pith. sign in

arxiv: 2606.26531 · v1 · pith:MUZJ5FAInew · submitted 2026-06-25 · 🌌 astro-ph.GA

UV Star-Formation Rates of the SHIELD Dwarf Galaxies

David G. Gormanous , Anjali S. Dziarski , John J. Salzer , John M. Cannon , Steven Janowiecki , Nathalie Haurberg , April Horton , Elizabeth A. K. Adams
show 6 more authors
Martha P. Haynes Myles J. Klapkowski Joshua R. Marine Kristen B. W. McQuinn Katherine L. Rhode Evan D. Skillman
This is my paper

Pith reviewed 2026-06-26 04:59 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords dwarf galaxiesstar formation ratesFUVGALEXHαSHIELDbaryonic massgas depletion
0
0 comments X

The pith

FUV star-formation rates remain reliable even at the lowest dwarf galaxy masses while Hα rates underestimate them.

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

The paper uses GALEX far-ultraviolet fluxes to calculate star-formation rates for the full sample of 75 gas-rich dwarf galaxies in the SHIELD survey. It finds that these FUV rates hold as consistent tracers down to the smallest masses studied, unlike Hα rates which can severely underestimate the true activity due to the bursty, stochastic nature of star formation in such systems. Baryonic mass emerges as the strongest mass-based predictor of the FUV SFR among the quantities examined. The galaxies also display long gas depletion timescales and high gas-to-stellar mass ratios, with 68 percent having more gas than stars.

Core claim

FUV SFRs appear to be robust tracers of star formation down to the very lowest galaxy masses included in this study. Baryonic mass is the best mass-related tracer for the prediction of the FUV SFR of the galaxies in our sample. When comparing the Hα SFRs to the FUV SFRs for SHIELD and other local galaxy surveys, we confirm and solidify previous results that show that Hα-based SFRs for dwarf galaxies can grossly underestimate the true rate of star formation, emphasizing the highly stochastic nature of star formation in extremely low-mass galaxies.

What carries the argument

GALEX FUV flux measurements converted to star-formation rates via standard calibrations, then compared against Hα SFRs and mass tracers including baryonic mass.

If this is right

  • Hα-based SFR estimates alone are unreliable for dwarf galaxies and should be supplemented or replaced by FUV data.
  • Baryonic mass provides the most direct mass-based way to predict FUV SFR in similar low-mass systems.
  • Star formation in these galaxies proceeds in a highly stochastic manner, producing long gas depletion times.
  • 68 percent of the sample galaxies are gas-dominated, with gas mass exceeding stellar mass.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Complete censuses of star formation across the galaxy population will require UV data for the numerous faint dwarfs that dominate number counts.
  • Models of galaxy assembly should build in bursty star-formation histories when simulating systems below 10^8 solar masses.
  • Surveys aiming to measure SFRs in low-mass galaxies should prioritize UV imaging over single-epoch Hα observations.

Load-bearing premise

Standard calibrations that turn observed FUV fluxes into star-formation rates remain accurate for these extremely low-mass, low-metallicity systems without large biases from dust, burst age, or metallicity effects.

What would settle it

Finding large systematic offsets between FUV SFRs and independent tracers such as infrared luminosity or radio continuum emission in a comparable sample of still lower-mass dwarfs would falsify the robustness of FUV as a tracer.

Figures

Figures reproduced from arXiv: 2606.26531 by Anjali S. Dziarski, April Horton, David G. Gormanous, Elizabeth A. K. Adams, Evan D. Skillman, John J. Salzer, John M. Cannon, Joshua R. Marine, Katherine L. Rhode, Kristen B. W. McQuinn, Martha P. Haynes, Myles J. Klapkowski, Nathalie Haurberg, Steven Janowiecki.

Figure 1
Figure 1. Figure 1: Graph of FUV flux vs. NUV flux for the SHIELD galaxies in units of counts s−1 . The blue diamonds indicate galaxies with measured FUV fluxes and the orange diamonds indicate galaxies with predicted FUV fluxes. The dashed line indicates a best fit line with a slope of 0.227. The scatter about the best fit line is 0.21 counts s−1 . 200232, AGC 238890, AGC 215282, and AGC 229052). One galaxy (AGC 124056) was … view at source ↗
Figure 2
Figure 2. Figure 2: Example images for four representative SHIELD galaxies. Each row contains three images of the same galaxy obtained in distinct wavelength regimes: NUV, optical, and NIR. The individual image cutouts are each 1 by 1 arcmin on a side. The four galaxies shown cover the full range of observed FUV SFR for the SHIELD sample, and are ordered by decreasing SFR: top – AGC 749237, log(SFRF UV ) = −2.16; 2nd row – AG… view at source ↗
Figure 3
Figure 3. Figure 3: Log-log graph of the CMD-based SFR200Myr (see text) vs. our FUV SFR. The solid line represents equality. The dashed line indicates a weighted bivariate best fit line with a slope of 0.95 ± 0.08. The orthogonal scatter about the best fit line is 0.21 dex. The general agreement between the two independent measures of the SFRs of the SHIELD galaxies shown in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Log-log graph of (Hα SFR/FUV SFR) vs. FUV SFR for SHIELD as well as other nearby galaxy surveys. The open diamonds indicate SHIELD galaxies whose Hα fluxes represent 3σ upper limits; upper-limit arrows are shown on only a few of these open diamonds. The solid line indicates (SF RHα/SF RF UV ) = 1. the IMF in the low-mass, low-SFR regime, combined with the non-uniform sampling of the star-formation his￾tory… view at source ↗
Figure 6
Figure 6. Figure 6: Log-log graph of H I gas mass vs. FUV SFR for SHIELD galaxies as well as other nearby galaxy surveys. The slope of the best fit and its uncertainty has been calculated for each sample, as well as a best fit line and its uncertainty for all of the samples combined. The SHIELD best fit line has been extended for clarity. paper (Haynes et al. 2011, 2018), but using our updated TRGB distances when these are av… view at source ↗
Figure 8
Figure 8. Figure 8: Log-log graph of Baryonic Mass vs. FUV SFR. The dashed line indicates a best fit line with a slope of 0.90 ± 0.02. The scatter about the best fit line is 0.26 dex. made to account for any molecular gas present in these systems, since the direct measurement of molecular gas via the detection of the CO rotational lines is notoriously difficult in metal-poor galaxies (Sage et al. 1992; Taylor et al. 1998; Ler… view at source ↗
Figure 9
Figure 9. Figure 9: Graph of the B-band absolute magnitude vs. the log of FUV SFR. The dashed line indicates a best fit line with a slope of 2.38 ± 0.04. The scatter about the best fit line is 0.87 magnitudes. tude scales with stellar mass in a predictable way. We include the same comparison samples employed in the previous plots in this subsection. Visual inspection sug￾gests a larger scatter in this “mass” - SFR comparison,… view at source ↗
Figure 10
Figure 10. Figure 10: Log-log graph of Hα-based gas depletion timescale (GDT) vs. FUV-based GDT. The solid black di￾agonal line represents equality between the two GDTs and the solid gray lines denote the age of the universe. about the lack of molecular gas measurements in dwarf galaxies, we will compute the GDT for each SHIELD galaxy using the following equation: GDT = Mgas/SF R, (7) where Mgas is given by equation 6. We comp… view at source ↗
Figure 12
Figure 12. Figure 12: Graph of the log of (Hα SFR/FUV SFR) vs. baryonic gas mass fraction (Mgas / Mbaryonic). The vertical solid line indicates Mgas = Mbaryonic (starless galaxies), and the horizontal solid line indicates SFRHα/SFRF UV = 1. A basic assumption that one might make is that a galaxy’s SFR depends on its gas fraction, since galaxies with higher gas content might be expected to be more ef￾fective at turning their ga… view at source ↗
Figure 13
Figure 13. Figure 13: Log-log graph of Stellar Mass vs. FUV sSFR. In addition to the SHIELD galaxies we include the comparison samples from FIGGS, LITTLE THINGS, VLA ANGST, and 11HUGs. The SHIELD galaxies anchor the low-mass end of plot. et al. 2011; Teich et al. 2016). This surface density of H I gas is usually considered to be the lower limit nec￾essary for the formation of giant H II region complexes (e.g., Skillman 1987; K… view at source ↗
read the original abstract

The Survey of HI in Extremely Low-mass Dwarfs (SHIELD) is a multi-wavelength observational project targeting gas-rich, star-forming dwarf galaxies at the faint end of the HI mass function. We present near-ultraviolet (NUV) and far-ultraviolet (FUV) flux measurements obtained from GALEX survey images and use these fluxes to derive FUV star-formation rates (SFRs) for all 75 SHIELD galaxies with GALEX data. This paper represents the first published analysis that makes use of the full SHIELD sample. We compare the FUV SFRs to a variety of physical quantities to better understand the nature of SHIELD galaxies. When comparing the H$\alpha$ SFRs to the FUV SFRs for SHIELD and other local galaxy surveys, we confirm and solidify previous results that show that H$\alpha$-based SFRs for dwarf galaxies can grossly underestimate the true rate of star formation, emphasizing the highly stochastic nature of star formation in extremely low-mass galaxies. We further show that FUV SFRs appear to be robust tracers of star formation down to the very lowest galaxy masses included in this study. We show that baryonic mass is the best mass-related tracer for the prediction of the FUV SFR of the galaxies in our sample. Not surprisingly, the SHIELD dwarf galaxies exhibit long gas-depletion timescales and large gas mass fractions: fully 68% of the SHIELD galaxies have gas masses that are larger than their stellar masses.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents NUV and FUV flux measurements from GALEX images for 75 SHIELD dwarf galaxies and derives FUV star-formation rates (SFRs). It compares these FUV SFRs to Hα SFRs from the same and other local surveys, to baryonic mass, and to other physical quantities. The central claims are that FUV SFRs remain robust tracers down to the lowest galaxy masses in the sample, that Hα-based SFRs grossly underestimate the true rate due to stochastic star formation in dwarfs, that baryonic mass is the best mass-related predictor of FUV SFR, and that the galaxies show long gas-depletion times with 68% having gas mass exceeding stellar mass.

Significance. If the FUV calibration holds without large systematic offsets, the work provides a valuable extension of SFR measurements to the faint end of the HI mass function, reinforcing evidence for highly stochastic star formation in low-mass systems and the limitations of Hα as a tracer. The sample size and direct comparison across surveys add weight to prior indications that UV-based rates are preferable for such galaxies.

major comments (2)
  1. [SFR derivation and abstract claims] The derivation of FUV SFRs (abstract and methods) applies standard GALEX calibrations to obtain the rates used for all subsequent claims, but supplies no explicit test (metallicity-dependent models, SED fitting cross-check, or variation of the conversion factor across the sample) for biases from low metallicity or bursty SFH. SHIELD galaxies have metallicities << solar; if the true conversion varies by >0.3 dex the 'robust tracer' conclusion and the Hα-underestimation result are conditional on an untested scaling.
  2. [Hα vs. FUV comparison] The comparison of Hα to FUV SFRs (abstract) concludes that Hα 'grossly underestimate[s]' the true rate, but this rests on the same unvalidated FUV calibration; without a quantitative assessment of possible systematic offsets in the FUV-to-SFR conversion the magnitude of the underestimation cannot be established.
minor comments (1)
  1. [Abstract] The abstract states results from data analysis but supplies no details on flux extraction, calibration, error propagation, sample selection, or comparison methodology; these should be added or referenced to allow evaluation of the measurements.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive comments, which highlight important considerations for the FUV SFR calibration. We address each major comment below and are prepared to revise the manuscript accordingly to strengthen the presentation of uncertainties.

read point-by-point responses

  1. Referee: [SFR derivation and abstract claims] The derivation of FUV SFRs (abstract and methods) applies standard GALEX calibrations to obtain the rates used for all subsequent claims, but supplies no explicit test (metallicity-dependent models, SED fitting cross-check, or variation of the conversion factor across the sample) for biases from low metallicity or bursty SFH. SHIELD galaxies have metallicities << solar; if the true conversion varies by >0.3 dex the 'robust tracer' conclusion and the Hα-underestimation result are conditional on an untested scaling.

    Authors: We acknowledge that the manuscript applies the standard GALEX FUV calibration without performing new metallicity-dependent modeling, SED fitting cross-checks, or explicit variation of the conversion factor within the sample. The calibration follows the widely adopted relation from Kennicutt & Evans (2012) and is consistent with its use in prior studies of low-mass galaxies. Our claim of robustness is based on the observed consistency of FUV SFR trends with other local surveys rather than a new internal validation. We will revise the methods and discussion sections to add an explicit paragraph discussing potential systematic offsets at low metallicity (<< solar) and bursty SFHs, citing relevant literature on calibration variations, to clarify the assumptions underlying the 'robust tracer' statement. revision: yes

  2. Referee: [Hα vs. FUV comparison] The comparison of Hα to FUV SFRs (abstract) concludes that Hα 'grossly underestimate[s]' the true rate, but this rests on the same unvalidated FUV calibration; without a quantitative assessment of possible systematic offsets in the FUV-to-SFR conversion the magnitude of the underestimation cannot be established.

    Authors: The statement that Hα SFRs grossly underestimate the true rate is based on the systematic offset observed between Hα and FUV measurements both within SHIELD and when compared to other local surveys. We agree that without a quantitative assessment of possible FUV calibration offsets the precise magnitude remains conditional. In revision we will expand the relevant section to include a quantitative discussion of typical offsets reported in the literature for low-metallicity systems and to temper the language around the magnitude of underestimation while retaining the qualitative conclusion supported by the multi-survey comparison. revision: yes

Circularity Check

0 steps flagged

No circularity in observational derivation chain

full rationale

The paper measures GALEX FUV/NUV fluxes for the SHIELD sample and applies standard calibrations to obtain FUV SFRs. These are then compared directly to Hα SFRs, baryonic mass, and other surveys without any parameter fitting to subsets of the data or presentation of fitted quantities as independent predictions. No self-definitional loops, no load-bearing self-citations that reduce the central claims to unverified inputs, and no ansatz smuggling or renaming of known results. The derivation chain consists of direct measurements and cross-survey comparisons that remain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Observational study relying on established UV-to-SFR conversion methods from prior literature; no free parameters or invented entities introduced in the abstract.

axioms (1)
  • domain assumption Standard calibration relating FUV luminosity to star-formation rate holds for these low-mass, low-metallicity systems.
    The derivation of FUV SFRs from measured fluxes depends on this established relation.

pith-pipeline@v0.9.1-grok · 5869 in / 1326 out tokens · 35997 ms · 2026-06-26T04:59:38.746744+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

55 extracted references · 50 canonical work pages · 3 internal anchors

  1. [1]

    , keywords =

    Aver, E., Berg, D. A., Hirschauer, A. S., et al. 2022, MNRAS, 510, 373, doi: 10.1093/mnras/stab3226

    work page doi:10.1093/mnras/stab3226 2022

  2. [2]

    Sharina, M. E. 2008a, MNRAS, 386, 138, doi: 10.1111/j.1365-2966.2008.13010.x 20Gormanous et al

    work page doi:10.1111/j.1365-2966.2008.13010.x 2008

  3. [3]

    C., & Szapudi, I

    Begum, A., Chengalur, J. N., Karachentsev, I. D., Sharina, M. E., & Kaisin, S. S. 2008b, MNRAS, 386, 1667, doi: 10.1111/j.1365-2966.2008.13150.x

    work page doi:10.1111/j.1365-2966.2008.13150.x 2008

  4. [4]

    , eprint =

    Bell, E. F., & de Jong, R. S. 2001, ApJ, 550, 212, doi: 10.1086/319728

    work page doi:10.1086/319728 2001

  5. [5]

    F., & Kennicutt, Jr., R

    Bell, E. F., & Kennicutt, Jr., R. C. 2001, ApJ, 548, 681, doi: 10.1086/319025

    work page doi:10.1086/319025 2001

  6. [6]

    2020, ApJ, 898, 68, doi: 10.3847/1538-4357/ab9ec0

    Helou, G. 2020, ApJ, 898, 68, doi: 10.3847/1538-4357/ab9ec0

    work page doi:10.3847/1538-4357/ab9ec0 2020

  7. [7]

    M., Giovanelli, R., Haynes, M

    Cannon, J. M., Giovanelli, R., Haynes, M. P., et al. 2011, ApJL, 739, L22, doi: 10.1088/2041-8205/739/1/L22

    work page doi:10.1088/2041-8205/739/1/l22 2011

  8. [8]

    , keywords =

    Cardamone, C., Schawinski, K., Sarzi, M., et al. 2009, MNRAS, 399, 1191, doi: 10.1111/j.1365-2966.2009.15383.x da Silva, R. L., Fumagalli, M., & Krumholz, M. 2012, ApJ, 745, 145, doi: 10.1088/0004-637X/745/2/145

    work page doi:10.1111/j.1365-2966.2009.15383.x 2009

  9. [9]

    A., Boquien, M., Turner, J

    Dale, D. A., Boquien, M., Turner, J. A., et al. 2023, The Astronomical Journal, 165, 260, doi: 10.3847/1538-3881/accffe

    work page doi:10.3847/1538-3881/accffe 2023

  10. [10]

    J., Lang , D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d

    work page doi:10.3847/1538-3881/ab089d 2019

  11. [11]

    L., & Krumholz, M

    Fumagalli, M., da Silva, R. L., & Krumholz, M. R. 2011, ApJL, 741, L26, doi: 10.1088/2041-8205/741/2/L26

    work page doi:10.1088/2041-8205/741/2/l26 2011

  12. [12]

    M.Glowacki,E.Elson,andR.Davé.MNRAS,498(3):3687–3702,Nov.2020

    Giovanelli, R., Haynes, M. P., Kent, B. R., et al. 2005, AJ, 130, 2598, doi: 10.1086/497431

    work page doi:10.1086/497431 2005

  13. [13]

    C., Salzer, J

    Haurberg, N. C., Salzer, J. J., Cannon, J. M., & Marshall, M. V. 2015, ApJ, 800, 121, doi: 10.1088/0004-637X/800/2/121

    work page doi:10.1088/0004-637x/800/2/121 2015

  14. [14]

    P., Giovanelli, R., Martin, A

    Haynes, M. P., Giovanelli, R., Martin, A. M., et al. 2011, AJ, 142, 170, doi: 10.1088/0004-6256/142/5/170

    work page doi:10.1088/0004-6256/142/5/170 2011

  15. [15]

    , author =

    Haynes, M. P., Giovanelli, R., Kent, B. R., et al. 2018, ApJ, 861, 49, doi: 10.3847/1538-4357/aac956

    work page doi:10.3847/1538-4357/aac956 2018

  16. [16]

    S., Salzer, J

    Hirschauer, A. S., Salzer, J. J., Haurberg, N., Gronwall, C., & Janowiecki, S. 2022, ApJ, 925, 131, doi: 10.3847/1538-4357/ac402a

    work page doi:10.3847/1538-4357/ac402a 2022

  17. [17]

    S., Salzer, J

    Hirschauer, A. S., Salzer, J. J., Skillman, E. D., et al. 2016, ApJ, 822, 108, doi: 10.3847/0004-637X/822/2/108

    work page doi:10.3847/0004-637x/822/2/108 2016

  18. [18]

    A., Elmegreen, B

    Hunter, D. A., Elmegreen, B. G., & Ludka, B. C. 2010, The Astronomical Journal, 139, 447, doi: 10.1088/0004-6256/139/2/447

    work page doi:10.1088/0004-6256/139/2/447 2010

  19. [19]

    A., Ficut-Vicas, D., Ashley, T., et al

    Hunter, D. A., Ficut-Vicas, D., Ashley, T., et al. 2012, AJ, 144, 134, doi: 10.1088/0004-6256/144/5/134

    work page doi:10.1088/0004-6256/144/5/134 2012

  20. [20]

    G., Haynes, M

    Jones, M. G., Haynes, M. P., Giovanelli, R., & Moorman, C. 2018, MNRAS, 477, 2, doi: 10.1093/mnras/sty521

    work page doi:10.1093/mnras/sty521 2018

  21. [21]

    Kennicutt, Jr., R. C. 1998a, ARA&A, 36, 189, doi: 10.1146/annurev.astro.36.1.189 —. 1998b, ApJ, 498, 541, doi: 10.1086/305588

    work page internal anchor Pith review doi:10.1146/annurev.astro.36.1.189

  22. [22]

    C., Kennicutt, Jr., R

    Lee, J. C., Kennicutt, Jr., R. C., Funes, S. J. J. G., Sakai, S., & Akiyama, S. 2009, ApJ, 692, 1305, doi: 10.1088/0004-637X/692/2/1305

    work page doi:10.1088/0004-637x/692/2/1305 2009

  23. [23]

    C., Gil de Paz, A., Tremonti, C., et al

    Lee, J. C., Gil de Paz, A., Tremonti, C., et al. 2009, The Astrophysical Journal, 706, 599, doi: 10.1088/0004-637X/706/1/599

    work page doi:10.1088/0004-637x/706/1/599 2009

  24. [24]

    S., & Schombert, J

    Lelli, F., McGaugh, S. S., & Schombert, J. M. 2016a, AJ, 152, 157, doi: 10.3847/0004-6256/152/6/157 —. 2016b, ApJL, 816, L14, doi: 10.3847/2041-8205/816/1/L14

    work page doi:10.3847/0004-6256/152/6/157 2041

  25. [25]

    D., Simon, J

    Leroy, A., Bolatto, A. D., Simon, J. D., & Blitz, L. 2005, ApJ, 625, 763, doi: 10.1086/429578

    work page doi:10.1086/429578 2005

  26. [26]

    Marine, J. R. 2023, Photometric Analysis of the Stellar Populations of 82 Low-Mass, Low Metallicity Galaxies using the Spitzer Space Telescope, Undergraduate Honors Thesis, Macalester College, St. Paul, MN

    2023

  27. [27]

    M., Papastergis, E., Giovanelli, R., et al

    Martin, A. M., Papastergis, E., Giovanelli, R., et al. 2010, ApJ, 723, 1359, doi: 10.1088/0004-637X/723/2/1359

    work page doi:10.1088/0004-637x/723/2/1359 2010

  28. [28]

    The Galaxy Evolution Explorer: A Space Ultraviolet Survey Mission

    Martin, D. C., Fanson, J., Schiminovich, D., et al. 2005, The Astrophysical Journal, 619, L1, doi: 10.1086/426387

    work page internal anchor Pith review doi:10.1086/426387 2005

  29. [29]

    Masters, K. L. 2005, PhD thesis, Cornell University, New York

    2005

  30. [30]

    McGaugh, S. S. 2012, AJ, 143, 40, doi: 10.1088/0004-6256/143/2/40

    work page doi:10.1088/0004-6256/143/2/40 2012

  31. [31]

    T., Teich, Y

    McNichols, A. T., Teich, Y. G., Nims, E., et al. 2016, ApJ, 832, 89, doi: 10.3847/0004-637X/832/1/89

    work page doi:10.3847/0004-637x/832/1/89 2016

  32. [32]

    Mitchell, N. P. 2015, The Astrophysical Journal, 808, 109, doi: 10.1088/0004-637X/808/2/109

    work page doi:10.1088/0004-637x/808/2/109 2015

  33. [33]

    McQuinn, K. B. W., Cannon, J. M., Dolphin, A. E., et al. 2014, ApJ, 785, 3, doi: 10.1088/0004-637X/785/1/3 —. 2015, ApJ, 802, 66, doi: 10.1088/0004-637X/802/1/66

    work page doi:10.1088/0004-637x/785/1/3 2014

  34. [34]

    McQuinn, K. B. W., Telidevara, A. K., Fuson, J., et al. 2021, ApJ, 918, 23, doi: 10.3847/1538-4357/ac03ae

    work page doi:10.3847/1538-4357/ac03ae 2021

  35. [35]

    McQuinn, K. B. W., Adams, E. A. K., Cannon, J. M., et al. 2022, ApJ, 940, 8, doi: 10.3847/1538-4357/ac9285

    work page doi:10.3847/1538-4357/ac9285 2022

  36. [36]

    M., Warren, S

    Ott, J., Stilp, A. M., Warren, S. R., et al. 2012, AJ, 144, 123, doi: 10.1088/0004-6256/144/4/123

    work page doi:10.1088/0004-6256/144/4/123 2012

  37. [37]

    Monthly Notices of the Royal Astronomical Society , volume =

    Roychowdhury, S., Chengalur, J. N., Kaisin, S. S., Begum, A., & Karachentsev, I. D. 2011, MNRAS, 414, L55, doi: 10.1111/j.1745-3933.2011.01055.x

    work page doi:10.1111/j.1745-3933.2011.01055.x 2011

  38. [38]

    Karachentsev, I. D. 2014, Monthly Notices of the Royal Astronomical Society, 445, 1392, doi: 10.1093/mnras/stu1814

    work page doi:10.1093/mnras/stu1814 2014

  39. [39]

    J., Salzer, J

    Sage, L. J., Salzer, J. J., Loose, H.-H., & Henkel, C. 1992, A&A, 265, 19

    1992

  40. [40]

    M., Charlot , S., et al

    Salim, S., Rich, R. M., Charlot, S., et al. 2007, ApJS, 173, 267, doi: 10.1086/519218 SHIELD UV SFRs21

    work page doi:10.1086/519218 2007

  41. [41]

    J., MacAlpine, G

    Salzer, J. J., MacAlpine, G. M., & Boroson, T. A. 1989, ApJS, 70, 447, doi: 10.1086/191345

    work page doi:10.1086/191345 1989

  42. [42]

    R., Skillman, E

    Savino, A., Weisz, D. R., Skillman, E. D., et al. 2022, The Astrophysical Journal, 938, 101, doi: 10.3847/1538-4357/ac91cb

    work page doi:10.3847/1538-4357/ac91cb 2022

  43. [43]

    J., Finkbeiner , D

    Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, The Astrophysical Journal, 500, 525, doi: 10.1086/305772

    work page internal anchor Pith review doi:10.1086/305772 1998

  44. [44]

    M., & McGaugh, S

    Schombert, J. M., & McGaugh, S. 2014, PASA, 31, e011, doi: 10.1017/pasa.2014.2

    work page doi:10.1017/pasa.2014.2 2014

  45. [45]

    K., Walter, F., et al

    Schruba, A., Leroy, A. K., Walter, F., et al. 2012, AJ, 143, 138, doi: 10.1088/0004-6256/143/6/138

    work page doi:10.1088/0004-6256/143/6/138 2012

  46. [46]

    2020, Star Formation in Low-Mass Dwarf

    Shepley, M. 2020, Star Formation in Low-Mass Dwarf

    2020

  47. [47]

    Skillman, E. D. 1987, in NASA Conference Publication, Vol. 2466, NASA Conference Publication, ed. C. J. Lonsdale Persson, 263–266

    1987

  48. [48]

    , eprint =

    Sullivan, M., Treyer, M. A., Ellis, R. S., et al. 2000, MNRAS, 312, 442, doi: 10.1046/j.1365-8711.2000.03140.x

    work page doi:10.1046/j.1365-8711.2000.03140.x 2000

  49. [49]

    L., Kobulnicky, H

    Taylor, C. L., Kobulnicky, H. A., & Skillman, E. D. 1998, The Astronomical Journal, 116, 2746, doi: 10.1086/300655

    work page doi:10.1086/300655 1998

  50. [50]

    G., McNichols, A

    Teich, Y. G., McNichols, A. T., Nims, E., et al. 2016, ApJ, 832, 85, doi: 10.3847/0004-637X/832/1/85

    work page doi:10.3847/0004-637x/832/1/85 2016

  51. [51]

    Magnetohydrodynamics on an Unstructured Moving Grid:

    Torres-Flores, S., Epinat, B., Amram, P., Plana, H., & Mendes de Oliveira, C. 2011, MNRAS, 416, 1936, doi: 10.1111/j.1365-2966.2011.19169.x

    work page doi:10.1111/j.1365-2966.2011.19169.x 2011

  52. [52]

    Walter, F., Brinks, E., de Blok, W. J. G., et al. 2008, The Astronomical Journal, 136, 2563, doi: 10.1088/0004-6256/136/6/2563

    work page doi:10.1088/0004-6256/136/6/2563 2008

  53. [53]

    R., Johnson, B

    Weisz, D. R., Johnson, B. D., Johnson, L. C., et al. 2012, ApJ, 744, 44, doi: 10.1088/0004-637X/744/1/44

    work page doi:10.1088/0004-637x/744/1/44 2012

  54. [54]

    K., Martin, D

    Wyder, T. K., Martin, D. C., Schiminovich, D., et al. 2007, ApJS, 173, 293, doi: 10.1086/521402

    work page doi:10.1086/521402 2007

  55. [55]

    E., & Wang, J

    Yang, H., Malhotra, S., Rhoads, J. E., & Wang, J. 2017, ApJ, 847, 38, doi: 10.3847/1538-4357/aa8809

    work page doi:10.3847/1538-4357/aa8809 2017