Pith. sign in

REVIEW 3 major objections 6 minor 141 references

The Stellar Mass Function of Gas-Rich Galaxies and the Underlying $M_{\rm HI}-M_{\rm star}$ Scaling Relation in the Local Universe

T0 review · 3 major / 6 minor · reviewed 2026-08-01 · deepseek-v4-flash

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

Pith's one-line read This paper claims that gas-rich galaxies selected by their atomic hydrogen content follow a single Schechter form for their stellar mass function, and that abundance matching this against the HI mass function yields a selection-free M_HI–M_

desk verdict Useful alpha.100 HI-selected GSMF and an abundance-matched scaling relation, but the 'free from selection bias' claim leans on an untested transfer of V_eff weights to the optical-limited subsample. read the letter →

arxiv 2607.21225 v1 pith:IW5NMW6P submitted 2026-07-23 astro-ph.GA astro-ph.CO

Tanya Tripty , Nishikanta Khandai , Saili Dutta , Khushi Sharma This is my paper · ORCID
classification astro-ph.GAastro-ph.CO
keywords galaxystellarmassfunctionHIALFALFAsurveyM_HI–M_starscalingrelationabundancematchingSchechter1/V_effmethod
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 measure the stellar mass function of galaxies selected by their atomic hydrogen (HI) content — rather than by their starlight — and to recover the relation between HI mass and stellar mass in a way that is not biased by how ALFALFA detects galaxies. Using the 100% ALFALFA catalog with optical counterparts from SDSS and UV/IR mass estimates from GSWLC-2, it finds that the HI-selected stellar mass function is consistent with a single Schechter function, and that red galaxies contribute about 54% of the stellar mass yet only ~18% of the number density. The main payoff is a M_HI–M_star relation obtained by abundance matching the HI mass function to the stellar mass function, which the authors argue is free from selection effects and agrees with a volume-limited sample and with the MIGHTEE-HI survey. If right, the result gives an unbiased view of how much atomic gas galaxies carry at a given stellar mass, and shows that gas-rich galaxies hold about a third of the local stellar mass density.

What carries the argument

The load-bearing machinery is the 1/V_eff method applied to the ALFALFA survey: a two-dimensional stepwise maximum likelihood fit to the HI mass–velocity width plane yields a normalized bivariate function, and each galaxy's effective maximum volume V_eff is then computed from it using the survey's 50% completeness relation for flux versus velocity width. These V_eff weights correct the HI-selected sample for selection effects and are reused to build the HI-selected stellar mass function by binning in stellar mass. The selection-free M_HI–M_star relation is then produced by abundance matching: the HI mass function and the stellar mass function are both normalized at the high-mass end, and mat

What would settle it

The paper's own volume-limited sample (0.0025 < z < 0.004) gives a direct, unbiased M_HI–M_star relation that agrees with the abundance-matched curve; the claim would be falsified if a larger volume-limited sample spanning a wider redshift range measured M_HI–M_star values that deviate from the abundance-matched curve by more than the combined uncertainties.

Watch

Extended reading notes

Core claim

The central claim is that once the ALFALFA selection function is corrected via a 1/V_eff weighting scheme, the stellar mass function of HI-selected galaxies is a single Schechter function with log10(M*/M⊙) = 10.83, α = −1.14, and φ* = 2.30×10^-3 h70^3 Mpc^-3 dex^-1 (GSWLC-calibrated masses). Red and blue populations are each also single Schechter functions, with red galaxies making up ~18% of the corrected number density but ~54% of the stellar mass density. Abundance matching the HI mass function against this GSMF yields an intrinsic M_HI–M_star relation that lies below the relation seen in the raw data — that is, the observed HI-selected relation is biased toward gas-rich galaxies at fixed

Load-bearing premise

The analysis assumes that applying the optical r-band magnitude cut (r < 17.77) and spectral-class cleaning does not bias the stellar-mass distribution relative to the full HI-selected sample, since the selection weights (V_eff) are computed from the larger HI sample and then transferred to the optical-limited subset without recomputation.

Editorial extensions

If this is right

  • Gas-rich galaxies contribute about 33% of the local stellar mass density and 39% of galaxy number counts when compared with an optically selected SDSS sample in a shared volume.
  • Red galaxies in the HI-selected population dominate the stellar mass budget (~54%) despite being only ~18% of the galaxies by number.
  • The corrected M_HI–M_star relation lies below the directly observed one at fixed stellar mass, implying that naive HI-selected relations overestimate typical gas content.
  • Dust-corrected (GSWLC-calibrated) stellar masses raise the characteristic mass of the HI-selected GSMF by about 0.2 dex relative to uncorrected KCORRECT masses, shifting the inferred stellar mass density by more than 1σ.

Reading between the lines

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

  • Editorial inference: the V_eff weights are computed from the full 19,838-galaxy HI sample and then applied to a subsample of 16,955 galaxies after optical magnitude and spectral-class cuts; recomputing the weights on the optical-limited subsample would test whether those cuts bias the stellar-mass distribution.
  • Editorial inference: the abundance-matched relation assumes monotonic rank correlation between HI mass and stellar mass; incorporating the full conditional distribution P(M_HI|M_star) (the paper itself adds scatter only through a σ=0.2 dex broadening) would provide a sturdier basis for the claimed unbiased relation.
  • Editorial inference: the ~33% and ~39% budget numbers rely on spectroscopic coverage that exists in only ~65% of the ALFALFA footprint; HI-selected galaxies outside that region could shift the global budget if their stellar masses differ systematically.
  • Editorial inference: the paper's machine-learning mass predictions reproduce the GSMF only in the well-sampled middle mass range; training the models to predict (M_star, V_eff) jointly, as the paper suggests, would be a direct test of whether low-mass-end distortions can be removed.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Request a human review

A listed scientist reviews the paper for a fee and the review publishes here regardless of verdict. See the reviewers or get listed.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper uses the ALFALFA 100% catalog with SDSS optical counterparts to construct an HI-selected galaxy stellar mass function (GSMF), recalibrating KCORRECT stellar masses with GSWLC-2 masses via a linear relation. Applying 1/V_eff weights derived from the 2DSWML bivariate HI mass–velocity-width function, the authors find that the HI-selected GSMF is described by a single Schechter function with {phi* = 2.30e-3, log10(M*/M_sun) = 10.83, alpha = -1.14} (Table 1, KCORRECT+GSWLC), and that red galaxies contribute ~18% of the number density but ~54% of the stellar mass density. Comparison with an optically selected SDSS sample in a common sub-volume yields gas-rich fractions of ~33% in stellar mass and ~39% in number counts. Finally, the authors abundance-match the HI-selected GSMF and HIMF to obtain an M_HI-M_star relation, claiming it is free from selection bias, and validate it against a local volume-limited sample and MIGHTEE-HI results.

Significance. If the central claims hold, the paper provides the first ALFALFA 100%-based, selection-corrected stellar mass function of gas-rich galaxies and a corresponding M_HI-M_star relation, which would be valuable for galaxy evolution studies and for comparisons with simulations. The analysis is thorough in several respects: it cross-checks the HIMF against Oman (2022), uses three stellar mass estimators, tests consistency with machine-learning mass predictions, and compares with an external interferometric survey (MIGHTEE-HI). These checks are genuine strengths and demonstrate careful attention to systematics. However, the headline claim that the derived M_HI-M_star relation is 'free from selection bias' is not fully secured by the presented tests, for the reasons detailed below.

major comments (3)
  1. [Sec. 2.1 / Sec. 3.3] The 1/V_eff weights used for the HI-selected GSMF are computed from the 19,838-galaxy sample (Sec. 3.1) and then applied to the 16,955 galaxies that pass the r_petro<17.77 and spectral-class cuts (Sec. 2.1). The manuscript does not demonstrate that these cuts are independent of M_star at fixed (M_HI, W50). The 2,298 excluded galaxies are likely to be preferentially faint, low-mass, or optically incomplete; if so, the weighted GSMF, the red/blue fractions (Table 2), and the abundance-matched relation in Fig. 9 are all biased. Appendix C concedes that the ML algorithms are 'trained to predict M_star rather than predicting both M_star and V_eff simultaneously' and therefore do not test the weight transfer. A direct test comparing M_HI, W50, and z distributions, or a re-estimation of V_eff on the optical-limited sample, is required to support the claim that the relation is 'free from selecti
  2. [Sec. 5 / Fig. 9] The 'underlying' M_HI-M_star relation is obtained by abundance matching the HIMF and GSMF derived from the same survey with the same 1/V_eff weights. It is therefore a restatement of those two mass functions rather than an independent measurement. The zero-scatter line is a rank-matching; the sigma=0.2 dex line is an assumed value, not constrained by the data. The uncertainty band shown in Fig. 9 includes only propagation from the mass-function fits, not the uncertainty in sigma or in the transfer of weights. The volume-limited check uses a very small sample (no N quoted) and the MIGHTEE comparison is encouraging, but the matching line is constructed with the same assumed sigma. To claim an unbiased relation, sigma should be fitted or explicitly marginalized, and the relation should be tested on an external sample, e.g. xGASS or the MIGHTEE data themselves.
  3. [Sec. 2.1 / Fig. 2] The KCORRECT-to-GSWLC linear calibration (Fig. 2, M_kcorrect = 0.917 M_GSWLC + 0.740) is fitted to the 8,280 galaxies with GSWLC masses and applied to the 8,677 without. The text states that only 49% of the sample has GSWLC estimates and that no suitable selection criteria were found, but no test is given that the GSWLC subset is representative in M_HI, W50, color, or redshift. The ML checks (Sec. 5, Appendix C) are trained and tested on GSWLC galaxies, so they do not validate extrapolation to the no-GSWLC population. A systematic difference would shift the M* and alpha values in Table 1 and the fractions in Table 2. A binned comparison of the two subsets, or a calibration stability test on random subsamples, would strengthen the analysis.
minor comments (6)
  1. [Sec. 2.2] The sentence 'We therefore removed these galaxies from our analysis. Finally, we are left with 45,609 galaxies' is contradictory; please clarify how many galaxies were removed and the selection criteria for the final optical sample.
  2. [Sec. 3.3 / Appendix B] The statement 'the GSMF for the total population is complete above the mass 6.2> M_HI>7.3' appears to contain a typo; it should presumably refer to M_star. Also, the completeness statement is not self-consistent with the adopted M_star>7.0 threshold.
  3. [Fig. 9 / Sec. 5] Please provide the number of galaxies in the volume-limited sample and the mass range they span; this is important for assessing the strength of the consistency check.
  4. [Appendix C] The statement that the algorithms 'had (wrongly) originally anticipated' is informal; please rephrase. Also, the ML cross-check is not a full validation of the calibration, as acknowledged.
  5. [Table 1] The chi^2_red values are reported without degrees of freedom or the fit range; please specify.
  6. [Figures] The name 'Schecter' appears in some figure captions; it should be 'Schechter' throughout.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the HI-selected GSMF, HIMF, and abundance-matched scaling relation are derived from data with explicit weights and validated against independent volume-limited and MIGHTEE-HI samples.

full rationale

The paper's central derivation chain is: (i) measure the HIMF via 2DSWML/V_eff; (ii) compute the HI-selected GSMF by binning stellar masses with the same V_eff weights; (iii) abundance-match the HIMF and GSMF to obtain the M_HI-M_star relation; and (iv) validate with a volume-limited sample and compare with MIGHTEE-HI. Each step is a standard estimator applied to data, not a reduction of the conclusion to its inputs. The V_eff weights are determined self-consistently from Eqs. (3)-(7), with normalization fixed at the complete high-mass end, and the resulting HIMF is explicitly checked against the independent Oman (2022) measurement. The abundance-matched relation is transparently a transformation of the two measured mass functions, not an independent prediction; but this is the stated method, not a hidden circularity. The volume-limited sample (z in [0.0025,0.004]) provides an external check that does not use 1/V_eff weighting, and the agreement with MIGHTEE-HI (Pan et al. 2023) is an independent benchmark. Self-citations to Dutta et al. (2020) concern the 2DSWML implementation and normalization convention; these are methodological, not load-bearing uniqueness claims, and the method is also standard (Loveday 2000; Zwaan et al. 2003). The sigma=0.2 dex scatter curve is an assumed illustrative value, explicitly deferred to future work, not a fitted prediction. The main residual concern is the transfer of V_eff weights from the 19,838-galaxy HI sample to the 16,955-galaxy r_petro<17.77 optical-limited subsample; if the optical cuts are correlated with stellar mass at fixed HI properties, the GSMF and derived scaling relation could be biased. This is a selection-bias/correctness risk, not circularity, because the paper does not define the GSMF in terms of the scaling relation or vice versa, and the volume-limited comparison provides partial external support.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The analysis rests on an empirical completeness relation, a linear mass calibration fitted to a 49% subset, and an adopted 0.2 dex scatter. These are the main inputs the reader must accept on faith; the remaining machinery is standard survey analysis.

free parameters (3)
  • KCORRECT-to-GSWLC linear calibration coefficients = slope=0.917, intercept=0.740
    Fit to 8,280 GSWLC galaxies in Sec. 2.1 and applied to all non-GSWLC galaxies in the final sample; this drives the stellar mass scale, phi*, and M* of every GSMF.
  • GSWLC mass-error relation coefficients = sigma_GSWLC = -0.021 * M_GSWLC + 0.250
    Fitted relation used to assign errors to calibrated masses; affects the quoted uncertainties and the Schechter fit chi-squared.
  • Intrinsic scatter sigma in abundance-matched M_HI-M_star relation = 0.2 dex
    Introduced by hand in Sec. 5 to align the abundance-matched relation with the observed data and MIGHTEE-HI mock; no fitting procedure or uncertainty is quoted for this value.
assumptions (5)
  • domain assumption ALFALFA 50% completeness relation Eq. 1 (Oman 2022)
    Used to compute V_eff and every HIMF/GSMF weight. If this empirical completeness curve is wrong, the claimed selection corrections shift.
  • domain assumption Optical r_petro<17.77 and spectral-class cuts do not bias the stellar-mass sample relative to the full HI sample
    V_eff weights are computed from the 19,838-galaxy HI sample and then applied to the 16,955-galaxy optical-limited stellar-mass sample without recomputation (Sec 2.1 and 3.3).
  • domain assumption GSWLC linear calibration extrapolates to galaxies without GSWLC masses
    Only 49% of ALFALFA galaxies have GSWLC masses; the linear relation is applied to the rest with no external validation of the extrapolation (Sec 2.1).
  • domain assumption Abundance matching assumes a monotonic M_HI-M_star relation with symmetric scatter
    The cumulative matching of HIMF and GSMF in Sec. 5 defines the relation; if the relation is non-monotonic or scatter is asymmetric, the inferred mapping is not the underlying relation.
  • domain assumption Red/blue split follows the Baldry et al. (2004) color-magnitude divider (Eq. 2)
    All red/blue fractions and population mass functions depend on this boundary.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Stellar Mass Function of Gas-Rich Galaxies and the Underlying $M_{\rm HI}-M_{\rm star}$ Scaling Relation in the Local Universe." pith.science (2026). https://pith.science/paper/IW5NMW6P

@misc{pith2026260721225,
  author       = {Pith},
  title        = {Pith review of: The Stellar Mass Function of Gas-Rich Galaxies and the Underlying $M_\rm HI-M_\rm star$ Scaling Relation in the Local Universe},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IW5NMW6P}},
  note         = {Machine review of arXiv:2607.21225}
}
abstract

We estimate the Galaxy Stellar Mass Function (GSMF) of HI gas-rich galaxies using the 100\% ALFALFA ($\alpha$) catalog, $\sim 98\%$ of which have optical counterparts in the Sloan Digital Sky Survey (SDSS) and a subset of them have counterparts in GALEX SDSS WISE Legacy Catalogue-2(GSWLC-2). We use the mass estimates from this subset which combines UV, optical and IR bands with individual dust corrections to recalibrate optical stellar mass estimates. We use a non-parametric method to estimate the GSMF of these gas-rich galaxies. The resulting, HI-selected GSMF is consistent with a single Schechter function with best-fit parameters $\left\{\phi_* (10^{-3}\, h_{70}^{3}\,\mathrm{Mpc}^{-3}\,\mathrm{dex}^{-1}), \log_{10} (M_*/M_{\odot}) + 2\log_{10} h_{70}, \alpha \right\} = \left\{2.30^{+0.12}_{-0.12}, \,10.83^{+0.01}_{-0.01},\, -1.14^{+0.02}_{-0.02}\right\} $. Additionally, the red and blue populations are each well described by a single Schechter function. After correcting for selection effects, we find that the red population accounts for only $\sim18\%$ of gas-rich galaxies by number, yet contributes $\sim54\%$ of the total stellar mass, with the blue population accounting for the rest. Using an optically selected sample and a joint optical-HI sample, we find gas-rich galaxies represent $\sim 33\%$ of the total stellar mass density and $\sim 39\%$ of the total galaxy number counts in the local Universe. We use the GSMF and the HI mass function (HIMF) of the HI-selected sample to obtain the $M_{\rm HI}-M_{\rm star}$ relation, which is free from selection bias.

Figures

Figures reproduced from arXiv: 2607.21225 by the authors.

Figure 1
Figure 1. The H I sample used in this work plotted in the RA-Dec plane. Each point is a galaxy with an H I detection. Galaxies inside the solid black boundary (regions 1 and 2) are used to construct H Iselected sample described in Sec. 2.1. The blue points represent galaxies outside the SDSS footprint. The yellow (green) points are galaxies which have (do not have) an OC. All yellow points have photometric detections. The gre… view at source ↗
Figure 2
Figure 2. Left: The scaling relation between GSWLC and Taylor stellar mass estimates for each galaxy (points). Right: The scaling relation between GSWLC and KCORRECT stellar mass estimates. The blue line with error bars is the mean relation in mass bins and their variance in each bin. The black dotted line is the 𝑦 = 𝑥 curve. The solid maroon line in the right panel shows the best fit linear relation 𝑀kcorrect = 0.917𝑀GSWLC +… view at source ↗
Figure 3
Figure 3. Left: The 𝛼.100 HIMF for our sample (data points with errors) is estimated based on the the 1/𝑉eff method. The solid line and the shaded region are the best-fit Schechter function and its uncertainty respectively. Comparison is made with the HIMF derived from the 𝛼.100 sample, based on similar quality cuts, survey volume, and completeness relation 1 of Oman (2022) (dotted line) Right: The HIMF for the total (black),… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Comparison of H I selected GSMFs derived using different stellar mass estimates.Top left: GSMF estimated using 𝑀Taylor. Top right: GSMF using 𝑀kcorrect. Bottom left: GSMF using 𝑀kcorrect+GSWLC. In each panel, the total (grey squares), blue (blue circles), and red (red …
Figure 5
Figure 5. Figure 5: The stellar mass function of the H I-selected sample derived using 𝑀kcorrect+GSWLC mass estimates (open squares, with the best-fit Schechter function and its 1𝜎 uncertainty shown as the black curve and grey shaded band) compared with Machine Learning models: Random For…
Figure 6
Figure 6. Figure 6: Optically selected GSMF: comparison between this work (filled squares) using SDSS DR15 in the redshift range 0.0025 < 𝑧 < 0.05 with the 𝑀kcorrect+GSWLC stellar mass estimate and previous measurements in the local Universe. These measurements are Baldry et al. (2012) (o…
Figure 7
Figure 7. Figure 7: Left: optically selected GSMF for the total(black), red(red) and blue(blue) populations (data points with error bars) and their corresponding double Schecter function (equation 10 fits and uncertainties, solid lines and shaded regions. The data points corresponding to …
Figure 8
Figure 8. Figure 8: Optically and joint optical-H I selected GSMF for the total(Left), blue(Middle), and red(Right) galaxy populations. In the upper panel, solid and dashed curves show the best-fit optical and H I Schecter function, respectively, with shaded bands denoting 1𝜎 uncertaintie…
Figure 9
Figure 9. Figure 9: Left: log10 (𝑀HI/𝑀⊙ ) as a function of redshift for ALFALFA galaxies (black points). Purple diamonds denote galaxies from the volume-limited sample in the redshift range 0.0025 ≤ 𝑧 ≤ 0.004. Middle: Comparison of stellar mass and H I mass scaling relations for ALFALFA g…
Figure 10
Figure 10. Figure 10: The H I selected 𝑀star − 𝑀HI scaling relations and uncertainties (shaded regions) for the total(black), red(red) and blue(blue) populations. The solid (dashed) line corresponds to the H I (joint optical-H I) selected sample. The solid line segments with numbers denote…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

141 extracted references · 62 canonical work pages

  1. [1]

    Quantifying the Bimodal Color‐Magnitude Distribution of Galaxies

    Baldry I. K., Glazebrook K., Brinkmann J., Ivezi \'c Z ., Lupton R. H., Nichol R. C., Szalay A. S., 2004, @doi [ ] 10.1086/380092 , https://ui.adsabs.harvard.edu/abs/2004ApJ...600..681B 600, 681

    work page doi:10.1086/380092 2004

  2. [5]

    A COMPREHENSIVE ANALYSIS OF UNCERTAINTIES AFFECTING THE STELLAR MASS-HALO MASS RELATION FOR 0 &lt; <i>z</i> &lt; 4

    Behroozi P. S., Conroy C., Wechsler R. H., 2010, @doi [ ] 10.1088/0004-637X/717/1/379 , https://ui.adsabs.harvard.edu/abs/2010ApJ...717..379B 717, 379

    work page doi:10.1088/0004-637x/717/1/379 2010

  3. [6]

    UniverseMachine: The correlation between galaxy growth and dark matter halo assembly from z = 0−10

    Behroozi P., Wechsler R. H., Hearin A. P., Conroy C., 2019, @doi [ ] 10.1093/mnras/stz1182 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.3143B 488, 3143

    work page doi:10.1093/mnras/stz1182 2019

  4. [7]

    The massive end of the luminosity and stellar mass functions: dependence on the fit to the light profile

    Bernardi M., Meert A., Sheth R. K., Vikram V., Huertas-Company M., Mei S., Shankar F., 2013, @doi [ ] 10.1093/mnras/stt1607 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.436..697B 436, 697

    work page Pith review doi:10.1093/mnras/stt1607 2013

  5. [8]

    Bigiel F., Leroy A., Walter F., Brinks E., de Blok W. J. G., Madore B., Thornley M. D., 2008, @doi [ ] 10.1088/0004-6256/136/6/2846 , https://ui.adsabs.harvard.edu/abs/2008AJ....136.2846B 136, 2846

    work page doi:10.1088/0004-6256/136/6/2846 2008

  6. [9]

    <i>K</i> -Corrections and Filter Transformations in the Ultraviolet, Optical, and Near-Infrared

    Blanton M. R., Roweis S., 2007, @doi [ ] 10.1086/510127 , https://ui.adsabs.harvard.edu/abs/2007AJ....133..734B 133, 734

    work page doi:10.1086/510127 2007

  7. [12]

    The Dust Content and Opacity of Actively Star‐forming Galaxies

    Calzetti D., Armus L., Bohlin R. C., Kinney A. L., Koornneef J., Storchi-Bergmann T., 2000, @doi [ ] 10.1086/308692 , https://ui.adsabs.harvard.edu/abs/2000ApJ...533..682C 533, 682

    work page Pith review doi:10.1086/308692 2000

  8. [14]

    Catinella B., et al., 2018, @doi [ ] 10.1093/mnras/sty089 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.476..875C 476, 875

    work page doi:10.1093/mnras/sty089 2018

Show all 141 references
  1. [16]

    H., 2009, @doi [ ] 10.1088/0004-637X/696/1/620 , https://ui.adsabs.harvard.edu/abs/2009ApJ...696..620C 696, 620

    Conroy C., Wechsler R. H., 2009, @doi [ ] 10.1088/0004-637X/696/1/620 , https://ui.adsabs.harvard.edu/abs/2009ApJ...696..620C 696, 620

    2009 doi

  2. [19]

    P., et al., 2022, @doi [ ] 10.1093/mnras/stac472 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.513..439D 513, 439

    Driver S. P., et al., 2022, @doi [ ] 10.1093/mnras/stac472 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.513..439D 513, 439

    2022 doi

  3. [20]

    Drory N., et al., 2009, @doi [ ] 10.1088/0004-637X/707/2/1595 , https://ui.adsabs.harvard.edu/abs/2009ApJ...707.1595D 707, 1595

    2009 doi

  4. [21]

    A., Crone Odekon M., Haynes M

    Durbala A., Finn R. A., Crone Odekon M., Haynes M. P., Koopmann R. A., O'Donoghue A. A., 2020, @doi [ ] 10.3847/1538-3881/abc018 , https://ui.adsabs.harvard.edu/abs/2020AJ....160..271D 160, 271

    2020 doi

  5. [22]

    Dutta S., Khandai N., 2021, @doi [ ] 10.1093/mnrasl/slaa178 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.500L..37D 500, L37

    2021 doi

  6. [23]

    Dutta S., Khandai N., Dey B., 2020, @doi [ ] 10.1093/mnras/staa864 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.2664D 494, 2664

    2020 doi

  7. [24]

    Dutta S., Khandai N., Rana S., 2022, @doi [ ] 10.1093/mnras/stab3618 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.511.2585D 511, 2585

    2022 doi

  8. [25]

    W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    Foreman-Mackey D., Hogg D. W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    2013 doi

  9. [26]

    H., 2001, @doi [The Annals of Statistics] 10.1214/aos/1013203451 , 29, 1189

    Friedman J. H., 2001, @doi [The Annals of Statistics] 10.1214/aos/1013203451 , 29, 1189

    2001

  10. [28]

    Giovanelli R., et al., 2005, @doi [ ] 10.1086/497431 , https://ui.adsabs.harvard.edu/abs/2005AJ....130.2598G 130, 2598

    2005 doi

  11. [29]

    J., Jing Y

    Guo H., Li C., Zheng Z., Mo H. J., Jing Y. P., Zu Y., Lim S. H., Xu H., 2017, @doi [ ] 10.3847/1538-4357/aa85e7 , https://ui.adsabs.harvard.edu/abs/2017ApJ...846...61G 846, 61

    2017 doi

  12. [30]

    P., et al., 2011, @doi [ ] 10.1088/0004-6256/142/5/170 , https://ui.adsabs.harvard.edu/abs/2011AJ....142..170H 142, 170

    Haynes M. P., et al., 2011, @doi [ ] 10.1088/0004-6256/142/5/170 , https://ui.adsabs.harvard.edu/abs/2011AJ....142..170H 142, 170

    2011 doi

  13. [31]

    P., et al., 2018, @doi [ ] 10.3847/1538-4357/aac956 , https://ui.adsabs.harvard.edu/abs/2018ApJ...861...49H 861, 49

    Haynes M. P., et al., 2018, @doi [ ] 10.3847/1538-4357/aac956 , https://ui.adsabs.harvard.edu/abs/2018ApJ...861...49H 861, 49

    2018 doi

  14. [32]

    P., Giovanelli R., Brinchmann J., 2012, @doi [ ] 10.1088/0004-637X/756/2/113 , https://ui.adsabs.harvard.edu/abs/2012ApJ...756..113H 756, 113

    Huang S., Haynes M. P., Giovanelli R., Brinchmann J., 2012, @doi [ ] 10.1088/0004-637X/756/2/113 , https://ui.adsabs.harvard.edu/abs/2012ApJ...756..113H 756, 113

    2012 doi

  15. [33]

    D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , 9, 90

    Hunter J. D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , 9, 90

    2007 doi

  16. [34]

    G., Haynes M

    Jones M. G., Haynes M. P., Giovanelli R., Moorman C., 2018, @doi [ ] 10.1093/mnras/sty521 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477....2J 477, 2

    2018 doi

  17. [35]

    https://api.semanticscholar.org/CorpusID:3815895

    Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Ye Q., Liu T.-Y., 2017, in Neural Information Processing Systems. https://api.semanticscholar.org/CorpusID:3815895

    2017

  18. [37]

    Kennicutt Jr. R. C., 1998, @doi [ ] 10.1086/305588 , https://ui.adsabs.harvard.edu/abs/1998ApJ...498..541K 498, 541

    1998 doi

  19. [38]

    IOS Press, p

    Kluyver T., Ragan-Kelley B., P \'e rez F., et al., 2016, in Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press, p. 87, @doi 10.3233/978-1-61499-649-1-87

    2016 doi

  20. [39]

    M., Graziani R., Hoffman Y., Pomar \`e de D., Shaya E

    Kourkchi E., Courtois H. M., Graziani R., Hoffman Y., Pomar \`e de D., Shaya E. J., Tully R. B., 2020, @doi [ ] 10.3847/1538-3881/ab620e , https://ui.adsabs.harvard.edu/abs/2020AJ....159...67K 159, 67

    2020 doi

  21. [40]

    S., Schombert J

    Lelli F., McGaugh S. S., Schombert J. M., 2016, @doi [ ] 10.3847/2041-8205/816/1/L14 , https://ui.adsabs.harvard.edu/abs/2016ApJ...816L..14L 816, L14

    2016 doi

  22. [41]

    K., Walter F., Brinks E., Bigiel F., de Blok W

    Leroy A. K., Walter F., Brinks E., Bigiel F., de Blok W. J. G., Madore B., Thornley M. D., 2008, @doi [ ] 10.1088/0004-6256/136/6/2782 , https://ui.adsabs.harvard.edu/abs/2008AJ....136.2782L 136, 2782

    2008 doi

  23. [42]

    J., Carollo C

    Lilly S. J., Carollo C. M., Pipino A., Renzini A., Peng Y., 2013, @doi [ ] 10.1088/0004-637X/772/2/119 , https://ui.adsabs.harvard.edu/abs/2013ApJ...772..119L 772, 119

    2013 doi

  24. [43]

    Louppe G., Wehenkel L., Sutera A., Geurts P., 2013, in Burges C. J. C., Bottou L., Ghahramani Z., Weinberger K. Q., eds, Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held De...

    2013

  25. [45]

    M., Obreschkow D., Jarvis M

    Maddox N., Hess K. M., Obreschkow D., Jarvis M. J., Blyth S.-L., 2015, @doi [ ] 10.1093/mnras/stu2532 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.447.1610M 447, 1610

    2015 doi

  26. [46]

    M., Papastergis E., Giovanelli R., Haynes M

    Martin A. M., Papastergis E., Giovanelli R., Haynes M. P., Springob C. M., Stierwalt S., 2010, @doi [ ] 10.1088/0004-637X/723/2/1359 , https://ui.adsabs.harvard.edu/abs/2010ApJ...723.1359M 723, 1359

    2010 doi

  27. [47]

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

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

    2005

  28. [48]

    S., Schombert J

    McGaugh S. S., Schombert J. M., Bothun G. D., de Blok W. J. G., 2000, @doi [ ] 10.1086/312628 , https://ui.adsabs.harvard.edu/abs/2000ApJ...533L..99M 533, L99

    2000 doi

  29. [51]

    P., Naab T., White S

    Moster B. P., Naab T., White S. D. M., 2013, @doi [ ] 10.1093/mnras/sts261 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.3121M 428, 3121

    2013 doi

  30. [52]

    G., et al., 2007, @doi [ ] 10.1086/517926 , https://ui.adsabs.harvard.edu/abs/2007ApJ...660L..43N 660, L43

    Noeske K. G., et al., 2007, @doi [ ] 10.1086/517926 , https://ui.adsabs.harvard.edu/abs/2007ApJ...660L..43N 660, L43

    2007 doi

  31. [53]

    A., 2022, @doi [ ] 10.1093/mnras/stab3164 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.3268O 509, 3268

    Oman K. A., 2022, @doi [ ] 10.1093/mnras/stab3164 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.3268O 509, 3268

    2022 doi

  32. [54]

    Pan H., et al., 2023, @doi [ ] 10.1093/mnras/stad2343 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.525..256P 525, 256

    2023 doi

  33. [55]

    M., 2013, PhD thesis, Cornell University, New York

    Papastergis E. M., 2013, PhD thesis, Cornell University, New York

    2013

  34. [56]

    P., Rodr \' guez-Puebla A., Jones M

    Papastergis E., Giovanelli R., Haynes M. P., Rodr \' guez-Puebla A., Jones M. G., 2013, @doi [ ] 10.1088/0004-637X/776/1/43 , https://ui.adsabs.harvard.edu/abs/2013ApJ...776...43P 776, 43

    2013 doi

  35. [58]

    A., et al., 2023, @doi [ ] 10.1093/mnras/stad1249 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5308P 522, 5308

    Ponomareva A. A., et al., 2023, @doi [ ] 10.1093/mnras/stad1249 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5308P 522, 5308

    2023 doi

  36. [59]

    H., Chengalur J

    Rhee J., Lah P., Briggs F. H., Chengalur J. N., Colless M., Willner S. P., Ashby M. L. N., Le F \`e vre O., 2018, @doi [ ] 10.1093/mnras/stx2461 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.1879R 473, 1879

    2018 doi

  37. [60]

    P., Tollerud E

    Robitaille T. P., Tollerud E. J., et al., 2013, @doi [Astronomy and Astrophysics] 10.1051/0004-6361/201322068 , 558, A33

    2013 doi

  38. [61]

    Salim S., et al., 2016, @doi [ ] 10.3847/0067-0049/227/1/2 , https://ui.adsabs.harvard.edu/abs/2016ApJS..227....2S 227, 2

    2016 doi

  39. [62]

    C., 2018, @doi [ ] 10.3847/1538-4357/aabf3c , https://ui.adsabs.harvard.edu/abs/2018ApJ...859...11S 859, 11

    Salim S., Boquien M., Lee J. C., 2018, @doi [ ] 10.3847/1538-4357/aabf3c , https://ui.adsabs.harvard.edu/abs/2018ApJ...859...11S 859, 11

    2018 doi

  40. [64]

    J., Finkbeiner D

    Schlegel D. J., Finkbeiner D. P., Davis M., 1998, @doi [ ] 10.1086/305772 , https://ui.adsabs.harvard.edu/abs/1998ApJ...500..525S 500, 525

    1998 doi

  41. [68]

    S., Cunha C

    Sheldon E. S., Cunha C. E., Mandelbaum R., Brinkmann J., Weaver B. A., 2012, @doi [ ] 10.1088/0067-0049/201/2/32 , https://ui.adsabs.harvard.edu/abs/2012ApJS..201...32S 201, 32

    2012 doi

  42. [69]

    S., Dav \'e R., 2015, @doi [ ] 10.1146/annurev-astro-082812-140951 , https://ui.adsabs.harvard.edu/abs/2015ARA&A..53...51S 53, 51

    Somerville R. S., Dav \'e R., 2015, @doi [ ] 10.1146/annurev-astro-082812-140951 , https://ui.adsabs.harvard.edu/abs/2015ARA&A..53...51S 53, 51

    2015 doi

  43. [70]

    A., et al., 2002, @doi [ ] 10.1086/342343 , https://ui.adsabs.harvard.edu/abs/2002AJ....124.1810S 124, 1810

    Strauss M. A., et al., 2002, @doi [ ] 10.1086/342343 , https://ui.adsabs.harvard.edu/abs/2002AJ....124.1810S 124, 1810

    2002 doi

  44. [72]

    A., et al., 2004, @doi [ ] 10.1086/423264 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..898T 613, 898

    Tremonti C. A., et al., 2004, @doi [ ] 10.1086/423264 , https://ui.adsabs.harvard.edu/abs/2004ApJ...613..898T 613, 898

    2004 doi

  45. [73]

    S., Werk J

    Tumlinson J., Peeples M. S., Werk J. K., 2017, @doi [ ] 10.1146/annurev-astro-091916-055240 , https://ui.adsabs.harvard.edu/abs/2017ARA&A..55..389T 55, 389

    2017 doi

  46. [74]

    C., Varoquaux G., 2011, @doi [Computing in Science and Engineering] 10.1109/MCSE.2011.37 , 13, 22

    Van Der Walt S., Colbert S. C., Varoquaux G., 2011, @doi [Computing in Science and Engineering] 10.1109/MCSE.2011.37 , 13, 22

    2011 doi

  47. [75]

    E., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , 17, 261

    Virtanen P., Gommers R., Oliphant T. E., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , 17, 261

    2020 doi

  48. [76]

    K., Schawinski K., Bruderer C., 2016, @doi [ ] 10.1093/mnras/stw756 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.459.2150W 459, 2150

    Weigel A. K., Schawinski K., Bruderer C., 2016, @doi [ ] 10.1093/mnras/stw756 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.459.2150W 459, 2150

    2016 doi

  49. [77]

    A., et al., 2003, @doi [ ] 10.1086/374944 , https://ui.adsabs.harvard.edu/abs/2003AJ....125.2842Z 125, 2842

    Zwaan M. A., et al., 2003, @doi [ ] 10.1086/374944 , https://ui.adsabs.harvard.edu/abs/2003AJ....125.2842Z 125, 2842

    2003 doi

  50. [78]

    , keywords =

    The Circumgalactic Medium. , keywords =. doi:10.1146/annurev-astro-091916-055240 , archivePrefix =. 1709.09180 , primaryClass =

    arXiv

  51. [79]

    , year = 1959, month = mar, volume =

    The Rate of Star Formation. , year = 1959, month = mar, volume =. doi:10.1086/146614 , adsurl =

    1959 doi

  52. [80]

    The Rate of Star Formation. II. The Rate of Formation of Stars of Different Mass. , year = 1963, month = apr, volume =. doi:10.1086/147553 , adsurl =

    1963 doi

  53. [81]

    , keywords =

    The Star Formation Law in Galactic Disks. , keywords =. doi:10.1086/167834 , adsurl =

    doi

  54. [82]

    , keywords =

    The Global Schmidt Law in Star-forming Galaxies. , keywords =. doi:10.1086/305588 , archivePrefix =. astro-ph/9712213 , primaryClass =

    arXiv

  55. [83]

    The Arecibo Legacy Fast ALFA Survey. I. Science Goals, Survey Design, and Strategy. , keywords =. doi:10.1086/497431 , archivePrefix =. astro-ph/0508301 , primaryClass =

    arXiv

  56. [84]

    , keywords =

    The HI Parkes All Sky Survey: southern observations, calibration and robust imaging. , keywords =. doi:10.1046/j.1365-8711.2001.04102.x , adsurl =

    2001

  57. [85]

    , keywords =

    MIGHTEE-H I: the M _ H I - M _ * relation over the last billion years. , keywords =. doi:10.1093/mnras/stad2343 , archivePrefix =. 2210.04651 , primaryClass =

    arXiv

  58. [86]

    , keywords =

    MIGHTEE-H I: the first MeerKAT H I mass function from an untargeted interferometric survey. , keywords =. doi:10.1093/mnras/stad1249 , archivePrefix =. 2304.13051 , primaryClass =

    arXiv

  59. [87]

    , keywords =

    The Star Formation Efficiency in Nearby Galaxies: Measuring Where Gas Forms Stars Effectively. , keywords =. doi:10.1088/0004-6256/136/6/2782 , archivePrefix =. 0810.2556 , primaryClass =

    arXiv

  60. [88]

    , keywords =

    The Star Formation Law in Nearby Galaxies on Sub-Kpc Scales. , keywords =. doi:10.1088/0004-6256/136/6/2846 , archivePrefix =. 0810.2541 , primaryClass =

    arXiv

  61. [89]

    , keywords =

    Theory of Star Formation. , keywords =. doi:10.1146/annurev.astro.45.051806.110602 , archivePrefix =. 0707.3514 , primaryClass =

    arXiv

  62. [90]

    Machine Learning , keywords =

    Random Forests. Machine Learning , keywords =. doi:10.1023/A:1010933404324 , adsurl =

    doi

  63. [91]

    Understanding variable importances in forests of randomized trees , booktitle =

    Gilles Louppe and Louis Wehenkel and Antonio Sutera and Pierre Geurts , editor =. Understanding variable importances in forests of randomized trees , booktitle =. 2013 , url =

    2013

  64. [92]

    arXiv e-prints , keywords =

    XGBoost: A Scalable Tree Boosting System. arXiv e-prints , keywords =. doi:10.48550/arXiv.1603.02754 , archivePrefix =. 1603.02754 , primaryClass =

    doi arXiv

  65. [93]

    , title =

    Friedman, Jerome H. , title =. The Annals of Statistics , volume =. 2001 , month =

    2001

  66. [94]

    Neural Information Processing Systems , year=

    LightGBM: A Highly Efficient Gradient Boosting Decision Tree , author=. Neural Information Processing Systems , year=

  67. [95]

    and Colbert, S

    Van Der Walt, S. and Colbert, S. C. and Varoquaux, G. , title =. Computing in Science and Engineering , volume =. 2011 , doi =

    2011

  68. [96]

    and Gommers, R

    Virtanen, P. and Gommers, R. and Oliphant, T. E. and others , title =. Nature Methods , volume =. 2020 , doi =

    2020

  69. [97]

    Robitaille, T. P. and Tollerud, E. J. and others , title =. Astronomy and Astrophysics , volume =. 2013 , doi =

    2013

  70. [98]

    Hunter, J. D. , title =. Computing in Science and Engineering , volume =. 2007 , doi =

    2007

  71. [99]

    Journal of Machine Learning Research , keywords =

    Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research , keywords =. doi:10.48550/arXiv.1201.0490 , archivePrefix =. 1201.0490 , primaryClass =

    doi arXiv

  72. [100]

    and Ragan-Kelley, B

    Kluyver, T. and Ragan-Kelley, B. and P. Jupyter Notebooks – a publishing format for reproducible computational workflows , booktitle =. 2016 , doi =

    2016

  73. [101]

    doi:10.25080/Majora-92bf1922-00a , adsurl =

    Data Structures for Statistical Computing in Python. doi:10.25080/Majora-92bf1922-00a , adsurl =

    doi

  74. [102]

    Computing in Science and Engineering , year = 2021, month = jul, volume =

    mpi4py: Status Update After 12 Years of Development. Computing in Science and Engineering , year = 2021, month = jul, volume =. doi:10.1109/MCSE.2021.3083216 , adsurl =

    2021

  75. [103]

    , keywords =

    The Clustering of ALFALFA Galaxies: Dependence on H I Mass, Relationship with Optical Samples, and Clues of Host Halo Properties. , keywords =. doi:10.1088/0004-637X/776/1/43 , archivePrefix =. 1308.2661 , primaryClass =

    arXiv

  76. [104]

    , keywords =

    Galactic star formation and accretion histories from matching galaxies to dark matter haloes. , keywords =. doi:10.1093/mnras/sts261 , archivePrefix =. 1205.5807 , primaryClass =

    arXiv

  77. [105]

    , keywords =

    UNIVERSEMACHINE: The correlation between galaxy growth and dark matter halo assembly from z = 0-10. , keywords =. doi:10.1093/mnras/stz1182 , archivePrefix =. 1806.07893 , primaryClass =

    arXiv

  78. [106]

    , keywords =

    A Comprehensive Analysis of Uncertainties Affecting the Stellar Mass-Halo Mass Relation for 0 < z < 4. , keywords =. doi:10.1088/0004-637X/717/1/379 , archivePrefix =. 1001.0015 , primaryClass =

    arXiv

  79. [107]

    , keywords =

    Connecting Galaxies, Halos, and Star Formation Rates Across Cosmic Time. , keywords =. doi:10.1088/0004-637X/696/1/620 , archivePrefix =. 0805.3346 , primaryClass =

    arXiv

  80. [108]

    , keywords =

    The distribution of neutral hydrogen in the colour-magnitude plane of galaxies. , keywords =. doi:10.1093/mnrasl/slaa178 , archivePrefix =. 2010.15140 , primaryClass =

    2010 arXiv

  81. [109]

    , keywords =

    Neutral hydrogen (H I) gas content of galaxies at z 0.32. , keywords =. doi:10.1093/mnras/stx2461 , archivePrefix =. 1709.07596 , primaryClass =

    arXiv

  82. [110]

    , keywords =

    The Arecibo Legacy Fast ALFA Survey: The Galaxy Population Detected by ALFALFA. , keywords =. doi:10.1088/0004-637X/756/2/113 , archivePrefix =. 1207.0523 , primaryClass =

    arXiv

  83. [111]

    , keywords =

    emcee: The MCMC Hammer. , keywords =. doi:10.1086/670067 , archivePrefix =. 1202.3665 , primaryClass =

    arXiv

  84. [112]

    , keywords =

    On the galaxy stellar mass function, the mass-metallicity relation and the implied baryonic mass function. , keywords =. doi:10.1111/j.1365-2966.2008.13348.x , archivePrefix =. 0804.2892 , primaryClass =

    2008

  85. [113]

    , keywords =

    Constraining the H I-Halo Mass Relation from Galaxy Clustering. , keywords =. doi:10.3847/1538-4357/aa85e7 , archivePrefix =. 1707.01999 , primaryClass =

    arXiv

  86. [114]

    The Arecibo Legacy Fast ALFA Survey. X. The H I Mass Function and \_H I from the 40\. , keywords =. doi:10.1088/0004-637X/723/2/1359 , archivePrefix =. 1008.5107 , primaryClass =

    arXiv

  87. [115]

    , keywords =

    The Bimodal Galaxy Stellar Mass Function in the COSMOS Survey to z -0.5ex 1: A Steep Faint End and a New Galaxy Dichotomy. , keywords =. doi:10.1088/0004-637X/707/2/1595 , archivePrefix =. 0910.5720 , primaryClass =

    arXiv

  88. [116]

    , keywords =

    Galaxy And Mass Assembly (GAMA): the galaxy stellar mass function at z < 0.06. , keywords =. doi:10.1111/j.1365-2966.2012.20340.x , archivePrefix =. 1111.5707 , primaryClass =

    2012

  89. [117]

    , keywords =

    K-Corrections and Filter Transformations in the Ultraviolet, Optical, and Near-Infrared. , keywords =. doi:10.1086/510127 , archivePrefix =. astro-ph/0606170 , primaryClass =

    arXiv

  90. [118]

    , keywords =

    Stellar population synthesis at the resolution of 2003. , keywords =. doi:10.1046/j.1365-8711.2003.06897.x , archivePrefix =. astro-ph/0309134 , primaryClass =

    2003

  91. [119]

    , keywords =

    The Dust Content and Opacity of Actively Star-forming Galaxies. , keywords =. doi:10.1086/308692 , archivePrefix =. astro-ph/9911459 , primaryClass =

    arXiv

  92. [120]

    , keywords =

    The ALFALFA-SDSS Galaxy Catalog. , keywords =. doi:10.3847/1538-3881/abc018 , archivePrefix =. 2011.02588 , primaryClass =

    2011

  93. [121]

    , keywords =

    The dark matter haloes of HI selected galaxies. , keywords =. doi:10.1093/mnras/stab3618 , archivePrefix =. 2108.03253 , primaryClass =

    arXiv

  94. [122]

    , keywords =

    The Arecibo Legacy Fast ALFA Survey: The .40 H I Source Catalog, Its Characteristics and Their Impact on the Derivation of the H I Mass Function. , keywords =. doi:10.1088/0004-6256/142/5/170 , archivePrefix =. 1109.0027 , primaryClass =

    arXiv

  95. [123]

    , keywords =

    The Arecibo Legacy Fast ALFA Survey: The ALFALFA Extragalactic H I Source Catalog. , keywords =. doi:10.3847/1538-4357/aac956 , archivePrefix =. 1805.11499 , primaryClass =

    arXiv

  96. [124]

    , keywords =

    The population of galaxies that contribute to the H I mass function. , keywords =. doi:10.1093/mnras/staa864 , archivePrefix =. 1909.03077 , primaryClass =

    1909 arXiv

  97. [125]

    , keywords =

    The ALFALFA H I mass function: a dichotomy in the low-mass slope and a locally suppressed `knee' mass. , keywords =. doi:10.1093/mnras/sty521 , archivePrefix =. 1802.00053 , primaryClass =

    arXiv

  98. [126]

    , keywords =

    GALEX-SDSS-WISE Legacy Catalog (GSWLC): Star Formation Rates, Stellar Masses, and Dust Attenuations of 700,000 Low-redshift Galaxies. , keywords =. doi:10.3847/0067-0049/227/1/2 , archivePrefix =. 1610.00712 , primaryClass =

    arXiv

  99. [127]

    , keywords =

    Dust Attenuation Curves in the Local Universe: Demographics and New Laws for Star-forming Galaxies and High-redshift Analogs. , keywords =. doi:10.3847/1538-4357/aabf3c , archivePrefix =. 1804.05850 , primaryClass =

    arXiv

  100. [128]

    Analysis of a complete galaxy redshift survey. II. The field-galaxy luminosity function. , keywords =. doi:10.1093/mnras/232.2.431 , adsurl =

    doi

  101. [129]

    , keywords =

    The ALFALFA H I velocity width function. , keywords =. doi:10.1093/mnras/stab3164 , archivePrefix =. 2108.08856 , primaryClass =

    arXiv

  102. [130]

    , keywords =

    Maps of Dust Infrared Emission for Use in Estimation of Reddening and Cosmic Microwave Background Radiation Foregrounds. , keywords =. doi:10.1086/305772 , archivePrefix =. astro-ph/9710327 , primaryClass =

    arXiv

  103. [131]

    , year = 1968, month = feb, volume =

    Space Distribution and Luminosity Functions of Quasi-Stellar Radio Sources. , year = 1968, month = feb, volume =. doi:10.1086/149446 , adsurl =

    1968 doi

  104. [132]

    , keywords =

    Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Main Galaxy Sample. , keywords =. doi:10.1086/342343 , archivePrefix =. astro-ph/0206225 , primaryClass =

    arXiv

  105. [133]

    , keywords =

    Galaxy And Mass Assembly (GAMA): stellar mass estimates. , keywords =. doi:10.1111/j.1365-2966.2011.19536.x , archivePrefix =. 1108.0635 , primaryClass =

    2011

  106. [134]

    , keywords =

    Stellar mass functions: methods, systematics and results for the local Universe. , keywords =. doi:10.1093/mnras/stw756 , archivePrefix =. 1604.00008 , primaryClass =

    arXiv

  107. [135]

    , keywords =

    Inclination-dependent Luminosity Function of Spiral Galaxies in the Sloan Digital Sky Survey: Implications for Dust Extinction. , keywords =. doi:10.1086/511131 , archivePrefix =. astro-ph/0611714 , primaryClass =

    arXiv

  108. [136]

    , keywords =

    Stochastic Processes as the Origin of the Double Power-law Shape of the Quasar Luminosity Function. , keywords =. doi:10.3847/1538-4357/ab86ab , archivePrefix =. 2004.07412 , primaryClass =

    2004 arXiv

  109. [137]

    , keywords =

    The 1000 Brightest HIPASS Galaxies: The H I Mass Function and _ HI. , keywords =. doi:10.1086/374944 , archivePrefix =. astro-ph/0302440 , primaryClass =

    arXiv

  110. [138]

    , keywords =

    Cosmicflows-3: Two Distance-Velocity Calculators. , keywords =. doi:10.3847/1538-3881/ab620e , archivePrefix =. 1912.07214 , primaryClass =

    1912 arXiv

  111. [139]

    , keywords =

    The massive end of the luminosity and stellar mass functions: dependence on the fit to the light profile. , keywords =. doi:10.1093/mnras/stt1607 , archivePrefix =. 1304.7778 , primaryClass =

    arXiv

  112. [140]

    , keywords =

    Galaxy And Mass Assembly (GAMA): end of survey report and data release 2. , keywords =. doi:10.1093/mnras/stv1436 , archivePrefix =. 1506.08222 , primaryClass =

    arXiv

  113. [141]

    , keywords =

    The K-band luminosity function of nearby field galaxies. , keywords =. doi:10.1046/j.1365-8711.2000.03179.x , archivePrefix =. astro-ph/9907179 , primaryClass =

    2000

  114. [142]

    Statistical analysis of ALFALFA galaxies: Insights in galaxy formation & near-field cosmology

  115. [143]

    Galaxy flows in and around the Local Supercluster

  116. [144]

    , year = 1976, month = jan, volume =

    An analytic expression for the luminosity function for galaxies. , year = 1976, month = jan, volume =. doi:10.1086/154079 , adsurl =

    1976 doi

  117. [145]

    , keywords =

    Galaxy And Mass Assembly (GAMA): Data Release 4 and the z < 0.1 total and z < 0.08 morphological galaxy stellar mass functions. , keywords =. doi:10.1093/mnras/stac472 , archivePrefix =. 2203.08539 , primaryClass =

    arXiv

  118. [146]

    , keywords =

    Photometric Redshift Probability Distributions for Galaxies in the SDSS DR8. , keywords =. doi:10.1088/0067-0049/201/2/32 , archivePrefix =. 1109.5192 , primaryClass =

    arXiv

  119. [147]

    , keywords =

    Predicting dust extinction from the stellar mass of a galaxy. , keywords =. doi:10.1111/j.1365-2966.2010.17321.x , archivePrefix =. 1007.1145 , primaryClass =

    2010

  120. [148]

    , keywords =

    Star Formation in AEGIS Field Galaxies since z=1.1: The Dominance of Gradually Declining Star Formation, and the Main Sequence of Star-forming Galaxies. , keywords =. doi:10.1086/517926 , archivePrefix =. astro-ph/0701924 , primaryClass =

    arXiv

  121. [149]

    Frontiers in Astronomy and Space Sciences , keywords =

    Past, Present and Future of the Scaling Relations of Galaxies and Active Galactic Nuclei. Frontiers in Astronomy and Space Sciences , keywords =. doi:10.3389/fspas.2021.694554 , archivePrefix =. 2109.06301 , primaryClass =

    2021

  122. [150]

    , keywords =

    The Origin of the Mass-Metallicity Relation: Insights from 53,000 Star-forming Galaxies in the Sloan Digital Sky Survey. , keywords =. doi:10.1086/423264 , archivePrefix =. astro-ph/0405537 , primaryClass =

    arXiv

  123. [151]

    , keywords =

    The Small Scatter of the Baryonic Tully-Fisher Relation. , keywords =. doi:10.3847/2041-8205/816/1/L14 , archivePrefix =. 1512.04543 , primaryClass =

    arXiv

  124. [152]

    , keywords =

    The Baryonic Tully-Fisher Relation. , keywords =. doi:10.1086/312628 , archivePrefix =. astro-ph/0003001 , primaryClass =

    arXiv

  125. [153]

    , keywords =

    Gas Regulation of Galaxies: The Evolution of the Cosmic Specific Star Formation Rate, the Metallicity-Mass-Star-formation Rate Relation, and the Stellar Content of Halos. , keywords =. doi:10.1088/0004-637X/772/2/119 , archivePrefix =. 1303.5059 , primaryClass =

    arXiv

  126. [154]

    , keywords =

    Physical Models of Galaxy Formation in a Cosmological Framework. , keywords =. doi:10.1146/annurev-astro-082812-140951 , archivePrefix =. 1412.2712 , primaryClass =

    arXiv

  127. [155]

    , keywords =

    xGASS: total cold gas scaling relations and molecular-to-atomic gas ratios of galaxies in the local Universe. , keywords =. doi:10.1093/mnras/sty089 , archivePrefix =. 1802.02373 , primaryClass =

    arXiv

  128. [156]

    Gas fraction scaling relations of massive galaxies and first data release

    The GALEX Arecibo SDSS Survey - I. Gas fraction scaling relations of massive galaxies and first data release. , keywords =. doi:10.1111/j.1365-2966.2009.16180.x , archivePrefix =. 0912.1610 , primaryClass =

    2009

  129. [157]

    , keywords =

    Variation of galactic cold gas reservoirs with stellar mass. , keywords =. doi:10.1093/mnras/stu2532 , archivePrefix =. 1412.0852 , primaryClass =

    arXiv

  130. [158]

    , keywords =

    Quantifying the Bimodal Color-Magnitude Distribution of Galaxies. , keywords =. doi:10.1086/380092 , archivePrefix =. astro-ph/0309710 , primaryClass =

    arXiv

  131. [159]

    N., 2013, Journal of Improbable Astronomy, 1, 1

    Author A. N., 2013, Journal of Improbable Astronomy, 1, 1

    2013

  132. [160]

    D., 2015, Journal of Interesting Stuff, 17, 198

    Jones C. D., 2015, Journal of Interesting Stuff, 17, 198

    2015

  133. [161]

    B., 2014, The Example Journal, 12, 345 (Paper I)

    Smith A. B., 2014, The Example Journal, 12, 345 (Paper I)

    2014

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.