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arxiv: 1907.02970 · v2 · pith:RU6Z4KHRnew · submitted 2019-07-05 · 🌌 astro-ph.GA

Beyond UVJ: More Efficient Selection of Quiescent Galaxies With UV / Mid-IR Fluxes

Pith reviewed 2026-05-25 01:51 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords quiescent galaxiesUVJ diagramspecific star formation ratecolor-color diagramsSED fittinggalaxy evolution3D-HST
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The pith

UVJ colors saturate below log(sSFR/yr^{-1}) ≲ -10.5 while far-UV and mid-IR fluxes continue to track lower star formation rates.

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

The paper establishes that the UVJ color-color diagram loses sensitivity once specific star formation rates fall below about 10 to the -10.5 per year, because further drops in star formation produce little additional change in those colors. Bayesian modeling of UVJ fluxes alone versus full UV-to-mid-IR SEDs confirms the saturation and shows that apparent UVJ trends with age, metallicity, and dust are driven by galaxy scaling relations rather than direct constraints from the colors. Far-UV and mid-IR fluxes keep correlating with sSFR at these lower levels, enabling new color-color selections that isolate galaxies with substantially less ongoing star formation. The paper supplies explicit selection criteria in these extended spaces as a function of target sSFR. Because the UVJ trends rest on evolving scaling relations, the diagram's utility is expected to change with cosmic time.

Core claim

The UVJ diagram saturates below log(sSFR/yr^{-1}) ≲ -10.5, with colors no longer changing substantially as sSFR decreases further. Far-UV and/or MIR fluxes maintain correlations with sSFR at these low levels and enable more efficient color-color selections of quiescent galaxies. The observed UVJ trends are driven by scaling relationships and are expected to evolve with cosmological time. Bayesian fits confirm these relations even when using nonparametric SFHs and flexible dust curves.

What carries the argument

The saturation threshold of UVJ colors at low sSFR together with the continued sensitivity of far-UV and mid-IR color combinations in new diagrams.

If this is right

  • UVJ-based selections of quiescent galaxies will become less effective or require redshift-dependent adjustments as scaling relations evolve.
  • New far-UV or mid-IR color selections can isolate samples with sSFR values well below the UVJ limit.
  • UVJ colors alone do not independently constrain stellar age or metallicity.
  • Full UV-MIR photometry supplies additional information on low-level star formation that UVJ fluxes alone cannot provide.

Where Pith is reading between the lines

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

  • The new color selections could be applied to deeper multi-wavelength surveys to identify quiescent galaxies at earlier cosmic epochs.
  • Redshift-dependent recalibration of the selection boundaries may be required because the underlying scaling relations change with time.
  • Cross-checks against independent low-sSFR indicators such as H-alpha equivalent width or radio continuum could test the efficiency of the proposed diagrams.

Load-bearing premise

The Prospector fits with nonparametric star-formation histories accurately recover the true relationships between observed colors and specific star formation rate without model-dependent biases.

What would settle it

A measurement in which UVJ colors continue to change appreciably below log(sSFR) = -10.5 when sSFR is determined independently from another indicator would falsify the saturation claim.

Figures

Figures reproduced from arXiv: 1907.02970 by Charlie Conroy, Joel Leja, Sandro Tacchella.

Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Median stellar population properties in the UVJ diagram after fitting synthetic UVJ fluxes (upper panels) and after fitting full SEDs of observed galaxies (lower panels). From right to left: mass-to-light ratio for the SDSS-g band relative to solar, dust optical depth, sSFR averaged over the most recent 100 Myr, average stellar age, and stellar metallicity. Each pixel shows either the median of the posteri… view at source ↗
Figure 3
Figure 3. Figure 3: Information gained after fitting synthetic UVJ fluxes (upper panels) and full observed SEDs (lower panels). The overall layout of the figure follows [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The correlation between sSFR and optical color, UVJ colors, UV-optical color and optical-MIR color is shown. The Pearson correlation coefficient between color and sSFR is shown in the corner. Optical and NIR colors begin to saturate at approximately sSFR ∼ 10−10.5 yr−1 whereas FUV-optical or optical-MIR colors continue to correlate with sSFR over a wide range of values. The median sSFR as a function of col… view at source ↗
Figure 5
Figure 5. Figure 5: Comparing the efficiency of sample selection by galaxy sSFR in different color-color spaces. In the upper row, from left to right, the panels show the canonical UVJ diagram, the FUV-V-J diagram, and the FUV-V-W3 diagram. In the lower row, the sample purity as a function of target sSFR is shown, both for the best-fitting colors (solid line) and modeling the effect of photometric uncertainty (dashed lines). … view at source ↗
read the original abstract

The UVJ color-color diagram is a popular and efficient method to distinguish between quiescent and star-forming galaxies through their rest-frame $U-V$ vs. $V-J$ colors. Here we explore the information content of this color-color space using the Bayesian inference machine Prospector. We fit the same physical model to two datasets: (i) UVJ fluxes alone, and (ii) full UV-mid IR (MIR) broadband SEDs from the 3D-HST survey. Notably this model uses both nonparametric SFHs and a flexible dust attenuation curve, both of which have the potential to `break' the typical correlations observed in UVJ color-color space. Instead, these fits confirm observed trends between UVJ colors and observed galaxy properties, including specific star formation rate (sSFR), dust attenuation, stellar age, and stellar metallicity. They also demonstrate that UVJ colors do not, on their own, constrain stellar age or metallicity; the observed trends in the UVJ diagram are instead driven by galaxy scaling relationships and thus will evolve with cosmological time. We also show that UVJ colors 'saturate' below $\log(\mathrm{sSFR/yr}^{-1})\lesssim -10.5$, i.e. changing sSFR no longer produces substantial changes in UVJ colors. We show that far-UV and/or MIR fluxes continue to correlate with sSFR down to low sSFRs and can be used in color-color diagrams to efficiently target galaxies with much lower levels of ongoing star formation. We provide selection criteria in these new color-color spaces as a function of desired sample sSFR.

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

1 major / 0 minor

Summary. The manuscript uses the Prospector code to perform Bayesian SED fits with nonparametric star-formation histories and flexible dust attenuation to two datasets from 3D-HST: (i) UVJ fluxes alone and (ii) full UV-mid-IR broadband photometry. It reports that UVJ colors saturate below log(sSFR/yr^{-1}) ≲ -10.5 while far-UV and/or MIR fluxes continue to track sSFR to lower values, enabling new color-color selection criteria for galaxies with lower ongoing star formation. The observed UVJ trends with sSFR, dust, age, and metallicity are attributed to galaxy scaling relations rather than direct constraints from the colors themselves, implying the diagram will evolve with cosmic time.

Significance. If the central results hold, the work supplies practical, redshift-dependent color selections that extend quiescent-galaxy targeting below the UVJ saturation regime and clarifies why standard UVJ correlations arise from scaling relations. The explicit comparison of UVJ-only versus full-SED fits under identical flexible models is a methodological strength, as is the demonstration that nonparametric SFHs and variable dust curves do not erase the observed trends.

major comments (1)
  1. [Results (sSFR-color mapping and saturation analysis)] The saturation threshold log(sSFR/yr^{-1}) ≲ -10.5 and the claim that FUV/MIR fluxes continue to correlate at lower sSFR are derived by mapping observed colors to sSFR values recovered from the Prospector full-SED fits. Because the identical nonparametric SFH parameterization and flexible dust curve are employed for both the UVJ-only and full-SED runs, any model-level degeneracy between recent SFR, older populations, and attenuation that suppresses UVJ variation at low sSFR will appear as saturation in the comparison. The manuscript supplies no external anchor (emission-line SFRs, resolved stellar populations, or mock catalogs with known truth) to demonstrate that the threshold is physical rather than an artifact of the fitting machinery. This is load-bearing for the central claim that the new FUV/MIR selections are more efficient at targeting lower-sSFR galaxies.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive report and for recognizing the methodological strengths of the work. We address the single major comment below regarding the robustness of the sSFR saturation threshold.

read point-by-point responses
  1. Referee: The saturation threshold log(sSFR/yr^{-1}) ≲ -10.5 and the claim that FUV/MIR fluxes continue to correlate at lower sSFR are derived by mapping observed colors to sSFR values recovered from the Prospector full-SED fits. Because the identical nonparametric SFH parameterization and flexible dust curve are employed for both the UVJ-only and full-SED runs, any model-level degeneracy between recent SFR, older populations, and attenuation that suppresses UVJ variation at low sSFR will appear as saturation in the comparison. The manuscript supplies no external anchor (emission-line SFRs, resolved stellar populations, or mock catalogs with known truth) to demonstrate that the threshold is physical rather than an artifact of the fitting machinery. This is load-bearing for the central claim that the new FUV/MIR selections are more efficient at targeting lower-sSFR galaxies.

    Authors: We agree that an independent external validation (e.g., emission-line SFRs or mocks with known truth) would provide the strongest possible confirmation that the saturation is physical. However, the sSFR values used for the mapping are not derived from UVJ data alone; they come from fits that incorporate the additional FUV and MIR photometry. These extra constraints allow the model to recover a wider dynamic range in sSFR, as demonstrated by the fact that the same nonparametric SFH and flexible dust model, when given only UVJ fluxes, produces broader posteriors and fails to track the lowest sSFR values. The observed UVJ colors (fixed data) show no further variation once the full-SED sSFR drops below ≲ -10.5, while the observed FUV/MIR colors continue to change. This differential behavior indicates that the information content of the UVJ bands themselves is limited at low sSFR, rather than the saturation being solely an artifact of shared model degeneracies. We will revise the manuscript to add an explicit discussion of this point and a caveat noting the absence of independent SFR anchors in the current analysis. revision: partial

Circularity Check

0 steps flagged

No significant circularity; claims rest on independent dataset comparison

full rationale

The paper fits the identical Prospector model (nonparametric SFHs + flexible dust) to two distinct datasets—UVJ fluxes alone versus full UV-MIR SEDs—then directly compares the recovered sSFR values and color trends. The reported UVJ saturation below log(sSFR) ≲ -10.5 and the continued correlation of FUV/MIR fluxes are outputs of this cross-dataset comparison, not quantities defined in terms of each other or renamed fitted parameters. No load-bearing step reduces by construction to a self-citation chain, ansatz, or input; the derivation remains externally anchored in the 3D-HST photometry.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis depends on the domain assumption that the Prospector physical model faithfully represents galaxy SEDs across the tested flux sets; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption The Prospector model with nonparametric SFHs and flexible dust attenuation provides unbiased recovery of galaxy properties when applied to both limited UVJ fluxes and full SEDs.
    This assumption underpins the comparison that reveals the saturation behavior and the value of additional bands.

pith-pipeline@v0.9.0 · 5837 in / 1383 out tokens · 28695 ms · 2026-05-25T01:51:51.153868+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

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    astro-ph.GA 2026-05 conditional novelty 5.0

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Reference graph

Works this paper leans on

45 extracted references · 45 canonical work pages · cited by 1 Pith paper · 4 internal anchors

  1. [1]

    J., Le F` evre, O., et al

    Arnouts, S., Walcher, C. J., Le F` evre, O., et al. 2007, A&A, 476, 137

  2. [2]

    2013, A&A, 558, A67

    Arnouts, S., Le Floc’h, E., Chevallard, J., et al. 2013, A&A, 558, A67

  3. [3]

    K., Glazebrook, K., Brinkmann, J., et al

    Baldry, I. K., Glazebrook, K., Brinkmann, J., et al. 2004, ApJ, 600, 681

  4. [4]

    L., Baldry, I

    Balogh, M. L., Baldry, I. K., Nichol, R., et al. 2004, ApJL, 615, L101

  5. [5]

    M., P´ erez-Gonz´ alez, P

    Barro, G., Faber, S. M., P´ erez-Gonz´ alez, P. G., et al. 2014, ApJ, 791, 52

  6. [6]

    MOSFIRE Spectroscopy of Quiescent Galaxies at 1.5 < z < 2.5. II - Star Formation Histories and Galaxy Quenching

    Belli, S., Newman, A. B., & Ellis, R. S. 2018, ArXiv e-prints, arXiv:1810.00008

  7. [7]

    M., et al

    Belli, S., Genzel, R., F¨ orster Schreiber, N. M., et al. 2017, ApJL, 841, L6

  8. [8]

    Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151

  9. [9]

    2003, PASP, 115, 763

    Chabrier, G. 2003, PASP, 115, 763

  10. [10]

    Charlot, S., & Fall, S. M. 2000, ApJ, 539, 718

  11. [11]

    2013, MNRAS, 432, 2061

    Chevallard, J., Charlot, S., Wandelt, B., & Wild, V. 2013, MNRAS, 432, 2061

  12. [12]

    Conroy, C., & Gunn, J. E. 2010, ApJ, 712, 833

  13. [13]

    E., & White, M

    Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486

  14. [14]

    2004, ApJ, 617, 746 Dav´ e, R., Rafieferantsoa, M

    Daddi, E., Cimatti, A., Renzini, A., et al. 2004, ApJ, 617, 746 Dav´ e, R., Rafieferantsoa, M. H., & Thompson, R. J. 2017, MNRAS, 471, 1671 D´ ıaz-Garc´ ıa, L. A., Cenarro, A. J., L´ opez-Sanjuan, C., et al. 2017, ArXiv e-prints, arXiv:1711.10590

  15. [15]

    The star-formation activity of IllustrisTNG galaxies: main sequence, UVJ diagram, quenched fractions, and systematics

    Donnari, M., Pillepich, A., Nelson, D., et al. 2018, arXiv e-prints, arXiv:1812.07584

  16. [16]

    Eales, S., de Vis, P., W. L. Smith, M., et al. 2017, MNRAS, 465, 3125

  17. [17]

    J., Faber, S

    Fang, J. J., Faber, S. M., Koo, D. C., et al. 2018, ApJ, 858, 100

  18. [18]

    G., et al

    Fumagalli, M., Labb´ e, I., Patel, S. G., et al. 2014, ApJ, 796, 35

  19. [19]

    Han, Z., Podsiadlowski, P., & Lynas-Gray, A. E. 2007, MNRAS, 380, 1098

  20. [20]

    R., van der Wel, A., Franx, M., et al

    Hill, A. R., van der Wel, A., Franx, M., et al. 2019, ApJ, 871, 76

  21. [21]

    2013, ApJS, 208, 19

    Hinshaw, G., Larson, D., Komatsu, E., et al. 2013, ApJS, 208, 19

  22. [22]

    J., Le F` evre, O., et al

    Ilbert, O., McCracken, H. J., Le F` evre, O., et al. 2013, A&A, 556, A55

  23. [23]

    Non-parametric Star Formation History Reconstruction with Gaussian Processes I: Counting Major Episodes of Star Formation

    Iyer, K. G., Gawiser, E., Faber, S. M., et al. 2019, arXiv e-prints, arXiv:1901.02877

  24. [24]

    K., Bureau, M., et al

    Jeong, H., Yi, S. K., Bureau, M., et al. 2009, MNRAS, 398, 2028

  25. [25]

    & Johnson, B

    Johnson, B., & Leja, J. 2017, bd-j/prospector: Initial release, , , doi:10.5281/zenodo.1116491. https://doi.org/10.5281/ zenodo.1116491 Kauffmann, G., Heckman, T. M., White, S. D. M., et al. 2003, MNRAS, 341, 33

  26. [26]

    D., Conroy, C., & van Dokkum, P

    Leja, J., Johnson, B. D., Conroy, C., & van Dokkum, P. 2018, ApJ, 854, 62

  27. [27]

    D., Conroy, C., van Dokkum, P

    Leja, J., Johnson, B. D., Conroy, C., van Dokkum, P. G., & Byler, N. 2017, ApJ, 837, 170

  28. [28]

    S., Jiang, D., Faber, S

    Liu, F. S., Jiang, D., Faber, S. M., et al. 2017, ApJL, 844, L2

  29. [29]

    C., Wyder, T

    Martin, D. C., Wyder, T. K., Schiminovich, D., et al. 2007, The Astrophysical Journal Supplement Series, 173, 342

  30. [30]

    2018, MNRAS, 473, 2098

    Merlin, E., Fontana, A., Castellano, M., et al. 2018, MNRAS, 473, 2098

  31. [31]

    2013, A&A, 558, A61

    Moresco, M., Pozzetti, L., Cimatti, A., et al. 2013, A&A, 558, A61

  32. [32]

    D., et al

    Narayanan, D., Dav´ e, R., Johnson, B. D., et al. 2018, MNRAS, 474, 1718 Pacifici, C., Kassin, S. A., Weiner, B. J., et al. 2016, ApJ, 832, 79

  33. [33]

    F., et al

    Papovich, C., Kawinwanichakij, L., Quadri, R. F., et al. 2018, ApJ, 854, 30

  34. [34]

    Salim, S., Boquien, M., & Lee, J. C. 2018, ApJ, 859, 11

  35. [35]

    2016, ApJ, 827, 20

    Salmon, B., Papovich, C., Long, J., et al. 2016, ApJ, 827, 20

  36. [36]

    2018, A&A, 618, A85

    Schreiber, C., Glazebrook, K., Nanayakkara, T., et al. 2018, A&A, 618, A85

  37. [37]

    E., Whitaker, K

    Skelton, R. E., Whitaker, K. E., Momcheva, I. G., et al. 2014, ApJS, 214, 24

  38. [38]

    Speagle, J. S. 2019, arXiv e-prints, arXiv:1904.02180

  39. [39]

    Straatman, C. M. S., Labb´ e, I., Spitler, L. R., et al. 2014, ApJL, 783, L14

  40. [40]

    R., et al

    Strateva, I., Ivezi´ c,ˇZ., Knapp, G. R., et al. 2001, AJ, 122, 1861

  41. [41]

    J., & Johnson, B

    Tacchella, S., Bose, S., Conroy, C., Eisenstein, D. J., & Johnson, B. D. 2018, ApJ, 868, 92

  42. [42]

    E., Kriek, M., van Dokkum, P

    Whitaker, K. E., Kriek, M., van Dokkum, P. G., et al. 2012, ApJ, 745, 179

  43. [43]

    E., van Dokkum, P

    Whitaker, K. E., van Dokkum, P. G., Brammer, G., et al. 2013, ApJL, 770, L39

  44. [44]

    J., Quadri, R

    Williams, R. J., Quadri, R. F., Franx, M., van Dokkum, P., & Labb´ e, I. 2009, ApJ, 691, 1879

  45. [45]

    2007, ApJ, 655, 51

    Wuyts, S., Labb´ e, I., Franx, M., et al. 2007, ApJ, 655, 51