Pith. sign in

REVIEW 3 major objections 5 minor 78 references

Baryonic Ecosystem in Galaxies (BEINGMgII). Host Galaxies of Ultra-strong MgII Absorbers in Subaru Hyper Suprime-Cam Survey

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read The paper identifies 136 faint host galaxies for ultra-strong Mg II absorbers at small impact parameters and argues that the gas traces disks and winds in normal star-forming galaxies.

desk verdict Largest USMgII host sample to date, but 63% of hosts rest on photo-z matches that the paper never shows are clean; still worth refereeing. read the letter →

arxiv 2412.07835 v1 pith:2VJI4QEF submitted 2024-12-10 astro-ph.GA

Ravi Joshi (IIA) , Sarbeswar Das , Michele Fumagalli , Matteo Fossati , Céline Péroux , Reena Chaudhary , Hassen M. Yesuf , Luis C. Ho This is my paper · ORCID
classification astro-ph.GA
keywords ultra-strongMgIIabsorbersquasarabsorptionlineshostgalaxyidentificationcircumgalacticmediumgalacticwindsmain-sequencegalaxiesphotometricredshiftsimpactparameter
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

Ultra-strong Mg II absorbers---quasar absorption systems with rest equivalent width $W_{2796} \ge 3$ Å---are rare and thought to trace violent gas outflows, but the galaxies that make them have been hard to identify because they sit in the quasar's glare. This paper searches deep multi-band optical images around 418 such absorbers along 412 sightlines and identifies 136 bona fide host galaxies at small impact parameters (median 11.4 kpc, range 5.2--23 kpc), a detection rate of about 38%. The hosts are mostly ordinary star-forming main-sequence galaxies, with about 21% starbursts, and the absorbing gas is preferentially located along the galaxies' major and minor axes, which the authors interpret as disk gas and large-scale winds. A factor-three galaxy overdensity within 50 kpc points to group and interaction environments as an additional route to the strongest Mg II absorption. If correct, these results would place ultra-strong Mg II absorption in normal, actively star-forming galaxies at kiloparsec scales rather than exclusively in extreme starbursts.

What carries the argument

The machinery that carries the argument is the host-identification pipeline: model and subtract the quasar point spread function in deep HSC images, stack the bands, extract sources at 3$\sigma$ within the SDSS fiber radius, fit each candidate with the BAGPIPES SED code at or near the absorber redshift, and search the coadded SDSS spectrum for [O II] $\lambda\lambda3727,3729$ emission at $z_{\rm abs}$. Individual [O II] detections at $\ge 2\sigma$ anchor 50 hosts; the remaining 86 are assigned by photometric redshift match within $\Delta z/(1+z)\le0.15$ together with a stacked [O II] detection at $3.1\sigma$. The geometric part of the argument is carried by the azimuthal angle $\phi$ between the quasar sightline and the galaxy major axis, measured from Sersic fits to galaxies with ellipticity $e>0.2$; splitting the sample into $\phi<30^\circ$ ('disk') and $\phi>50^\circ$ ('wind') subsets is what produces the bimodal distribution and the contrasting $W_{2796}$--$\rho$ behavior.

What would settle it

A decisive test is to obtain deep rest-frame optical spectra (targeting [O II] or H$\alpha$) for the 86 galaxies classified as hosts only on photometric redshift and count how many show emission at the absorber redshift; if the confirmed fraction drops well below the ~80% implied by the stacked detection, chance alignment contaminates the sample and the claimed geometry results lose their footing.

Watch

Extended reading notes

Core claim

The paper's central claim is that a statistically significant fraction of ultra-strong Mg II absorbers are hosted by faint galaxies at small impact parameters: 136 bona fide hosts along 412 quasar sightlines, with a median impact parameter of 11.4 kpc and a 38% detection rate when at least four optical passbands are available. The hosts span stellar masses $8.65 \le \log M_\star/M_\odot \le 11.67$ with an average star formation rate of $30.7\,M_\odot\,\mathrm{yr}^{-1}$, and they lie on the main sequence of star-forming galaxies (21% are starbursts, and all meet the SFR threshold for launching strong outflows). Geometrically, the absorbers prefer the major and minor axes of their hosts: among 40 systems with robust position angles, 52% lie within 30° of the minor axis and 30% within 30° of the major axis, and the strongest absorbers ($W_{2796}>4$ Å) all belong to the minor-axis 'wind' subset. The wind subset shows near-constant $W_{2796}$ out to $\sim$23 kpc while the disk subset shows smaller scatter, which the paper argues explains the large scatter in the classical $W_{2796}$--impact-parameter anti-correlation. Non-zero [O II] emission along sightlines with no detected stellar counterpart further suggests that some absorbers arise in galaxies fainter than the image depth.

Load-bearing premise

The load-bearing premise is that a galaxy seen near the quasar is genuinely the absorber host when its photometric redshift agrees with the absorber redshift within $\Delta z/(1+z)\le0.15$, even though 86 of the 136 hosts have no individual spectroscopic confirmation; if a large fraction of these are chance alignments, the detection rate, the host mass/SFR distributions, and the disk/wind geometry results would be biased.

Editorial extensions

If this is right

  • If the 38% detection rate holds, the median impact parameter of 11.4 kpc makes ultra-strong Mg II absorption a kiloparsec-scale phenomenon rather than a diffuse halo phenomenon.
  • The main-sequence nature of the hosts means $W_{2796}\ge3$ Å can be produced by moderate star formation, and the 21% starburst fraction bounds how much extreme star formation contributes to the class.
  • The bimodal azimuthal distribution implies that integral-field spectrograph surveys should see biconical outflows along the minor axes and co-rotating gas along the major axes of USMgII hosts.
  • The wind subset's flat $W_{2796}$ with impact parameter and the factor-three overdensity within 50 kpc require both outflows and group environments in models of the strongest Mg II absorbers.
  • The non-zero [O II] along 'clear' sightlines suggests a population of host galaxies fainter than $r\sim26$ that deeper imaging or spectroscopy should reveal.

Reading between the lines

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

  • A statistical test the paper does not perform: place random HSC sources at the same impact parameters and ask what fraction would match the absorber redshift within $\Delta z/(1+z)\le0.15$; that would quantify the chance-alignment contamination in the 86 photometric-only hosts.
  • If the wind-subset interpretation is right, the SDSS spectra of those systems should show broader and more asymmetric Mg II profiles than the disk subset; a line-profile analysis of the existing spectra would be a cheap, immediate check.
  • The 'dark galaxy' candidates along clear sightlines connect naturally to the low-mass satellites that simulations predict account for a large fraction of absorbers; targeted narrow-band [O II] or integral-field searches around those quasars could find them directly.
  • The photo-z matching criterion itself is likely redshift- and mass-dependent, so the claimed host fractions should be re-derived in narrow redshift bins once spectroscopic confirmation is available.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper searches for host galaxies of ultra-strong Mg II absorbers (W2796 >= 3 Å) at small impact parameters (5–23 kpc) using deep Hyper Suprime-Cam imaging and SDSS spectroscopy. From 418 absorbers, the authors identify 50 galaxies through individual [O II] emission at >= 2σ and add 86 further candidates using BAGPIPES SED fits with photometric redshifts matched to the absorber redshift, or with redshift fixed to z_abs, yielding a total of 136 'bona fide' hosts. They report a ~38% detection rate, typical star-forming main-sequence host properties (21% starbursts), a preference for absorption along galaxy major and minor axes, a W2796–impact parameter relation with a wind/disk dichotomy, and an overdensity of galaxies around USMgII sightlines. The paper argues that USMgII absorbers trace disk gas and large-scale outflows, with some systems possibly hosted by unseen faint galaxies.

Significance. If the host identifications are reliable, this is one of the largest samples of ultra-strong Mg II absorber hosts in the small-impact-parameter regime, and the stacking analysis plus the public catalog would be valuable for future studies of CGM gas flows. The direct [O II] detections (50 systems) and the high-significance stacked [O II] signal for the full host sample are solid observational results. The paper also uses a carefully constructed control sample for the environmental overdensity measurement. However, the central claim that the SED-fitted sample of 130 galaxies represents genuine USMgII hosts rests on a photo-z matching procedure that has not been validated against a control field, and several of the physical conclusions (main-sequence offset, starburst fraction, wind/disk geometry) depend on this same SED-fitted sample. The statistical evidence for the geometric bimodality is also moderate (N≈40, KS P=0.084). With appropriate robustness tests and a clearer separation of secure and candidate hosts, the paper would make a solid contribution.

major comments (3)
  1. [Sec. 3.3 / Table 1] The 'bona fide' host sample includes 86 systems with individual [O II] below 2σ that are accepted based on a photo-z match within Δz/(1+z) ≤ 0.15 or a z-fixed SED fit. With only 4–5 HSC bands and sparse, shallow NIR data, this tolerance is broad (Δz ≈ 0.3 at z = 1), and the search radius of 1.5″ admits a non-negligible prior of unrelated faint galaxies. The stacked [O II] detection at 3.1σ for this subset does not exclude large contamination: even if only ~40–60% of the candidates are real, a stack of the real members could still reach a similar significance. The paper should compare the photo-z-only subset with the 50 direct detections in terms of redshift distribution, stellar mass, SFR, and azimuthal-angle distribution, and should estimate the chance-alignment rate using random quasar positions. Without this, the physical results in Sections 4.2–4.4, which use all 130 SED-fitted galaxies, may be biased by contamination.
  2. [Sec. 4.4 / Fig. 7] The claim that USMgII absorbers preferentially align along the major and minor axes rests on a small subsample (40 galaxies with ellipticity e>0.2, 38 with reliable PA) and a KS test between the 'wind' and 'disk' distributions that gives P=0.084, i.e., not significant at the usual threshold. The binning into 30-degree intervals with 30%, 52%, and 18% is suggestive but no significance level is quoted for the departure from a uniform distribution. The thresholds α≤30° and α≥50° are chosen post hoc and not varied; the paper should test the robustness of the bimodality to these thresholds and to the inclusion of the 10 additionally incorporated MEGAFLOW systems.
  3. [Sec. 4.3 / Fig. 6] The W2796–ρ analysis uses a literature compilation with heterogeneous selection functions and redshift ranges, and the wind/disk subsets are separated by arbitrary azimuthal-angle cuts. The quoted difference in mean W2796 (3.59±0.08 Å for wind versus 3.28±0.06 Å for disk) is small relative to the scatter, and no significance test for the difference between the two subsets is given. Moreover, because most of the USMgII galaxies in this figure come from the photo-z-selected sample, any systematic error in the host identifications propagates directly into the claimed wind/disk dichotomy. The authors should report a KS or rank-sum test between the wind and disk W2796 distributions and should repeat the fit excluding the photo-z-only candidates.
minor comments (5)
  1. [Sec. 4.1 / Fig. 3] The text reports both 'about 40%' and 'a detection rate of 38%' for the host detection rate; the denominator (all HSC-covered sightlines with 4+ passbands, or those with a detected potential host) should be stated unambiguously.
  2. [Sec. 4.2] The phrase 'average logM⋆ of 9.90 M⊙' should read 'average log(M⋆/M⊙) = 9.90', and the quoted average SFR of 30.72 M⊙ yr−1 should be accompanied by a dispersion or uncertainty.
  3. [Fig. 5] The caption mentions 'shaded and hatched regions' but does not identify which region corresponds to the 1σ confidence interval of the main-sequence fit or to the starburst definition; please clarify.
  4. [Table 1] The summary counts are hard to reconcile: 120 systems have [O II] < 2σ among the 170 within 1.5″, yet categories IB, II, and IIIB sum to 120 only if one reads the table carefully; a brief note explaining the column logic would improve readability.
  5. [Sec. 3.2] The 2.8σ [O II] detection along 'clear' sightlines is marginal; the interpretation of this signal as evidence of faint or dark galaxies should be presented as tentative, especially given the possibility of extended [O II] from a galaxy group or a distant source.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the host-galaxy SED parameters are measured after sample selection, not used to define the selection, and the stacked [OII] check is a post-hoc validation rather than a fitted input.

full rationale

The paper's central claims—detection rate, main-sequence/starburst fraction, and wind/disk azimuthal bimodality—are measurements made on a host-galaxy sample, not quantities that reduce by construction to the selection inputs. The host catalogue combines 50 individual [OII] detections with 86 objects selected via BAGPIPES photometric-redshift agreement or a good z-fixed SED fit. The derived stellar masses and SFRs do not enter the selection criterion: an object is classified as a host based on redshift agreement or fit quality, not on its resulting SFR or mass. The 3.1-sigma [OII] stack for the 86 photo-z-selected objects is computed after sample definition (Sec. 3.3: 'Using 86, out of the above 130, absorbers with [Oii]<2 sigma level and best photo-z estimates, we further generate the spectral stack'), so it is a consistency check, not a fitted input. The abstract phrase 'Utilizing the [OII] emission from the stacked spectrum... we further identify 86 galaxies' slightly misstates the chronological order, but the body text makes clear the stack validates rather than defines the subset. The W2796-rho relation is fitted to external literature samples (MAGIICAT, MEGAFLOW, MAGG, 3D-HST, Guha & Srianand), and the USMgII points are compared against it, not fitted to produce the conclusion. The wind/disk separation is a binning of measured azimuthal angles, and the associated W2796 comparisons are direct observations. Self-citations (e.g., Joshi et al. 2017b, 2018) support the established strong-MgII/[OII] connection and are not load-bearing. The photo-z contamination concern raised by the skeptical reader is a real robustness and correctness risk, but it is selection bias, not logical circularity: a contaminated sample would bias the measurements without making any derived quantity equal to its own input by construction. No load-bearing uniqueness claims, ansatz-by-citation, or renamed-known-result steps are present.

Assumptions & free parameters 6 free parameters · 5 assumptions · 1 invented entities

The central claim rests on standard cosmological conversion, on the assumption that photometric-redshift matches within 0.15 identify true absorber hosts for most of the sample, on the assumption that residual quasar light after PSF subtraction does not create spurious galaxies, and on the assumption that stacked [O II] at z_abs originates from the associated galaxy population. The SED-derived masses and SFRs, the photo-z tolerance, and the wind/disk angle cuts are chosen or fitted values that shape the final results.

free parameters (6)
  • log-linear W2796-rho fit offset alpha = alpha = 0.628 (+0.021/-0.019)
    Fitted to the combined sample (this work plus MAGIICAT, MEGAFLOW, MAGG, Huang, 3D-HST, Guha & Srianand) to model the equivalent width-impact parameter relation; used to quantify deviation of USMgII systems.
  • log-linear W2796-rho slope beta = beta = -0.018 (+0.001/-0.001)
    Same fit as alpha; the slope encodes the radial dependence of absorption strength.
  • SED model parameters (stellar mass, SFR, age, metallicity, Av) = log M* 8.65-11.67 solar masses; average SFR 30.72 M_sun/yr
    BAGPIPES fits to HSC/VISTA/unWISE photometry for 130 galaxies; these parameters drive the main sequence / starburst classification and wind threshold comparisons.
  • photo-z matching tolerance = Delta z / (1+z) <= 0.15
    Chosen threshold to declare a photometric source a USMgII host; 86 of 136 hosts are identified this way without individual [O II] detection.
  • wind/disk azimuthal angle thresholds = phi > 50 deg wind, phi < 30 deg disk
    Hand-chosen division of the 38-system subset to separate wind and disk origins; the 30-60 deg bin has only 18% of systems, and KS test P=0.084.
  • ellipticity threshold = e > 0.2
    Required for reliable position angle; reduces geometry sample to 40 systems.
assumptions (5)
  • domain assumption Flat LCDM cosmology with H0=70 km/s/Mpc, Omega_m=0.3, Omega_Lambda=0.7
    Stated in Section 1; used to convert angular impact parameters to kpc.
  • domain assumption Quasar light can be modeled as a PSF plus a Sersic host profile; residuals after subtraction are attributed to real sources
    Section 3.1; false-positive detections from imperfect PSF subtraction are mitigated by multi-band consistency and visual inspection, but residuals cannot be fully excluded.
  • domain assumption If at least 2Re of a candidate galaxy lies within the 1.5 arcsec SDSS/BOSS fiber, [O II] emission from the galaxy will appear in the quasar spectrum at z_abs
    Sections 3.1-3.2; used to link photometric detections to [O II] searches.
  • domain assumption A source with photometric redshift matching z_abs within Delta z/(1+z)<=0.15 is physically associated with the absorber
    Sections 3.3 and 4.1; for 86 systems this is the primary association criterion; photo-z outliers would contaminate the host sample.
  • domain assumption Median stacking of spectra preserves [O II] flux at the absorber redshift
    Used in Sections 3.2-3.3 to claim statistical detection of [O II] in subsets; assumes no systematic wavelength error and no contamination from sky residuals.
invented entities (1)
  • Unseen faint or dark galaxies as hosts of USMgII absorbers along clear sightlines
    purpose: Explains the 2.8 sigma [O II] detection in stacked spectra of 175 clear sightlines with no detected stellar counterpart, and supports the claim that USMgII systems may originate from galaxies fainter than the HSC detection limit.
    No individual detection or direct imaging evidence; one stacked-image example and reference to Das et al. in preparation. The [O II] signal could also arise from residual quasar light, extended emission from a distant galaxy, or intragroup gas.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Baryonic Ecosystem in Galaxies (BEINGMgII). Host Galaxies of Ultra-strong MgII Absorbers in Subaru Hyper Suprime-Cam Survey." pith.science (2026). https://pith.science/paper/2VJI4QEF

@misc{pith2026241207835,
  author       = {Pith},
  title        = {Pith review of: Baryonic Ecosystem in Galaxies (BEINGMgII). Host Galaxies of Ultra-strong MgII Absorbers in Subaru Hyper Suprime-Cam Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2VJI4QEF}},
  note         = {Machine review of arXiv:2412.07835}
}
abstract

We study the galaxies hosting ultra-strong MgII (USMgII) absorbers at small impact parameters of $\sim$2" (5 - 20 kpc), spanning a redshift range of $0.4 \le z \le 1.7$, using deep, high-resolution images from Hyper Suprime-Cam Subaru Strategic Survey and spectra from SDSS survey. From a total of 418 USMgII absorbers with $W_{2796}\ \ge 3 \mathring{A}$, along 412 quasar sightlines, we detect 50 galaxies based on [O II] $\lambda\lambda$3727,3729 nebular emission detected at $\ge 2\sigma$ level. Utilizing the [O II] emission from the stacked spectrum and employing the best-fit galaxy SED template, we further identify 86 galaxies, leading to a total of 136 bona fide USMgII galaxies. With a prerequisite of having a minimum of four HSC passbands available, we find a detection rate of $\sim$38% at an average impact parameter of 11.4 kpc. We find that galaxies hosting USMgII systems are typically star-forming main sequence galaxies, with 21% exhibiting a starburst nature. The non-zero [O II] emission along the `clear' sightlines, with no stellar counterpart, hints that the USMgII absorbers may likely emanate from the unseen faint galaxies near the quasar. The USMgII absorbers preferentially align along the major and minor axes of the galaxy, which suggests that they originate in the disk or large-scale wind. We show that the distribution of $W_{2796}$ as a function of impact parameter indicates a discernible radial dependence for the `disk' and `wind' subsets, with the observed large scatter in $W_{2796}$ potentially attributed to large-scale outflows. The quasar sightline hosting USMgII systems show a factor three higher galaxy surface density at impact parameters of $\lesssim 50$kpc, highlights the multiple pathways giving rise to USMgII absorption.

Figures

Figures reproduced from arXiv: 2412.07835 by the authors.

Figure 1
Figure 1. The postage stamp HSC color composite images, centered on the quasar, are depicted with gray contours. The SDSS fiber, with a radius of 1.5 ′′, is indicated by dashed circles, while the cyan-colored aperture highlights the USMgII host galaxy. The first three rows exhibit an example set of potential host detections at close quasar proximity for USMgII systems with direct [O ii] detection (at 2σ level), followed by [O… view at source ↗
Figure 2
Figure 2. The postage stamp HSC color composite images, centered on the quasar, are depicted with gray contours. The SDSS fiber, with a radius of 1.5 ′′, is indicated by a dashed yellow circle, while the cyan-colored aperture highlights the USMgII host galaxy. The second column ex￾hibits the corresponding multi-band best-fit SED model at the absorber redshift. Besides the HSC optical passbands, we also search for near￾infrare… view at source ↗
Figure 3
Figure 3. Distribution of the detection rate of USMgII absorber galax￾ies with redshift. The square shows the average r−band magnitude of galaxies per redshift bin, along with the 16th and 84th percentiles. 4. Results 4.1. Detection rate of USMgII absorber galaxies In the early efforts to map the host galaxies and environments of USMgII absorbers, Nestor et al. (2007) utilized the deep optical images that revealed the existen… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: A potential galaxy candidate from the faint end population at zabs = 1.018, along J234451.70+000603.2 quasar sightline at zqso = 2.4. Left panel: The postage stamp HSC multi-band images after quasar re￾moval. Top right panel: HSC five band median coadded image showing …
Figure 5
Figure 5. Figure 5: Main sequence, SFR versus stellar mass, USMgII absorber host galaxies. The dashed-blue and solid-red lines depict the best linear fit for main sequence galaxies at 0.5 ≤ z < 1 and 1 ≤ z < 2, respectively while the solid-green line represents the sequence for starburst …
Figure 6
Figure 6. Figure 6: Bottom panel: Equivalent width (W2796) versus impact parame￾ter (ρ) relation for MgII galaxies. The best-fit anti-correlation between W2796 and ρ is shown as a solid line along with the 16th and 84th percentiles indicated as a hatched region. The best-fitting relations…
Figure 7
Figure 7. Figure 7: Left Panel: Distribution of Mg ii absorbers as a function of azimuthal angle for the USMgII host galaxy with ellipticity e ≥ 0.2. The symbol size represents the W2796/W2600 with the color denoting the strength of equivalent width (W2796). Right Panel: same as left for …
Figure 8
Figure 8. Figure 8: The relative abundances, [X/Zn], along detection versus clear sightlines, for several species compared with the depletion patterns of the Milky Way halo (dashed line) and warm Disc (dashed-dot line) gas from Welty et al. (1999). The zero point of the ordinate correspon…
Figure 9
Figure 9. Figure 9: Excess surface density of galaxies along USMgII sightlines as a function of impact parameter with respect to a control quasar set of similar redshift and color. A clear overdensity of galaxies is evident for ρ ≲ 50kpc. The inset shows the same for galaxies selected bas…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

78 extracted references · 33 canonical work pages

  1. [1]

    N., Adelman-McCarthy, J

    Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2009, ApJS, 182, 543

    2009

  2. [2]

    2022, PASJ, 74, 247

    Aihara, H., AlSayyad, Y ., Ando, M., et al. 2022, PASJ, 74, 247

    2022

  3. [3]

    D., Allende Prieto, C., et al

    Alam, S., Albareti, F. D., Allende Prieto, C., et al. 2015, ApJS, 219, 12

    2015

  4. [4]

    2022, MNRAS, 513, 3210

    Anand, A., Kauffmann, G., & Nelson, D. 2022, MNRAS, 513, 3210

    work page 2022

  5. [5]

    & Arnouts, S

    Bertin, E. & Arnouts, S. 1996, A&AS, 117, 393

    1996

  6. [6]

    2002, in Astronomical Society of the Pacific Conference Series, V ol

    Bertin, E., Mellier, Y ., Radovich, M., et al. 2002, in Astronomical Society of the Pacific Conference Series, V ol. 281, Astronomical Data Analysis Software and Systems XI, ed. D. A. Bohlender, D. Durand, & T. H. Handley, 228

    2002

  7. [7]

    Bielby, R., Crighton, N. H. M., Fumagalli, M., et al. 2017, MNRAS, 468, 1373

    2017

  8. [8]

    I., Grogin, N., & Koekemoer, A

    Bisigello, L., Caputi, K. I., Grogin, N., & Koekemoer, A. 2018, A&A, 609, A82

    work page 2018

Show all 78 references
  1. [9]

    A., Churchill, C

    Bond, N. A., Churchill, C. W., Charlton, J. C., & V ogt, S. S. 2001, ApJ, 562, 641

    2001

  2. [10]

    J., Knobel, C., et al

    Bordoloi, R., Lilly, S. J., Knobel, C., et al. 2011, ApJ, 743, 10 Bouché, N., Hohensee, W., Vargas, R., et al. 2012, MNRAS, 426, 801 Bouché, N., Murphy, M. T., Péroux, C., et al. 2007, ApJ, 669, L5

    2011

  3. [11]

    L., & Storchi-Bergmann, T

    Calzetti, D., Kinney, A. L., & Storchi-Bergmann, T. 1994, ApJ, 429, 582

    1994

  4. [12]

    C., McLure, R

    Carnall, A. C., McLure, R. J., Dunlop, J. S., & Davé, R. 2018, Monthly Notices of the Royal Astronomical Society, 480, 4379–4401

    2018

  5. [13]

    L., Fielding, D

    Carr, C., Bryan, G. L., Fielding, D. B., Pandya, V ., & Somerville, R. S. 2023, ApJ, 949, 21

    2023

  6. [14]

    D., et al

    Chen, H.-W., Boettcher, E., Johnson, S. D., et al. 2019, ApJ, 878, L33

    2019

  7. [15]

    E., Gauthier, J.-R., et al

    Chen, H.-W., Helsby, J. E., Gauthier, J.-R., et al. 2010, ApJ, 714, 1521

    2010

  8. [16]

    F., Genel, S., et al

    DeFelippis, D., Bouché, N. F., Genel, S., et al. 2021, ApJ, 923, 56

    2021

  9. [17]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168

    2019

  10. [18]

    2023, MNRAS, 522, 535

    Dutta, R., Fossati, M., Fumagalli, M., et al. 2023, MNRAS, 522, 535

    2023

  11. [19]

    2020, MNRAS, 499, 5022

    Dutta, R., Fumagalli, M., Fossati, M., et al. 2020, MNRAS, 499, 5022

    2020

  12. [20]

    2018, A&A, 616, A110

    Elbaz, D., Leiton, R., Nagar, N., et al. 2018, A&A, 616, A110

    2018

  13. [21]

    2018, A&A, 617, A62

    Feltre, A., Bacon, R., Tresse, L., et al. 2018, A&A, 617, A62

    2018

  14. [22]

    P., & Bridges, M

    Feroz, F., Hobson, M. P., & Bridges, M. 2009, Monthly Notices of the Royal Astronomical Society, 398, 1601–1614

    2009

  15. [23]

    2019, MNRAS, 484, 2212

    Fossati, M., Fumagalli, M., Gavazzi, G., et al. 2019, MNRAS, 484, 2212

    2019

  16. [24]

    2013, MNRAS, 432, 1444

    Gauthier, J.-R. 2013, MNRAS, 432, 1444

    2013

  17. [25]

    Guha, L. K. & Srianand, R. 2023, MNRAS, 519, 3319

    2023

  18. [26]

    K., Srianand, R., Dutta, R., et al

    Guha, L. K., Srianand, R., Dutta, R., et al. 2022, MNRAS, 513, 3836

    2022

  19. [27]

    K., Srianand, R., & Petitjean, P

    Guha, L. K., Srianand, R., & Petitjean, P. 2024, MNRAS, 527, 5075

    2024

  20. [28]

    F., et al

    Guo, Y ., Bacon, R., Bouché, N. F., et al. 2023, Nature, 624, 53

    2023

  21. [29]

    M., Lehnert, M

    Heckman, T. M., Lehnert, M. D., Strickland, D. K., & Armus, L. 2000, ApJS, 129, 493

    2000

  22. [30]

    H., Martin, C

    Ho, S. H., Martin, C. L., & Schaye, J. 2020, ApJ, 904, 76

    2020

  23. [31]

    A., et al

    Huang, Y .-H., Chen, H.-W., Shectman, S. A., et al. 2021, MNRAS, 502, 4743

    2021

  24. [32]

    J., et al

    Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, A&A, 457, 841

    2006

  25. [33]

    2020, PASJ, 72, 83

    Ishino, T., Matsuoka, Y ., Koyama, S., et al. 2020, PASJ, 72, 83

    2020

  26. [34]

    Jenkins, E. B. 1986, ApJ, 304, 739

    1986

  27. [35]

    2017b, MNRAS, 471, 1910

    Joshi, R., Srianand, R., Petitjean, P., & Noterdaeme, P. 2017b, MNRAS, 471, 1910

    1910

  28. [36]

    2018, MNRAS, 476, 210

    Joshi, R., Srianand, R., Petitjean, P., & Noterdaeme, P. 2018, MNRAS, 476, 210

    2018

  29. [37]

    G., Churchill, C

    Kacprzak, G. G., Churchill, C. W., Evans, J. L., Murphy, M. T., & Steidel, C. C. 2011, MNRAS, 416, 3118

    2011

  30. [38]

    G., Churchill, C

    Kacprzak, G. G., Churchill, C. W., & Nielsen, N. M. 2012, ApJ, 760, L7

    2012

  31. [39]

    G., Martin, C

    Kacprzak, G. G., Martin, C. L., Bouché, N., et al. 2014, ApJ, 792, L12

    2014

  32. [40]

    E., Forster Schreiber, N

    Kehoe, E., Shapley, A. E., Forster Schreiber, N. M., et al. 2024, arXiv e-prints, arXiv:2406.07621

    2024 arXiv

  33. [41]

    2014, ApJ, 795, 31

    Lan, T.-W., Ménard, B., & Zhu, G. 2014, ApJ, 795, 31

    2014

  34. [42]

    Lan, T.-W. & Mo, H. 2018, ApJ, 866, 36

    2018

  35. [43]

    F., Creech, S., Brammer, G., et al

    Lundgren, B. F., Creech, S., Brammer, G., et al. 2021, ApJ, 913, 50

    2021

  36. [44]

    2017, ApJ, 846, 4

    Mas-Ribas, L., Miralda-Escudé, J., Pérez-Ràfols, I., et al. 2017, ApJ, 846, 4

    2017

  37. [45]

    2012, AJ, 144, 159 Ménard, B., Wild, V ., Nestor, D., et al

    Matsuoka, Y ., Ienaka, N., Oyabu, S., Wada, K., & Takino, S. 2012, AJ, 144, 159 Ménard, B., Wild, V ., Nestor, D., et al. 2011, MNRAS, 417, 801

    2012

  38. [46]

    Murray, N., Ménard, B., & Thompson, T. A. 2011, ApJ, 735, 66

    2011

  39. [47]

    2019, MNRAS, 490, 3234

    Nelson, D., Pillepich, A., Springel, V ., et al. 2019, MNRAS, 490, 3234

    2019

  40. [48]

    B., Johnson, B

    Nestor, D. B., Johnson, B. D., Wild, V ., et al. 2011, MNRAS, 412, 1559

    2011

  41. [49]

    B., Turnshek, D

    Nestor, D. B., Turnshek, D. A., Rao, S. M., & Quider, A. M. 2007, ApJ, 658, 185

    2007

  42. [50]

    M., Churchill, C

    Nielsen, N. M., Churchill, C. W., & Kacprzak, G. G. 2013, ApJ, 776, 115

    2013

  43. [51]

    2014, A&A, 566, A24

    Noterdaeme, P., Petitjean, P., Pâris, I., et al. 2014, A&A, 566, A24

    2014

  44. [52]

    2010, MNRAS, 403, 906

    Noterdaeme, P., Srianand, R., & Mohan, V . 2010, MNRAS, 403, 906

    2010

  45. [53]

    Oppenheimer, B. D. & Davé, R. 2006, MNRAS, 373, 1265 Péroux, C., Nelson, D., van de V oort, F., et al. 2020, MNRAS, 499, 2462 Péroux, C., Rahmani, H., Quiret, S., et al. 2017, MNRAS, 464, 2053

    2006

  46. [54]

    L., Rupke, D

    Perrotta, S., Coil, A. L., Rupke, D. S. N., et al. 2024, arXiv e-prints, arXiv:2409.10013

    2024 arXiv

  47. [55]

    2023, MNRAS, 519, 1526

    Popesso, P., Concas, A., Cresci, G., et al. 2023, MNRAS, 519, 1526

    2023

  48. [56]

    M., Turnshek, D

    Rao, S. M., Turnshek, D. A., & Nestor, D. B. 2006, ApJ, 636, 610

    2006

  49. [57]

    2018, ApJ, 853, 95

    Moustakas, J. 2018, ApJ, 853, 95

    2018

  50. [58]

    Rubin, K. H. R., Juarez, C., Cooksey, K. L., et al. 2022, ApJ, 936, 171

    2022

  51. [59]

    Rubin, K. H. R., Prochaska, J. X., Koo, D. C., Phillips, A. C., & Weiner, B. J. 2010, ApJ, 712, 574

    2010

  52. [60]

    Rupke, D. S. N., Coil, A., Geach, J. E., et al. 2019, Nature, 574, 643

    2019

  53. [61]

    2021, kasperschmidt/FELIS: FELIS version 1.0

    Schmidt, K. 2021, kasperschmidt/FELIS: FELIS version 1.0

    2021

  54. [62]

    F., Zabl, J., et al

    Schroetter, I., Bouché, N. F., Zabl, J., et al. 2019, MNRAS, 490, 4368

    2019

  55. [63]

    1996, ApJ, 462, 643

    Srianand, R. 1996, ApJ, 462, 643

    1996

  56. [64]

    C., Erb, D

    Steidel, C. C., Erb, D. K., Shapley, A. E., et al. 2010, ApJ, 717, 289

    2010

  57. [65]

    & Price, K

    Storn, R. & Price, K. 1997, Journal of Global Optimization, 11, 341

    1997

  58. [66]

    A., Whichard, Z

    Straka, L. A., Whichard, Z. L., Kulkarni, V . P., et al. 2013, MNRAS, 436, 3200

    2013

  59. [67]

    Suresh, J., Nelson, D., Genel, S., Rubin, K. H. R., & Hernquist, L. 2019, MN- RAS, 483, 4040

    2019

  60. [68]

    A., Heckman, T

    Tremonti, C. A., Heckman, T. M., Kauffmann, G., et al. 2004, ApJ, 613, 898

    2004

  61. [69]

    S., & Werk, J

    Tumlinson, J., Peeples, M. S., & Werk, J. K. 2017, ARA&A, 55, 389 van der Wel, A., Franx, M., van Dokkum, P. G., et al. 2014, ApJ, 788, 28

    2017

  62. [70]

    2005, ARA&A, 43, 769

    Veilleux, S., Cecil, G., & Bland-Hawthorn, J. 2005, ARA&A, 43, 769

    2005

  63. [71]

    E., Frisch, P

    Welty, D. E., Frisch, P. C., Sonneborn, G., & York, D. G. 1999, ApJ, 512, 636

    1999

  64. [72]

    2023, MNRAS, 523, 676

    Weng, S., Péroux, C., Karki, A., et al. 2023, MNRAS, 523, 676

    2023

  65. [73]

    2024, MNRAS, 527, 3494

    Weng, S., Péroux, C., Ramesh, R., et al. 2024, MNRAS, 527, 3494

    2024

  66. [74]

    J., et al

    Wuyts, S., Franx, M., Cox, T. J., et al. 2009, ApJ, 696, 348

    2009

  67. [75]

    G., Khare, P., Vanden Berk, D., et al

    York, D. G., Khare, P., Vanden Berk, D., et al. 2006, MNRAS, 367, 945

    2006

  68. [76]

    F., Schroetter, I., et al

    Zabl, J., Bouché, N. F., Schroetter, I., et al. 2019, MNRAS, 485, 1961

    2019

  69. [77]

    F., Wisotzki, L., et al

    Zabl, J., Bouché, N. F., Wisotzki, L., et al. 2021, MNRAS, 507, 4294

    2021

  70. [78]

    B., et al

    Zibetti, S., Ménard, B., Nestor, D. B., et al. 2007, ApJ, 658, 161 Article number, page 12 of 12

    2007

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.