REVIEW 3 major objections 5 minor 90 references
A multi-wavelength investigation of spiral structures in $z > 1$ galaxies with JWST
T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash
Pith's one-line read Spiral arms in z ~ 1.5 galaxies show density-wave shock signatures in nine of eighteen measured arms.
desk verdict First z>1 spiral offset measurements are real; the density-wave/tidal/clumpy split rests on an assumed trailing direction and needs kinematics. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The diagnostic is a two-band flux-peak offset. After subtracting a bulge-disk model fitted with GALIGHT, the residual near-infrared image is deprojected to a face-on geometry using the disk axis ratio, and a polar-shapelet transform (an expansion of the deprojected image into smooth azimuthal basis functions) keeps only azimuthal modes m = 2, 3, 4 to isolate the spiral pattern. A GALFIT model of disk plus amplified spiral components then produces segmentation maps from which arm skeletons are traced, and transverse masks are placed along each skeleton in steps of two pixels. At each step a skewed Gaussian is fit to the F150W and F444W flux profiles; the sign and magnitude of the peak offset, with a measured systematic uncertainty of ~0.13 kpc, is the arm's classification criterion. The physical interpretation leans on the local-universe result that dust produced in a spiral shock sits on the leading side of the arm, so a positive offset (optical ahead of near-infrared) marks a trailing density wave, a negative offset marks a tidally accelerated or leading wave, and no offset marks a co-rotating material origin.
What would settle it
Map the velocity field of one positive-offset arm (for example with ALMA CO or JWST/NIRSpec Hα) and check whether the arm pattern is indeed slower than the local disk rotation, as a trailing density wave requires; if the arm co-rotates with the disk or the optical peak trails in a galaxy without a companion, the offset-sign interpretation fails.
Extended reading notes
Core claim
On JWST/NIRCam images of eight spectroscopically confirmed massive star-forming galaxies at z ≈ 1.5, the paper detects eighteen spiral arms in residual images after subtracting a bulge-plus-disk model from the rest-frame near-infrared F444W data. Along each arm it measures a cross-arm flux profile in PSF-matched F150W (rest-frame optical) and F444W (rest-frame near-infrared) images, fits a skewed Gaussian to each profile, and records the peak-to-peak offset. Fifteen arms are detected in both bands. Nine have robust positive offsets of 0.2–0.8 kpc, meaning the optical peak lies ahead of the near-infrared arm in the direction of spiral propagation; the paper attributes these to dust produced in spiral shocks on the leading side of density-wave arms. Five arms have negative offsets of 0.2–0.8 kpc, three of them in two galaxies with clear companion interactions, and are attributed to tidally accelerated waves. Three remaining arms are detected only in F444W, and are attributed to stochastic clumpy star formation without a systematic velocity offset between arm and disk. The paper concludes that the population of z > 1 spirals is a mixture of these mechanisms, analogous to the local universe.
Load-bearing premise
The interpretation requires that dust created in a spiral shock lies on the leading side of the arm, dimming young stars so that their optical light peaks slightly ahead of the arm, exactly as in local spirals; if high-redshift clumpy disks place dust differently, or if the PSF-matching and deprojection steps create a systematic optical-ahead shift, the arm-by-arm classification loses its meaning even if the measured offsets are real.
Editorial extensions
If this is right
- Nine of the eighteen arms show the positive optical-ahead offset expected for density-wave shocks, so density waves appear to be a genuine, common mechanism for spiral structure at z ≈ 1.5.
- Five arms show the opposite sign, with three in interacting systems, indicating that tidal interactions can push spiral waves into the regime where they lead rather than trail the disk.
- The three arms detected only in the near-infrared imply a population of spiral features whose star formation is too clumpy or dust-obscured to produce a coherent optical arm, likely stochastic in origin.
- The offset varies along individual arms and appears to decline at large radius in at least one galaxy, a trend expected as arms approach corotation, so larger samples could use such gradients to estimate pattern speeds.
- Because the method measures offsets in a fixed, reproducible way, it can be applied directly to other JWST imaging surveys to build a statistical census of spiral-arm origin at high redshift.
Reading between the lines
- If offset sign is a faithful formation-mechanism tag, then the mix of signs in this small sample implies that at z ≈ 1.5 no single process dominates spiral-arm formation, and that kinematic follow-up, not morphology alone, is needed to confirm each arm's origin.
- A natural test would be to compare offset amplitude with gas fraction or clumpiness across a larger sample: the density-wave picture predicts stronger, more coherent positive offsets in smoother, less clumpy disks, and weaker or absent offsets in clumpy ones.
- The paper deliberately leaves out galaxies with faint, patchy arms, so the true fraction of stochastic, flocculent spirals at z > 1 is probably higher than the three-of-eighteen arms found here; simulations that add clumpy star formation to mock JWST images could calibrate this selection bias.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper uses JWST/NIRCam COSMOS-Web imaging of eight massive star-forming galaxies at z_spec ~ 1.5 to measure offsets between rest-frame optical (F150W) and rest-frame near-IR (F444W) flux distributions across 18 spiral arms. Spiral arm locations are determined from F444W bulge+disk subtraction, deprojection, polar-shapelet filtering of m=2-4 modes, GALFIT spiral models, and skeletonization; fluxes are then mapped in fixed masks perpendicular to the arm paths. The authors report offsets of order 0.2-0.8 kpc for 14 of 18 arms, with 9 arms showing positive offsets (interpreted as density-wave shocks), 5 showing negative offsets (interpreted as tidally driven arms), and the remaining arms showing no offset or no optical detection (interpreted as stochastic/clumpy star formation). The paper presents this as evidence that spiral arms at z > 1 have a multi-faceted origin similar to the local Universe.
Significance. If the physical interpretation holds, this is the first systematic, model-based quantification of spiral-arm color offsets at z > 1 and would open a genuinely new observational window on spiral-arm dynamics at cosmic noon. The methodological pipeline is transparent and largely reproducible with public tools and data: PSF-matched images, bulge+disk subtraction, shapelet-based arm isolation, model skeletons, skewed-Gaussian peak fitting, and an injected point-source estimate of systematic uncertainty (0.13 kpc). Credit is due for attempting a quantitative route where previous work at these redshifts was mostly visual. The weakness is interpretive: the sign of the offset is defined relative to an assumed trailing-wave propagation direction, no kinematic confirmation is available, the arm-by-arm counts are not statistically significant, and the sample is small and SFR-selected. As a method paper and pilot measurement the work is valuable; as a demonstration of the density-wave/tidal/clumpy classification it is not yet conclusive.
major comments (3)
- [Sec. 4, Sec. 5] The sign convention for positive vs. negative offsets is fixed by assigning each arm a propagation direction 'based on the expected propagation for density waves' (Sec. 4, paragraph beginning 'Additionally, we assign a direction'). This is equivalent to assuming all 18 arms are trailing. The subsequent classification of the five negative-offset arms as tidal is therefore not independent of the density-wave hypothesis: if any of these arms are leading density waves, their negative offsets would be the expected density-wave signature, not a tidal signature. The paper itself notes in Sec. 1 that strong tidal perturbations can produce leading waves (Thomasson et al. 1989; Buta et al. 1992, 2003), and three of the five negative-offset arms lie in the two galaxies with visible companions. Without an independent measurement of disk rotation direction and near/far side, or at least a quantitative defense of the trailing assumption, the abstract's conclusion that 'these offsets reflect the presence of density waves' and the 9/18 density-wave vs. 5/18 tidal tally are not uniquely determined by the data.
- [Sec. 4, Fig. 6] The sign distribution does not provide statistically significant support for a physical dichotomy. Among the 15 arms detected in both bands, 10 show positive offsets and 5 negative; the two-sided binomial probability of a result this extreme under a symmetric null is about p ~ 0.3. The text calls this a 'marginal bias toward positive values,' but the abstract and summary convert this into a confident 9/18 density-wave / 5/18 tidal classification. The paper should report a confidence interval on the fraction of positive offsets, account for the clustering of arms within only eight galaxies, and avoid drawing mechanistic conclusions from the raw sign counts alone.
- [Sec. 3.3, Sec. 2] The systematic uncertainty of 0.13 kpc is estimated with an injected point source that is run through the deprojection and reversal procedure, but the polar-shapelet transform used to determine mask positions is explicitly excluded from the error estimate, and the GALFIT spiral model that defines the arm paths is not part of the injection test either. In addition, the F150W image is PSF-matched to F444W with a Gaussian kernel (Sec. 2); any residual PSF mismatch or a wavelength-dependent centroid shift from the matching procedure would directly bias the measured offsets, whose quoted values are only ~0.2-0.8 kpc. The authors should quantify the PSF-matching contribution by repeating the measurement with the opposite matching direction or with simulated disks containing known injected offsets, and should include the arm-location uncertainty in the reported error budget.
minor comments (5)
- [Sec. 4, Eq. (1)] The parameter F in Eq. (1) combines an absolute offset in kpc with a dimensionless significance ratio; setting alpha = 0.5 treats these two quantities as commensurable, but no sensitivity test for alpha is reported. At minimum, the authors should show that the arm-by-arm classification is unchanged for alpha in a reasonable range such as 0.2-0.8.
- [Abstract, Sec. 4] The arm counts are presented inconsistently: the abstract refers to 'the remaining cases with no detected offsets' after 9 positive and 5 negative arms, leaving four arms, while Sec. 4 says three arms are not detected in F150W and one additional arm has a positive offset below the 0.13 kpc threshold. Please clarify in the abstract or text that the 'no offset' category includes both non-detections in F150W and sub-threshold offsets.
- [Fig. 1 caption] The caption states that 'the final two in red' denote possible interacting companions, but the red coloring is not visible in the monochrome/printed version of the figure and the IDs are not otherwise flagged in the text; please add an explicit marker or mention the IDs in the caption.
- [Throughout] The manuscript would benefit from an arm-by-arm table listing each arm's host ID, m-component, measured maximum offset, its uncertainty, the F-statistic value, and the assigned mechanism; this would greatly improve reproducibility given that the classification is the central result.
- [Throughout] Several LaTeX accent artifacts remain in the text (e.g., 'S ersic', 'F aisst', 'Mart ´ ınez-Garc ´ ıa'); these should be cleaned during production to avoid rendering issues.
Circularity Check
Measured offsets carry the result; the interpretive framework is external and testable, not definitional.
full rationale
The derivation chain is observational rather than circular: F150W and F444W flux peaks are measured by fitting skewed Gaussians to flux profiles across masks placed on model-derived spiral skeletons (Secs. 3.2-3.3). The resulting sign counts (9 positive, 5 negative, 3 none) are raw measurements, not outputs of a model fitted to the density-wave/tidal/clumpy classification. Eq. (1) merely selects the maximum offset per arm and does not encode the physical mechanism. The Sec. 5 interpretation imports local-universe priors, notably the dust-on-leading-side picture from Yu & Ho (2018) and the kinematic assumption that arms trail below corotation; these are external assumptions, not identities within the paper. The self-citations (Kalita et al. 2024a,b; Yu & Ho 2018, 2020) supply the sample and interpretive framework but do not force the measured sign distribution. The Sec. 4 statement that arm direction is assigned based on expected density-wave propagation could be wrong for individual interacting systems, but that is a scientific limitation or robustness concern, not a circular reduction: the measured offsets remain independent of that assignment. No equation equates a fitted parameter with a predicted quantity, and no claim reduces by construction to its input.
Assumptions & free parameters
free parameters (3)
- alpha in F statistic =
0.5
- m=2,3,4 harmonic filtering
- Mask dimensions =
15 x 3 pixels
assumptions (3)
- domain assumption The near-IR bulge-disk model gives the correct inclination and deprojection for the galaxy disk
- domain assumption Spiral shock dust resides on the leading side of the density-wave arm in high-redshift galaxies, as inferred locally
- domain assumption Trailing arms and sub-corotation dynamics apply to z~1.5 disks
Cite this review
Pith. "Pith review of A multi-wavelength investigation of spiral structures in $z > 1$ galaxies with JWST." pith.science (2026). https://pith.science/paper/XTSXO2XF
@misc{pith2026250103325,
author = {Pith},
title = {Pith review of: A multi-wavelength investigation of spiral structures in $z > 1$ galaxies with JWST},
year = {2026},
howpublished = {\url{https://pith.science/paper/XTSXO2XF}},
note = {Machine review of arXiv:2501.03325}
}
abstract
Recent JWST observations have revealed the prevalence of spiral structures at $z > 1$. Unlike in the local Universe, the origin and the consequence of spirals at this epoch remain unexplored. We use public JWST/NIRCam data from the COSMOS-Web survey to map spiral structures in eight massive ($> 10^{10.5}\,\rm M_{\odot}$) star-forming galaxies at $z_{\rm spec} \sim 1.5$. We present a method for systematically quantifying spiral arms at $z>1$, enabling direct measurements of flux distributions. Using rest-frame near-IR images, we construct morphological models accurately tracing spiral arms. We detect offsets ($\sim 0.2 - 0.8\,\rm kpc$) between the rest-frame optical and near-IR flux distributions across most arms. Drawing parallels to the local Universe, we conclude that these offsets reflect the presence of density waves. For nine out of eighteen arms, the offsets indicate spiral shocks triggered by density waves. Five arms have offsets in the opposite direction and are likely associated with tidal interactions. For the remaining cases with no detected offsets, we suggest that stochastic 'clumpy' star formation is the primary driver of their formation. In conclusion, we find a multi-faceted nature of spiral arms at $z > 1$, similar to that in the local Universe.
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Works this paper leans on
-
[1]
Athanassoula, E., Romero-G´ omez, M., & Masdemont, J. J. 2009, MNRAS, 394, 67, doi: 10.1111/j.1365-2966.2008.14273.x
arXiv 2009
-
[2]
Baba, J., Saitoh, T. R., & Wada, K. 2013, ApJ, 763, 46, doi: 10.1088/0004-637X/763/1/46
-
[3]
2011, in Astronomical Society of the Pacific Conference Series, Vol
Bertin, E. 2011, in Astronomical Society of the Pacific Conference Series, Vol. 442, Astronomical Data Analysis Software and Systems XX, ed. I. N. Evans, A. Accomazzi, D. J. Mink, & A. H. Rots, 435
2011
-
[4]
2008, Galactic Dynamics: Second Edition (Princeton University Press)
Binney, J., & Tremaine, S. 2008, Galactic Dynamics: Second Edition (Princeton University Press)
2008
-
[5]
2018, Physics of the Dark Universe, 22, 189, doi: 10.1016/j.dark.2018.11.002
Birrer, S., & Amara, A. 2018, Physics of the Dark Universe, 22, 189, doi: 10.1016/j.dark.2018.11.002
-
[6]
2021, The Journal of Open Source Software, 6, 3283, doi: 10.21105/joss.03283
Birrer, S., Shajib, A., Gilman, D., et al. 2021, The Journal of Open Source Software, 6, 3283, doi: 10.21105/joss.03283
-
[7]
2003, MNRAS, 344, 358, doi: 10.1046/j.1365-8711.2003.06613.x 11
Bottema, R. 2003, MNRAS, 344, 358, doi: 10.1046/j.1365-8711.2003.06613.x 11
arXiv 2003
-
[8]
Buta, R., Crocker, D. A., & Byrd, G. G. 1992, AJ, 103, 1526, doi: 10.1086/116165
Show all 90 references
-
[9]
J., Byrd, G
Buta, R. J., Byrd, G. G., & Freeman, T. 2003, AJ, 125, 634, doi: 10.1086/345821
2003 doi
-
[10]
J., Sheth, K., Athanassoula, E., et al
Buta, R. J., Sheth, K., Athanassoula, E., et al. 2015, ApJS, 217, 32, doi: 10.1088/0067-0049/217/2/32
2015 doi
-
[11]
M., Kartaltepe, J
Casey, C. M., Kartaltepe, J. S., Drakos, N. E., et al. 2023, ApJ, 954, 31, doi: 10.3847/1538-4357/acc2bc
2023 doi
-
[12]
2023, MNRAS, 520, 2180, doi: 10.1093/mnras/stac3791
Claeyssens, A., Adamo, A., Richard, J., et al. 2023, MNRAS, 520, 2180, doi: 10.1093/mnras/stac3791
2023 doi
-
[13]
2016, A&A, 591, A49, doi: 10.1051/0004-6361/201527866
Contini, T., Epinat, B., Bouch´ e, N., et al. 2016, A&A, 591, A49, doi: 10.1051/0004-6361/201527866
2016 doi
-
[14]
2010, ApJ, 713, 686, doi: 10.1088/0004-637X/713/1/686 de Vaucouleurs, G
Daddi, E., Bournaud, F., Walter, F., et al. 2010, ApJ, 713, 686, doi: 10.1088/0004-637X/713/1/686 de Vaucouleurs, G. 1959, Handbuch der Physik, 53, 275, doi: 10.1007/978-3-642-45932-0 7
2010 doi
-
[15]
2020, ApJ, 888, 37, doi: 10.3847/1538-4357/ab5b90
Ding, X., Silverman, J., Treu, T., et al. 2020, ApJ, 888, 37, doi: 10.3847/1538-4357/ab5b90
2020 doi
-
[16]
2014, PASA, 31, e035, doi: 10.1017/pasa.2014.31
Dobbs, C., & Baba, J. 2014, PASA, 31, e035, doi: 10.1017/pasa.2014.31
2014 doi
-
[18]
L., Theis, C., Pringle, J
Dobbs, C. L., Theis, C., Pringle, J. E., & Bate, M. R. 2010, MNRAS, 403, 625, doi: 10.1111/j.1365-2966.2009.16161.x
2010
-
[19]
2018, A&A, 616, A110, doi: 10.1051/0004-6361/201732370
Elbaz, D., Leiton, R., Nagar, N., et al. 2018, A&A, 616, A110, doi: 10.1051/0004-6361/201732370
2018 doi
-
[20]
G., Bournaud, F., & Elmegreen, D
Elmegreen, B. G., Bournaud, F., & Elmegreen, D. M. 2008, ApJ, 688, 67, doi: 10.1086/592190
2008 doi
-
[21]
M., & Elmegreen, B
Elmegreen, D. M., & Elmegreen, B. G. 1987, ApJ, 314, 3, doi: 10.1086/165034 —. 2014, ApJ, 781, 11, doi: 10.1088/0004-637X/781/1/11
1987 doi
-
[22]
M., Elmegreen, B
Elmegreen, D. M., Elmegreen, B. G., & Dressler, A. 1982, MNRAS, 201, 1035, doi: 10.1093/mnras/201.4.1035
1982 doi
- [23]
-
[24]
B., & Drory, N
Fisher, D. B., & Drory, N. 2008, AJ, 136, 773, doi: 10.1088/0004-6256/136/2/773 F¨ orster Schreiber, N. M., Shapley, A. E., Genzel, R., et al. 2011, ApJ, 739, 45, doi: 10.1088/0004-637X/739/1/45
2008 doi
-
[25]
S., Baba, J., Saitoh, T
Fujii, M. S., Baba, J., Saitoh, T. R., et al. 2011, ApJ, 730, 109, doi: 10.1088/0004-637X/730/2/109
2011 doi
-
[26]
E., Smail, I., Moran, S
Geach, J. E., Smail, I., Moran, S. M., et al. 2011, ApJL, 730, L19, doi: 10.1088/2041-8205/730/2/L19
2011 doi
-
[27]
2011, ApJ, 733, 101, doi: 10.1088/0004-637X/733/2/101
Genzel, R., Newman, S., Jones, T., et al. 2011, ApJ, 733, 101, doi: 10.1088/0004-637X/733/2/101
2011 doi
-
[28]
B., Liu, D., et al
Genzel, R., Jolly, J. B., Liu, D., et al. 2023, ApJ, 957, 48, doi: 10.3847/1538-4357/acef1a
2023 doi
-
[29]
Gerola, H., & Seiden, P. E. 1978, ApJ, 223, 129, doi: 10.1086/156243
1978 doi
-
[30]
L., Swinbank, A
Gillman, S., Tiley, A. L., Swinbank, A. M., et al. 2020, MNRAS, 492, 1492, doi: 10.1093/mnras/stz3576
2020 doi
-
[31]
M., & Clarke, C
Gittins, D. M., & Clarke, C. J. 2004, MNRAS, 349, 909, doi: 10.1111/j.1365-2966.2004.07560.x G´ omez-Guijarro, C., Toft, S., Karim, A., et al. 2018, ApJ, 856, 121, doi: 10.3847/1538-4357/aab206
2004
- [32]
-
[33]
C., Bell, E
Guo, Y., Ferguson, H. C., Bell, E. F., et al. 2015, ApJ, 800, 39, doi: 10.1088/0004-637X/800/1/39
2015 doi
- [34]
-
[35]
M., Johnson, H
Harrison, C. M., Johnson, H. L., Swinbank, A. M., et al. 2017, MNRAS, 467, 1965, doi: 10.1093/mnras/stx217
2017 doi
-
[36]
Hubble, E. P. 1926, ApJ, 63, 236, doi: 10.1086/142976
1926 doi
-
[37]
2023, ApJL, 948, L13, doi: 10.3847/2041-8213/accd6d
Jacobs, C., Glazebrook, K., Calabr` o, A., et al. 2023, ApJL, 948, L13, doi: 10.3847/2041-8213/accd6d
2023 doi
-
[38]
1994, A&A, 287, 55
Jungwiert, B., & Palous, J. 1994, A&A, 287, 55
1994
-
[39]
S., Silverman, J
Kalita, B. S., Silverman, J. D., Daddi, E., et al. 2024a, ApJ, 960, 25, doi: 10.3847/1538-4357/acfee4
-
[40]
S., Suzuki, T
Kalita, B. S., Suzuki, T. L., Kashino, D., et al. 2024b, MNRAS, doi: 10.1093/mnras/stae2781
-
[41]
Kalnajs, A. J. 1973, PASA, 2, 174, doi: 10.1017/S1323358000013461
1973 doi
-
[42]
D., Rodighiero, G., et al
Kashino, D., Silverman, J. D., Rodighiero, G., et al. 2013, ApJL, 777, L8, doi: 10.1088/2041-8205/777/1/L8
2013 doi
-
[43]
D., Sanders, D., et al
Kashino, D., Silverman, J. D., Sanders, D., et al. 2019, ApJS, 241, 10, doi: 10.3847/1538-4365/ab06c4
2019 doi
-
[44]
C., & Clarke, C
Kendall, S., Kennicutt, R. C., & Clarke, C. 2011, MNRAS, 414, 538, doi: 10.1111/j.1365-2966.2011.18422.x
2011
-
[45]
1995, in Proceedings of ICNN’95 - International Conference on Neural Networks, doi: 10.1109/ICNN.1995.488968
Kennedy, J., & Eberhart, R. 1995, in Proceedings of ICNN’95 - International Conference on Neural Networks, doi: 10.1109/ICNN.1995.488968
1995
-
[46]
2014, MNRAS, 440, 208, doi: 10.1093/mnras/stu276
Kim, Y., & Kim, W.-T. 2014, MNRAS, 440, 208, doi: 10.1093/mnras/stu276
2014 doi
-
[47]
2024, ApJL, 968, L15, doi: 10.3847/2041-8213/ad43eb
Kuhn, V., Guo, Y., Martin, A., et al. 2024, ApJL, 968, L15, doi: 10.3847/2041-8213/ad43eb
2024 doi
-
[48]
J., Ilbert, O., et al
Laigle, C., McCracken, H. J., Ilbert, O., et al. 2016, ApJS, 224, 24, doi: 10.3847/0067-0049/224/2/24
2016 doi
-
[49]
S., et al
Lang, P., Wuyts, S., Somerville, R. S., et al. 2014, ApJ, 788, 11, doi: 10.1088/0004-637X/788/1/11
2014 doi
-
[50]
D., Daddi, E., et al
Liu, Z., Silverman, J. D., Daddi, E., et al. 2024, ApJ, 968, 15, doi: 10.3847/1538-4357/ad4096
2024 doi
-
[51]
M., Jonsson, P., Cox, T
Lotz, J. M., Jonsson, P., Cox, T. J., et al. 2011, ApJ, 742, 103, doi: 10.1088/0004-637X/742/2/103
2011 doi
-
[52]
1970, ApJL, 159, L151, doi: 10.1086/180500
Lynds, R. 1970, ApJL, 159, L151, doi: 10.1086/180500
1970 doi
-
[53]
2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615
Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615
2014 doi
-
[54]
2019, A&A, 621, L6, doi: 10.1051/0004-6361/201834456 12
Marasco, A., Fraternali, F., Posti, L., et al. 2019, A&A, 621, L6, doi: 10.1051/0004-6361/201834456 12
2019 doi
-
[55]
J., Haeussler, B., et al
Margalef-Bentabol, B., Conselice, C. J., Haeussler, B., et al. 2022, MNRAS, 511, 1502, doi: 10.1093/mnras/stac080 Mart ´ ınez-Garc ´ ıa, E. E., & Gonz´ alez-L´ opezlira, R. A. 2013, ApJ, 765, 105, doi: 10.1088/0004-637X/765/2/105 Mart ´ ınez-Garc ´ ıa, E. E., Gonz´ alez-L´ ope...
2022 doi
-
[56]
2023, MNRAS, 524, 18, doi: 10.1093/mnras/stad1805
Puerari, I. 2023, MNRAS, 524, 18, doi: 10.1093/mnras/stad1805
2023 doi
- [57]
-
[58]
E., Schinnerer, E., Garc ´ ıa-Burillo, S., et al
Meidt, S. E., Schinnerer, E., Garc ´ ıa-Burillo, S., et al. 2013, ApJ, 779, 45, doi: 10.1088/0004-637X/779/1/45
2013 doi
- [59]
-
[60]
B., & Abraham, R
Nair, P. B., & Abraham, R. G. 2010, ApJS, 186, 427, doi: 10.1088/0067-0049/186/2/427
2010 doi
-
[61]
2001, AJ, 121, 1024, doi: 10.1086/318762
Nomura, H., & Kamaya, H. 2001, AJ, 121, 1024, doi: 10.1086/318762
2001 doi
-
[62]
H., Kim, W.-T., Lee, H
Oh, S. H., Kim, W.-T., Lee, H. M., & Kim, J. 2008, ApJ, 683, 94, doi: 10.1086/588184
2008 doi
-
[63]
Y., Ho, L
Peng, C. Y., Ho, L. C., Impey, C. D., & Rix, H.-W. 2010, AJ, 139, 2097, doi: 10.1088/0004-6256/139/6/2097
2010 doi
-
[64]
L., Garuda, N., et al
Polletta, M., Frye, B. L., Garuda, N., et al. 2024, A&A, 690, A285, doi: 10.1051/0004-6361/202450671
2024 doi
-
[65]
M., & Pezzulli, G
Posti, L., Fraternali, F., Di Teodoro, E. M., & Pezzulli, G. 2018, A&A, 612, L6, doi: 10.1051/0004-6361/201833091
2018 doi
-
[66]
2021, MNRAS, 508, 5217, doi: 10.1093/mnras/stab2914
Puglisi, A., Daddi, E., Valentino, F., et al. 2021, MNRAS, 508, 5217, doi: 10.1093/mnras/stab2914
2021 doi
-
[67]
Reynolds, J. H. 1927, The Observatory, 50, 185
1927
-
[68]
Roberts, W. W. 1969, ApJ, 158, 123, doi: 10.1086/150177
1969 doi
-
[69]
C., Daddi, E., et al
Rujopakarn, W., Williams, C. C., Daddi, E., et al. 2023, ApJL, 948, L8, doi: 10.3847/2041-8213/accc82
2023 doi
-
[70]
2000, MNRAS, 319, 377, doi: 10.1046/j.1365-8711.2000.03650.x
Salo, H., & Laurikainen, E. 2000, MNRAS, 319, 377, doi: 10.1046/j.1365-8711.2000.03650.x
2000
-
[71]
2023, ApJ, 951, 147, doi: 10.3847/1538-4357/acd5d6
Sattari, Z., Mobasher, B., Chartab, N., et al. 2023, ApJ, 951, 147, doi: 10.3847/1538-4357/acd5d6
2023 doi
-
[72]
2007, ApJS, 172, 1, doi: 10.1086/516585
Scoville, N., Aussel, H., Brusa, M., et al. 2007, ApJS, 172, 1, doi: 10.1086/516585
2007 doi
-
[73]
A., & Carlberg, R
Sellwood, J. A., & Carlberg, R. G. 1984, ApJ, 282, 61, doi: 10.1086/162176 —. 2019, MNRAS, 489, 116, doi: 10.1093/mnras/stz2132
1984 doi
-
[74]
Shetty, R., & Ostriker, E. C. 2008, ApJ, 684, 978, doi: 10.1086/590383
2008 doi
-
[75]
Shu, F. H. 1970, ApJ, 160, 89, doi: 10.1086/150409 —. 2016, ARA&A, 54, 667, doi: 10.1146/annurev-astro-081915-023426
1970 doi
-
[76]
D., Kashino, D., Sanders, D., et al
Silverman, J. D., Kashino, D., Sanders, D., et al. 2015, ApJS, 220, 12, doi: 10.1088/0067-0049/220/1/12
2015 doi
-
[77]
P., & Alexander, P
Sleath, J. P., & Alexander, P. 1995, MNRAS, 275, 507, doi: 10.1093/mnras/275.2.507
1995 doi
-
[78]
Silverman, J. D. 2014, ApJS, 214, 15, doi: 10.1088/0067-0049/214/2/15
2014 doi
-
[79]
P., Swinbank, A
Stott, J. P., Swinbank, A. M., Johnson, H. L., et al. 2016, MNRAS, 457, 1888, doi: 10.1093/mnras/stw129
2016 doi
-
[80]
J., Genzel, R., Neri, R., et al
Tacconi, L. J., Genzel, R., Neri, R., et al. 2010, Nature, 463, 781, doi: 10.1038/nature08773
2010 doi
-
[81]
2024a, A&A, 684, A23, doi: 10.1051/0004-6361/202347255
Tan, Q.-H., Daddi, E., de Souza Magalh˜ aes, V., et al. 2024a, A&A, 684, A23, doi: 10.1051/0004-6361/202347255
- [82]
-
[83]
J., Sundelius, B., et al
Thomasson, M., Donner, K. J., Sundelius, B., et al. 1989, A&A, 211, 25
1989
-
[84]
2014, ApJ, 782, 68, doi: 10.1088/0004-637X/782/2/68
Toft, S., Smolˇ ci´ c, V., Magnelli, B., et al. 2014, ApJ, 782, 68, doi: 10.1088/0004-637X/782/2/68
2014 doi
-
[85]
1969, ApJ, 158, 899, doi: 10.1086/150250 —
Toomre, A. 1969, ApJ, 158, 899, doi: 10.1086/150250 —. 1977, ARA&A, 15, 437, doi: 10.1146/annurev.aa.15.090177.002253
1969
-
[86]
1972, ApJ, 178, 623, doi: 10.1086/151823
Toomre, A., & Toomre, J. 1972, ApJ, 178, 623, doi: 10.1086/151823
1972 doi
-
[87]
W., Lintott, C
Willett, K. W., Lintott, C. J., Bamford, S. P., et al. 2013, MNRAS, 435, 2835, doi: 10.1093/mnras/stt1458
2013 doi
-
[88]
Yu, S.-Y., Cheng, C., Pan, Y., Sun, F., & Li, Y. A. 2023, A&A, 676, A74, doi: 10.1051/0004-6361/202346140
2023 doi
-
[89]
Yu, S.-Y., & Ho, L. C. 2018, ApJ, 869, 29, doi: 10.3847/1538-4357/aaeacd —. 2020, ApJ, 900, 150, doi: 10.3847/1538-4357/abac5b
2018 doi
-
[90]
C., Barth, A
Yu, S.-Y., Ho, L. C., Barth, A. J., & Li, Z.-Y. 2018, ApJ, 862, 13, doi: 10.3847/1538-4357/aacb25
2018 doi
-
[91]
C., & Wang, J
Yu, S.-Y., Ho, L. C., & Wang, J. 2021, ApJ, 917, 88, doi: 10.3847/1538-4357/ac0c77
2021 doi
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