu/abs/2019yCat.7283....0F p

    Flesch E. W., 2019a, VizieR Online Data Catalog, https://ui.adsabs.harvard.edu/abs/2019yCat.7283....0F p. VII/283

  34. [45]

    W., 2019b, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2019arXiv191205614F p

    Flesch E. W., 2019b, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2019arXiv191205614F p. arXiv:1912.05614

    1912 arXiv

  35. [46]

    Gaia Collaboration et al., 2016, @doi [ ] 10.1051/0004-6361/201629272 , https://ui.adsabs.harvard.edu/abs/2016A&A...595A...1G 595, A1

    2016 doi

  36. [47]

    Gaia Collaboration et al., 2021a, @doi [ ] 10.1051/0004-6361/202039657 , https://ui.adsabs.harvard.edu/abs/2021A&A...649A...1G 649, A1

    doi

  37. [48]

    Gaia Collaboration et al., 2021b, @doi [ ] 10.1051/0004-6361/202039588 , https://ui.adsabs.harvard.edu/abs/2021A&A...649A...7G 649, A7

    doi

  38. [49]

    arXiv:2208.00211

    Gaia Collaboration et al., 2022, arXiv e-prints, https://ui.adsabs.harvard.edu/abs/2022arXiv220800211G p. arXiv:2208.00211

    2022 arXiv

  39. [50]

    Gaia Collaboration et al., 2023, @doi [ ] 10.1051/0004-6361/202243232 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A..41G 674, A41

    2023 doi

  40. [51]

    P., et al., 2006, @doi [ ] 10.1007/s11214-006-8315-7 , https://ui.adsabs.harvard.edu/abs/2006SSRv..123..485G 123, 485

    Gardner J. P., et al., 2006, @doi [ ] 10.1007/s11214-006-8315-7 , https://ui.adsabs.harvard.edu/abs/2006SSRv..123..485G 123, 485

    2006 doi

  41. [52]

    Geha M., et al., 2003, @doi [ ] 10.1086/344947 , https://ui.adsabs.harvard.edu/abs/2003AJ....125....1G 125, 1

    2003 doi

  42. [53]

    D., Meixner M., Meade M., Whitney B

    Gordon K. D., Meixner M., Meade M., Whitney B. A., Engelbracht C. W., Bot C., 2011, @doi [ ] 10.1088/0004-6256/142/4/102 , 142

    2011 doi

  43. [54]

    J., et al., 2010, @doi [ ] 10.1051/0004-6361/201014519 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L...3G 518, L3

    Griffin M. J., et al., 2010, @doi [ ] 10.1051/0004-6361/201014519 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L...3G 518, L3

    2010 doi

  44. [55]

    J., et al., 2017, @doi [ ] 10.1051/0004-6361/201629225 , https://ui.adsabs.harvard.edu/abs/2017A&A...600A..82G 600, A82

    Grin N. J., et al., 2017, @doi [ ] 10.1051/0004-6361/201629225 , https://ui.adsabs.harvard.edu/abs/2017A&A...600A..82G 600, A82

    2017 doi

  45. [56]

    Groenewegen M. A. T., Blommaert J. A. D. L., 1998, , https://ui.adsabs.harvard.edu/abs/1998A&A...332...25G 332, 25

    1998

  46. [57]

    Groenewegen M. A. T., et al., 2019, @doi [ ] 10.1051/0004-6361/201833904 , https://ui.adsabs.harvard.edu/abs/2019A&A...622A..63G 622, A63

    2019 doi

  47. [58]

    Groenewegen M. A. T., et al., 2020, @doi [ ] 10.1051/0004-6361/201937271 , https://ui.adsabs.harvard.edu/abs/2020A&A...636A..48G 636, A48

    2020 doi

  48. [59]

    Gullieuszik M., et al., 2012, @doi [ ] 10.1051/0004-6361/201117493 , https://ui.adsabs.harvard.edu/abs/2012A&A...537A.105G 537, A105

    2012 doi

  49. [60]

    M., Buckley D

    Haberl F., Maitra C., Vasilopoulos G., Maggi P., Udalski A., Monageng I. M., Buckley D. A. H., 2022, @doi [ ] 10.1051/0004-6361/202243301 , https://ui.adsabs.harvard.edu/abs/2022A&A...662A..22H 662, A22

    2022 doi

  50. [61]

    Haberl F., et al., 2023, @doi [ ] 10.1051/0004-6361/202245807 , https://ui.adsabs.harvard.edu/abs/2023A&A...671A..90H 671, A90

    2023 doi

  51. [62]

    B., Heathcote S

    Hamuy M., Suntzeff N. B., Heathcote S. R., Walker A. R., Gigoux P., Phillips M. M., 1994, @doi [ ] 10.1086/133417 , https://ui.adsabs.harvard.edu/abs/1994PASP..106..566H 106, 566

    1994 doi

  52. [63]

    C., Alexander D

    Hickox R. C., Alexander D. M., 2018, @doi [ ] 10.1146/annurev-astro-081817-051803 , https://ui.adsabs.harvard.edu/abs/2018ARA&A..56..625H 56, 625

    2018 doi

  53. [64]

    Hony S., et al., 2011, @doi [ ] 10.1051/0004-6361/201116845 , https://ui.adsabs.harvard.edu/abs/2011A&A...531A.137H 531, A137

    2011 doi

  54. [65]

    M., et al., 2013, @doi [ ] 10.1093/mnras/stt030 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.430.2047H 430, 2047

    Hopkins A. M., et al., 2013, @doi [ ] 10.1093/mnras/stt030 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.430.2047H 430, 2047

    2013 doi

  55. [66]

    E., et al., 2001, @doi [ ] 10.1086/321420 , https://ui.adsabs.harvard.edu/abs/2001ApJ...554..742H 554, 742

    Hornschemeier A. E., et al., 2001, @doi [ ] 10.1086/321420 , https://ui.adsabs.harvard.edu/abs/2001ApJ...554..742H 554, 742

    2001 doi

  56. [67]

    W., et al., 2014, @doi [ ] 10.1017/pasa.2014.36 , https://ui.adsabs.harvard.edu/abs/2014PASA...31...41H 31, e041

    Hotan A. W., et al., 2014, @doi [ ] 10.1017/pasa.2014.36 , https://ui.adsabs.harvard.edu/abs/2014PASA...31...41H 31, e041

    2014 doi

  57. [68]

    W., et al., 2021, @doi [ ] 10.1017/pasa.2021.1 , https://ui.adsabs.harvard.edu/abs/2021PASA...38....9H 38, e009

    Hotan A. W., et al., 2021, @doi [ ] 10.1017/pasa.2021.1 , https://ui.adsabs.harvard.edu/abs/2021PASA...38....9H 38, e009

    2021 doi

  58. [69]

    D., et al., 2016, @doi [ ] 10.1051/0004-6361/201527398 , https://ui.adsabs.harvard.edu/abs/2016A&A...588A..93I 588, A93

    Ivanov V. D., et al., 2016, @doi [ ] 10.1051/0004-6361/201527398 , https://ui.adsabs.harvard.edu/abs/2016A&A...588A..93I 588, A93

    2016 doi

  59. [70]

    Ivezi \'c Z ., et al., 2019, @doi [ ] 10.3847/1538-4357/ab042c , https://ui.adsabs.harvard.edu/abs/2019ApJ...873..111I 873, 111

    2019 doi

  60. [71]

    J., et al., 2013, @doi [ ] 10.1093/mnras/sts118 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.1281J 428, 1281

    Jarvis M. J., et al., 2013, @doi [ ] 10.1093/mnras/sts118 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.1281J 428, 1281

    2013 doi

  61. [72]

    Johnston S., et al., 2008, @doi [Experimental Astronomy] 10.1007/s10686-008-9124-7 , https://ui.adsabs.harvard.edu/abs/2008ExA....22..151J 22, 151

    2008 doi

  62. [73]

    P., Woudt P

    Jones H., Saunders W., Colless M., Read M., Parker Q., Watson F., Campbell L., 2005, in Fairall A. P., Woudt P. A., eds, Astronomical Society of the Pacific Conference Series Vol. 329, Nearby Large-Scale Structures and the Zone of Avoidance. p. 11

    2005

  63. [75]

    C., et al., 2017, @doi [ ] 10.1093/mnras/stx1101 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470.3250J 470, 3250

    Jones O. C., et al., 2017, @doi [ ] 10.1093/mnras/stx1101 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.470.3250J 470, 3250

    2017 doi

  64. [76]

    D., et al., 2019, @doi [ ] 10.1093/mnras/stz2650 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1202J 490, 1202

    Joseph T. D., et al., 2019, @doi [ ] 10.1093/mnras/stz2650 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.1202J 490, 1202

    2019 doi

  65. [77]

    R., Van Winckel H., 2014, @doi [ ] 10.1093/mnras/stt2033 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.439.2211K 439, 2211

    Kamath D., Wood P. R., Van Winckel H., 2014, @doi [ ] 10.1093/mnras/stt2033 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.439.2211K 439, 2211

    2014 doi

  66. [78]

    M., Golawala M., Hulse J

    Khoshgoftaar T. M., Golawala M., Hulse J. V., 2007, in 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007). pp 310--317, @doi 10.1109/ICTAI.2007.46

    2007 doi

  67. [79]

    A., Oliveira J

    Kinson D. A., Oliveira J. M., van Loon J. T., 2021, @doi [ ] 10.1093/mnras/stab2386 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.5106K 507, 5106

    2021 doi

  68. [80]

    A., Oliveira J

    Kinson D. A., Oliveira J. M., van Loon J. T., 2022, @doi [ ] 10.1093/mnras/stac2692 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517..140K 517, 140

    2022 doi

  69. [81]

    A., Nordsieck K

    Kobulnicky H. A., Nordsieck K. H., Burgh E. B., Smith M. P., Percival J. W., Williams T. B., O'Donoghue D., 2003, in Iye M., Moorwood A. F. M., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 4841, Instrument Design and Performance for Opt...

    2003 doi

  70. [82]

    Kokusho T., Torii H., Kaneda H., Fukui Y., Tachihara K., 2023, @doi [ ] 10.3847/1538-4357/ace10e , https://ui.adsabs.harvard.edu/abs/2023ApJ...953..104K 953, 104

    2023 doi

  71. [83]

    S., Udalski A., 2011, @doi [ ] 10.1088/0067-0049/194/2/22 , 194

    Koz owski S., Kochanek C. S., Udalski A., 2011, @doi [ ] 10.1088/0067-0049/194/2/22 , 194

    2011 doi

  72. [84]

    Koz owski S., et al., 2012, @doi [ ] 10.1088/0004-637X/746/1/27 , https://ui.adsabs.harvard.edu/abs/2012ApJ...746...27K 746, 27

    2012 doi

  73. [85]

    Koz owski S., et al., 2013, @doi [ ] 10.1088/0004-637X/775/2/92 , https://ui.adsabs.harvard.edu/abs/2013ApJ...775...92K 775, 92

    2013 doi

  74. [86]

    Kumar A., et al., 2012, @doi [Journal of Astronomical Telescopes, Instruments, and Systems] 10.1117/12.924507 , 8443

    2012 doi

  75. [87]

    Lacy M., et al., 2004, @doi [ ] 10.1086/422816 , https://ui.adsabs.harvard.edu/abs/2004ApJS..154..166L 154, 166

    2004 doi

  76. [88]

    B., Oey M

    Lamb J. B., Oey M. S., Segura-Cox D. M., Graus A. S., Kiminki D. C., Golden-Marx J. B., Parker J. W., 2016, @doi [ ] 10.3847/0004-637X/817/2/113 , https://ui.adsabs.harvard.edu/abs/2016ApJ...817..113L 817, 113

    2016 doi

  77. [89]

    Maitra C., et al., 2019, @doi [ ] 10.1093/mnras/stz2831 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.490.5494M 490, 5494

    2019 doi

  78. [90]

    J., Vasilopoulos G., Sasaki M., Filipovi \'c M

    Maitra C., Haberl F., Maggi P., Kavanagh P. J., Vasilopoulos G., Sasaki M., Filipovi \'c M. D., Udalski A., 2021a, @doi [ ] 10.1093/mnras/stab716 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.504..326M 504, 326

    doi

  79. [91]

    Maitra C., Haberl F., Vasilopoulos G., Ducci L., Dennerl K., Carpano S., 2021b, @doi [ ] 10.1051/0004-6361/202039468 , https://ui.adsabs.harvard.edu/abs/2021A&A...647A...8M 647, A8

    doi

  80. [92]

    J., Curran J., Hunstead R

    Mauch T., Murphy T., Buttery H. J., Curran J., Hunstead R. W., Piestrzynski B., Robertson J. G., Sadler E. M., 2003, @doi [ ] 10.1046/j.1365-8711.2003.06605.x , https://ui.adsabs.harvard.edu/abs/2003MNRAS.342.1117M 342, 1117

    2003

  81. [93]

    McConnell D., et al., 2016, @doi [ ] 10.1017/pasa.2016.37 , https://ui.adsabs.harvard.edu/abs/2016PASA...33...42M 33, e042

    2016 doi

  82. [94]

    McConnell D., et al., 2020, @doi [ ] 10.1017/pasa.2020.41 , https://ui.adsabs.harvard.edu/abs/2020PASA...37...48M 37, e048

    2020 doi

  83. [95]

    Meixner M., et al., 2006, @doi [ ] 10.1086/508185 , https://ui.adsabs.harvard.edu/abs/2006AJ....132.2268M 132, 2268

    2006 doi

  84. [96]

    Meixner M., et al., 2010, @doi [ ] 10.1051/0004-6361/201014662 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L..71M 518, L71

    2010 doi

  85. [97]

    S., Rana D

    More A. S., Rana D. P., 2017, in 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM). pp 72--78, @doi 10.1109/ICISIM.2017.8122151

    2017

  86. [98]

    Murphy T., et al., 2010, @doi [ ] 10.1111/j.1365-2966.2009.15961.x , 402, 2403

    2010

  87. [99]

    Nandra K., et al., 2015, @doi [ ] 10.1088/0067-0049/220/1/10 , https://ui.adsabs.harvard.edu/abs/2015ApJS..220...10N 220, 10

    2015 doi

  88. [100]

    Netzer H., 2015, @doi [ ] 10.1146/annurev-astro-082214-122302 , https://ui.adsabs.harvard.edu/abs/2015ARA&A..53..365N 53, 365

    2015 doi

  89. [101]

    F., Levesque E

    Neugent K. F., Levesque E. M., Massey P., Morrell N. I., Drout M. R., 2020, @doi [ ] 10.3847/1538-4357/ababaa , https://ui.adsabs.harvard.edu/abs/2020ApJ...900..118N 900, 118

    2020 doi

  90. [102]

    L., et al., 2017, @doi [ ] 10.3847/1538-3881/aa8d1c , https://ui.adsabs.harvard.edu/abs/2017AJ....154..199N 154, 199

    Nidever D. L., et al., 2017, @doi [ ] 10.3847/1538-3881/aa8d1c , https://ui.adsabs.harvard.edu/abs/2017AJ....154..199N 154, 199

    2017 doi

  91. [103]

    Nikutta R., Hunt-Walker N., Nenkova M., Ivezi\' c v., Elitzur M., 2014, @doi [ ] 10.1093/mnras/stu1087 , 442, 3361

    2014 doi

  92. [104]

    No \"e l N. E. D., Conn B. C., Carrera R., Read J. I., Rix H. W., Dolphin A., 2013, @doi [ ] 10.1088/0004-637X/768/2/109 , https://ui.adsabs.harvard.edu/abs/2013ApJ...768..109N 768, 109

    2013 doi

  93. [105]

    No \"e l N. E. D., Conn B. C., Read J. I., Carrera R., Dolphin A., Rix H. W., 2015, @doi [ ] 10.1093/mnras/stv1614 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.452.4222N 452, 4222

    2015 doi

  94. [106]

    P., Hopkins A

    Norris R. P., Hopkins A. M., Afonso J., Brown S., Condon J. J., 2011, @doi [ ] 10.1071/AS11021 , 28

    2011 doi

  95. [107]

    M., et al., 2011, @doi [ ] 10.1111/j.1745-3933.2010.00990.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.411L..36O 411, L36

    Oliveira J. M., et al., 2011, @doi [ ] 10.1111/j.1745-3933.2010.00990.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.411L..36O 411, L36

    2011

  96. [108]

    M., et al., 2013, @doi [ ] 10.1093/mnras/sts250 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.3001O 428, 3001

    Oliveira J. M., et al., 2013, @doi [ ] 10.1093/mnras/sts250 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.3001O 428, 3001

    2013 doi

  97. [109]

    M., et al., 2019, @doi [ ] 10.1093/mnras/stz2810 , 490, 3909

    Oliveira J. M., et al., 2019, @doi [ ] 10.1093/mnras/stz2810 , 490, 3909

    2019 doi

  98. [110]

    Padovani P., et al., 2017, @doi [ ] 10.1007/s00159-017-0102-9 , https://ui.adsabs.harvard.edu/abs/2017A&ARv..25....2P 25, 2

    2017 doi

  99. [111]

    C., Grocholski A

    Parisi M. C., Grocholski A. J., Geisler D., Sarajedini A., Clari \'a J. J., 2009, @doi [ ] 10.1088/0004-6256/138/2/517 , https://ui.adsabs.harvard.edu/abs/2009AJ....138..517P 138, 517

    2009 doi

  100. [112]

    C., Geisler D., Grocholski A

    Parisi M. C., Geisler D., Grocholski A. J., Clari \'a J. J., Sarajedini A., 2010, @doi [ ] 10.1088/0004-6256/139/3/1168 , https://ui.adsabs.harvard.edu/abs/2010AJ....139.1168P 139, 1168

    2010 doi

  101. [113]

    C., Gramajo L

    Parisi M. C., Gramajo L. V., Geisler D., Dias B., Clari \'a J. J., Da Costa G., Grebel E. K., 2022, @doi [ ] 10.1051/0004-6361/202142597 , https://ui.adsabs.harvard.edu/abs/2022A&A...662A..75P 662, A75

    2022 doi

  102. [114]

    Pedregosa F., et al., 2011, Journal of Machine Learning Research, 12, 2825

    2011

  103. [115]

    M., et al., 2021, @doi [ ] 10.1093/mnras/stab1858 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.506.3540P 506, 3540

    Pennock C. M., et al., 2021, @doi [ ] 10.1093/mnras/stab1858 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.506.3540P 506, 3540

    2021 doi

  104. [116]

    M., et al., 2022, @doi [ ] 10.1093/mnras/stac2096 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515.6046P 515, 6046

    Pennock C. M., et al., 2022, @doi [ ] 10.1093/mnras/stac2096 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.515.6046P 515, 6046

    2022 doi

  105. [117]

    L., et al., 2010, @doi [ ] 10.1051/0004-6361/201014759 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L...1P 518, L1

    Pilbratt G. L., et al., 2010, @doi [ ] 10.1051/0004-6361/201014759 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L...1P 518, L1

    2010 doi

  106. [118]

    Poglitsch A., et al., 2010, @doi [ ] 10.1051/0004-6361/201014535 , https://ui.adsabs.harvard.edu/abs/2010A&A...518L...2P 518, L2

    2010 doi

  107. [119]

    A., Parker Q

    Reid W. A., Parker Q. A., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21471.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.425..355R 425, 355

    2012

  108. [120]

    Reis I., Baron D., Shahaf S., 2018, @doi [ ] 10.3847/1538-3881/aaf101 , 157

    2018 doi

  109. [121]

    T., 2014, @doi [ ] 10.1093/mnras/stu1807 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445.2214R 445, 2214

    Rezaeikh S., Javadi A., Khosroshahi H., van Loon J. T., 2014, @doi [ ] 10.1093/mnras/stu1807 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445.2214R 445, 2214

    2014 doi

  110. [122]

    Ripepi V., et al., 2015, @doi [ ] 10.1093/mnras/stu2260 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.446.3034R 446, 3034

    2015 doi

  111. [123]

    Roman-Duval J., et al., 2019, @doi [ ] 10.3847/1538-4357/aaf8bb , https://ui.adsabs.harvard.edu/abs/2019ApJ...871..151R 871, 151

    2019 doi

  112. [124]

    Rubele S., et al., 2012, @doi [ ] 10.1051/0004-6361/201117863 , https://ui.adsabs.harvard.edu/abs/2012A&A...537A.106R 537, A106

    2012 doi

  113. [125]

    Rubele S., et al., 2015, @doi [ ] 10.1093/mnras/stv141 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.449..639R 449, 639

    2015 doi

  114. [126]

    Rubele S., et al., 2018, @doi [ ] 10.1093/mnras/sty1279 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.478.5017R 478, 5017

    2018 doi

  115. [127]

    Ruffle P. M. E., et al., 2015, @doi [ ] 10.1093/mnras/stv1106 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.451.3504R 451, 3504

    2015 doi

  116. [128]

    Salvato M., et al., 2018, @doi [ ] 10.1093/mnras/stx2651 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.4937S 473, 4937

    2018 doi

  117. [129]

    F., Finkbeiner D

    Schlafly E. F., Finkbeiner D. P., 2011, @doi [ ] 10.1088/0004-637X/737/2/103 , https://ui.adsabs.harvard.edu/abs/2011ApJ...737..103S 737, 103

    2011 doi

  118. [130]

    F., Meisner A

    Schlafly E. F., Meisner A. M., Green G. M., 2019, @doi [ ] 10.3847/1538-4365/aafbea , https://ui.adsabs.harvard.edu/abs/2019ApJS..240...30S 240, 30

    2019 doi

  119. [131]

    J., Finkbeiner D

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

    1998 doi

  120. [132]

    Schumacher G., Mondaca E., Warner M., Martinez M., Estay O., Abbott T. M. C., 2010, in Radziwill N. M., Bridger A., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 7740, Software and Cyberinfrastructure for Astronomy. p. 77402H, @doi 10.11...

    2010 doi

  121. [133]

    P., Looney L

    Seale J. P., Looney L. W., Chu Y.-H., Gruendl R. A., Brandl B., Chen C. H. R., Brandner W., Blake G. A., 2009, @doi [ ] 10.1088/0004-637X/699/1/150 , https://ui.adsabs.harvard.edu/abs/2009ApJ...699..150S 699, 150

    2009 doi

  122. [134]

    J., Dudik R

    Secrest N. J., Dudik R. P., Dorland B. N., Zacharias N., Makarov V., Fey A., Frouard J., Finch C., 2015, @doi [ ] 10.1088/0067-0049/221/1/12 , https://ui.adsabs.harvard.edu/abs/2015ApJS..221...12S 221, 12

    2015 doi

  123. [135]

    A., Stanghellini L., Mutchler M., Balick B., Blades J

    Shaw R. A., Stanghellini L., Mutchler M., Balick B., Blades J. C., 2001, @doi [ ] 10.1086/319013 , https://ui.adsabs.harvard.edu/abs/2001ApJ...548..727S 548, 727

    2001 doi

  124. [136]

    A., Bolatto A

    Sheets H. A., Bolatto A. D., van Loon J. T., Sandstrom K., Simon J. D., Oliveira J. M., Barb \'a R. H., 2013, @doi [ ] 10.1088/0004-637X/771/2/111 , https://ui.adsabs.harvard.edu/abs/2013ApJ...771..111S 771, 111

    2013 doi

  125. [137]

    F., et al., 2006, @doi [ ] 10.1086/498708 , https://ui.adsabs.harvard.edu/abs/2006AJ....131.1163S 131, 1163

    Skrutskie M. F., et al., 2006, @doi [ ] 10.1086/498708 , https://ui.adsabs.harvard.edu/abs/2006AJ....131.1163S 131, 1163

    2006 doi

  126. [138]

    J., Sakelliou I., 2001, @doi [ ] 10.1051/0004-6361:20000065 , https://ui.adsabs.harvard.edu/abs/2001A&A...365L.273S 365, L273

    Soria R., Wu K., Page M. J., Sakelliou I., 2001, @doi [ ] 10.1051/0004-6361:20000065 , https://ui.adsabs.harvard.edu/abs/2001A&A...365L.273S 365, L273

    2001 doi

  127. [139]

    Stern D., Eisenhardt P., Gorjian V., Kochanek C., 2005, @doi [ ] 10.1086/432523 , 631

    2005 doi

  128. [140]

    Stern D., et al., 2012, @doi [ ] 10.1088/0004-637X/753/1/30 , https://ui.adsabs.harvard.edu/abs/2012ApJ...753...30S 753, 30

    2012 doi

  129. [141]

    W., Rix H.-W., Eilers A.-C., Fabbian G., Blanton M., Alonso D., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2306.17749 , https://ui.adsabs.harvard.edu/abs/2023arXiv230617749S p

    Storey-Fisher K., Hogg D. W., Rix H.-W., Eilers A.-C., Fabbian G., Blanton M., Alonso D., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2306.17749 , https://ui.adsabs.harvard.edu/abs/2023arXiv230617749S p. arXiv:2306.17749

    2023 doi arXiv

  130. [142]

    Sturm R., Haberl F., Pietsch W., Ballet J., Hatzidimitriou D., Buckley D. A. H., 2013a, @doi [ ] 10.3847/1538-4365/aa7053 , 558

    doi

  131. [143]

    Sturm R., et al., 2013b, @doi [ ] 10.1051/0004-6361/201219935 , https://ui.adsabs.harvard.edu/abs/2013A&A...558A...3S 558, A3

    doi

  132. [144]

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

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

    2005

  133. [145]

    A., Bianchi L., Simons R., 2014, in American Astronomical Society Meeting Abstracts \#223

    Thilker D. A., Bianchi L., Simons R., 2014, in American Astronomical Society Meeting Abstracts \#223. p. 355.11

    2014

  134. [146]

    L., ed., Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol

    Tody D., 1986, in Crawford D. L., ed., Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 627, Instrumentation in astronomy VI. p. 733, @doi 10.1117/12.968154

    1986 doi

  135. [147]

    J., Brissenden R

    Tody D., 1993, in Hanisch R. J., Brissenden R. J. V., Barnes J., eds, Astronomical Society of the Pacific Conference Series Vol. 52, Astronomical Data Analysis Software and Systems II. p. 173

    1993

  136. [148]

    Vasiliev E., 2018, @doi [ ] 10.1093/mnrasl/sly168 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.481L.100V 481, L100

    2018 doi

  137. [149]

    R., et al., 2014, @doi [ ] 10.1051/0004-6361/201323082 , https://ui.adsabs.harvard.edu/abs/2014A&A...564A..40W 564, A40

    Walborn N. R., et al., 2014, @doi [ ] 10.1051/0004-6361/201323082 , https://ui.adsabs.harvard.edu/abs/2014A&A...564A..40W 564, A40

    2014 doi

  138. [151]

    A., et al., 2020b, @doi [ ] 10.1051/0004-6361/201937353 , https://ui.adsabs.harvard.edu/abs/2020A&A...641A.136W 641, A136

    Webb N. A., et al., 2020b, @doi [ ] 10.1051/0004-6361/201937353 , https://ui.adsabs.harvard.edu/abs/2020A&A...641A.136W 641, A136

    doi

  139. [152]

    Wenger M., et al., 2000, @doi [ ] 10.1051/aas:2000332 , https://ui.adsabs.harvard.edu/abs/2000A&AS..143....9W 143, 9

    2000 doi

  140. [153]

    T., 2020, in Ballester P., Ibsen J., Solar M., Shortridge K., eds, Astronomical Society of the Pacific Conference Series Vol

    Whiting M. T., 2020, in Ballester P., Ibsen J., Solar M., Shortridge K., eds, Astronomical Society of the Pacific Conference Series Vol. 522, Astronomical Data Analysis Software and Systems XXVII. p. 469

    2020

  141. [154]

    L., et al., 2010, @doi [ ] 10.1088/0004-6256/140/6/1868 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1868W 140, 1868

    Wright E. L., et al., 2010, @doi [ ] 10.1088/0004-6256/140/6/1868 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1868W 140, 1868

    2010 doi

  142. [155]

    Zivkov V., et al., 2018, @doi [ ] 10.1051/0004-6361/201833951 , https://ui.adsabs.harvard.edu/abs/2018A&A...620A.143Z 620, A143

    2018 doi

  143. [156]

    Zivkov V., et al., 2020, @doi [ ] 10.1093/mnras/staa626 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494..458Z 494, 458

    2020 doi

  144. [157]

    S., et al., 2012, in McLean I

    de Jong R. S., et al., 2012, in McLean I. S., Ramsay S. K., Takami H., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 8446, Ground-based and Airborne Instrumentation for Astronomy IV. p. 84460T ( @eprint arXiv 1206.6885 ), @doi 10.1117/12.926239

    2012 arXiv

  145. [158]

    de Jong J. T. A., et al., 2017, @doi [ ] 10.1051/0004-6361/201730747 , https://ui.adsabs.harvard.edu/abs/2017A&A...604A.134D 604, A134

    2017 doi

  146. [159]

    L., et al., 2020, @doi [ ] 10.1051/0004-6361/201936361 , https://ui.adsabs.harvard.edu/abs/2020A&A...636A..54V 636, A54

    van Gelder M. L., et al., 2020, @doi [ ] 10.1051/0004-6361/201936361 , https://ui.adsabs.harvard.edu/abs/2020A&A...636A..54V 636, A54

    2020 doi

  147. [160]

    van Jaarsveld N., Buckley D. A. H., McBride V. A., Haberl F., Vasilopoulos G., Maitra C., Udalski A., Miszalski B., 2018, @doi [ ] 10.1093/mnras/stx3270 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.3253V 475, 3253

    2018 doi

  148. [161]

    T., Sansom A., 2015, @doi [ ] 10.1093/mnras/stv1787 , 453

    van Loon J. T., Sansom A., 2015, @doi [ ] 10.1093/mnras/stv1787 , 453

    2015 doi

  149. [162]

    T., et al., 1998, , https://ui.adsabs.harvard.edu/abs/1998A&A...329..169V 329, 169

    van Loon J. T., et al., 1998, , https://ui.adsabs.harvard.edu/abs/1998A&A...329..169V 329, 169

    1998

  150. [163]

    T., Zijlstra A

    van Loon J. T., Zijlstra A. A., Groenewegen M. A. T., 1999a, , https://ui.adsabs.harvard.edu/abs/1999A&A...346..805V 346, 805

  151. [164]

    T., Groenewegen M

    van Loon J. T., Groenewegen M. A. T., de Koter A., Trams N. R., Waters L. B. F. M., Zijlstra A. A., Whitelock P. A., Loup C., 1999b, , https://ui.adsabs.harvard.edu/abs/1999A&A...351..559V 351, 559

  152. [165]

    T., Cioni M

    van Loon J. T., Cioni M. R. L., Zijlstra A. A., Loup C., 2005, @doi [ ] 10.1051/0004-6361:20042555 , https://ui.adsabs.harvard.edu/abs/2005A&A...438..273V 438, 273

    2005 doi

  153. [166]

    T., Marshall J

    van Loon J. T., Marshall J. R., Cohen M., Matsuura M., Wood P. R., Yamamura I., Zijlstra A. A., 2006, @doi [ ] 10.1051/0004-6361:20054222 , https://ui.adsabs.harvard.edu/abs/2006A&A...447..971V 447, 971

    2006 doi

  154. [167]

    T., Cohen M., Oliveira J

    van Loon J. T., Cohen M., Oliveira J. M., Matsuura M., McDonald I., Sloan G. C., Wood P. R., Zijlstra A. A., 2008, @doi [ ] 10.1051/0004-6361:200810036 , https://ui.adsabs.harvard.edu/abs/2008A&A...487.1055V 487, 1055

    2008 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.