Pith. sign in

REVIEW 4 major objections 3 minor 136 references

RIDEN pilot survey: broad-band selection of candidate quasars with extended Lyman-$\alpha$ nebulae using CLAUDS-HSC-SSP-DUNES$^2$ joint data

T0 review · 4 major / 3 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read Extended Lyman-alpha nebulae around $z=1.9$--$3.0$ quasars can be found in ordinary broad-band images; this pilot finds 24 candidates across 13 square degrees and paves the path to a Rubin-LSST-wide census.

desk verdict A careful pilot of a known broad-band Ly-alpha selection method, useful for LSST planning, but the candidate list is not yet proven and a false-positive control is missing. read the letter →

arxiv 2506.04570 v1 pith:GGHP6XBE submitted 2025-06-05 astro-ph.GA

Rhythm Shimakawa , Satoshi Kikuta , Haruka Kusakabe , Marcin Sawicki , Yongming Liang , Rieko Momose , Stephen Gwyn , Guillaume Desprez This is my paper
classification astro-ph.GA
keywords Lyman-alphanebulaequasarbroad-bandselectionELANcircumgalacticmediumRubinLSSTphotometricredshiftsHSC-SSP
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 show that huge Lyman-alpha nebulae around quasars can be found without expensive narrow-band or integral-field observations, using only the broad-band images that large surveys already produce. Around 483 spectroscopically confirmed quasars at $z=1.9$--$3.0$ in 13 square degrees of deep $u$-to-$K$ imaging, the authors extrapolate a continuum image from two redder bands into the $u$ or $g$ band, subtract it, and look for residual extended emission. They find 24 candidate nebulae with $u$- or $g$-band excess spanning 50--170 kpc, three of which rival known giant nebulae in size and asymmetry. The Rubin Observatory's LSST will cover 18,000 square degrees in the same bands, so the same method could scan hundreds of thousands of quasars and, for the first time, measure how common these giant gas reservoirs are and where they live.

What carries the argument

The broad-band colour selection is the mechanism: three images per quasar — one Ly$\alpha$-band ($u$, $u^*$, or $g$) and two redder continuum bands ($g,r$ or $r,i$) — are combined through empirical colour-term relations (Eqs. 1--6) to build a pseudo-continuum image of the Ly$\alpha$ band, which is then subtracted to leave a Ly$\alpha$ map. The colour terms and their small redshift dependences are fitted to thousands of spec-$z$ galaxies from the same imaging, and an SED-fitting photometric-redshift catalogue is used to mask projected neighbours outside $|\delta z| > 0.1$, rejecting about 95% of $i<25$ sources as irrelevant foreground or background objects. Residual flux is converted to Ly$\alpha$ surface brightness using the filter width, the filter transmission at the quasar's Ly$\alpha$ redshift, and a pixel area, then scaled by $(1+z)^4$ to a common $z=2.3$ for comparison. Detection requires a 2$\sigma$ excess in binned pixels, and a nebula is defined by an effective area above 40 arcsec$^2$ after masking.

What would settle it

Run the identical colour-term subtraction and nebula selection on thousands of random sky positions or on non-quasar galaxies matched to the same photometric-redshift distribution and imaging depth; if the rate of 50-170 kpc excess regions approaches the roughly 5% rate found around quasars, the candidates are dominated by colour-term systematics. Spectroscopy settles it directly: integral-field or narrow-band observations of the 24 candidates should show extended Ly-alpha with rest-frame equivalent width above roughly 100-200 A and spatial morphology matching the broad-band excess.

Watch

Extended reading notes

Core claim

The central claim is that extended Lyman-$\alpha$ nebulae around $z=1.9$--$3.0$ quasars are detectable as a residual excess in a single broad-band image after subtracting an empirically extrapolated continuum. For quasars at $z=1.90$--$2.23$ the Ly$\alpha$ line falls in the $u$ (or $u^*$) band, and for $z=2.34$--$3.00$ in the $g$ band; the continuum at that wavelength is estimated from two redder bands using colour-term relations calibrated on 1,541--1,822 galaxies with spectroscopic redshifts, with a small redshift correction. Photometric-redshift masking removes foreground and background objects that would mimic nebulae, and the central quasar is masked. Applying this to 483 quasars yields 24 candidates with effective Ly$\alpha$ areas above 40 arcsec$^2$, corresponding to sizes of roughly 50--170 kpc, whose radial surface-brightness profiles agree with the exponential profiles measured by integral-field surveys, with scale lengths of about 15--17 kpc. The authors also report no significant difference in nebular asymmetry between the $z\sim2.1$ and $z\sim2.8$ samples, and no statistically significant environmental overdensity around quasars with large nebulae compared to control samples, although the largest nebula sits near a density peak.

Load-bearing premise

The colour-term relations that predict Ly-alpha-band flux from redder bands were fitted to ordinary galaxies, not quasars; the method assumes those relations hold for quasar host galaxies and their surroundings, so that any leftover u- or g-band light is genuinely extended Ly-alpha emission rather than a mismatch in the continuum extrapolation or light from other ultraviolet lines.

Editorial extensions

If this is right

  • If the method is valid, the Rubin LSST's 18,000 square degrees in the same $u,g,r,i$ bands would allow the selection to run over roughly 200,000 quasars, expanding the surveyed volume by a factor of hundreds relative to current narrow-band surveys.
  • A statistically meaningful census of giant (ELAN-like) nebulae becomes possible, including their number density and their association with protoclusters, which presently rests on a handful of objects.
  • The $u$-band path extends Ly$\alpha$ searches to $z=1.9$--$2.3$, where the filter is about two times narrower than the $g$-band, giving better line contrast and reaching surface brightnesses as low as roughly $0.5\times10^{-17}\,\mathrm{erg\,s^{-1}\,cm^{-2}\,arcsec^{-2}}$.
  • The three giant candidates, with projected extents beyond 130 kpc and asymmetric shapes, would join the rare Type I ELAN class if confirmed, providing targets for detailed studies of gas around massive haloes.

Reading between the lines

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

  • The null environmental result may be a depth effect: the Monte Carlo simulations show the method recovers only about 10% of the Ly$\alpha$ flux beyond 40 pkpc, so any environmental trends acting on the outer nebular gas would largely escape this selection.
  • A direct test of the systematics would be to apply the same colour-term subtraction to spectroscopically confirmed non-quasar galaxies at the same redshifts; the false-positive rate of 'nebulae' around those galaxies would calibrate how much of the 5% detection rate is continuum mismatch rather than real Ly$\alpha$ emission.
  • Because the quasar sample is fainter than the integral-field-survey quasars ($-27.7 < M_i < -24.9$ versus $M_i < -27$), the absence of morphological evolution between $z\sim2.1$ and $z\sim2.8$ cannot yet be compared with earlier claims of evolving asymmetry; LSST's larger sample would allow a luminosity-matched comparison.
  • The same broad-band trick could in principle be pushed to the $g$-band at $z=3$--$3.5$ or the $i$-band at $z\sim4$, but the paper's own colour-term analysis shows IGM absorption increasingly corrupts the continuum extrapolation beyond $z=3$, setting a practical boundary for the method.
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

4 major / 3 minor

Summary. The paper presents a pilot broad-band search for extended Lyman-alpha nebulae around 483 SDSS/BOSS quasars at z=1.9-3.0, using deep u-to-K imaging from the CLAUDS-HSC-SSP-DUNES2 joint dataset. The method constructs a continuum image from two broad bands (Eqs. 1-6), subtracts it from the Ly-alpha band, masks foreground/background objects with photometric redshifts, and selects 24 candidate quasar nebulae with effective Ly-alpha area Area_eff>40 arcsec2. The authors then measure radial profiles, covering fractions, asymmetry, and environmental overdensities, concluding that there is no significant redshift evolution of asymmetry and no environmental dependence, while noting the lack of spectroscopic confirmation and various data limitations.

Significance. If validated, the broad-band Ly-alpha imaging technique would be a powerful way to discover giant Ly-alpha nebulae over very wide fields, including the upcoming Rubin/LSST survey. The paper is careful in tone: candidates are explicitly called candidates, the absence of spectroscopic confirmation is acknowledged, and several limitations are stated in the text. The public release of the candidate catalogue and processed images is a strength. However, the central claim that 24 candidate nebulae exhibit genuine u/g-band excess rests on an empirical continuum extrapolation and has not been tested against control samples or systematics. The paper is therefore a useful pilot and method demonstration, but its main quantitative result is not yet firmly established.

major comments (4)
  1. [Section 4.1, Area_eff selection] The selection of 24 quasar nebulae via Area_eff>40 arcsec2 is not accompanied by any control experiment using stars, random sky positions, or non-quasar galaxies processed through the same pipeline. The 5.9% detection fraction (24/409) therefore has no demonstrated excess over the expected rate of spurious extended residuals from PSF wings, colour-term mismatch, or unmasked projected neighbours. The central claim of the abstract depends on this comparison, so a control experiment should be added.
  2. [Section 3, Eqs. (1)-(6)] The colour-term relations are fitted to 1,541-1,822 spectroscopic galaxies, with SDSS/eBOSS quasars explicitly excluded. Quasar rest-UV continua are often AGN power laws rather than galaxy SEDs, so these galaxy-calibrated relations may be systematically offset when applied to the quasar targets. Because the continuum subtraction is performed pixel-by-pixel over the full cutout, even a small systematic offset will produce a spatially extended residual that survives the 2.5-arcsec central mask and mimics a low-surface-brightness nebula. The paper should validate the colour terms on quasar spectra, on quasars whose Ly-alpha does not fall in the Ly-alpha band, or on stars, and quantify the resulting spatial residual.
  3. [Section 3, Eq. (8) and Section 5.1] The photometric-redshift outlier rate is f_outlier=0.13 for i<25 sources at z=1.9-3.0, and the mask only removes objects with |delta z|>0.1. A substantial fraction of foreground/background galaxies therefore remains unmasked and can create asymmetric extended features in the Ly-alpha residual images. This concern is directly relevant to the morphology analysis: Section 5.1 notes that three of the nebulae with alpha<0.5 have bright companions in the Ly-alpha images, suggesting that their high asymmetry is not intrinsic. The paper should quantify the probability that an Area_eff>40 arcsec2 detection is produced by an unmasked interloper, for example by injecting fake galaxies into the images.
  4. [Section 5.2] The environmental analysis uses photometric redshifts with outlier rates of 0.3-0.4 at |delta z|>0.05, and the control and quasar samples are both subject to the same projection effects. The KS tests therefore have limited power, and the conclusion that there is 'no environmental dependence' is not strongly supported. This is acknowledged in the text, but given that the absence of environmental dependence is one of the three headline results, the limitation should be stated more prominently and the density measurement should include an uncertainty estimate arising from photo-z scatter.
minor comments (3)
  1. [Section 6] The Conclusions state that the authors obtain 39 and 17 quasars with large Ly-alpha nebulae in the two redshift intervals, but Section 4.1 and Table 2 report 8, 10, and 6 in the three selections, totaling 24. The abstract also says 24. This numerical inconsistency should be corrected.
  2. [Section 4.1] The notation for the background standard deviation is inconsistent: the text uses both bg_std and bg_std_z, and Figure 6 uses bd_std_z. Please unify the notation.
  3. [Section 5.1] The definition of 'Type I ELAN' from Li et al. (2024) is cited as '>100 pkpc with M_UV<-22', but M_UV is not defined at that point. Please define the absolute UV magnitude and specify the band or wavelength.

Circularity Check

0 steps flagged · score 2.0 of 10

No load-bearing circularity: the Ly-alpha maps are calibrated on an external spec-z galaxy sample with quasars excluded; the self-citation to Shimakawa (2022) supplies the method only, not the evidence for the detections.

full rationale

The derivation chain is not circular. The continuum images subtracted to form the Ly-alpha maps are built from empirical color-term relations (Eqs. 1-6) fitted to 1,541-1,822 spectroscopic galaxies from external surveys (3D-HST, DEIMOS 10k, GAMA DR3, PRIMUS, SDSS DR15, VVDS), and Section 3 explicitly states that SDSS/eBOSS quasars are removed from the fitting sample. Thus the residual u/g-band flux attributed to Ly-alpha is not forced by the target quasars' own photometry; any AGN-continuum color mismatch would be a systematic error, not a circular reduction. The candidate selection (Area_eff > 40 arcsec^2) is a measurement on these residual maps, not a prediction of a fitted quantity. The radial-profile fit and the Monte Carlo covering-fraction simulation reuse the same 24 detected nebulae, but the paper uses them as descriptive consistency tools and not as independent validation; the abstract's central claim is the detection itself. Self-citations to Shimakawa (2022) provide the methodological template and area definitions, but the present detections are new measurements on HCD-JF data and are not justified by that citation alone, and Prescott et al. (2012, 2013) are also cited as external confirmation of the broad-band technique. The paper explicitly flags the lack of spectroscopic confirmation and the ~13% photo-z outlier rate, which are correctness risks rather than circularity. Finding: no significant circularity beyond normal, non-load-bearing methodological self-citation.

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

The central analysis rests on empirically calibrated color terms and several hand-chosen thresholds. No new physical entities are introduced. The main assumptions are that the color-term calibration transfers to quasars, that residual broad-band excess is Ly-alpha, and that photo-z from u-to-K photometry is adequate for masking and density estimation.

free parameters (5)
  • Color-term coefficients (a,b) in Eqs 1-3 = e.g., (1.763, -0.754) for u-band; (1.633, -0.618) for u*; (1.350, -0.338) for g
    Fitted to 1,541-1,822 spec-z galaxies; used to extrapolate continuum into the Ly-alpha band, so errors propagate into Ly-alpha excess maps.
  • Redshift-dependent color corrections (c,d) in Eqs 4-6 = (-0.470, -0.908) for u; (-0.560, -1.184) for u*; (-0.422, +1.120) for g
    Additional linear redshift trends fitted to the same spec-z sample; correct color terms as a function of redshift.
  • Effective area threshold Area_eff = 40 arcsec squared
    Hand-chosen threshold to define quasar nebulae for morphological analysis; corresponds to circularized radius 30 pkpc, intended to avoid the central PSF mask.
  • Background noise cut bg_std_z = 0.67 in units of 10^-17 erg/s/cm^2/arcsec^2
    Quality cut excluding 74 quasars with noisy Ly-alpha images; affects all subsequent sample comparisons.
  • Photo-z masking threshold |delta-z| = 0.1
    Chosen to mask foreground and background projected neighbors; impacts both Ly-alpha maps and density measurements.
assumptions (5)
  • standard math Cosmology: flat Lambda-CDM with Omega_m=0.310, Omega_Lambda=0.689, H0=67.7 km/s/Mpc
    Used for distance and physical scale conversions (Section 1).
  • domain assumption Empirical color-term relations calibrated on non-quasar spec-z galaxies apply to quasar hosts and surrounding nebulae
    Section 3: the continuum extrapolation (Eqs 1-6) is assumed to hold for quasar environments, though quasars are excluded from the fit.
  • domain assumption Residual flux in the Ly-alpha band after continuum subtraction is dominated by Ly-alpha emission
    Section 3: contributions from other UV lines (HeII, metal lines) are assumed an order of magnitude fainter.
  • domain assumption Photometric redshifts from Mizuki with u-to-K photometry are accurate enough for masking and density estimates
    Sections 3 and 5.2: used to mask contaminants and to count neighbor overdensities with delta-z = +/-0.05.
  • domain assumption Broad-band selection detects Ly-alpha only for equivalent widths greater than about 100-200 Angstroms
    Section 3: stated limitation; thus the sample is biased toward high equivalent width nebulae.

how reviews work

0 comments
Cite this review

Pith. "Pith review of RIDEN pilot survey: broad-band selection of candidate quasars with extended Lyman-$\alpha$ nebulae using CLAUDS-HSC-SSP-DUNES$^2$ joint data." pith.science (2026). https://pith.science/paper/GGHP6XBE

@misc{pith2026250604570,
  author       = {Pith},
  title        = {Pith review of: RIDEN pilot survey: broad-band selection of candidate quasars with extended Lyman-$\alpha$ nebulae using CLAUDS-HSC-SSP-DUNES$^2$ joint data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GGHP6XBE}},
  note         = {Machine review of arXiv:2506.04570}
}
abstract

The Vera C. Rubin Observatory will conduct the Legacy Survey of Space and Time (LSST), delivering deep, multi-band ($ugrizy$) imaging data across 18,000 square degrees over the next decade. Before this ultra-wide-field survey, we constructed a broad-band Ly$\alpha$ imaging toward 483 SDSS/BOSS quasars at $z=$ 1.9-3.0, using deep, wide-field ultraviolet to near-infrared ($u$-to-$K$) data from the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP), the CFHT Large Area U-band Deep Survey (CLAUDS), the Deep UKIRT Near-Infrared Steward Survey (DUNES$^2$), and additional public data covering 13 square degrees. Our broad-band selection allowed us to select 24 candidate quasar nebulae that exhibit $u$ or $g$ band excess over 50-170 kpc, some of which exhibit asymmetrical extended features similar to those seen in previously discovered giant nebulae. We then investigated whether the Ly$\alpha$ morphology of quasar nebulae differs between two redshift intervals, $z=$ 1.9-2.3 and $z=$ 2.3-3.0, and examined environmental dependence based on a control sample. Comparison results show no significant difference in asymmetry within Ly$\alpha$ nebulae between the two redshift intervals. Furthermore, we found no systematic differences in overdensities around the complete quasar samples, quasars with large Ly$\alpha$ nebulae, and control samples, while the most extended nebula appears to be located in the high-density region. Further verification analyses are required since the current dataset lacks spectroscopic confirmation for both quasar nebulae and their surrounding neighbours. Nevertheless, the results demonstrate the great potential of the Rubin LSST to discover giant Ly$\alpha$ nebulae on an unprecedented scale.

Figures

Figures reproduced from arXiv: 2506.04570 by the authors.

Figure 1
Figure 1. Effective throughput of broad-band filters (𝑢𝑔𝑟 𝑖𝑧𝑦) on Megacam and HSC (top) and LSST Camera (bottom). Ly𝛼 emissions from our target quasars at 𝑧 = 1.9–3.0 can be traced using 𝑢, 𝑢 ∗ , or 𝑔-band filters. in high-redshift protoclusters (Toshikawa et al. 2024), and derivations of star-formation histories of early-type galaxies (Ali et al. 2024). The HSC Joint-Data cover four survey fields, E-COSMOS, DEEP2- 3, ELAIS-N… view at source ↗
Figure 2
Figure 2. Survey fields of the HSC-SSP Deep layers (grey open circles), CLAUDS (cyan-filled regions), and a compilation of multiple near-infrared imaging surveys, including DUNES2 , UKIDSS, UltraVISTA, VIDEO (red open or red-hatched squares). Black cross symbols depict our target quasars at 𝑧 = 1.9–3.0. Refer to Coupon et al. (2018); Bosch et al. (2018); Aihara et al. (2019) for details on these catalogue flags. Consequently,… view at source ↗
Figure 3
Figure 3. Redshift distributions of the colour terms in the three broad-band selections, from left to right: 𝑔𝑟 − 𝑢, 𝑔𝑟 − 𝑢 ∗ , and 𝑟 𝑖 − 𝑔. In the first row, purple and grey dots indicate spec-𝑧 sources at the target redshift in each selection ( [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: (Top) Photometric redshifts versus spectroscopic redshifts for 124,268 spec-𝑧 sources (𝑖 < 25 mag), with additional selection thresh￾olds (stellar_mass> 107.5 and photoz_risk_best< 0.2) consistent with those in Tanaka et al. (2018). (Bottom) Dispersion (𝜎conv) and outl…
Figure 5
Figure 5. Figure 5: From left to right, RGB colour cutouts, broad-band coadd images with Ly𝛼 contributions (Ly𝛼-bands: 𝑢, 𝑢 ∗ , 𝑔), extrapolated broad-band images (𝑔𝑟𝑧, 𝑔𝑟𝑧, 𝑟 𝑖𝑧), and Ly𝛼 maps (𝑔𝑟𝑧 − 𝑢, 𝑔𝑟𝑧 − 𝑢 ∗ , 𝑟 𝑖𝑧 − 𝑔) without and with masks. The colour maps in the fourth and fifth…
Figure 6
Figure 6. Figure 6: Ly𝛼 areas of 483 quasars plotted against (left) redshift and (right) the standard deviation of redshift dimming-corrected background flux (bg_std𝑧) in the unit of 10−17 erg s−1 cm−2 arcsec−2 . (Top) Effective Ly𝛼 ar￾eas (Area_eff), accounting for object masking of proj…
Figure 9
Figure 9. Figure 9: Same as [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 10
Figure 10. Figure 10: An example of the image segmentation to (object_id = 75338966231962053). The left panel is a masked Ly𝛼 image (same as in [PITH_FULL_IMAGE:figures/full_fig_p010_10.png]
Figure 11
Figure 11. Figure 11: Flux-weighted asymmetry versus projected maximum extent for 24 quasar nebulae at 𝑧 = 1.9–3.0 with effective Ly𝛼 areas > 40 arcsec2 . The inset panel in the lower right corner shows the comparison of Ly𝛼 morphology between the 𝑢-selected samples at 𝑧 = 1.90–2.23 (blue …
Figure 12
Figure 12. Figure 12: (Left) 𝑁-th nearest neighbour densities for quasars and control samples, shown for 𝑁 = 3, 10, 30 (first to third rows, respectively). Purple symbols represent 409 quasars, with symbol sizes scaled by effective Ly𝛼 area (Area_eff). Grey dots show 𝑖-band magnitude limit…
Figure 13
Figure 13. Figure 13: On-sky distribution of photo-𝑧 selected neighbours within 𝛿𝑧 ± 0.05 (white open circles) surrounding the most extended quasar nebula in our sample (the white open square, object_id = 37484563299081621 at 𝑧 = 2.226). The background image is an RGB cutout based on the 𝑢…
Figure 14
Figure 14. Figure 14: Ly𝛼 surface brightness (SBLy𝛼) limit versus (left) the number of quasars and (right) the survey volume in various survey programs as given in the figures (Erb et al. 2011; Matsuda et al. 2011; Prescott et al. 2012; Borisova et al. 2016; Arrigoni Battaia et al. 2019; C…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

136 extracted references · 19 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    S., Ahumada R., Almeida A., et al

    Aguado D. S., Ahumada R., Almeida A., et al. 2019, @doi [ApJS] 10.3847/1538-4365/aaf651 , https://ui.adsabs.harvard.edu/abs/2019ApJS..240...23A 240, 23

    doi:10.3847/1538-4365/aaf651 2019

  3. [3]

    2018, @doi [PASJ] 10.1093/pasj/psx066 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...4A 70, S4

    Aihara H., Arimoto N., Armstrong R., et al. 2018, @doi [PASJ] 10.1093/pasj/psx066 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...4A 70, S4

    doi:10.1093/pasj/psx066 2018

  4. [4]

    2019, @doi [PASJ] 10.1093/pasj/psz103 , https://ui.adsabs.harvard.edu/abs/2019PASJ...71..114A 71, 114

    Aihara H., AlSayyad Y., Ando M., et al. 2019, @doi [PASJ] 10.1093/pasj/psz103 , https://ui.adsabs.harvard.edu/abs/2019PASJ...71..114A 71, 114

    doi:10.1093/pasj/psz103 2019

  5. [5]

    2022, @doi [PASJ] 10.1093/pasj/psab122 , https://ui.adsabs.harvard.edu/abs/2022PASJ...74..247A 74, 247

    Aihara H., AlSayyad Y., Ando M., et al. 2022, @doi [PASJ] 10.1093/pasj/psab122 , https://ui.adsabs.harvard.edu/abs/2022PASJ...74..247A 74, 247

    doi:10.1093/pasj/psab122 2022

  6. [6]

    S., De Propris R., Chung C., et al

    Ali S. S., De Propris R., Chung C., et al. 2024, @doi [ApJ] 10.3847/1538-4357/ad3209 , https://ui.adsabs.harvard.edu/abs/2024ApJ...966...50A 966, 50

    doi:10.3847/1538-4357/ad3209 2024

  7. [7]

    X., Hennawi J

    Arrigoni Battaia F., Prochaska J. X., Hennawi J. F., et al. 2018, @doi [MNRAS] 10.1093/mnras/stx2465 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.3907A 473, 3907

    doi:10.1093/mnras/stx2465 2018

  8. [8]

    F., Prochaska J

    Arrigoni Battaia F., Hennawi J. F., Prochaska J. X., et al. 2019, @doi [MNRAS] 10.1093/mnras/sty2827 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.3162A 482, 3162

    doi:10.1093/mnras/sty2827 2019

Show all 136 references
  1. [9]

    C., et al

    Arrigoni Battaia F., Obreja A., Chen C. C., et al. 2023, @doi [ ] 10.1051/0004-6361/202245520 , https://ui.adsabs.harvard.edu/abs/2023A&A...676A..51A 676, A51

    2023 doi

  2. [10]

    P., Tollerud E

    Astropy Collaboration Robitaille T. P., Tollerud E. J., et al. 2013, @doi [A&A] 10.1051/0004-6361/201322068 , https://ui.adsabs.harvard.edu/abs/2013A&A...558A..33A 558, A33

    2013 doi

  3. [11]

    2010, in McLean I

    Bacon R., Accardo M., Adjali L., et al. 2010, in McLean I. S., Ramsay S. K., Takami H., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 7735, Ground-based and Airborne Instrumentation for Astronomy III. p. 773508, @doi 10.1117/12.856027

    2010 doi

  4. [12]

    2023, @doi [ ] 10.1051/0004-6361/202244187 , https://ui.adsabs.harvard.edu/abs/2023A&A...670A...4B 670, A4

    Bacon R., Brinchmann J., Conseil S., et al. 2023, @doi [ ] 10.1051/0004-6361/202244187 , https://ui.adsabs.harvard.edu/abs/2023A&A...670A...4B 670, A4

    2023 doi

  5. [14]

    K., Liske J., Brown M

    Baldry I. K., Liske J., Brown M. J. I., et al. 2018, @doi [MNRAS] 10.1093/mnras/stx3042 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.474.3875B 474, 3875

    2018 doi

  6. [15]

    J., Lacey C

    Benson A. J., Lacey C. G., Baugh C. M., et al. 2002, @doi [MNRAS] 10.1046/j.1365-8711.2002.05387.x , https://ui.adsabs.harvard.edu/abs/2002MNRAS.333..156B 333, 156

    2002

  7. [16]

    M., Jarvis M., 2002, @doi [AJ] 10.1086/338085 , https://ui.adsabs.harvard.edu/abs/2002AJ....123..583B 123, 583

    Bernstein G. M., Jarvis M., 2002, @doi [AJ] 10.1086/338085 , https://ui.adsabs.harvard.edu/abs/2002AJ....123..583B 123, 583

    2002 doi

  8. [17]

    B., Ivezi \'c Z ., Jones R

    Bianco F. B., Ivezi \'c Z ., Jones R. L., et al. 2022, @doi [ ] 10.3847/1538-4365/ac3e72 , https://ui.adsabs.harvard.edu/abs/2022ApJS..258....1B 258, 1

    2022 doi

  9. [18]

    S., Schlegel D

    Bolton A. S., Schlegel D. J., Aubourg \'E ., et al. 2012, @doi [AJ] 10.1088/0004-6256/144/5/144 , https://ui.adsabs.harvard.edu/abs/2012AJ....144..144B 144, 144

    2012 doi

  10. [19]

    J., et al

    Borisova E., Cantalupo S., Lilly S. J., et al. 2016, @doi [ApJ] 10.3847/0004-637X/831/1/39 , https://ui.adsabs.harvard.edu/abs/2016ApJ...831...39B 831, 39

    2016 doi

  11. [20]

    2018, @doi [PASJ] 10.1093/pasj/psx080 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...5B 70, S5

    Bosch J., Armstrong R., Bickerton S., et al. 2018, @doi [PASJ] 10.1093/pasj/psx080 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...5B 70, S5

    2018 doi

  12. [21]

    Dobb's Journal of Software Tools

    Bradski G., 2000, Dr. Dobb's Journal of Software Tools

    2000

  13. [22]

    B., van Dokkum P

    Brammer G. B., van Dokkum P. G., Franx M., et al. 2012, @doi [ApJS] 10.1088/0067-0049/200/2/13 , https://ui.adsabs.harvard.edu/abs/2012ApJS..200...13B 200, 13

    2012 doi

  14. [23]

    J., Warren S

    Bunker A. J., Warren S. J., Hewett P. C., et al. 1995, @doi [MNRAS] 10.1093/mnras/273.2.513 , https://ui.adsabs.harvard.edu/abs/1995MNRAS.273..513B 273, 513

    1995 doi

  15. [24]

    2017, @doi [ApJ] 10.3847/1538-4357/aa5d14 , https://ui.adsabs.harvard.edu/abs/2017ApJ...837...71C 837, 71

    Cai Z., Fan X., Yang Y., et al. 2017, @doi [ApJ] 10.3847/1538-4357/aa5d14 , https://ui.adsabs.harvard.edu/abs/2017ApJ...837...71C 837, 71

    2017 doi

  16. [25]

    2018, @doi [ApJL] 10.3847/2041-8213/aacce6 , https://ui.adsabs.harvard.edu/abs/2018ApJ...861L...3C 861, L3

    Cai Z., Hamden E., Matuszewski M., et al. 2018, @doi [ApJL] 10.3847/2041-8213/aacce6 , https://ui.adsabs.harvard.edu/abs/2018ApJ...861L...3C 861, L3

    2018 doi

  17. [26]

    X., et al

    Cai Z., Cantalupo S., Prochaska J. X., et al. 2019, @doi [ApJS] 10.3847/1538-4365/ab4796 , https://ui.adsabs.harvard.edu/abs/2019ApJS..245...23C 245, 23

    2019 doi

  18. [27]

    430, Gas Accretion onto Galaxies

    Cantalupo S., 2017, in Fox A., Dav \'e R., eds, Astrophysics and Space Science Library Vol. 430, Gas Accretion onto Galaxies. p. 195 ( @eprint arXiv 1612.00491 ), @doi 10.1007/978-3-319-52512-9_9

    2017 arXiv

  19. [28]

    X., et al

    Cantalupo S., Arrigoni-Battaia F., Prochaska J. X., et al. 2014, @doi [Natur] 10.1038/nature12898 , https://ui.adsabs.harvard.edu/abs/2014Natur.506...63C 506, 63

    2014 doi

  20. [29]

    J., et al

    Cantalupo S., Pezzulli G., Lilly S. J., et al. 2019, @doi [MNRAS] 10.1093/mnras/sty3481 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.483.5188C 483, 5188

    2019 doi

  21. [30]

    Cassata P., Tasca L. A. M., Le F \`e vre O., et al. 2015, @doi [A&A] 10.1051/0004-6361/201423824 , https://ui.adsabs.harvard.edu/abs/2015A&A...573A..24C 573, A24

    2015 doi

  22. [31]

    B., 2013, @doi [ApJ] 10.1088/0004-637X/776/2/84 , https://ui.adsabs.harvard.edu/abs/2013ApJ...776...84C 776, 84

    Chaudhuri A., Majumdar S., Nath B. B., 2013, @doi [ApJ] 10.1088/0004-637X/776/2/84 , https://ui.adsabs.harvard.edu/abs/2013ApJ...776...84C 776, 84

    2013 doi

  23. [32]

    Chen C.-C., Arrigoni Battaia F., Emonts B. H. C., et al. 2021, @doi [ApJ] 10.3847/1538-4357/ac2b9d , https://ui.adsabs.harvard.edu/abs/2021ApJ...923..200C 923, 200

    2021 doi

  24. [33]

    L., Newman J

    Coil A. L., Newman J. A., Croton D., et al. 2008, @doi [ApJ] 10.1086/523639 , https://ui.adsabs.harvard.edu/abs/2008ApJ...672..153C 672, 153

    2008 doi

  25. [34]

    L., Blanton M

    Coil A. L., Blanton M. R., Burles S. M., et al. 2011, @doi [ApJ] 10.1088/0004-637X/741/1/8 , https://ui.adsabs.harvard.edu/abs/2011ApJ...741....8C 741, 8

    2011 doi

  26. [35]

    2025, arXiv, https://ui.adsabs.harvard.edu/abs/2025arXiv250503897C p

    Coloma Puga M., Balmaverde B., Capetti A., et al. 2025, arXiv, https://ui.adsabs.harvard.edu/abs/2025arXiv250503897C p. arXiv:2505.03897

    2025 arXiv

  27. [36]

    J., Moustakas J., Blanton M

    Cool R. J., Moustakas J., Blanton M. R., et al. 2013, @doi [ApJ] 10.1088/0004-637X/767/2/118 , https://ui.adsabs.harvard.edu/abs/2013ApJ...767..118C 767, 118

    2013 doi

  28. [37]

    2018, @doi [PASJ] 10.1093/pasj/psx047 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...7C 70, S7

    Coupon J., Czakon N., Bosch J., et al. 2018, @doi [PASJ] 10.1093/pasj/psx047 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...7C 70, S7

    2018 doi

  29. [38]

    2023, @doi [A&A] 10.1051/0004-6361/202243363 , https://ui.adsabs.harvard.edu/abs/2023A&A...670A..82D 670, A82

    Desprez G., Picouet V., Moutard T., et al. 2023, @doi [A&A] 10.1051/0004-6361/202243363 , https://ui.adsabs.harvard.edu/abs/2023A&A...670A..82D 670, A82

    2023 doi

  30. [39]

    arXiv:1704.03416

    Dijkstra M., 2017, @doi [arXiv] 10.48550/arXiv.1704.03416 , https://ui.adsabs.harvard.edu/abs/2017arXiv170403416D p. arXiv:1704.03416

    2017 doi arXiv

  31. [40]

    B., Farina E

    Drake A. B., Farina E. P., Neeleman M., et al. 2019, @doi [ApJ] 10.3847/1538-4357/ab2984 , https://ui.adsabs.harvard.edu/abs/2019ApJ...881..131D 881, 131

    2019 doi

  32. [41]

    K., Bogosavljevi \'c M., Steidel C

    Erb D. K., Bogosavljevi \'c M., Steidel C. C., 2011, @doi [ApJL] 10.1088/2041-8205/740/1/L31 , https://ui.adsabs.harvard.edu/abs/2011ApJ...740L..31E 740, L31

    2011 doi

  33. [42]

    P., Arrigoni-Battaia F., Costa T., et al

    Farina E. P., Arrigoni-Battaia F., Costa T., et al. 2019, @doi [ApJ] 10.3847/1538-4357/ab5847 , https://ui.adsabs.harvard.edu/abs/2019ApJ...887..196F 887, 196

    2019 doi

  34. [43]

    2010, @doi [ApJ] 10.1088/0004-637X/725/1/633 , https://ui.adsabs.harvard.edu/abs/2010ApJ...725..633F 725, 633

    Faucher-Gigu \`e re C.-A., Kere s D., Dijkstra M., et al. 2010, @doi [ApJ] 10.1088/0004-637X/725/1/633 , https://ui.adsabs.harvard.edu/abs/2010ApJ...725..633F 725, 633

    2010 doi

  35. [44]

    K., et al

    Fossati M., Fumagalli M., Lofthouse E. K., et al. 2021, @doi [MNRAS] 10.1093/mnras/stab660 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503.3044F 503, 3044

    2021 doi

  36. [45]

    2018, @doi [PASJ] 10.1093/pasj/psx079 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...3F 70, S3

    Furusawa H., Koike M., Takata T., et al. 2018, @doi [PASJ] 10.1093/pasj/psx079 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...3F 70, S3

    2018 doi

  37. [46]

    2012, @doi [ApJ] 10.1088/0004-637X/749/2/169 , https://ui.adsabs.harvard.edu/abs/2012ApJ...749..169G 749, 169

    Galametz A., Stern D., De Breuck C., et al. 2012, @doi [ApJ] 10.1088/0004-637X/749/2/169 , https://ui.adsabs.harvard.edu/abs/2012ApJ...749..169G 749, 169

    2012 doi

  38. [47]

    2010, @doi [MNRAS] 10.1111/j.1365-2966.2010.16941.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.407..613G 407, 613

    Goerdt T., Dekel A., Sternberg A., et al. 2010, @doi [MNRAS] 10.1111/j.1365-2966.2010.16941.x , https://ui.adsabs.harvard.edu/abs/2010MNRAS.407..613G 407, 613

    2010

  39. [48]

    2007, @doi [ApJ] 10.1086/520324 , https://ui.adsabs.harvard.edu/abs/2007ApJ...667...79G 667, 79

    Gronwall C., Ciardullo R., Hickey T., et al. 2007, @doi [ApJ] 10.1086/520324 , https://ui.adsabs.harvard.edu/abs/2007ApJ...667...79G 667, 79

    2007 doi

  40. [49]

    2020, @doi [ApJ] 10.3847/1538-4357/ab9b7f , https://ui.adsabs.harvard.edu/abs/2020ApJ...898...26G 898, 26

    Guo Y., Maiolino R., Jiang L., et al. 2020, @doi [ApJ] 10.3847/1538-4357/ab9b7f , https://ui.adsabs.harvard.edu/abs/2020ApJ...898...26G 898, 26

    2020 doi

  41. [50]

    R., Millman K

    Harris C. R., Millman K. J., van der Walt S. J., et al. 2020, @doi [Natur] 10.1038/s41586-020-2649-2 , https://ui.adsabs.harvard.edu/abs/2020Natur.585..357H 585, 357

    2020 doi

  42. [51]

    2017, @doi [A&A] 10.1051/0004-6361/201731579 , https://ui.adsabs.harvard.edu/abs/2017A&A...608A..10H 608, A10

    Hashimoto T., Garel T., Guiderdoni B., et al. 2017, @doi [A&A] 10.1051/0004-6361/201731579 , https://ui.adsabs.harvard.edu/abs/2017A&A...608A..10H 608, A10

    2017 doi

  43. [52]

    2018, @doi [ApJ] 10.3847/1538-4357/aabacf , https://ui.adsabs.harvard.edu/abs/2018ApJ...858...77H 858, 77

    Hasinger G., Capak P., Salvato M., et al. 2018, @doi [ApJ] 10.3847/1538-4357/aabacf , https://ui.adsabs.harvard.edu/abs/2018ApJ...858...77H 858, 77

    2018 doi

  44. [53]

    A., Wylezalek D., Kurk J

    Hatch N. A., Wylezalek D., Kurk J. D., et al. 2014, @doi [MNRAS] 10.1093/mnras/stu1725 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.445..280H 445, 280

    2014 doi

  45. [54]

    F., Prochaska J

    Hennawi J. F., Prochaska J. X., Cantalupo S., et al. 2015, @doi [Sci] 10.1126/science.aaa5397 , https://ui.adsabs.harvard.edu/abs/2015Sci...348..779H 348, 779

    2015 doi

  46. [55]

    D., 2007, @doi [CSE] 10.1109/MCSE.2007.55 , https://ui.adsabs.harvard.edu/abs/2007CSE.....9...90H 9, 90

    Hunter J. D., 2007, @doi [CSE] 10.1109/MCSE.2007.55 , https://ui.adsabs.harvard.edu/abs/2007CSE.....9...90H 9, 90

    2007 doi

  47. [56]

    B., et al

    Iqbal A., Majumdar S., Nath B. B., et al. 2017, @doi [MNRAS] 10.1093/mnras/stx1999 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.472..713I 472, 713

    2017 doi

  48. [57]

    M., Tyson J

    Ivezi \'c Z ., Kahn S. M., Tyson J. A., et al. 2019, @doi [ApJ] 10.3847/1538-4357/ab042c , https://ui.adsabs.harvard.edu/abs/2019ApJ...873..111I 873, 111

    2019 doi

  49. [58]

    J., Bonfield D

    Jarvis M. J., Bonfield D. G., Bruce V. A., et al. 2013, @doi [MNRAS] 10.1093/mnras/sts118 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.428.1281J 428, 1281

    2013 doi

  50. [59]

    F., Blain A

    Jones S. F., Blain A. W., Lonsdale C., et al. 2015, @doi [MNRAS] 10.1093/mnras/stv214 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.448.3325J 448, 3325

    2015 doi

  51. [60]

    2007, @doi [ApJ] 10.1086/518410 , https://ui.adsabs.harvard.edu/abs/2007ApJ...663..765K 663, 765

    Kashikawa N., Kitayama T., Doi M., et al. 2007, @doi [ApJ] 10.1086/518410 , https://ui.adsabs.harvard.edu/abs/2007ApJ...663..765K 663, 765

    2007 doi

  52. [61]

    2018, @doi [PASJ] 10.1093/pasj/psy056 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70...66K 70, 66

    Kawanomoto S., Uraguchi F., Komiyama Y., et al. 2018, @doi [PASJ] 10.1093/pasj/psy056 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70...66K 70, 66

    2018 doi

  53. [62]

    2022, @doi [A&A] 10.1051/0004-6361/202141900 , https://ui.adsabs.harvard.edu/abs/2022A&A...659A.183K 659, A183

    Kerutt J., Wisotzki L., Verhamme A., et al. 2022, @doi [A&A] 10.1051/0004-6361/202141900 , https://ui.adsabs.harvard.edu/abs/2022A&A...659A.183K 659, A183

    2022 doi

  54. [63]

    S., Hennawi J

    Khrykin I. S., Hennawi J. F., Worseck G., et al. 2021, @doi [MNRAS] 10.1093/mnras/stab1288 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505..649K 505, 649

    2021 doi

  55. [64]

    2017, @doi [ApJ] 10.3847/1538-4357/aa72f0 , https://ui.adsabs.harvard.edu/abs/2017ApJ...841..128K 841, 128

    Kikuta S., Imanishi M., Matsuoka Y., et al. 2017, @doi [ApJ] 10.3847/1538-4357/aa72f0 , https://ui.adsabs.harvard.edu/abs/2017ApJ...841..128K 841, 128

    2017 doi

  56. [65]

    2019, @doi [PASJ] 10.1093/pasj/psz055 , https://ui.adsabs.harvard.edu/abs/2019PASJ...71L...2K 71, L2

    Kikuta S., Matsuda Y., Cen R., et al. 2019, @doi [PASJ] 10.1093/pasj/psz055 , https://ui.adsabs.harvard.edu/abs/2019PASJ...71L...2K 71, L2

    2019 doi

  57. [66]

    2021, @doi [ApJ] 10.3847/1538-4357/abbe89 , https://ui.adsabs.harvard.edu/abs/2021ApJ...909..119K 909, 119

    Kimock B., Narayanan D., Smith A., et al. 2021, @doi [ApJ] 10.3847/1538-4357/abbe89 , https://ui.adsabs.harvard.edu/abs/2021ApJ...909..119K 909, 119

    2021 doi

  58. [67]

    2025, @doi [ApJ] 10.3847/1538-4357/ada5f4 , https://ui.adsabs.harvard.edu/abs/2025ApJ...980..104K 980, 104

    Kiyota T., Ando M., Tanaka M., et al. 2025, @doi [ApJ] 10.3847/1538-4357/ada5f4 , https://ui.adsabs.harvard.edu/abs/2025ApJ...980..104K 980, 104

    2025 doi

  59. [68]

    2018, @doi [PASJ] 10.1093/pasj/psx069 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...2K 70, S2

    Komiyama Y., Obuchi Y., Nakaya H., et al. 2018, @doi [PASJ] 10.1093/pasj/psx069 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...2K 70, S2

    2018 doi

  60. [69]

    2022, @doi [ApJ] 10.3847/1538-4357/ac4cb1 , https://ui.adsabs.harvard.edu/abs/2022ApJ...927...53K 927, 53

    Kooistra R., Inoue S., Lee K.-G., et al. 2022, @doi [ApJ] 10.3847/1538-4357/ac4cb1 , https://ui.adsabs.harvard.edu/abs/2022ApJ...927...53K 927, 53

    2022 doi

  61. [70]

    W., Schlegel D

    Lang D., Hogg D. W., Schlegel D. J., 2016, @doi [AJ] 10.3847/0004-6256/151/2/36 , https://ui.adsabs.harvard.edu/abs/2016AJ....151...36L 151, 36

    2016 doi

  62. [71]

    M., Kulkarni S

    Law N. M., Kulkarni S. R., Dekany R. G., et al. 2009, @doi [PASP] 10.1086/648598 , https://ui.adsabs.harvard.edu/abs/2009PASP..121.1395L 121, 1395

    2009 doi

  63. [72]

    J., Almaini O., et al

    Lawrence A., Warren S. J., Almaini O., et al. 2007, @doi [MNRAS] 10.1111/j.1365-2966.2007.12040.x , https://ui.adsabs.harvard.edu/abs/2007MNRAS.379.1599L 379, 1599

    2007

  64. [73]

    2013, @doi [A&A] 10.1051/0004-6361/201322179 , https://ui.adsabs.harvard.edu/abs/2013A&A...559A..14L 559, A14

    Le F \`e vre O., Cassata P., Cucciati O., et al. 2013, @doi [A&A] 10.1051/0004-6361/201322179 , https://ui.adsabs.harvard.edu/abs/2013A&A...559A..14L 559, A14

    2013 doi

  65. [74]

    F., Stark C., et al

    Lee K.-G., Hennawi J. F., Stark C., et al. 2014, @doi [ApJL] 10.1088/2041-8205/795/1/L12 , https://ui.adsabs.harvard.edu/abs/2014ApJ...795L..12L 795, L12

    2014 doi

  66. [75]

    F., White M., et al

    Lee K.-G., Hennawi J. F., White M., et al. 2016, @doi [ApJ] 10.3847/0004-637X/817/2/160 , https://ui.adsabs.harvard.edu/abs/2016ApJ...817..160L 817, 160

    2016 doi

  67. [76]

    2018, @doi [ApJS] 10.3847/1538-4365/aace58 , https://ui.adsabs.harvard.edu/abs/2018ApJS..237...31L 237, 31

    Lee K.-G., Krolewski A., White M., et al. 2018, @doi [ApJS] 10.3847/1538-4365/aace58 , https://ui.adsabs.harvard.edu/abs/2018ApJS..237...31L 237, 31

    2018 doi

  68. [77]

    I., et al

    Li X., Ragosta F., Clarkson W. I., et al. 2022, @doi [ ] 10.3847/1538-4365/ac3bca , https://ui.adsabs.harvard.edu/abs/2022ApJS..258....2L 258, 2

    2022 doi

  69. [78]

    2024, @doi [ApJS] 10.3847/1538-4365/ad812c , https://ui.adsabs.harvard.edu/abs/2024ApJS..275...27L 275, 27

    Li M., Zhang H., Cai Z., et al. 2024, @doi [ApJS] 10.3847/1538-4365/ad812c , https://ui.adsabs.harvard.edu/abs/2024ApJS..275...27L 275, 27

    2024 doi

  70. [79]

    M., et al

    Liu C., Gebhardt K., Cooper E. M., et al. 2022, @doi [ApJS] 10.3847/1538-4365/ac6ba6 , https://ui.adsabs.harvard.edu/abs/2022ApJS..261...24L 261, 24

    2022 doi

  71. [80]

    W., Higley A

    Lyke B. W., Higley A. N., McLane J. N., et al. 2020, @doi [ApJS] 10.3847/1538-4365/aba623 , https://ui.adsabs.harvard.edu/abs/2020ApJS..250....8L 250, 8

    2020 doi

  72. [81]

    2021, @doi [MNRAS] 10.1093/mnras/staa3277 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502..494M 502, 494

    Mackenzie R., Pezzulli G., Cantalupo S., et al. 2021, @doi [MNRAS] 10.1093/mnras/staa3277 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.502..494M 502, 494

    2021 doi

  73. [82]

    C., Darvish B., Lin Z., et al

    Martin D. C., Darvish B., Lin Z., et al. 2023, @doi [Nature Astronomy] 10.1038/s41550-023-02054-1 , https://ui.adsabs.harvard.edu/abs/2023NatAs...7.1390M 7, 1390

    2023 doi

  74. [83]

    2024, @doi [ApJ] 10.3847/1538-4357/ad3a67 , https://ui.adsabs.harvard.edu/abs/2024ApJ...969...56M 969, 56

    Massingill K., Mason B., Lacy M., et al. 2024, @doi [ApJ] 10.3847/1538-4357/ad3a67 , https://ui.adsabs.harvard.edu/abs/2024ApJ...969...56M 969, 56

    2024 doi

  75. [84]

    2004, @doi [AJ] 10.1086/422020 , https://ui.adsabs.harvard.edu/abs/2004AJ....128..569M 128, 569

    Matsuda Y., Yamada T., Hayashino T., et al. 2004, @doi [AJ] 10.1086/422020 , https://ui.adsabs.harvard.edu/abs/2004AJ....128..569M 128, 569

    2004 doi

  76. [85]

    2011, @doi [MNRAS] 10.1111/j.1745-3933.2010.00969.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.410L..13M 410, L13

    Matsuda Y., Yamada T., Hayashino T., et al. 2011, @doi [MNRAS] 10.1111/j.1745-3933.2010.00969.x , https://ui.adsabs.harvard.edu/abs/2011MNRAS.410L..13M 410, L13

    2011

  77. [86]

    J., 1993, @doi [ARA&A] 10.1146/annurev.aa.31.090193.003231 , https://ui.adsabs.harvard.edu/abs/1993ARA&A..31..639M 31, 639

    McCarthy P. J., 1993, @doi [ARA&A] 10.1146/annurev.aa.31.090193.003231 , https://ui.adsabs.harvard.edu/abs/1993ARA&A..31..639M 31, 639

    1993

  78. [87]

    J., Spinrad H., Djorgovski S., et al

    McCarthy P. J., Spinrad H., Djorgovski S., et al. 1987, @doi [ApJL] 10.1086/184951 , https://ui.adsabs.harvard.edu/abs/1987ApJ...319L..39M 319, L39

    1987 doi

  79. [88]

    J., Milvang-Jensen B., Dunlop J., et al

    McCracken H. J., Milvang-Jensen B., Dunlop J., et al. 2012, @doi [A&A] 10.1051/0004-6361/201219507 , https://ui.adsabs.harvard.edu/abs/2012A&A...544A.156M 544, A156

    2012 doi

  80. [89]

    Miley G., De Breuck C., 2008, @doi [A&ARv] 10.1007/s00159-007-0008-z , https://ui.adsabs.harvard.edu/abs/2008A&ARv..15...67M 15, 67

    2008 doi

  81. [90]

    2018, @doi [PASJ] 10.1093/pasj/psx063 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...1M 70, S1

    Miyazaki S., Komiyama Y., Kawanomoto S., et al. 2018, @doi [PASJ] 10.1093/pasj/psx063 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...1M 70, S1

    2018 doi

  82. [91]

    G., Brammer G

    Momcheva I. G., Brammer G. B., van Dokkum P. G., et al. 2016, @doi [ApJS] 10.3847/0067-0049/225/2/27 , https://ui.adsabs.harvard.edu/abs/2016ApJS..225...27M 225, 27

    2016 doi

  83. [92]

    A., Eilers A.-C., Davies F

    Morey K. A., Eilers A.-C., Davies F. B., et al. 2021, @doi [ApJ] 10.3847/1538-4357/ac1c70 , https://ui.adsabs.harvard.edu/abs/2021ApJ...921...88M 921, 88

    2021 doi

  84. [93]

    C., et al

    Morrissey P., Matuszewski M., Martin D. C., et al. 2018, @doi [ ] 10.3847/1538-4357/aad597 , https://ui.adsabs.harvard.edu/abs/2018ApJ...864...93M 864, 93

    2018 doi

  85. [94]

    D., Palanque-Delabrouille N., Prakash A., et al

    Myers A. D., Palanque-Delabrouille N., Prakash A., et al. 2015, @doi [ApJS] 10.1088/0067-0049/221/2/27 , https://ui.adsabs.harvard.edu/abs/2015ApJS..221...27M 221, 27

    2015 doi

  86. [95]

    B., et al

    Newville M., Stensitzki T., Allen D. B., et al. 2014, LMFIT: Non-Linear Least-Square Minimization and Curve-Fitting for Python , Zenodo, @doi 10.5281/zenodo.11813

    2014 doi

  87. [96]

    A., et al

    Nowotka M., Chen C.-C., Battaia F. A., et al. 2022, @doi [A&A] 10.1051/0004-6361/202040133 , https://ui.adsabs.harvard.edu/abs/2022A&A...658A..77N 658, A77

    2022 doi

  88. [97]

    B., Martin C., Matuszewski M., et al

    O'Sullivan D. B., Martin C., Matuszewski M., et al. 2020, @doi [ApJ] 10.3847/1538-4357/ab838c , https://ui.adsabs.harvard.edu/abs/2020ApJ...894....3O 894, 3

    2020 doi

  89. [98]

    B., Gunn J

    Oke J. B., Gunn J. E., 1983, @doi [ApJ] 10.1086/160817 , https://ui.adsabs.harvard.edu/abs/1983ApJ...266..713O 266, 713

    1983 doi

  90. [99]

    B., Peebles P

    Partridge R. B., Peebles P. J. E., 1967, @doi [ApJ] 10.1086/149079 , https://ui.adsabs.harvard.edu/abs/1967ApJ...147..868P 147, 868

    1967 doi

  91. [100]

    2024, @doi [ ] 10.1051/0004-6361/202348659 , https://ui.adsabs.harvard.edu/abs/2024A&A...684A.119P 684, A119

    Pensabene A., Cantalupo S., Cicone C., et al. 2024, @doi [ ] 10.1051/0004-6361/202348659 , https://ui.adsabs.harvard.edu/abs/2024A&A...684A.119P 684, A119

    2024 doi

  92. [101]

    2020, @doi [A&A] 10.1051/0004-6361/201833910 , https://ui.adsabs.harvard.edu/abs/2020A&A...641A...6P 641, A6

    Planck Collaboration Aghanim N., Akrami Y., et al. 2020, @doi [A&A] 10.1051/0004-6361/201833910 , https://ui.adsabs.harvard.edu/abs/2020A&A...641A...6P 641, A6

    2020 doi

  93. [102]

    W., Arnaud M., Piffaretti R., et al

    Pratt G. W., Arnaud M., Piffaretti R., et al. 2010, @doi [A&A] 10.1051/0004-6361/200913309 , https://ui.adsabs.harvard.edu/abs/2010A&A...511A..85P 511, A85

    2010 doi

  94. [103]

    Prescott M. K. M., Dey A., Jannuzi B. T., 2012, @doi [ApJ] 10.1088/0004-637X/748/2/125 , https://ui.adsabs.harvard.edu/abs/2012ApJ...748..125P 748, 125

    2012 doi

  95. [104]

    Prescott M. K. M., Dey A., Jannuzi B. T., 2013, @doi [ApJ] 10.1088/0004-637X/762/1/38 , https://ui.adsabs.harvard.edu/abs/2013ApJ...762...38P 762, 38

    2013 doi

  96. [105]

    S., Kotilainen J., 2020, @doi [ApJS] 10.3847/1538-4365/ab99c5 , https://ui.adsabs.harvard.edu/abs/2020ApJS..249...17R 249, 17

    Rakshit S., Stalin C. S., Kotilainen J., 2020, @doi [ApJS] 10.3847/1538-4365/ab99c5 , https://ui.adsabs.harvard.edu/abs/2020ApJS..249...17R 249, 17

    2020 doi

  97. [106]

    H., et al

    Ramakrishnan V., Moon B., Im S. H., et al. 2023, @doi [ApJ] 10.3847/1538-4357/acd341 , https://ui.adsabs.harvard.edu/abs/2023ApJ...951..119R 951, 119

    2023 doi

  98. [107]

    R., Law N

    Rau A., Kulkarni S. R., Law N. M., et al. 2009, @doi [PASP] 10.1086/605911 , https://ui.adsabs.harvard.edu/abs/2009PASP..121.1334R 121, 1334

    2009 doi

  99. [108]

    2021, pandas-dev/pandas: Pandas 1.3.2 , Zenodo, @doi 10.5281/zenodo.5203279

    Reback J., jbrockmendel McKinney W., et al. 2021, pandas-dev/pandas: Pandas 1.3.2 , Zenodo, @doi 10.5281/zenodo.5203279

    2021 doi

  100. [109]

    A., Comparat J., Prada F., et al

    Rodr \' guez-Torres S. A., Comparat J., Prada F., et al. 2017, @doi [MNRAS] 10.1093/mnras/stx454 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468..728R 468, 728

    2017 doi

  101. [110]

    2019, @doi [MNRAS] 10.1093/mnras/stz2522 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.5202S 489, 5202

    Sawicki M., Arnouts S., Huang J., et al. 2019, @doi [MNRAS] 10.1093/mnras/stz2522 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.5202S 489, 5202

    2019 doi

  102. [111]

    J., Finkbeiner D

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

    1998 doi

  103. [112]

    Shimakawa R., 2022, @doi [MNRAS] 10.1093/mnras/stac1575 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.514.3910S 514, 3910

    2022 doi

  104. [113]

    A., Gunn J

    Smee S. A., Gunn J. E., Uomoto A., et al. 2013, @doi [AJ] 10.1088/0004-6256/146/2/32 , https://ui.adsabs.harvard.edu/abs/2013AJ....146...32S 146, 32

    2013 doi

  105. [114]

    C., Bogosavljevi \'c M., Shapley A

    Steidel C. C., Bogosavljevi \'c M., Shapley A. E., et al. 2011, @doi [ApJ] 10.1088/0004-637X/736/2/160 , https://ui.adsabs.harvard.edu/abs/2011ApJ...736..160S 736, 160

    2011 doi

  106. [115]

    A., Weinberg D

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

    2002 doi

  107. [116]

    2024, @doi [ApJ] 10.3847/1538-4357/ad65d7 , https://ui.adsabs.harvard.edu/abs/2024ApJ...972...82S 972, 82

    Suzuki Y., Uchiyama H., Matsuoka Y., et al. 2024, @doi [ApJ] 10.3847/1538-4357/ad65d7 , https://ui.adsabs.harvard.edu/abs/2024ApJ...972...82S 972, 82

    2024 doi

  108. [117]

    S., Chiba M., et al

    Takada M., Ellis R. S., Chiba M., et al. 2014, @doi [PASJ] 10.1093/pasj/pst019 , https://ui.adsabs.harvard.edu/abs/2014PASJ...66R...1T 66, R1

    2014 doi

  109. [118]

    Tanaka M., 2015, @doi [ApJ] 10.1088/0004-637X/801/1/20 , https://ui.adsabs.harvard.edu/abs/2015ApJ...801...20T 801, 20

    2015 doi

  110. [119]

    2018, @doi [PASJ] 10.1093/pasj/psx077 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...9T 70, S9

    Tanaka M., Coupon J., Hsieh B.-C., et al. 2018, @doi [PASJ] 10.1093/pasj/psx077 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S...9T 70, S9

    2018 doi

  111. [120]

    B., 2005, in Shopbell P., Britton M., Ebert R., eds, Astronomical Society of the Pacific Conference Series Vol

    Taylor M. B., 2005, in Shopbell P., Britton M., Ebert R., eds, Astronomical Society of the Pacific Conference Series Vol. 347, Astronomical Data Analysis Software and Systems XIV. p. 29

    2005

  112. [121]

    2025, @doi [ApJL] 10.3847/2041-8213/adb0ba , https://ui.adsabs.harvard.edu/abs/2025ApJ...980L..43T 980, L43

    Tornotti D., Fumagalli M., Fossati M., et al. 2025, @doi [ApJL] 10.3847/2041-8213/adb0ba , https://ui.adsabs.harvard.edu/abs/2025ApJ...980L..43T 980, L43

    2025 doi

  113. [122]

    2024, @doi [MNRAS] 10.1093/mnras/stad3162 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.6276T 527, 6276

    Toshikawa J., Wuyts S., Kashikawa N., et al. 2024, @doi [MNRAS] 10.1093/mnras/stad3162 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.6276T 527, 6276

    2024 doi

  114. [123]

    S., Werk J

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

    2017 doi

  115. [124]

    2018, @doi [PASJ] 10.1093/pasj/psx112 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S..32U 70, S32

    Uchiyama H., Toshikawa J., Kashikawa N., et al. 2018, @doi [PASJ] 10.1093/pasj/psx112 , https://ui.adsabs.harvard.edu/abs/2018PASJ...70S..32U 70, S32

    2018 doi

  116. [125]

    2019, @doi [ApJ] 10.3847/1538-4357/aaef7b , https://ui.adsabs.harvard.edu/abs/2019ApJ...870...45U 870, 45

    Uchiyama H., Kashikawa N., Overzier R., et al. 2019, @doi [ApJ] 10.3847/1538-4357/aaef7b , https://ui.adsabs.harvard.edu/abs/2019ApJ...870...45U 870, 45

    2019 doi

  117. [126]

    2020, @doi [ApJ] 10.3847/1538-4357/abc47b , https://ui.adsabs.harvard.edu/abs/2020ApJ...905..125U 905, 125

    Uchiyama H., Akiyama M., Toshikawa J., et al. 2020, @doi [ApJ] 10.3847/1538-4357/abc47b , https://ui.adsabs.harvard.edu/abs/2020ApJ...905..125U 905, 125

    2020 doi

  118. [127]

    2019, @doi [Sci] 10.1126/science.aaw5949 , https://ui.adsabs.harvard.edu/abs/2019Sci...366...97U 366, 97

    Umehata H., Fumagalli M., Smail I., et al. 2019, @doi [Sci] 10.1126/science.aaw5949 , https://ui.adsabs.harvard.edu/abs/2019Sci...366...97U 366, 97

    2019 doi

  119. [128]

    L., Sabhlok S., et al

    Vayner A., Zakamska N. L., Sabhlok S., et al. 2023, @doi [MNRAS] 10.1093/mnras/stac3537 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519..961V 519, 961

    2023 doi

  120. [129]

    P., R \"o ttgering H

    Venemans B. P., R \"o ttgering H. J. A., Miley G. K., et al. 2007, @doi [A&A] 10.1051/0004-6361:20053941 , https://ui.adsabs.harvard.edu/abs/2007A&A...461..823V 461, 823

    2007 doi

  121. [130]

    Villar-Mart \' n M., 2007, @doi [NewAR] 10.1016/j.newar.2006.11.017 , https://ui.adsabs.harvard.edu/abs/2007NewAR..51..194V 51, 194

    2007 doi

  122. [131]

    D., Ross N

    White M., Myers A. D., Ross N. P., et al. 2012, @doi [MNRAS] 10.1111/j.1365-2966.2012.21251.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.424..933W 424, 933

    2012

  123. [132]

    L., Eisenhardt P

    Wright E. L., Eisenhardt P. R. M., Mainzer A. K., et al. 2010, @doi [AJ] 10.1088/0004-6256/140/6/1868 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.1868W 140, 1868

    2010 doi

  124. [133]

    H., et al

    Zehavi I., Zheng Z., Weinberg D. H., et al. 2005, @doi [ApJ] 10.1086/431891 , https://ui.adsabs.harvard.edu/abs/2005ApJ...630....1Z 630, 1

    2005 doi

  125. [134]

    H., et al

    Zehavi I., Zheng Z., Weinberg D. H., et al. 2011, @doi [ApJ] 10.1088/0004-637X/736/1/59 , https://ui.adsabs.harvard.edu/abs/2011ApJ...736...59Z 736, 59

    2011 doi

  126. [135]

    2025a, @doi [MNRAS] 10.1093/mnras/staf260 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.538..503Z 538, 503

    Zhang H., Behroozi P., Volonteri M., et al. 2025a, @doi [MNRAS] 10.1093/mnras/staf260 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.538..503Z 538, 503

    doi

  127. [136]

    2025b, @doi [ApJ] 10.3847/1538-4357/adb41b , https://ui.adsabs.harvard.edu/abs/2025ApJ...981...70Z 981, 70

    Zhang H., Cai Z., Li M., et al. 2025b, @doi [ApJ] 10.3847/1538-4357/adb41b , https://ui.adsabs.harvard.edu/abs/2025ApJ...981...70Z 981, 70

    doi

  128. [137]

    van Ojik R., Roettgering H. J. A., Miley G. K., et al. 1997, A&A, https://ui.adsabs.harvard.edu/abs/1997A&A...317..358V 317, 358

    1997

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.