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REVIEW 3 major objections 4 minor 37 references

The SOMA-POL Survey. I. Polarization and magnetic field properties of massive protostars

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The long axes of 29 massive protostar cores align either nearly parallel or nearly perpendicular to the local magnetic field, a bimodal pattern implying magnetic fields regulate massive star formation.

desk verdict Useful new survey, but the bimodal alignment claim rests on marginal p-values and unpropagated orientation errors, and the abstract overstates the p'-Lbol/Menv correlation. read the letter →

arxiv 2507.02384 v1 pith:MY54YRGD submitted 2025-07-03 astro-ph.GA

Tuva Källberg , Chi Yan Law , Jonathan C. Tan , Kate Pattle , Zacariyya Khan This is my paper · ORCID
classification astro-ph.GA
keywords magneticfieldsdustpolarizationmassivestarformationprotostarscloud-fieldalignmentgrainsubmillimeterpolarimetrybimodaldistribution
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 paper sets out to answer a live question in star formation: whether magnetic fields actively regulate the birth of massive stars or are too weak to matter. Using JCMT-POL2 850-micron polarimetry of 29 massive protostars in 13 regions, the authors compare each core's elongation direction with the local magnetic field direction on scales of about 0.6 pc or less. The relative angles do not scatter randomly: they cluster into two preferred states, nearly parallel and nearly perpendicular, with Kuiper-test and Monte Carlo p-values at or below 0.05. The paper reads this pattern as evidence that magnetic fields are dynamically important in high-mass core formation, the same regulatory role previously seen only in low-mass clouds. A secondary result, a falling polarization fraction as the luminosity-to-envelope-mass ratio rises, points to dust or grain-alignment properties changing as protostars evolve.

What carries the argument

The load-bearing quantity is the relative orientation angle $\theta_{\rm rel}$, measured between the mean magnetic field orientation inside a source's optimal aperture and the structural elongation direction defined by that same aperture. The mean field direction comes from a weighted circular mean of polarization angles rotated by 90 degrees (elongated dust grains emit with their long axes perpendicular to the field); the elongation comes from two independent techniques, Hessian matrix analysis of the Stokes I map and the autocorrelation function of Stokes I with ellipse fitting, so any claimed bimodality must survive a change of method. Parallel alignment is defined as $\theta_{\rm rel}$ within $0^\circ$–$30^\circ$ and perpendicular as $60^\circ$–$90^\circ$. Statistical significance is judged against a uniform distribution on $[0^\circ, 90^\circ]$ using a one-sample Kuiper test, backed by Monte Carlo trials ($10^6$ draws) that count how often a random histogram produces as large a fraction of near-parallel and near-perpendicular sources as the data show.

What would settle it

Re-run the one-sample Kuiper and Monte Carlo tests on the source-scale relative orientation with orientation uncertainties propagated (for example, from the scatter of the autocorrelation ellipse fits across contour levels, or by restricting to sources with aspect ratio above 1.3): if the bimodality no longer reaches $p \lesssim 0.05$ under either weighting, the claim that magnetic fields dynamically regulate massive cores on roughly 0.6 pc scales would lose its statistical support. A complementary check is higher-resolution sub-arcsecond polarimetry of the same cores to see whether the parallel and perpendicular classifications of the magnetic field geometry survive at smaller scales.

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Extended reading notes

Core claim

The paper's central claim is a statistically significant bimodal distribution between the SOMA source orientations and the local magnetic field orientations. Inside each source's optimal aperture (typically $\lesssim 0.6$ pc), the mean magnetic field direction, inferred from 850-micron dust polarization under the standard assumption that grains align with their long axes perpendicular to the field, is either within 30 degrees of the source's long axis or within 30 degrees of perpendicular, with few sources at intermediate angles. The pattern holds across two independent structure-orientation methods (Hessian matrix analysis and autocorrelation of the Stokes I map) and two statistical tests (a one-sample Kuiper test and $10^6$-iteration Monte Carlo simulations), giving $p = 0.010/0.012$ for Hessian-derived orientations and $p = 0.050/0.045$ for autocorrelation-derived orientations. The same analysis at the larger filament scale shows no significant bimodality ($p \gtrsim 0.1$), which the authors attribute to noisier, more diffuse emission defining the filaments. In the authors' framing, the bimodality means magnetic fields play a dynamically important, regulative role in high-mass star-forming regions, similar to results previously reported for low-mass regions.

Load-bearing premise

The results depend on treating each source's measured elongation direction as accurate, yet several sources are nearly round (aspect ratio as low as 1.04) and have essentially undefined long axes; if the uncertainty or systematic bias in these structural orientations is comparable to the 30-degree bins used to define parallel and perpendicular, the bimodality could be an artifact of the measurement rather than a real property of massive cores.

Editorial extensions

If this is right

  • Magnetic fields are dynamically important on the roughly 0.6 pc scales of massive protostellar cores, favoring core-accretion models in which fields regulate collapse and fragmentation over models that ignore them.
  • The bimodal alignment pattern previously found in low-mass Gould Belt clouds now extends to the high-mass regime, implying a common magnetic-regulation mechanism across stellar masses.
  • Grain alignment persists up to the highest intensities probed (the $p'$–$I$ index $\alpha \approx 0.6$–$0.8$ at high intensity), so polarized dust emission remains a usable magnetic field tracer in dense massive cores.
  • The significant anti-correlation between debiased polarization fraction and $L_{\rm bol}/M_{\rm env}$ means observable polarization properties systematically change as a protostar evolves.
  • The lack of bimodality at filament scales and of inter-source field correlations suggests that on larger scales, gravity-driven motions in the protocluster potential take over from magnetic regulation.

Reading between the lines

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

  • Editorial extension: the bimodality predicts a resolvable morphological dichotomy at sub-arcsecond scales—parallel-class cores should show fields running along the long axis (gas accumulating along field lines) and perpendicular-class cores should show fields draped across the core (field-guided compression)—which higher-resolution interferometric polarimetry could test directly.
  • Editorial extension: the cleanest internal check is to restrict the Kuiper and Monte Carlo statistics to sources with well-defined long axes (aspect ratio > 1.3); the sample's nearly round sources, such as G28.20 A at 1.04, make the current significance partly dependent on measuring a direction that is almost undefined.
  • Editorial extension: the opposite-signed $p'$–dispersion trend relative to low-mass regions is plausibly driven by noisier, lower-intensity sightlines in this sample; re-binning the data at matched signal-to-noise would reveal whether the trend is a Ricean noise effect or a physical difference.
  • Editorial extension: if the $p'$–$L_{\rm bol}/M_{\rm env}$ anti-correlation strengthens with a larger sample, polarization fraction could serve as a cheap evolutionary-stage diagnostic for massive protostars, complementing SED-based classification.
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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 / 4 minor

Summary. The paper presents JCMT POL-2 850 µm polarization observations of 13 SOMA massive star-forming regions, yielding 29 massive protostars. It derives mean magnetic field orientations, polarization fractions, angular dispersions, and structural orientation angles via Hessian matrix and autocorrelation analyses, and compares these with SED-derived protostellar properties. The main claimed result is a statistically significant bimodal distribution of the relative orientation between the local magnetic field and the source elongation on SOMA-aperture scales (Kuiper/Monte Carlo p ≈ 0.01–0.05), while no bimodality is found for filament-scale alignment. Secondary results include a p′–I power-law slope α ≈ 0.6–0.8 at high intensity, correlations between polarization fraction/dispersion and intensity, and a reported anti-correlation between p′ and Lbol/Menv.

Significance. If the bimodality is robust, this would be an important extension of the low-mass Gould Belt cloud-field alignment results to high-mass protostellar cores, supporting a dynamically important role for magnetic fields in massive star formation. The paper's strengths are the direct POL-2 survey of a well-studied SOMA sample, the use of two structural orientation estimators, and the quantitative comparison against uniform distributions with both Kuiper and Monte Carlo tests. The SED modeling connects polarization properties to evolutionary stage. The main caveats are that the formal test treats orientation angles as exact and the autocorrelation p-value sits at the conventional threshold; these need to be addressed before the central claim is fully load-bearing.

major comments (3)
  1. [§6.2.1, Tables 4–5] The p-values in Table 5 are computed treating each source elongation angle as exact, but Table 4 provides no per-source uncertainty for the Hessian orientations and the autocorrelation orientations vary by tens of degrees across contour levels; several sources have aspect ratios close to unity (G28.20 A: 1.04; G40.62: 1.07; G32.03 A: 1.14). Because the parallel/perpendicular classification uses 30° bins, an orientation error of roughly 30° can move a source between bins; for example, AFGL 5180 A has θrel,SOMA ≈ 69.5° from the Hessian method and ≈59.1° from the autocorrelation method, straddling the 60° boundary. The paper should propagate orientation uncertainties into the statistical test (e.g., Monte Carlo resampling of orientations using the contour-level scatter and cross-method differences, or repeating the test after excluding low-aspect-ratio sources) and should report region-level bootstrap p-values. Until this is done, the p=0.050/0.045 autocorrelation result does not robustly support the word “statistically confirm” in the abstract.
  2. [§6.5, Table 6, Abstract] The Spearman test for p′ versus Lbol/Menv gives p=0.052, which is not below the conventional 0.05 threshold; the abstract's claim of a “statistically significant anti-correlation” is therefore overstated. Please either adopt an explicitly justified one-sided test, report an effect size with its uncertainty, or soften the wording so that it accurately reflects the marginal significance.
  3. [§6.2.1] The claim that the bimodality is confirmed “independent of the methods to measure structural orientation” is stronger than the evidence supports, because the Hessian and autocorrelation methods use the same Stokes I maps, the same apertures, and the same noise realization; agreement between them cannot remove a systematic bias shared by both. In addition, the 29 sources come from only 13 regions and are not fully independent, so the effective sample size is smaller than 29. A region-level bootstrap or a mixed-effects treatment should be added before claiming method independence.
minor comments (4)
  1. [Fig. 5 caption, §6.2.1] The text refers to panels “3c)” and “3d)” when describing Figure 5; these should be panels (c) and (d) of Figure 5.
  2. [§6.2] The sentence “Only data points with SNRI < 10 are used here as well” appears to be a typo; given the earlier masking it should presumably read SNR I > 10.
  3. [Table 3, G28.20 B] The equally weighted mean field orientation for G28.20 B is listed as −84.7° while the intensity-weighted value is 34.9°; this large difference should be verified and the table should clarify how angle wraparound is handled.
  4. [Table 7, §6.6] The source is named “G28.8 A” in Table 7 and in the text of §6.6, but everywhere else in the paper it is G28.20 A; please make the naming consistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the bimodality test compares independently measured polarization and column-density orientations against a uniform null; no fitted parameter is relabeled as a prediction.

full rationale

The paper's central claims are derived from independent observables and null-hypothesis tests. Magnetic field orientations come from POL-2 Stokes Q/U maps (Eqs. 1-3), source and filament elongations come from Hessian matrix and autocorrelation analyses of Stokes I maps, and the statistical significance of the resulting bimodal distribution is assessed with a one-sample Kuiper test against an analytic uniform CDF and with Monte Carlo simulations that draw random orientations from a uniform distribution. No equation in the paper reduces a predicted quantity to an input by construction: the SED-derived Lbol, Menv, and Lbol/Menv values come from a separate radiative transfer grid (Zhang & Tan 2018) implemented via sedcreator, not from the polarization data, and the p'-I power-law slopes and dispersion correlations are descriptive fits rather than fitted parameters presented as independent predictions. Self-citations appear (e.g., SOMA survey, Zhang & Tan 2018 grid, earlier work by Law et al.), but they supply context, data, or modeling tools; the load-bearing bimodality result is tested against a uniform null and does not depend on those citations as proof. The concern that near-circular sources (aspect ratios close to 1) have poorly constrained orientation angles is a measurement-uncertainty or robustness issue, not a circularity of the derivation chain.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims rest on standard assumptions of dust grain alignment and on the reliability of the SED-derived evolutionary indicator. No new free parameters are fitted for the main bimodality result, but the bin definitions and the manually chosen break point are choices that affect secondary results.

free parameters (3)
  • Break point of p'-I broken power law = not stated numerically, identified by eye in Fig. 4
    The transition between the noise-dominated and alignment-dominated regimes is chosen manually, affecting the reported slopes alpha=0.76 (beam) and alpha=0.60 (SOMA).
  • Bimodality bin boundaries = 0-30 deg (parallel), 60-90 deg (perpendicular)
    The definition of parallel and perpendicular bins is a modeling choice that enters the Monte Carlo p-values; other bin choices would change the result.
  • Gaussian fit parameters for theta_rel histograms = means and dispersions in Fig. 5 legends
    These describe the bimodal shape but are not used for the formal significance test.
assumptions (4)
  • domain assumption Dust grains have their long axes aligned perpendicular to the magnetic field (RAT alignment), so theta_B = theta_p + 90 degrees (Eq. 3).
    The entire B-field inference rests on this standard but unproven-in-this-paper assumption; for the specific grains and radiation field in massive protostellar cores, alignment efficiency could differ.
  • domain assumption The SOMA aperture, defined by 70 micron flux convergence, encloses the core whose orientation is compared to the B-field.
    If the aperture is not matched to the structure being shaped by the B-field, the measured relative orientation may not be physically meaningful.
  • domain assumption The Zhang & Tan (2018) turbulent core accretion model grid provides reliable Lbol and Menv estimates.
    The SED-derived evolutionary indicator Lbol/Menv is used in Section 6.5; if the grid is biased, the anti-correlation interpretation changes.
  • standard math Uniform distribution is the appropriate null hypothesis for the relative orientation test.
    The Kuiper test and Monte Carlo compare to a uniform distribution, assuming isotropy of orientations; this is standard.

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Cite this review

Pith. "Pith review of The SOMA-POL Survey. I. Polarization and magnetic field properties of massive protostars." pith.science (2026). https://pith.science/paper/MY54YRGD

@misc{pith2026250702384,
  author       = {Pith},
  title        = {Pith review of: The SOMA-POL Survey. I. Polarization and magnetic field properties of massive protostars},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MY54YRGD}},
  note         = {Machine review of arXiv:2507.02384}
}
abstract

The role of magnetic fields in regulating the formation of massive stars remains much debated. Here we present sub-millimeter polarimetric observations with JCMT-POL2 at $850\:\mu$m of 13 regions of massive star formation selected from the SOFIA Massive (SOMA) star formation survey, yielding a total of 29 massive protostars. Our investigation of the $p'-I$ relationship suggests that grain alignment persists up to the highest intensities. We examine the relative orientations between polarization-inferred magnetic field direction and source column density elongation direction on small and large scales. On small scales, we find a bimodal distribution of these relative orientations, i.e., with an excess of near-parallel and near-perpendicular orientations. By applying a one-sample Kuiper test and Monte Carlo simulations to compare to a relative orientation distribution drawn from a uniform distribution, we statistically confirm this bimodal distribution, independent of the methods to measure structural orientation. This bimodal distribution suggests that magnetic fields are dynamically important on the local scales ($\lesssim 0.6\:$pc) of massive protostellar cores. We also examine how basic polarization properties of overall degree of polarization and local dispersion in polarization vector orientations depend on intrinsic protostellar properties inferred from spectral energy distribution (SED) modeling. We find a statistically significant anti-correlation between the debiased polarized fraction and the luminosity to mass ratio, $L_{\rm bol}/M_{\rm env}$, which hints at a change in the dust properties for protostellar objects at different evolutionary stages.

Figures

Figures reproduced from arXiv: 2507.02384 by the authors.

Figure 1
Figure 1. JCMT-POL2 850 µ m continuum images of the 7 SOMA regions with one source or binary sources. The sources selected for analysis are marked with black circles with a radius corresponding to the optimal aperture defined in §4. The black solid circle in the top left panel represents the JCMT beam aperture. Here, all images has a field of view of 3′ . in background-subtracted flux is <10%, indicating that further increase… view at source ↗
Figure 2
Figure 2. Cont. of [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. illustrates the method used for dispersion calculation for source AFGL 5180 A. The dispersion was calculated for two values of d, i.e., equal to the SOMA aperture and two times the beam aperture (14”). These results are listed as DBeam, and DSOMA in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Average debiased polarization fraction within beam aperture (a) and SOMA aperture (b) plotted against Stokes intensity. The data points are color coded by signal to noise ratio in polarization fraction, and data points for sources in the same region are marked with the…
Figure 5
Figure 5. Figure 5: Sub-figures (a) and (b) shows the distribution of relative orientation between the magnetic field orientation and the source orientation derived using the Hessian matrix analysis, and the autocorrelation function respectively. Additionally two independent Gaussian were…
Figure 6
Figure 6. Figure 6: The figure shows the relative orientation between the source/filament orientation and average magnetic field orientation on the SOMA scale plotted against the ratio of bolometric luminosity to envelope mass. Figure (a) shows the relative source orientation, and figure …
Figure 7
Figure 7. Figure 7: Angular difference between the magnetic field orientation for individual pair of sources within the same region as a function of the distance between the sources. The region of the pair is indicated by the marker. We applied a Kuiper test to the distribution of angular…
Figure 8
Figure 8. Figure 8: Angular dispersion at SOMA aperture plotted as a function of the mean intensity within the SOMA aperture (a) and debiased polarization fraction on SOMA scale (b). The data points are color-coded by Lbol/Menv from [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 12
Figure 12. Figure 12 [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]
Figure 9
Figure 9. Figure 9: Polarization fraction on SOMA scale against the bolometric luminosity (a) and the ratio between bolometric luminosity and envelope mass (b) from [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Polarization fraction on the SOMA scale against the surface density of the clump environment and star mass from [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: Polarization fraction on the SOMA scale against envelope mass from [PITH_FULL_IMAGE:figures/full_fig_p014_11.png]
Figure 12
Figure 12. Figure 12: The figure illustrates the θOutflow for the four sources. The blue dashed line shows the blue-shifted outflow orientation. The pink dashed line is the red-shifted outflow, and the white line is the average outflow orientation. The black line is the mean magnetic field…

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Works this paper leans on

37 extracted references · 34 canonical work pages

  1. [1]

    A., Aghanim, N., Alina, D., et al

    Ade, P. A., Aghanim, N., Alina, D., et al. 2015, Astronomy & Astrophysics, 576, A105

    work page 2015

  2. [2]

    2016, Astronomy & Astrophysics, 595, A57

    Alina, D., Montier, L., Ristorcelli, I., et al. 2016, Astronomy & Astrophysics, 595, A57

    work page 2016

  3. [3]

    2025, arXiv e-prints, arXiv:2501.13931

    Alves, J., Lombardi, M., & Lada, C. 2025, arXiv e-prints, arXiv:2501.13931

    arXiv 2025

  4. [4]

    Andersson, B., Lazarian, A., & Vaillancourt, J. E. 2015, Annual Review of As- tronomy and Astrophysics, 53, 501

    work page 2015

  5. [5]

    A., Bate, M

    Bonnell, I. A., Bate, M. R., Clarke, C. J., & Pringle, J. E. 2001, MNRAS, 323, 785

    2001

  6. [6]

    C., Li, D., Heiles, C., et al

    Ching, T. C., Li, D., Heiles, C., et al. 2022, Nature, 601, 49

    work page 2022

  7. [7]

    2024, Astronomy & Astrophysics, 682, A2 Davis Jr, L

    Crowe, S., Fedriani, R., Tan, J., et al. 2024, Astronomy & Astrophysics, 682, A2 Davis Jr, L. & Greenstein, J. L. 1951, Astrophysical Journal, vol. 114, p. 206, 114, 206

    work page 2024

  8. [8]

    S., et al

    Doi, Y ., Hasegawa, T., Furuya, R. S., et al. 2020, The Astrophysical Journal, 899, 28

    work page 2020

Show all 37 references
  1. [9]

    C., Telkamp, Z., et al

    Fedriani, R., Tan, J. C., Telkamp, Z., et al. 2023, ApJ, 942, 7

    2023

  2. [10]

    M., Beltrán, M

    Girart, J. M., Beltrán, M. T., Zhang, Q., Rao, R., & Estalella, R. 2009, Science, 324, 1408

    2009

  3. [11]

    2018, arXiv preprint arXiv:1811.03100 Grudi´c, M

    Gordon, M., Lopez-Rodriguez, E., Andersson, B.-G., et al. 2018, arXiv preprint arXiv:1811.03100 Grudi´c, M. Y ., Guszejnov, D., Offner, S. S. R., et al. 2022, MNRAS, 512, 216

    2018 arXiv

  4. [12]

    Gu, Q. et al. 2019, The Astrophysical Journal Letters, 871, L15

    2019

  5. [13]

    Kunz, M. W. & Mouschovias, T. C. 2009, MNRAS, 399, L94

    2009

  6. [14]

    2020, Monthly Notices of the Royal Astronomical Society, 498, 850

    Law, C.-Y ., Li, H.-B., Cao, Z., & Ng, C.-Y . 2020, Monthly Notices of the Royal Astronomical Society, 498, 850

    2020

  7. [15]

    Y ., Li, H

    Law, C. Y ., Li, H. B., & Leung, P. 2019, Monthly Notices of the Royal Astro- nomical Society, 484, 3604

    2019

  8. [16]

    C., Gorai, P., et al

    Law, C.-Y ., Tan, J. C., Gorai, P., et al. 2022, The Astrophysical Journal, 939, 120

    2022

  9. [17]

    C., Skalidis, R., et al

    Law, C.-Y ., Tan, J. C., Skalidis, R., et al. 2024, The Astrophysical Journal, 967, 157 Le Gouellec, V . J., Maury, A., Guillet, V ., et al. 2020, Astronomy & Astrophysics, 644, A11

    2024

  10. [18]

    2014, Protostars and planets VI, 101

    Li, H., Goodman, A., Sridharan, T., et al. 2014, Protostars and planets VI, 101

    2014

  11. [19]

    2013, Monthly Notices of the Royal Astronomical Society, 436, 3707

    Li, H.-b., Fang, M., Henning, T., & Kainulainen, J. 2013, Monthly Notices of the Royal Astronomical Society, 436, 3707

    2013

  12. [20]

    2017, Nature Astronomy, 1, 0158

    Li, H.-b., Jiang, H., Fan, X., Gu, Q., & Zhang, Y . 2017, Nature Astronomy, 1, 0158

    2017

  13. [21]

    C., De Buizer, J

    Liu, M., Tan, J. C., De Buizer, J. M., et al. 2020, The Astrophysical Journal, 904, 75

    2020

  14. [22]

    McKee, C. F. & Tan, J. C. 2002, Nature, 416, 59

    2002

  15. [23]

    McKee, C. F. & Tan, J. C. 2003, The Astrophysical Journal, 585, 850

    2003

  16. [24]

    2021, The Astrophysical Journal, 922, 210

    Panopoulou, G., Dickinson, C., Readhead, A., Pearson, T., & Peel, M. 2021, The Astrophysical Journal, 922, 210

    2021

  17. [25]

    2022, arXiv preprint arXiv:2203.11179

    Pattle, K., Fissel, L., Tahani, M., Liu, T., & Ntormousi, E. 2022, arXiv preprint arXiv:2203.11179

    2022 arXiv

  18. [26]

    2019, The Astrophysical Journal, 880, 27

    Pattle, K., Lai, S.-P., Hasegawa, T., et al. 2019, The Astrophysical Journal, 880, 27

    2019

  19. [27]

    C., et al

    Pillai, T., Kauffmann, J., Tan, J. C., et al. 2015, ApJ, 799, 74 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2015, A&A, 576, A104

    2015

  20. [28]

    & Walch, S

    Seifried, D. & Walch, S. 2015, Monthly Notices of the Royal Astronomical So- ciety, 452, 2410

    2015

  21. [29]

    1962, in Advances in Astronomy and Astrophysics, V ol

    Serkowski, K. 1962, in Advances in Astronomy and Astrophysics, V ol. 1 (Else- vier), 289–352

    1962

  22. [30]

    D., Beuther, H., Rugel, M., et al

    Soler, J. D., Beuther, H., Rugel, M., et al. 2019, A&A, 622, A166

    2019

  23. [31]

    D., Beuther, H., Syed, J., et al

    Soler, J. D., Beuther, H., Syed, J., et al. 2020, Astronomy & Astrophysics, 642, A163

    2020

  24. [32]

    D., Hennebelle, P., Martin, P

    Soler, J. D., Hennebelle, P., Martin, P. G., et al. 2013, ApJ, 774, 128

    2013

  25. [33]

    C., Beltrán, M

    Tan, J. C., Beltrán, M. T., Caselli, P., et al. 2014, Protostars and Planets VI, 149

    2014

  26. [34]

    C., et al

    Telkamp, Z., Fedriani, R., Tan, J. C., et al. 2025, The Astrophysical Journal, 986, 15

    2025

  27. [35]

    C., Christie, D., et al

    Wu, B., Tan, J. C., Christie, D., et al. 2017, ApJ, 841, 88

    2017

  28. [36]

    M., et al

    Zhang, Q., Qiu, K., Girart, J. M., et al. 2014, ApJ, 792, 116

    2014

  29. [37]

    & Tan, J

    Zhang, Y . & Tan, J. C. 2018, The Astrophysical Journal, 853, 18 Article number, page 14 of 17 T. Källberg et al.: SOMA-POL I Fig. 12. The figure illustrates the θOutflow for the four sources. The blue dashed line shows the blue-shifted outflow orientation. The pink dashed lin...

    2018

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