Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

CU-JADE: A Method for Traversing Extinction Jumps along the Line of Sight

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read By treating a star's line-of-sight extinction as a staircase of discrete jumps, CU-JADE locates molecular cloud layers—even weak ones—and maps dust in three dimensions out to 4 kiloparsecs.

desk verdict A genuinely new CUSUM-based tool for extinction-jump distances with a useful public catalog, but the paper overstates weak-jump sensitivity and needs stronger validation before the central claims hold. read the letter →

arxiv 2507.18002 v2 pith:5O76SNUM submitted 2025-07-24 astro-ph.GA

Shiyu Zhang , Yang Su , Xuepeng Chen , Min Fang , Fujun Du , Shaobo Zhang , Qing-Zeng Yan , Xin Liu
show 3 more authors
Miaomiao Zhang Yan Sun Ji Yang
This is my paper · ORCID
classification astro-ph.GA
keywords CUSUMchange-pointdetectioninterstellarextinctionmolecularclouddistances3Ddustmapline-of-sightCepheusCygnusRift
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

CU-JADE is a new statistical tool for turning stellar distance–extinction diagrams into a list of discrete dust layers along a given line of sight. The paper argues that a cumulative-sum (CUSUM) change-point detector can pick out the abrupt rises in extinction produced by molecular clouds, even when the jump is only about 0.15 mag in visual extinction and when several clouds overlap along the same sightline. If that claim holds, astronomers can measure distances to Milky Way molecular clouds in crowded Galactic-plane regions where single-jump models fail, and can build full-sky three-dimensional dust maps out to 4 kpc from existing stellar catalogs alone. The paper applies CU-JADE to the Cepheus and Cygnus regions and reports first distances for cometary clouds associated with the Cygnus OB2/OB1 associations at about 1.7 kpc.

What carries the argument

The central object is the CUSUM statistic $S_i = S_{i-1} + (\bar{A} - A_i)$, computed over stars sorted by distance. Its maximum-to-minimum excursion $S_{\mathrm{diff}}$ is compared to a shuffled null distribution, scaled by a factor $C$, to decide whether a jump is real; after each accepted jump the dataset is split and the process repeats. This converts the problem of counting clouds along a sightline into a recursive one-dimensional change-point search, with the scaling factor $C$ as the single tunable knob controlling the trade-off between detection completeness and false positives.

What would settle it

Generate a synthetic D–A dataset containing only a smooth extinction gradient with slope k ≈ 0.003 mag pc⁻¹ and no discrete clouds, run CU-JADE with C=1, and count the detected change points; the paper's Appendix A.3 predicts six false jumps near the midpoints of the iteration intervals, so a materially different count (or no false jumps) would contradict the claimed behavior.

Watch

Extended reading notes

Core claim

The central discovery claimed is that the line-of-sight extinction profile toward any star can be treated as a sequence of abrupt jumps, and that these jumps can be located recursively with a CUSUM statistic. Starting from a binned distance–extinction sequence, the method computes the cumulative sum of deviations from the mean, identifies the maximum excursion as a candidate jump, validates it by comparing its excursion to a null distribution built from shuffled sequences, splits the data at the accepted jump, and repeats until no further jumps pass the threshold. The paper asserts this procedure is sensitive to weak jumps (ΔA_V ≈ 0.15 mag) and shows no systematic offset when matched against 75 maser parallaxes; it also provides distance uncertainties via bootstrap resampling. The method is then used to build an all-sky, distance-resolved dust map out to 4 kpc, and to assign distances to multilayer molecular gas structures in Cepheus and Cygnus.

Load-bearing premise

The method assumes that a real cloud appears as a sharp upward step in extinction over a short distance range, and that any smooth, gradual rise in extinction along the line of sight will not masquerade as a jump once the confidence scaling factor is tuned; the paper's own appendix shows that a pure gradient produces a parabolic CUSUM curve that is mistaken for six false jumps unless that factor is chosen carefully.

Editorial extensions

If this is right

  • Distances to molecular clouds in crowded Galactic-plane sightlines become measurable even when only weak extinction jumps of ~0.15 mag are present.
  • Full-sky 3D extinction maps out to 4 kpc can be constructed from existing stellar catalogs, revealing kpc-scale cavities and coherent spiral-arm structure.
  • The method can resolve multiple gas layers along a single line of sight, as demonstrated for the Cepheus Flare and the Cygnus Rift region.
  • First distances to cometary clouds near Cyg OB2/OB1 at about 1.7 kpc support their physical association with the massive star-forming clusters.
  • Combining CU-JADE distance jumps with CO surveys provides a way to identify CO-dark molecular gas that has no bright CO emission.

Reading between the lines

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

  • Because CU-JADE only needs distance and extinction estimates per star, the same algorithm could be applied to near-infrared or mid-infrared extinction tracers, which would extend distance measurements to clouds currently hidden behind the 'extinction wall' at large distances.
  • The C factor's role in balancing precision and recall suggests the method's false-positive rate is not an intrinsic property; calibrating C per sightline or per stellar-density regime could make the jump catalog more reliable in complex regions.
  • The 3D jump catalog could be cross-correlated with HI and CO velocity data to separate dust layers that are physically distinct but appear at the same distance, offering a purely geometric check of kinematic distance assignments.
Share X Bluesky LinkedIn Reddit HN

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 manuscript proposes CU-JADE, a CUSUM-based change-point detection method for identifying extinction jumps in distance–extinction (D–A) diagrams, with a shuffled-CUSUM confidence level, bootstrap distance uncertainties, and recursive splitting to find multiple jumps. The method is validated on mock D–A datasets and against 75 maser parallaxes, then applied to the Cepheus and Cygnus regions and used to construct an all-sky 3D extinction map out to 4 kpc. The central claims are that CU-JADE detects abrupt jumps with minimal systematic errors, improves completeness of distance measurements for weak jumps with ΔA_V ≳ 0.15 mag, and resolves multiple molecular gas layers along the line of sight.

Significance. If the claims are substantiated, CU-JADE would be a useful addition to the extinction-jump toolkit: it is conceptually simple, does not require an assumed number of layers, produces bootstrap uncertainties, and is applicable to both high-latitude and crowded Galactic-plane sightlines. The paper's assets include a large mock-data validation campaign, a public data/code release at ScienceDB, and scientific applications that recover known structures (Cepheus Flare layers, Cygnus Rift clouds) and identify new distance measurements, notably cometary clouds at ~1.7 kpc associated with Cyg OB1/OB2. The method's reduced 'finger-of-god' artifacts in face-on projections are a plausible practical advantage over hierarchical inversion maps. However, several load-bearing claims, especially the weak-jump sensitivity at 0.15 mag and the 'minimal systematic errors' statement, are not quantitatively established by the validation as currently presented.

major comments (4)
  1. [Abstract; Section 4.1; Table 1] The headline claim that CU-JADE improves completeness 'even for extinction values as low as ΔA_V ≳ 0.15 mag' is not directly supported by the mock validation. The mock ΔA_G values are drawn from ξ(0.2)+0.15 mag while σ is drawn from ξ(0.2)+0.2 mag, so the simulations test a population with typical ΔA≈0.35–0.5 mag rather than the 0.15 mag boundary, and no stratified recall or F1 by ΔA or σ bin is reported. At the C=1 setting recommended for weak jumps, Table 1 gives overall F1 scores of 0.76–0.80 for 1–3 jumps, but also FP rates of 0.37–0.47 per sightline and a first-jump F1 of only 0.53 for three jumps; at C=3, recall for three jumps drops to 0.36 (F1=0.52). The table reports only means, so the dispersion across mock groups is unknown. Please report recall, precision, and F1 as functions of ΔA, σ, and n, including the 0.15 mag bin, with standard deviations, so the stated completeness threshold can actually be evaluated.
  2. [Section 4.2] The maser validation is not an independent test of weak-jump sensitivity. The text states that the crossmatch corresponds to ΔA_G ≳ 0.5 mag, and the 46/75 (61%) detection rate is obtained with C=3 followed by post-hoc lowering of C for six additional samples ('we lowered the scaling factor appropriately'). Matching is manual, and 76% of masers at 2.5–3 kpc are missed. This validates detection of strong single jumps, not ΔA_V ≳ 0.15 mag jumps, and therefore does not substantiate the abstract's 'minimal systematic errors on observed data' claim for the weak-jump regime. Please provide an observed validation sample with known weak jumps, or explicitly restrict the claims to strong jumps.
  3. [Section 2.2; Section 5.1.2] The all-sky map is presented with the claim of minimal systematic errors, but Section 2.2 explicitly states 'we did not conduct further quantitative comparison of the systematic differences between the catalogs', and Section 5.1.2 gives only a qualitative comparison with Vergely et al. (2022). There is no quantitative comparison of distance residuals, completeness as a function of A_V or distance, or angular-resolution effects against existing 3D extinction maps or the maser/YOC samples used for the figure overlays. The systematic-error claim should either be supported with quantitative validation or removed or tempered.
  4. [Appendix A.3; Section 4.1] The false-jump problem from smooth extinction gradients is acknowledged but is not tested in the mock validation that supports the weak-jump claim. The statistical mocks use slopes k drawn from [1,6]×10^-5 mag pc^-1 (Section 4.1), while Appendix A.3 shows that C=1 produces six false change points on a pure gradient and that slope-induced false detections become problematic around k ≳ 0.003 mag pc^-1. Real Galactic-plane sightlines can plausibly reach such gradients in dense complexes, and the all-sky map is produced with C=2, a regime whose false-positive/false-negative trade-off against slope has not been quantified. Please report the fraction of sightlines with large fitted k and test CU-JADE on mocks with k spanning at least 10^-4 to 10^-2 mag pc^-1.
minor comments (5)
  1. [Section 3.2.1] The sentence following Eq. (7), 'the value of CL now is only influenced by the number density of the samples', is confusing because Eq. (7) explicitly contains ΔA and σ as well; please rephrase to indicate that for a given stellar population the remaining tunable factor is the sampling density.
  2. [Table 1] The grouping of rows by C is easy to misread because the C labels are isolated at the start of each block; please format the table with explicit C columns or panel labels, and define the 'Gap' statistic with its sign convention in the table note.
  3. [Figure 1] The caption refers to panels (a)–(g), but the panel labels are not clearly visible in the reproduced figure; please ensure every subplot is labeled inside the figure itself.
  4. [Section 4.1] The mock parameter statement 'ΔA_G ∼ ξ(0.2) + 0.15 mag' and 'σ ∼ ξ(0.2) + 0.2 mag' should report the mean and dispersion of these distributions so that the reader can interpret the 'weak' regime being tested.
  5. [Section 5.1.1; Table 2] The description of the catalog column Ncomp says it includes jumps with ΔA_V < 0.15 mag, while the text says the map retains jumps exceeding 0.15 mag; please clarify whether the published table contains sub-threshold components and how they are flagged.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: CU-JADE's jump detection is tested against independently generated mock data and maser parallaxes, and the central derivation does not reduce to its inputs.

full rationale

I traced the paper's derivation chain and found no step in which a claimed prediction or first-principles result is equivalent to its inputs by construction. The CUSUM statistic, shuffling-based confidence level, and iterative splitting (Section 3.1) are a standard change-point procedure applied to D-A data, not a renamed known result. The approximate relation CL ∝ n^{1/2} ΔA σ^{-1} (Eq. 7) is explicitly an empirical scaling informed by simulations (Appendix A.2), including a fitted weight φ (Eq. A7), but this relation is not used as a substitute for detection; detection relies on the permutation distribution and the scaling factor C, which is tuned on mock and maser data. The mock validation (Section 4.1) and maser validation (Section 4.2) are external tests of the method: mock data contain pre-set jumps, and maser parallaxes are independent distance anchors. The self-citations to Zhang et al. (2024) are used for comparative context and cloud identification in Cygnus, not as an unverified premise that forces the conclusions. The paper itself admits some limitations that affect validity, such as the statement in Section 2.2 that no quantitative comparison of systematic differences between catalogs was conducted, and the low recall for three weak jumps in Table 1 (C=1, Type 3: recall 0.3566, F1 0.5207) undercuts the abstract's completeness claim. These are correctness and calibration concerns, not circularity: the method's output is not statistically forced by a fitted parameter renamed as a prediction, nor does any load-bearing derivation depend on a self-citation chain. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The method introduces no new physical entities. Its central claim rests on a step-function model of extinction, a shuffle-based null distribution, the accuracy of public stellar catalogs, and the adequacy of an approximate CL scaling that contains a fitted parameter. The tuning parameters C, CL threshold, the 0.15 mag cutoff, spatial sampling, and beta are all hand-set or fit, and they materially affect which jumps are reported.

free parameters (6)
  • C (scaling factor for confidence threshold) = 1-3 (5 in illustrative figure)
    Introduced in Section 3.1 to modulate Sdiff; tuned empirically using mock data (Section 4.1) and maser validation (Section 4.2). C=3 used for masers, C=1 for weak jumps, C=2 for all-sky map.
  • CL threshold = 0.95
    Predefined percentile for accepting a jump point; adjustable, as stated in Section 3.1.
  • Delta AV threshold (0.15 mag) = 0.15 mag
    Adopted in Section 5.1.1 to retain jumps in the all-sky map, based on extinction precision and H2 threshold, but this selection affects the completeness of the catalog.
  • Phi (weight coefficient in Eq. A7) = greater than 1, not specified
    Empirical weight in the fitted relation between Sdiff, Delta A, and sigma (Appendix A.2); introduces a hand-fitted element into the 'derivation' of CL.
  • Spatial sampling size L0 and grid spacing L = 1 deg and 30 arcmin (all-sky); 10 arcmin and 2 arcmin (Cygnus)
    Chosen sampling parameters in Sections 5.1 and 5.2.2 affect resolution and the number of stars per line of sight, and therefore detection sensitivity.
  • Beta in Mode II scaling (C*sqrt(beta*n)) = not specified
    Introduced in Section 3.2.2 as a constant for fine-tuning Mode II; the paper does not give its value or how to set it.
assumptions (5)
  • domain assumption The line-of-sight extinction profile is a staircase: piecewise constant plateaus separated by abrupt jumps (Eq. A1).
    Used throughout the method; Appendix A.3 shows that smooth gradients (slope k) produce false jumps, so this step-function assumption is load-bearing.
  • domain assumption Shuffling the extinction values while keeping distances fixed yields a valid null distribution for Sdiff.
    Core to the confidence assessment in Step 2 (Section 3.1). The data are distance-ordered; if correlated structure exists without a jump, the shuffled null may not represent noise.
  • domain assumption Gaia, SHEDR3, and ZGR23 stellar distances and extinctions are accurate enough to detect jumps at the claimed 0.15 mag level, and stellar completeness is sufficient behind dust walls.
    The method's sensitivity is limited by stellar parameter uncertainties and sample density, as acknowledged in Sections 4.2 and 5.2.2 for the extinction-wall effect.
  • ad hoc to paper The approximate scaling CL proportional to sqrt(n) * Delta A / sigma (Eq. 7) is a valid basis for Mode II threshold adjustment.
    Eq. (7) is derived from a fitted approximation (Eq. A7) with free parameter phi, so it is not a fully first-principles derivation.
  • standard math Standard Gaussian random-walk expectations for CUSUM statistics (Appendix A.2, Eqs. A4-A6).
    Used to argue that Sdiff scales as sqrt(n) * sigma and to support the shuffle-based noise model.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CU-JADE: A Method for Traversing Extinction Jumps along the Line of Sight." pith.science (2026). https://pith.science/paper/5O76SNUM

@misc{pith2026250718002,
  author       = {Pith},
  title        = {Pith review of: CU-JADE: A Method for Traversing Extinction Jumps along the Line of Sight},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5O76SNUM}},
  note         = {Machine review of arXiv:2507.18002}
}
abstract

Although interstellar dust extinction serves as a powerful distance estimator, the solar system's location within the Galactic plane complicates distance determinations, especially for molecular clouds (MCs) at varying distances along the line of sight (LoS). The presence of complex extinction patterns along the LoS introduces degeneracies, resulting in less accurate distance measurements to overlapping MCs in crowded regions of the Galactic plane. In this study, we develop the CUSUM-based Jump-point Analysis for Distance Estimation (CU-JADE), a novel method designed to help mitigate these observational challenges. The key strengths of CU-JADE include: (1) sensitivity to detect abrupt jumps in Distance-$A_{\lambda}$ ($D$-$A$) datasets, (2) minimal systematic errors as demonstrated on both mock and observed data, and (3) the ability to combine CUSUM analysis with multiwavelength data to improve the completeness of distance measurements for nearby gas structures, even for extinction values as low as $\Delta A_{V} \gtrsim 0.15$ mag. By combining CO survey data with a large sample of stars characterized by high-precision parallaxes and extinctions, we uncovered the multilayered molecular gas distribution in the high-latitude Cepheus region. We also determined accurate distances to MCs beyond the Cygnus Rift by analyzing the intricate structure of gas and extinction within the Galactic plane. Additionally, we constructed a full-sky 3D extinction map extending to 4 kpc, which provides critical insights into dense interstellar medium components dominated by molecular hydrogen. These results advance our understanding of the spatial distribution and physical properties of MCs across the Milky Way.

Figures

Figures reproduced from arXiv: 2507.18002 by the authors.

Figure 1
Figure 1. This flowchart illustrates the process of identifying extinction-jump points in a D-A diagram using the CU-JADE method. The process is demonstrated through a simple example with two jump points. Initialization: Begin with a set of simulated stellar samples exhibiting two jump points on the D-A diagram (subplot (a)). These jump points are indicated by blue lines. CUSUM Statistic Calculation: Compute the CUSUM statist… view at source ↗
Figure 2
Figure 2. An illustration of our multi-extinction-jump model (here in Mode I, C=3 for the left column, C=1 for the middle and right columns) applied to the mock data. Different rows set the jump points number from 1 to 6, with distances at [300, 700, 1200, 1800, 2500, 3300] pc, respectively. The three columns mirror different LoS extinction properties (see the text). Gray dots are the produced mock data and black dots are bin… view at source ↗
Figure 3
Figure 3. The distance correspondence between parallaxes from masers and those derived from our multi-extinction-jump model. Orange triangles highlight masers in the Cygnus region, while blue triangles represent those outside the Cygnus region. our method successfully identified multiple extinction jumps associated with MCs linked to masers in star-forming regions beyond the Cygnus Rift (highlighted as orange points in [PITH… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: The sky map displays the distances corresponding to the largest ∆AV component along the LoS in a Mollweide view. Some well-studied regions are indicated on the map. It should be noted that the values are likely to represent the MCs’ distances, but they could also corre…
Figure 5
Figure 5. Figure 5: A 2D histogram illustrating ∆AV weighted extinction jumps is presented in polar coordinates. The map has been smoothed using a uniform filter in Scipy with a kernel size of 3. The blue sector represents Cepheus, and the orange sector is Cygnus. Yellow squares with erro…
Figure 6
Figure 6. Figure 6: Face-on maps from different star catalogs/techniques. Panels (a) and (b) present Polar coordinate dust maps of ZGR23. Panel (a) is produced by our method, and panel (b) is generated using a simple spatial binning technique as outlined by Zhang et al. (2023). Panel (c) …
Figure 7
Figure 7. Figure 7: CfA 12CO and extinction map toward the Cepheus region. Panel (a) depicts the extinction between 200 and 500 pc using a colormap, while contours show the smoothed integrated intensity of 12CO emission within the LSR velocity range of -6 to 15 km s−1 . Panel (b) is simil…
Figure 8
Figure 8. Figure 8: Overlay of the identified MWISP 13CO cloud structures (white contours) within 1.1 kpc (clouds in the Cygnus Rift from Zhang et al. 2024) based on the extinction jumps detected within 1.1 kpc. The colormap shows the distance of the largest extinction in the distance int…
Figure 9
Figure 9. Figure 9: For the case far from the Cygnus Rift, we show four examples with good matches between extinction and the integrated intensity of MWISP 12CO, and continuous distances for their structures. The colormap represents the distance of the strongest extinction component withi…
Figure 10
Figure 10. Figure 10: For the case close to the projected edge of the Cygnus Rift, four examples showing an anticorrelation between extinction and integrated CO intensity in dense regions, which can also serve as strong evidence for determining distances. Here, the greyscale image represen…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DHARA: Data Handling and Automated Reduction pipeline for AIMPOL

    astro-ph.IM 2026-07 accept novelty 6.0 of 10

    An automated Python pipeline for AIMPOL dual-beam polarimetry recovers literature polarization values within 2σ for standards and the Alessi 1 cluster and is adaptable to similar instruments.

Reference graph

Works this paper leans on

108 extracted references · 13 canonical work pages · cited by 1 Pith paper

  1. [1]

    O., & Shah, D

    Alanqary, A., Alomar, A. O., & Shah, D. 2021, in Advances in Neural Information Processing Systems, ed. A. Beygelzimer, Y. Dauphin, P. Liang, & J. W. Vaughan. https://openreview.net/forum?id=i0DmV60aeK

    2021

  2. [2]

    A., et al

    Alves , J., Zucker , C., Goodman , A. A., et al. 2020, , 578, 237, 10.1038/s41586-019-1874-z

    doi:10.1038/s41586-019-1874-z 2020

  3. [3]

    Aminikhanghahi, S., & Cook, D. J. 2017, Knowledge and Information Systems, 51, 339, 10.1007/s10115-016-0987-z

    doi:10.1007/s10115-016-0987-z 2017

  4. [4]

    Anders , F., Khalatyan , A., Queiroz , A. B. A., et al. 2022, , 658, A91, 10.1051/0004-6361/202142369

    doi:10.1051/0004-6361/202142369 2022

  5. [5]

    2018, , 616, A8, 10.1051/0004-6361/201732516

    Andrae , R., Fouesneau , M., Creevey , O., et al. 2018, , 616, A8, 10.1051/0004-6361/201732516

    doi:10.1051/0004-6361/201732516 2018

  6. [6]

    2023, , 674, A27, 10.1051/0004-6361/202243462

    Andrae , R., Fouesneau , M., Sordo , R., et al. 2023, , 674, A27, 10.1051/0004-6361/202243462

    doi:10.1051/0004-6361/202243462 2023

  7. [7]

    P., Tollerud , E

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

    doi:10.1051/0004-6361/201322068 2013

  8. [8]

    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

    doi:10.3847/1538-3881/aabc4f 2018

Show all 108 references
  1. [9]

    M., Lim , P

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

    2022 doi

  2. [10]

    Ballesteros-Paredes , J., Rom \'a n-Z \'u \ n iga , C., Salom \'e , Q., Zamora-Avil \'e s , M., & Jim \'e nez-Donaire , M. J. 2019, , 490, 2648, 10.1093/mnras/stz2575

    2019 doi

  3. [11]

    T., Watkins , E

    Barnes , A. T., Watkins , E. J., Meidt , S. E., et al. 2023, , 944, L22, 10.3847/2041-8213/aca7b9

    2023 doi

  4. [12]

    Barry, D., & Hartigan, J. A. 1993, Journal of the American Statistical Association, 88, 309. http://www.jstor.org/stable/2290726

    1993

  5. [13]

    2020, , 643, A36, 10.1051/0004-6361/202038593

    Bellomi , E., Godard , B., Hennebelle , P., et al. 2020, , 643, A36, 10.1051/0004-6361/202038593

    2020 doi

  6. [14]

    R., Wright , N

    Berlanas , S. R., Wright , N. J., Herrero , A., Drew , J. E., & Lennon , D. J. 2019, , 484, 1838, 10.1093/mnras/stz117

    2019 doi

  7. [15]

    C., Savage , B

    Bohlin , R. C., Savage , B. D., & Drake , J. F. 1978, , 224, 132, 10.1086/156357

    1978 doi

  8. [16]

    Bok , B. J. 1937, The Distribution of the Stars in Space

    1937

  9. [17]

    D., Wolfire , M., & Leroy , A

    Bolatto , A. D., Wolfire , M., & Leroy , A. K. 2013, , 51, 207, 10.1146/annurev-astro-082812-140944

    2013 doi

  10. [18]

    A., Clayton , G

    Cardelli , J. A., Clayton , G. C., & Mathis , J. S. 1989, , 345, 245, 10.1086/167900

    1989 doi

  11. [19]

    Q., Huang , Y., Yuan , H

    Chen , B. Q., Huang , Y., Yuan , H. B., et al. 2019, , 483, 4277, 10.1093/mnras/sty3341

    2019 doi

  12. [20]

    Q., Li , G

    Chen , B. Q., Li , G. X., Yuan , H. B., et al. 2020, , 493, 351, 10.1093/mnras/staa235

    2020 doi

  13. [21]

    R., McLeod , A

    Chevance , M., Krumholz , M. R., McLeod , A. F., et al. 2023, in Astronomical Society of the Pacific Conference Series, Vol. 534, Protostars and Planets VII, ed. S. Inutsuka , Y. Aikawa , T. Muto , K. Tomida , & M. Tamura , 1, 10.48550/arXiv.2203.09570

    2023 doi arXiv

  14. [22]

    J., & Lo , K

    Claussen , M. J., & Lo , K. Y. 1986, , 308, 592, 10.1086/164529

    1986 doi

  15. [23]

    M., Hartmann , D., & Thaddeus , P

    Dame , T. M., Hartmann , D., & Thaddeus , P. 2001, , 547, 792, 10.1086/318388

    2001 doi

  16. [24]

    M., & Thaddeus , P

    Dame , T. M., & Thaddeus , P. 2022, , 262, 5, 10.3847/1538-4365/ac7e53

    2022 doi

  17. [25]

    Draine , B. T. 2003, , 41, 241, 10.1146/annurev.astro.41.011802.094840

    2003 arXiv

  18. [26]

    2011, Physics of the Interstellar and Intergalactic Medium

    ---. 2011, Physics of the Interstellar and Intergalactic Medium

    2011

  19. [27]

    2024, , 685, A82, 10.1051/0004-6361/202347628

    Edenhofer , G., Zucker , C., Frank , P., et al. 2024, , 685, A82, 10.1051/0004-6361/202347628

    2024 doi

  20. [28]

    2024, The Multiple Change-in-Gaussian-Mean Problem

    Fearnhead, P., & Fryzlewicz, P. 2024, The Multiple Change-in-Gaussian-Mean Problem. 2405.06796

    2024 arXiv

  21. [29]

    A., Langer , W

    Frerking , M. A., Langer , W. D., & Wilson , R. W. 1982, , 262, 590, 10.1086/160451

    1982 doi

  22. [30]

    Gaia Collaboration , Brown , A. G. A., Vallenari , A., et al. 2021, , 649, A1, 10.1051/0004-6361/202039657

    2021 doi

  23. [31]

    Gaia Collaboration , Vallenari , A., Brown , A. G. A., et al. 2023, , 674, A1, 10.1051/0004-6361/202243940

    2023 doi

  24. [32]

    M., Tumlinson , J., & Danforth , C

    Gillmon , K., Shull , J. M., Tumlinson , J., & Danforth , C. 2006, , 636, 891, 10.1086/498053

    2006 doi

  25. [33]

    Girardi , L., Groenewegen , M. A. T., Hatziminaoglou , E., & da Costa , L. 2005, , 436, 895, 10.1051/0004-6361:20042352

    2005 doi

  26. [34]

    A., Pineda , J

    Goodman , A. A., Pineda , J. E., & Schnee , S. L. 2009, , 692, 91, 10.1088/0004-637X/692/1/91

    2009 doi

  27. [35]

    M., Hivon , E., Banday , A

    G \'o rski , K. M., Hivon , E., Banday , A. J., et al. 2005, , 622, 759, 10.1086/427976

    2005 doi

  28. [36]

    M., Schlafly , E., Zucker , C., Speagle , J

    Green , G. M., Schlafly , E., Zucker , C., Speagle , J. S., & Finkbeiner , D. 2019, , 887, 93, 10.3847/1538-4357/ab5362

    2019 doi

  29. [37]

    A., Casandjian , J.-M., & Terrier , R

    Grenier , I. A., Casandjian , J.-M., & Terrier , R. 2005, Science, 307, 1292, 10.1126/science.1106924

    2005 doi

  30. [38]

    A., Lebrun , F., Arnaud , M., Dame , T

    Grenier , I. A., Lebrun , F., Arnaud , M., Dame , T. M., & Thaddeus , P. 1989, , 347, 231, 10.1086/168112

    1989 doi

  31. [39]

    L., Chen , B

    Guo , H. L., Chen , B. Q., & Liu , X. W. 2022, , 511, 2302, 10.1093/mnras/stac213

    2022 doi

  32. [40]

    Heyer , M., & Dame , T. M. 2015, , 53, 583, 10.1146/annurev-astro-082214-122324

    2015 doi

  33. [41]

    L., & Reffert , S

    Hunt , E. L., & Reffert , S. 2023, , 673, A114, 10.1051/0004-6361/202346285

    2023 doi

  34. [42]

    J., Reid , M

    Hyland , L. J., Reid , M. J., Orosz , G., et al. 2023, , 953, 21, 10.3847/1538-4357/acdbc5

    2023 doi

  35. [43]

    Kalberla , P. M. W., Kerp , J., & Haud , U. 2020, , 639, A26, 10.1051/0004-6361/202037602

    2020 doi

  36. [44]

    A., Zasowski , G., Rix , H.-W., et al

    Kollmeier , J. A., Zasowski , G., Rix , H.-W., et al. 2017, arXiv e-prints, arXiv:1711.03234, 10.48550/arXiv.1711.03234

    2017 doi arXiv

  37. [45]

    J., & Dame , T

    Lada , C. J., & Dame , T. M. 2020, , 898, 3, 10.3847/1538-4357/ab9bfb

    2020 doi

  38. [46]

    J., Lombardi , M., & Alves , J

    Lada , C. J., Lombardi , M., & Alves , J. F. 2010, , 724, 687, 10.1088/0004-637X/724/1/687

    2010 doi

  39. [47]

    L., et al

    Lallement , R., Babusiaux , C., Vergely , J. L., et al. 2019, , 625, A135, 10.1051/0004-6361/201834695

    2019 doi

  40. [48]

    S., & Dor \'e , O

    Lenz , D., Hensley , B. S., & Dor \'e , O. 2017, , 846, 38, 10.3847/1538-4357/aa84af

    2017 doi

  41. [49]

    K., Bolatto , A., Gordon , K., et al

    Leroy , A. K., Bolatto , A., Gordon , K., et al. 2011, , 737, 12, 10.1088/0004-637X/737/1/12

    2011 doi

  42. [50]

    2024, fastcpd: Fast Change Point Detection in R

    Li, X., & Zhang, X. 2024, fastcpd: Fast Change Point Detection in R. 2404.05933

    2024 arXiv

  43. [51]

    2024, , 685, L12, 10.1051/0004-6361/202450067

    Luo , G., Li , D., Zhang , Z.-Y., et al. 2024, , 685, L12, 10.1051/0004-6361/202450067

    2024 doi

  44. [52]

    1982, , 109, 213

    Lynga , G. 1982, , 109, 213

    1982

  45. [53]

    S., Mezger , P

    Mathis , J. S., Mezger , P. G., & Panagia , N. 1983, , 128, 212

    1983

  46. [54]

    2023, , 674, A3, 10.1051/0004-6361/202243880

    Montegriffo , P., De Angeli , F., Andrae , R., et al. 2023, , 674, A3, 10.1051/0004-6361/202243880

    2023 doi

  47. [55]

    2021, , 653, A33, 10.1051/0004-6361/202040073

    Nogueras-Lara , F., Sch \"o del , R., & Neumayer , N. 2021, , 653, A33, 10.1051/0004-6361/202040073

    2021 doi

  48. [56]

    B., De Biasi , M

    Orellana , R. B., De Biasi , M. S., & Pa \' z , L. G. 2021, , 502, 6080, 10.1093/mnras/stab457

    2021 doi

  49. [57]

    V., Zucker , C., Clemens , D., et al

    Panopoulou , G. V., Zucker , C., Clemens , D., et al. 2024, arXiv e-prints, arXiv:2406.03765, 10.48550/arXiv.2406.03765

    2024 doi arXiv

  50. [58]

    H., & Reed , B

    Pantaleoni Gonz \'a lez , M., Ma \' z Apell \'a niz , J., Barb \'a , R. H., & Reed , B. C. 2021, , 504, 2968, 10.1093/mnras/stab688

    2021 doi

  51. [59]

    2024, , 684, A162, 10.1051/0004-6361/202349015

    Pelgrims , V., Mandarakas , N., Skalidis , R., et al. 2024, , 684, A162, 10.1051/0004-6361/202349015

    2024 doi

  52. [60]

    E., Caselli , P., & Goodman , A

    Pineda , J. E., Caselli , P., & Goodman , A. A. 2008, , 679, 481, 10.1086/586883

    2008 doi

  53. [61]

    2020, , 641, A12, 10.1051/0004-6361/201833885

    Planck Collaboration , Aghanim , N., Akrami , Y., et al. 2020, , 641, A12, 10.1051/0004-6361/201833885

    2020 doi

  54. [62]

    Queiroz , A. B. A., Anders , F., Santiago , B. X., et al. 2018, , 476, 2556, 10.1093/mnras/sty330

    2018 doi

  55. [63]

    L., & Wright , N

    Quintana , A. L., & Wright , N. J. 2021, , 508, 2370, 10.1093/mnras/stab2663

    2021 doi

  56. [64]

    J., Menten , K

    Reid , M. J., Menten , K. M., Brunthaler , A., et al. 2014, , 783, 130, 10.1088/0004-637X/783/2/130

    2014 doi

  57. [65]

    2019, , 885, 131, 10.3847/1538-4357/ab4a11

    ---. 2019, , 885, 131, 10.3847/1538-4357/ab4a11

    2019 doi

  58. [66]

    , S., Bailer-Jones , C

    Rezaei Kh. , S., Bailer-Jones , C. A. L., Hogg , D. W., & Schultheis , M. 2018, , 618, A168, 10.1051/0004-6361/201833284

    2018 doi

  59. [67]

    , S., Bailer-Jones , C

    Rezaei Kh. , S., Bailer-Jones , C. A. L., Soler , J. D., & Zari , E. 2020, , 643, A151, 10.1051/0004-6361/202038708

    2020 doi

  60. [68]

    Riaz, M., Abbas, N., & Does, R. J. M. M. 2011, Quality and Reliability Engineering International, 27, 415 , 10.1002/qre.1124

    2011 doi

  61. [69]

    F., & Finkbeiner , D

    Schlafly , E. F., & Finkbeiner , D. P. 2011, , 737, 103, 10.1088/0004-637X/737/2/103

    2011 doi

  62. [70]

    F., Meisner , A

    Schlafly , E. F., Meisner , A. M., & Green , G. M. 2019, , 240, 30, 10.3847/1538-4365/aafbea

    2019 doi

  63. [71]

    F., Green , G., Finkbeiner , D

    Schlafly , E. F., Green , G., Finkbeiner , D. P., et al. 2014, , 786, 29, 10.1088/0004-637X/786/1/29

    2014 doi

  64. [72]

    J., Finkbeiner , D

    Schlegel , D. J., Finkbeiner , D. P., & Davis , M. 1998, , 500, 525, 10.1086/305772

    1998 doi

  65. [73]

    2006, , 458, 855, 10.1051/0004-6361:20065088

    Schneider , N., Bontemps , S., Simon , R., et al. 2006, , 458, 855, 10.1051/0004-6361:20065088

    2006 doi

  66. [74]

    2007, , 474, 873, 10.1051/0004-6361:20077540

    Schneider , N., Simon , R., Bontemps , S., Comer \'o n , F., & Motte , F. 2007, , 474, 873, 10.1051/0004-6361:20077540

    2007 doi

  67. [75]

    M., Danforth , C

    Shull , J. M., Danforth , C. W., & Anderson , K. L. 2021, , 911, 55, 10.3847/1538-4357/abe707

    2021 doi

  68. [76]

    F., Hopkins , P

    Skalidis , R., Goldsmith , P. F., Hopkins , P. F., & Ponnada , S. B. 2024, , 682, A161, 10.1051/0004-6361/202347968

    2024 doi

  69. [77]

    F., Cutri , R

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

    2006 doi

  70. [78]

    M., Rivolo , A

    Solomon , P. M., Rivolo , A. R., Barrett , J., & Yahil , A. 1987, , 319, 730, 10.1086/165493

    1987 doi

  71. [79]

    1993, Baltic Astronomy, 2, 171, 10.1515/astro-1993-0202

    Straizys , V., Kazlauskas , A., Vansevicius , V., & Cernis , K. 1993, Baltic Astronomy, 2, 171, 10.1515/astro-1993-0202

    1993 doi

  72. [80]

    2019, , 240, 9, 10.3847/1538-4365/aaf1c8

    Su , Y., Yang , J., Zhang , S., et al. 2019, , 240, 9, 10.3847/1538-4365/aaf1c8

    2019 doi

  73. [81]

    2020, , 893, 91, 10.3847/1538-4357/ab7fff

    Su , Y., Yang , J., Yan , Q.-Z., et al. 2020, , 893, 91, 10.3847/1538-4357/ab7fff

    2020 doi

  74. [82]

    2023, Research in Astronomy and Astrophysics, 23, 015019, 10.1088/1674-4527/aca64a

    Sun , L., Chen , X., Feng , J., et al. 2023, Research in Astronomy and Astrophysics, 23, 015019, 10.1088/1674-4527/aca64a

    2023 doi

  75. [83]

    2024 a , , 168, 203, 10.3847/1538-3881/ad7b2f

    Sun , M., Jiang , B., Guo , H., & Cui , W. 2024 a , , 168, 203, 10.3847/1538-3881/ad7b2f

    2024 doi

  76. [84]

    2024 b , , 977, L35, 10.3847/2041-8213/ad9605

    Sun , Y., Yang , J., Zhang , S., et al. 2024 b , , 977, L35, 10.3847/2041-8213/ad9605

    2024 doi

  77. [85]

    2021, , 505, 5164, 10.1093/mnras/stab1496

    Szil \'a gyi , M., Kun , M., & \'A brah \'a m , P. 2021, , 505, 5164, 10.1093/mnras/stab1496

    2021 doi

  78. [86]

    2000, Qual

    Taylor, W. 2000, Qual. Eng., 1

    2000

  79. [87]

    2020, Signal Processing, 167, 107299, https://doi.org/10.1016/j.sigpro.2019.107299

    Truong, C., Oudre, L., & Vayatis, N. 2020, Signal Processing, 167, 107299, https://doi.org/10.1016/j.sigpro.2019.107299

    2020

  80. [88]

    2020, , 72, 50, 10.1093/pasj/psaa018

    VERA Collaboration , Hirota , T., Nagayama , T., et al. 2020, , 72, 50, 10.1093/pasj/psaa018

    2020 doi

  81. [89]

    L., Lallement , R., & Cox , N

    Vergely , J. L., Lallement , R., & Cox , N. L. J. 2022, , 664, A174, 10.1051/0004-6361/202243319

    2022 doi

  82. [90]

    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

  83. [91]

    D., Waititu, G

    Wambui, G. D., Waititu, G. A., & Wanjoya, A. 2015, American Journal of Theoretical and Applied Statistics, 4, 581, 10.11648/j.ajtas.20150406.30

    2015 doi

  84. [92]

    2025, arXiv e-prints, arXiv:2505.00407, 10.48550/arXiv.2505.00407

    Wang , F., Fang , M., Fu , X., et al. 2025, arXiv e-prints, arXiv:2505.00407, 10.48550/arXiv.2505.00407

    2025 doi arXiv

  85. [93]

    J., Barnes , A

    Watkins , E. J., Barnes , A. T., Henny , K., et al. 2023, , 944, L24, 10.3847/2041-8213/aca6e4

    2023 doi

  86. [94]

    1923, Astronomische Nachrichten, 219, 109, 10.1002/asna.19232190702

    Wolf , M. 1923, Astronomische Nachrichten, 219, 109, 10.1002/asna.19232190702

    1923 doi

  87. [95]

    G., Hollenbach , D., & McKee , C

    Wolfire , M. G., Hollenbach , D., & McKee , C. F. 2010, , 716, 1191, 10.1088/0004-637X/716/2/1191

    2010 doi

  88. [96]

    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

  89. [97]

    J., Zheng , X

    Xu , Y., Reid , M. J., Zheng , X. W., & Menten , K. M. 2006, Science, 311, 54, 10.1126/science.1120914

    2006 doi

  90. [98]

    2019 a , , 885, 19, 10.3847/1538-4357/ab458e

    Yan , Q.-Z., Yang , J., Sun , Y., Su , Y., & Xu , Y. 2019 a , , 885, 19, 10.3847/1538-4357/ab458e

    2019 doi

  91. [99]

    2019 b , , 624, A6, 10.1051/0004-6361/201834337

    Yan , Q.-Z., Zhang , B., Xu , Y., et al. 2019 b , , 624, A6, 10.1051/0004-6361/201834337

    2019 doi

  92. [100]

    1997, , 110, 21, 10.1086/312994

    Yonekura , Y., Dobashi , K., Mizuno , A., Ogawa , H., & Fukui , Y. 1997, , 110, 21, 10.1086/312994

    1997 doi

  93. [101]

    2021, , 257, 51, 10.3847/1538-4365/ac242a

    Yuan , L., Yang , J., Du , F., et al. 2021, , 257, 51, 10.3847/1538-4365/ac242a

    2021 doi

  94. [102]

    2024, , 167, 220, 10.3847/1538-3881/ad2fcb

    Zhang , S., Su , Y., Chen , X., et al. 2024, , 167, 220, 10.3847/1538-3881/ad2fcb

    2024 doi

  95. [103]

    2023, in Proceedings of Machine Learning Research, Vol

    Zhang, X., & Dawn, T. 2023, in Proceedings of Machine Learning Research, Vol. 206, Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, ed. F. Ruiz, J. Dy, & J.-W. van de Meent (PMLR), 1129--1143. https://proceedings.mlr.press/v206/zhang23b.html

    2023

  96. [104]

    M., & Rix , H.-W

    Zhang , X., Green , G. M., & Rix , H.-W. 2023, , 524, 1855, 10.1093/mnras/stad1941

    2023 doi

  97. [105]

    2020, , 891, 137, 10.3847/1538-4357/ab75ef

    Zhao , H., Jiang , B., Li , J., et al. 2020, , 891, 137, 10.3847/1538-4357/ab75ef

    2020 doi

  98. [106]

    2024, , 971, 167, 10.3847/1538-4357/ad66cd

    Zhu , Z.-K., Fang , M., Lu , Z.-J., et al. 2024, , 971, 167, 10.3847/1538-4357/ad66cd

    2024 doi

  99. [107]

    S., Schlafly , E

    Zucker , C., Speagle , J. S., Schlafly , E. F., et al. 2019, , 879, 125, 10.3847/1538-4357/ab2388

    2019 doi

  100. [108]

    A., Alves , J., et al

    Zucker , C., Goodman , A. A., Alves , J., et al. 2022, , 601, 334, 10.1038/s41586-021-04286-5

    2022 doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.