Pith. sign in

REVIEW 5 major objections 5 minor 2 cited by

The g'-band luminosity function of quiescent red-sequence galaxies in the Coma cluster is a double Schechter function with a steep faint-end slope of alpha_2 = -1.539, probing to M about -11.3 mag.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

The Coma cluster's quenched galaxy luminosity function has a steep double-Schechter faint-end slope alpha_2 = -1.539 ± 0.024 down to M ≈ -11.3 g' mag.

T0 review reviewed 2026-08-04 challenge →

load-bearing objection A careful, benchmark-quality Coma GLF, but the steep faint-end slope rests on a completeness systematic that needs a sensitivity test and a background prior that needs more than two reference fields. the 5 major comments →

arxiv 2510.26889 v2 pith:5ITWZEOM submitted 2025-10-30 astro-ph.GA

Galaxy Luminosity Function of the Coma Cluster from Deep u'-g'-r' Wendelstein Imaging Data

Raphael Z\"oller , Matthias Kluge , Ralf Bender , Jan-Niklas Pippert , Benjamin Seidel , Claus G\"ossl , Ulrich Hopp , Hanna Kellermann
show 4 more authors
Christoph Ries Arno Riffeser Michael Schmidt Luis Thomas
This is my paper
classification astro-ph.GA
keywords galaxy luminosity functionComa clusterdwarf galaxiesred sequencequenched galaxiesultra-diffuse galaxiesgalaxy cluster membershipgalaxy photometry
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 aims to establish the g'-band luminosity function of quiescent red-sequence galaxies in the Coma cluster from deep wide-field imaging covering about 1.5 square degrees, reaching absolute magnitudes near -11.3. It argues that a single Schechter function cannot describe the data; a double Schechter function with a steep faint-end slope of alpha_2 = -1.539 is required, implying a rapidly growing population of faint quiescent dwarf galaxies including compact dwarfs and ultra-diffuse galaxies. The result matters as a benchmark for cosmological simulations of galaxy clusters, where the faint-end slope and normalization depend on baryonic feedback and dark matter models. The paper also performs a direct comparison with a constrained simulation of a Coma-like environment, finding broad agreement but a deficit of bright galaxies and an excess of dwarfs, and stresses that normalization as well as slope must be compared.

Core claim

Using fully automated detection, Sersic fitting, and color-based membership selection, the authors identify 5161 quiescent cluster member candidates in Coma. After correcting for incompleteness with injection-recovery tests and for foreground/background contamination with two identically analyzed reference fields, they fit the unbinned galaxy counts with a double Schechter function in the g' band. The best-fit parameters are M* = -20.15 (+0.26/-0.30), log10(Phi*_1)=1.92, alpha_1=0.06, log10(Phi*_2)=1.405, alpha_2=-1.539 ± 0.024 g' mag, spanning -24.5 to -11.3 mag. The steep faint-end slope shows no downturn down to the survey limit, contradicting some earlier claims of a declining GLF at fai

What carries the argument

The central object is the double Schechter luminosity function, whose steep second component alpha_2 carries the claim of a rising dwarf population. The machinery that makes the measurement trustworthy is fully automated: quiescent selection in the u'-g' versus g'-r' color-color diagram plus red-sequence membership, single-Sersic fits for faint galaxies and isophotal models for bright ones, injection-recovery tests for completeness, and identical analysis of two reference fields for contamination, combined in an unbinned maximum-likelihood fit with uncertainties propagated from the completeness and background posteriors.

Load-bearing premise

The two reference fields, analyzed identically, faithfully represent the number and luminosity distribution of foreground and background galaxies along the Coma line of sight; if the true background differs beyond the cosmic variance seen between the fields, the faint-end slope and normalization shift.

What would settle it

Measure the same GLF using spectroscopic redshifts to directly identify Coma members instead of statistical background subtraction, and check whether alpha_2 remains -1.54 within the quoted errors. Alternatively, apply the identical pipeline to another massive cluster with a different line of sight; if the steep slope disappears when the background is measured a different way, the claim fails.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • The Coma cluster hosts a substantial population of faint quiescent dwarfs, including compact dwarfs and ultra-diffuse galaxies, with no turnover down to M ≈ -11.3 g' mag.
  • The measured GLF provides a benchmark for cosmological simulations: galaxy formation models must reproduce a steep faint-end slope of about -1.54 and a normalization consistent with the observed counts.
  • A direct comparison between observation and simulation must use the same survey area, filter, and line-of-sight selection; comparing slopes alone can be misleading.
  • The steep faint-end slope is more in line with cold dark matter than warm dark matter, but baryonic physics and feedback remain entangled, so a dark-matter conclusion is not yet possible.
  • Earlier reports of a faint-end downturn in Coma are not confirmed; the GLF continues to rise.

Where Pith is reading between the lines

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

  • If the steep slope holds, the integrated stellar mass in galaxies below the detection limit may be non-negligible, and future deeper surveys could test whether the slope steepens further.
  • The reference-field contamination method could be applied to other galaxy clusters; the cosmic-variance uncertainty measured between fields offers a way to design survey strategies for background control.
  • Combining the GLF with structural parameter densities, as the authors hint, would isolate whether compact dwarfs or ultra-diffuse galaxies dominate the faint end, a testable prediction.
  • The apples-to-apples comparison suggests simulation subgrid feedback, not dark matter alone, drives the bright-end deficit; rerunning the same comparison after varying feedback prescriptions would isolate that effect.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper presents deep u'g'r' Wendelstein imaging of ~1.5 deg^2 around the Coma cluster, plus two reference fields, and derives the g'-band luminosity function of quenched red-sequence galaxies. The pipeline uses SExtractor detection, isophotal modeling of bright galaxies, fully automated GALFIT Sersic fits for faint galaxies, u'-g' vs. g'-r' color-color selection with a 3-sigma red-sequence cut, injection-recovery completeness corrections, and contamination subtraction using identically processed reference fields. The central claim is that the GLF is well described by a double Schechter function with M* = -20.15(+0.26/-0.30) g' mag, log10(Phi*_1) = 1.92(+0.09/-0.11), alpha_1 = 0.06(+0.32/-0.29), log10(Phi*_2) = 1.405(+0.088/-0.095), and alpha_2 = -1.539(+0.024/-0.024), probing to M ~ -11.3 g' mag, and that this provides a benchmark for simulations.

Significance. The data set and methodology are substantial: fully automated processing avoids visual-inspection selection biases; the injection-recovery test uses an unbinned Bernoulli likelihood; completeness and background uncertainties are propagated through 1000 nested-sampling realizations; and the comparison with the SLOW constrained simulation is a genuine apples-to-apples attempt. If the central result survives scrutiny, it is one of the most precise Coma GLFs at the faint end and a meaningful test for baryonic feedback and dark matter models. The steep alpha_2 is outside some current simulation predictions, but disagreement with consensus is not by itself a flaw. The main risks are systematic: the completeness input distribution, the two-field background estimate, and the construction of the unbinned likelihood.

major comments (5)
  1. [Abstract] The arXiv metadata abstract and the body abstract report different central results. The metadata abstract gives a single Schechter GLF with M* = -21.71 and alpha = -1.444 and 'more than 6000' candidates; the body abstract and Section 4.1 give a double Schechter GLF with M* = -20.15, alpha_2 = -1.539, and 5161 candidates. This is not cosmetic: the headline result and sample size are inconsistent across the manuscript. Harmonize the two abstracts and check all quoted numbers.
  2. [Sec. 3.6, Fig. 10] The completeness correction is the main faint-end lever, but the injection-recovery test draws mock structural parameters 'from our final Coma faint galaxy sample', i.e., the same catalog whose incompleteness is being measured. If the pipeline preferentially loses low-surface-brightness or compact dwarfs, those populations are underrepresented in the injected set and C(M) is biased high. Since completeness is modeled only as a 3rd-order polynomial in M, this structural selection effect is not captured; the quoted alpha_2 uncertainty excludes it. At M >~ -14, where C drops steeply, even a small relative bias in C propagates into a large error in the corrected counts. Please test sensitivity by injecting from a deliberately broadened structural-parameter distribution (e.g., the pre-selection SExtractor catalog, or literature UDG/compact-dwarf parameters) or by including R_e and mu_e in the
  3. [Eqs. 11-13] The unbinned likelihood is not written as a standard inhomogeneous Poisson point-process likelihood. With n_i = 1/A, Eq. 11 becomes log L = -integral f dM + (1/A) sum log f(M_i), whereas the correct Poisson likelihood is log L = -A integral f dM + sum log[A f(M_i)]. The 1/A factor changes the relative weight of the normalization integral and the data sum; in Eq. 13, where A_f and A_b differ, it also changes the relative weight of the bright and faint samples. This can bias M* and alpha_2. Please replace this with the standard unbinned Poisson likelihood, or justify the per-area weighting and show that the best-fit parameters are insensitive to it.
  4. [Sec. 3.7, Eq. 10, Fig. 13] The reference-field background fit quotes alpha_BG = +1.703, but Eq. 10 with alpha_BG + 1 in the exponent then makes the background counts decline steeply toward faint magnitudes, contradicting the text's statement that the reference-field faint-end slopes match well and the rising grey data points in Fig. 12. If the analysis actually used alpha_BG = -1.703, the sign should be stated consistently; if it used +1.703, the contamination model in Eq. 13 is misspecified and the faint-end subtraction, and therefore alpha_2, is affected. Please clarify the sign convention and re-run the GLF fit with the corrected background model.
  5. [Sec. 3.7, Eqs. 12-13] The contamination correction rests on only two reference fields. The combined posterior is bimodal in M*_BG and log10(Phi*_BG), and the field-to-field difference is interpreted as cosmic variance; this brackets variance between Ref1 and Ref2 but not the possibility that the true line-of-sight background toward Coma differs systematically from both. Please add an external check (a third reference field, photometric-redshift counts, or mock catalogs) or explicitly quote how alpha_2 changes under alternative background priors, e.g., using Ref1 alone, Ref2 alone, or a flat faint-end background.
minor comments (5)
  1. [Sec. 4.2, Eqs. 14-18] Eq. (14) and Eq. (15) are identical; the R-band to g'-band conversion block should be cleaned up and the derivation consolidated.
  2. [Fig. 12 caption] 'not included included for the double Schechter fit' has a duplicated word and should read 'not included in the double Schechter fit'.
  3. [Sec. 3.6] The clipping of the completeness polynomial to [1e-6, 1-1e-6] and the 'clip-off magnitude' are mentioned but never defined. State the adopted magnitude limit for the GLF sample and how it relates to the clipping.
  4. [Sec. 3.5 and Summary] The faint sample is quoted as 4829 candidates in Sec. 3.5 and the final sample as 5161 in the Summary. Clarify that the latter includes bright galaxies and state the bright/faint split explicitly.
  5. [Fig. 15] The axis label 'Mtot [g' mag]' is not defined in the text; use M_g' or define Mtot.

Circularity Check

1 steps flagged

Background model's M* is seeded by the Coma cluster's own binned M*, so the final M*/normalization are partly self-referential; the steep faint-end slope itself is independently supported.

specific steps
  1. self definitional [Section 4.1, paragraph following Eq. (12)]
    "Note here that multiple of the magnitude bins of the reference fields have 0 counts. Hence, we assume that M*_BG is similar to M* derived from the binned Coma GLF fit and use this as a Gaussian prior with sigma=0.5 mag."

    The background Schechter model used in the final Coma likelihood (Eq. 13) has its bright-end parameter M*_BG initialized with a Gaussian prior centered on the Coma cluster M* from a binned fit to the same Coma data. Since reference-field magnitude bins have 0 counts at the bright end, this prior dominates: Figure 13 gives M*_BG = -20.13(+0.83/-0.66), essentially the prior center. The final unbinned fit then solves for the cluster M* and normalizations with a background model that already encodes the cluster M*. Hence the reported M* = -20.15 and log10(Phi*) are partially inherited from the input binned fit rather than independently determined. The faint-end slope alpha_2 is less contaminated because it is constrained by the numerous faint reference-field galaxies, so the steep-slope claim

full rationale

The central GLF measurement is mostly self-contained: the analysis uses new photometry, an injection-recovery completeness correction (Section 3.6), two independently observed reference fields for contamination (Section 3.7), and an unbinned nested-sampling fit (Eq. 13). The literature comparison and the SLOW simulation are external benchmarks, not inputs. The pipeline is adopted from Zöller et al. (2024), but the present paper re-describes and re-validates the automated fitting, so that self-citation is not a load-bearing, unverified uniqueness claim. The one genuinely self-referential step is the prior on the background Schechter M*: it is set equal to the Coma cluster M* from the binned fit to the same data. Because the reference fields have essentially no bright galaxies, this prior dominates the background bright-end (Figure 13), and the final unbinned Coma fit then uses that background model. Thus the reported M* and Phi* values are partly inherited from the binned Coma fit rather than being a clean independent measurement. The steep faint-end slope alpha_2 is anchored by the abundant faint reference-field counts and by the faint-end Coma counts, so the paper's main physical claim does not reduce to the self-referential prior. The completeness injection-recovery also draws structural parameters from the final catalog; this is a standard internal calibration but can bias the faint-end correction if low-surface-brightness dwarfs are under-represented. That is a systematic risk rather than a definitional circularity. Overall: one partial circularity, not a fully forced result.

Axiom & Free-Parameter Ledger

3 free parameters · 5 axioms · 0 invented entities

No new physical entities are introduced. The free parameters are all selection thresholds and calibration fits for the galaxy sample. The most important are the red-sequence fit, the color cuts, and indirectly the background M* prior which couples the contamination model to the Coma M*.

free parameters (3)
  • Red-sequence intercept and slope = g'-r' = (-0.023 +/- 0.001) m_g' + (1.03 +/- 0.02)
    Fitted by linear regression to KDE ridge points in color-magnitude space; used to define cluster membership. Not directly fitted to the GLF, but affects sample selection.
  • Intrinsic red-sequence width (3 sigma) = 0.1 mag
    Adopted from Kluge et al. (2024); controls member selection purity vs completeness.
  • Color-color selection thresholds = u'-g' > g'-r' + 0.1; u'-g' > 0.55; g'-r' < 1
    Adjusted from Zoller et al. (2024) by a 0.2 mag shift; hand-chosen to include transitioning galaxies.
axioms (5)
  • domain assumption Quiescent red-sequence galaxies dominate the cluster center population and can be separated from interlopers by u'-g' vs g'-r' and g'-r' red sequence membership.
    Central for membership selection (Sec. 3.5; Eqs. 3-6). If significant star-forming or blue dwarf members are missed, the faint-end slope is underestimated.
  • domain assumption The two reference fields are representative of the interloper population in the Coma field.
    Required for contamination subtraction (Sec. 3.7). Only two fields; cosmic variance estimated from their difference. Authors acknowledge this limitation.
  • domain assumption Injection-recovery of artificial Sersic galaxies drawn from the final sample fairly represents the true completeness of the detection and measurement pipeline.
    Completeness function is derived from this test; if real galaxies have different morphologies, blending, or backgrounds than the injected models, the completeness correction biases the GLF (Sec. 3.6).
  • domain assumption Single Sersic models are adequate for faint dwarf galaxies, and GALFIT parameter uncertainties are reliable.
    Structural parameters and magnitudes of faint galaxies come from these fits (Sec. 3.4). Adoption from Zoller et al. (2024).
  • standard math The assumed cosmology (H0=69.6, Omega_m=0.286) and distance modulus of 35.03 for Coma.
    Converts apparent to absolute magnitudes (Sec. 1). Standard, small effect.

reviewed 2026-08-04 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Galaxy Luminosity Function of the Coma Cluster from Deep $u'-g'-r'$ Wendelstein Imaging Data." pith.science (2026). https://pith.science/paper/5ITWZEOM

@misc{pith2026251026889,
  author       = {Pith},
  title        = {Pith review of: Galaxy Luminosity Function of the Coma Cluster from Deep $u'-g'-r'$ Wendelstein Imaging Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5ITWZEOM}},
  note         = {Machine review of arXiv:2510.26889}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

We derive the $g'$ band galaxy luminosity function (GLF) of quenched resolved galaxies in the Coma cluster from a deep-imaging survey with $\approx1.5\,\mathrm{deg^2}$ around the cluster center. The dataset comprises deep $u'$-, $g'$-, and $r'$-band data obtained with the Wendelstein Wide Field Imager on the $2.1\,$m Fraunhofer Wendelstein Telescope reaching median $3\sigma$ surface brightness limits in 10" $\times$ 10" boxes of $\mathrm{ (30.0\,u',\,\,29.6\,g',\,\,28.7\,r')\,mag\,arcsec^{-2}}$. We measure structural parameters across a large dynamic range in galaxy brightness ($-24.5\,g'\,\mathrm{mag} \lessapprox M\lessapprox-11.3\,g'\,\mathrm{mag}$), from the brightest cluster galaxy to low-luminosity dwarfs, including compact dwarf galaxies and ultra-diffuse galaxies. We automatically identify more than 6000 cluster member candidates based on their membership on the quiescent sequence in the $u'-g'$ versus $g'-r'$ color-color diagram. The structural parameters of bright galaxies are obtained via isophotal modeling, and fully automated parametric image fitting for faint ones. Injection-recovery tests and two identically analyzed reference fields provide statistical corrections for completeness and contamination, yielding a representative GLF that reliably probes the faint end and may serve as a benchmark for future studies. We report a best-fit single Schechter $g'$ band GLF with $M^\star=-21.71^{+0.26}_{-0.29}\,g'\,\mathrm{mag}$, $\log_{10}(\phi^\star\,[\mathrm{deg}^{-2}\,\mathrm{mag}^{-1}])=1.355^{+0.076}_{-0.079}$, and a comparatively steep faint-end slope $\alpha=-1.444^{+0.015}_{-0.015}$. A directly matched comparison with the Coma counterpart in SLOW, a constrained cosmological simulation using CDM, shows broad agreement between the GLFs down to the simulation limit of $M = -15.5\,g'\,\mathrm{mag}$, despite a deficit of bright galaxies.

Figures

Figures reproduced from arXiv: 2510.26889 by Arno Riffeser, Benjamin Seidel, Christoph Ries, Claus G\"ossl, Hanna Kellermann, Jan-Niklas Pippert, Luis Thomas, Matthias Kluge, Michael Schmidt, Ralf Bender, Raphael Z\"oller, Ulrich Hopp.

Figure 1
Figure 1. Figure 1: 10 × 10 times binned cutout of the Coma g ′ band stack created without sigma clipping (left), with 3 − σ-clipping applied (middle), and with our masking method applied (right). low S/N regions in the outskirts. The resulting survey footprint is visualized as a color image in [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: 6 × 6 binned u ′ − g ′ − r ′ color image of the Coma cluster survey region (1.22 deg × 1.22 deg). Note that the bright star models were reinjected into the actual science stacks with one-third of their actual flux for visualization only. nal models. For NGC 4839, no iteration is needed. The models of NGC 4889, NGC 4874, and NGC 4839 are sub￾tracted from the star-subtracted image stack [PITH_FULL_IMAGE:fig… view at source ↗
Figure 3
Figure 3. Figure 3: 3σ depth on a 10′′ × 10′′ scale of our Coma cluster data (left), as well as of our two reference fields Ref1 (middle), and Ref2 (right) in the u ′ (top), g ′ (middle), and r ′ -band (bottom) [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: u ′ − g ′ − r ′ image of the central region of the Coma cluster before subtracting NGC 4889 (including the ICL) and NGC 4874 (left) and after (right). in the following used to preselect cluster member candi￾dates and as initial parameters for a more precise mea￾surement using GALFIT (Peng et al. 2010). We create two object catalogs, one for large and bright sources and one for relatively small and faint so… view at source ↗
Figure 5
Figure 5. Figure 5: Smoothed g ′ band image before (left panel) and after (middle panel) the aggressive background subtraction. The created masks (blue) for faint point sources are shown in the right panel, applied to the non-smoothed image. ground subtraction efficiently removes the outskirts of galaxies or even a whole diffuse galaxy such as DF15 (van Dokkum et al. 2015) as shown in [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Selected outputs of the fitting routine for LEDA 44562 in the g ′ band. The ellipticity, position angle, and central coordinates are fixed after the orange marked isophote. The background is determined at the red-marked position. In the surface brightness profile plot, the green dots are the data points used for the fit, the solid green line corresponds to the best-fit S´ersic profile, and the dashed green… view at source ↗
Figure 7
Figure 7. Figure 7: Unmasked cutout around the UDG DF15, the automatically masked image, the best-fit model, and the residual image (f.l.t.r.) ing of the fits. The main reason is to avoid an observer bias, i.e., unintentionally creating better manual masks for the galaxies in the Coma image than for the refer￾ence images or manually rejecting fewer fit results, which could potentially lead to an incorrect completeness cor￾rec… view at source ↗
Figure 8
Figure 8. Figure 8: u ′ − g ′ vs. g ′ − r ′ number density color-color diagram for the Coma cluster. Galaxies in the top left of the red visualized selection cutoffs are considered to be quenched. The selection criteria are given by: u ′ − g ′ > g′ − r + 0.1 (3) u ′ − g ′ > 0.55 (4) g ′ − r ′ < 1 (5) In [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: g ′ − r ′ color-magnitude diagram of the Coma cluster. The KDE ridge points are depicted as black dots, the best-fit red sequence as a solid black line, and the limits of the intrinsic red sequence as dashed black lines. Galaxies that deviate more than 3 × ∆(g ′ − r ′ ) from these limits are removed from the sample. The galaxies rejected by this red sequence selection are visualized in blue, and those that… view at source ↗
Figure 10
Figure 10. Figure 10: Completeness Function Coma Field derive the Bayesian posterior distribution and the best￾fit parameters using the dynamic nested sampling pack￾age DYNESTY (Speagle 2020). We use a Bernoulli distri￾bution for the injection recovery test: L = p k (1 − p) 1−k (7) Here, we use the unbinned data from the injection re￾covery test, where k = 1 is recovered and k = 0 is not recovered, compute a log-likelihood for… view at source ↗
Figure 11
Figure 11. Figure 11: Completeness Functions Reference Fields with a bin width of 0.5 mag. In [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: The best-fit GLF of the Coma cluster is visualized as a blue solid line with its 1σ uncertainty interval in shaded blue. The bright component of the best-fit double Schechter function is depicted as dotted red line and the faint component as dotted orange line. The completeness corrected counts per bin of galaxies from the reference fields are shown as light-grey data points for Ref1 and as dark-grey data… view at source ↗
Figure 13
Figure 13. Figure 13: Combined posterior distribution of the fits of a GLF using a Schechter function for both reference fields individually. The contours correspond to the 0.5σ, 1σ, 2σ, and 3σ levels. We propagate the uncertainties again by running the nested sampling 1000 times, randomly drawing the pa￾rameters for C(M) and ΦBG from their posteriors, nor￾malizing the resulting posteriors, combining them, and deriving the bes… view at source ↗
Figure 14
Figure 14. Figure 14: Posterior distribution of the fit of a GLF using a double Schechter function excluding NGC 4889, NGC 4874, and NGC 4839. The contours correspond to the 0.5σ, 1σ, 2σ, and 3σ levels. a large variety of dwarf galaxies from compact dwarf galaxies to UDGs (Z¨oller et al. in prep.). This allows us to derive a representative GLF that also probes the faint end well. As most of the literature GLFs were derived usi… view at source ↗
Figure 15
Figure 15. Figure 15: Comparison with GLF of the Coma cluster from the literature. Our best-fit GLF of the Coma cluster is visualized as a blue solid line with its 1σ uncertainty interval in shaded blue. The completeness and contamination corrected bin counts representing the galaxies used for the unbinned GLF fitting procedure are shown in blue, and the three brightest galaxies, NGC 4889, NGC 4874, and NGC 4839, which were no… view at source ↗
Figure 16
Figure 16. Figure 16: M∗ – M scaling for the simulated Coma cluster. The magnitude cutoff at M = −15.5 g ′ mag is highlighted by the red horizontal line. mass cut of 5×108M⊙ we applied due to the mass reso￾lution of the simulation. This can be seen in [PITH_FULL_IMAGE:figures/full_fig_p021_16.png] view at source ↗

discussion (0)

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

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Rates of tidal disruption events from constrained cosmological simulations of the local Universe: population properties and implications for transient surveys

    astro-ph.HE 2026-07 conditional novelty 6.0

    In simulated local clusters, tidal disruption events are dominated by cuspy satellite galaxies in extended halos, with a volumetric rate of ~600 Gpc^-3 yr^-1 inherited from empirical TDE scaling relations.

  2. Cutting with precision -- Leveraging Collapse Volumes to generate the next generation of zoom-in initial conditions

    astro-ph.CO 2026-06 unverdicted novelty 5.0

    Presents a new technique using collapse volumes to generate stable, low-contamination zoom-in initial conditions for SLOW constrained simulations, with tests on 20 regions showing pristine volumes beyond 6 virial radii.

Reference graph

Works this paper leans on

81 extracted references · 9 canonical work pages · cited by 2 Pith papers

  1. [1]

    D., Allende Prieto, C., et al

    Alam, S., Albareti, F. D., Allende Prieto, C., et al. 2015, ApJS, 219, 12, doi: 10.1088/0067-0049/219/1/12

    doi:10.1088/0067-0049/219/1/12 2015

  2. [2]

    Andreon, S., & Cuillandre, J. C. 2002, ApJ, 569, 144, doi: 10.1086/339261

    work page doi:10.1086/339261 2002

  3. [3]

    H., Hearin, A

    Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. 2019, MNRAS, 488, 3143, doi: 10.1093/mnras/stz1182

    doi:10.1093/mnras/stz1182 2019

  4. [5]

    L., Larson, D., Weiland, J

    Bennett, C. L., Larson, D., Weiland, J. L., & Hinshaw, G. 2014, ApJ, 794, 135, doi: 10.1088/0004-637X/794/2/135

    doi:10.1088/0004-637x/794/2/135 2014

  5. [6]

    M., Nichol, R

    Bernstein, G. M., Nichol, R. C., Tyson, J. A., Ulmer, M. P., & Wittman, D. 1995, AJ, 110, 1507, doi: 10.1086/117624

    doi:10.1086/117624 1995

  6. [7]

    2006, in Astronomical Society of the Pacific Conference Series, Vol

    Bertin, E. 2006, in Astronomical Society of the Pacific Conference Series, Vol. 351, Astronomical Data Analysis Software and Systems XV, ed. C. Gabriel, C. Arviset, D. Ponz, & S. Enrique, 112

    2006

  7. [8]

    2010, SWarp: Resampling and Co-adding FITS Images Together, Astrophysics Source Code Library, record ascl:1010.068

    Bertin, E. 2010, SWarp: Resampling and Co-adding FITS Images Together, Astrophysics Source Code Library, record ascl:1010.068

    2010

  8. [9]

    2011, in Astronomical Society of the Pacific Conference Series, Vol

    Bertin, E. 2011, in Astronomical Society of the Pacific Conference Series, Vol. 442, Astronomical Data Analysis Software and Systems XX, ed. I. N. Evans, A. Accomazzi, D. J. Mink, & A. H. Rots, 435

    2011

  9. [10]

    1996, A&AS, 117, 393, doi: 10.1051/aas:1996164

    Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393, doi: 10.1051/aas:1996164

    doi:10.1051/aas:1996164 1996

  10. [11]

    Binggeli, B., Sandage, A., & Tammann, G. A. 1985, AJ, 90, 1681, doi: 10.1086/113874

    doi:10.1086/113874 1985

  11. [12]

    R., Lupton, R

    Blanton, M. R., Lupton, R. H., Schlegel, D. J., et al. 2005, ApJ, 631, 208, doi: 10.1086/431416

    doi:10.1086/431416 2005

  12. [13]

    G., Lucey, J

    Bower, R. G., Lucey, J. R., & Ellis, R. S. 1992, MNRAS, 254, 589, doi: 10.1093/mnras/254.4.589

    doi:10.1093/mnras/254.4.589 1992

  13. [14]

    2020, astropy/photutils: 1.0.0, 1.0.0, Zenodo, doi: 10.5281/zenodo.4044744

    Bradley, L., Sip˝ ocz, B., Robitaille, T., et al. 2020, astropy/photutils: 1.0.0, 1.0.0, Zenodo, doi: 10.5281/zenodo.4044744

    doi:10.5281/zenodo.4044744 2020

  14. [15]

    S., & Boylan-Kolchin, M

    Bullock, J. S., & Boylan-Kolchin, M. 2017, ARA&A, 55, 343, doi: 10.1146/annurev-astro-091916-055313

    doi:10.1146/annurev-astro-091916-055313 2017

  15. [16]

    1979, ApJ, 228, 939, doi: 10.1086/156922

    Cash, W. 1979, ApJ, 228, 939, doi: 10.1086/156922

    doi:10.1086/156922 1979

  16. [17]

    V., & Zolotukhin, I

    Chilingarian, I. V., & Zolotukhin, I. Y. 2012, MNRAS, 419, 1727, doi: 10.1111/j.1365-2966.2011.19837.x

    arXiv 2012

  17. [18]

    C., Bolzonella, M., Boselli, A., et al

    Cuillandre, J. C., Bolzonella, M., Boselli, A., et al. 2025, A&A, 697, A11, doi: 10.1051/0004-6361/202450808

    doi:10.1051/0004-6361/202450808 2025

  18. [19]

    2024, A&A, 692, A81, doi: 10.1051/0004-6361/202450021

    Damiano, A., Valentini, M., Borgani, S., et al. 2024, A&A, 692, A81, doi: 10.1051/0004-6361/202450021

    doi:10.1051/0004-6361/202450021 2024

  19. [20]

    G., Pilipenko, S., et al

    Dolag, K., Sorce, J. G., Pilipenko, S., et al. 2023, A&A, 677, A169, doi: 10.1051/0004-6361/202346213

    doi:10.1051/0004-6361/202346213 2023

  20. [21]

    M., et al

    Dolag, K., Remus, R.-S., Valenzuela, L. M., et al. 2025, arXiv e-prints, arXiv:2504.01061, doi: 10.48550/arXiv.2504.01061

    doi:10.48550/arxiv.2504.01061 2025

  21. [22]

    1980, ApJ, 236, 351, doi: 10.1086/157753

    Dressler, A. 1980, ApJ, 236, 351, doi: 10.1086/157753

    doi:10.1086/157753 1980

  22. [23]

    S., & Peterson, B

    Efstathiou, G., Ellis, R. S., & Peterson, B. A. 1988, MNRAS, 232, 431, doi: 10.1093/mnras/232.2.431 Euclid Collaboration, Scaramella, R., Amiaux, J., et al. 2022, A&A, 662, A112, doi: 10.1051/0004-6361/202141938 24Z ¨oller et al. Euclid Collaboration, Mellier, Y., Abdurro’uf, et al. 2025, A&A, 697, A1, doi: 10.1051/0004-6361/202450810

    doi:10.1093/mnras/232.2.431 1988

  23. [24]

    M., Willmer, C

    Faber, S. M., Willmer, C. N. A., Wolf, C., et al. 2007, ApJ, 665, 265, doi: 10.1086/519294

    doi:10.1086/519294 2007

  24. [25]

    2016, ApJ, 824, 10, doi: 10.3847/0004-637X/824/1/10

    Ferrarese, L., Cˆ ot´ e, P., S´ anchez-Janssen, R., et al. 2016, ApJ, 824, 10, doi: 10.3847/0004-637X/824/1/10

    doi:10.3847/0004-637x/824/1/10 2016

  25. [26]

    A., Magnier, E

    Flewelling, H. A., Magnier, E. A., Chambers, K. C., et al. 2020, ApJS, 251, 7, doi: 10.3847/1538-4365/abb82d Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2021, A&A, 649, A1, doi: 10.1051/0004-6361/202039657 G´ omez, P. L., Nichol, R. C., Miller, C. J., et al. 2003, ApJ, 584, 210, doi: 10.1086/345593 G¨ ossl, C. A., & Riffeser, A. 2002, A&A, ...

    doi:10.3847/1538-4365/abb82d 2020

  26. [27]

    Gruen, D., Seitz, S., & Bernstein, G. M. 2014, PASP, 126, 158, doi: 10.1086/675080

    doi:10.1086/675080 2014

  27. [28]

    R., Duc, P.-A., et al

    Habas, R., Marleau, F. R., Duc, P.-A., et al. 2020, MNRAS, 491, 1901, doi: 10.1093/mnras/stz3045 Hern´ andez-Mart ´ ınez, E., Dolag, K., Seidel, B., et al. 2024, A&A, 687, A253, doi: 10.1051/0004-6361/202449460

    doi:10.1093/mnras/stz3045 2020

  28. [29]

    2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Hopp, U., Bender, R., Grupp, F., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9145, Ground-based and Airborne Telescopes V, ed. L. M. Stepp, R. Gilmozzi, & H. J. Hall, 91452D, doi: 10.1117/12.2054498

    doi:10.1117/12.2054498 2014

  29. [30]

    Hunter, J. D. 2007, CSE, 9, 90, doi: 10.1109/MCSE.2007.55 Ivezi´ c,ˇZ., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c

    doi:10.1109/mcse.2007.55 2007

  30. [31]

    P., Richards, G

    Jester, S., Schneider, D. P., Richards, G. T., et al. 2005, AJ, 130, 873, doi: 10.1086/432466

    doi:10.1086/432466 2005

  31. [32]

    Kauffmann, G., White, S. D. M., & Guiderdoni, B. 1993, MNRAS, 264, 201, doi: 10.1093/mnras/264.1.201

    doi:10.1093/mnras/264.1.201 1993

  32. [33]

    C., Brough, S., Dolag, K., et al

    Kimmig, L. C., Brough, S., Dolag, K., et al. 2025, Astronomy & Astrophysics, 700, A95, doi: 10.1051/0004-6361/202554777

    doi:10.1051/0004-6361/202554777 2025

  33. [34]

    2020, PhD thesis, Ludwig-Maximilians University of Munich, Germany

    Kluge, M. 2020, PhD thesis, Ludwig-Maximilians University of Munich, Germany

    2020

  34. [35]

    2023, ApJS, 267, 41, doi: 10.3847/1538-4365/ace052

    Kluge, M., & Bender, R. 2023, ApJS, 267, 41, doi: 10.3847/1538-4365/ace052

    work page doi:10.3847/1538-4365/ace052 2023

  35. [36]

    2021, ApJS, 252, 27, doi: 10.3847/1538-4365/abcda6

    Kluge, M., Bender, R., Riffeser, A., et al. 2021, ApJS, 252, 27, doi: 10.3847/1538-4365/abcda6

    doi:10.3847/1538-4365/abcda6 2021

  36. [37]

    2023, MNRAS, 521, 4852, doi: 10.1093/mnras/stad882

    Dolfi, A. 2023, MNRAS, 521, 4852, doi: 10.1093/mnras/stad882

    doi:10.1093/mnras/stad882 2023

  37. [38]

    2020, ApJS, 247, 43, doi: 10.3847/1538-4365/ab733b

    Kluge, M., Neureiter, B., Riffeser, A., et al. 2020, ApJS, 247, 43, doi: 10.3847/1538-4365/ab733b

    doi:10.3847/1538-4365/ab733b 2020

  38. [39]

    2024, A&A, 688, A210, doi: 10.1051/0004-6361/202349031

    Kluge, M., Comparat, J., Liu, A., et al. 2024, A&A, 688, A210, doi: 10.1051/0004-6361/202349031

    doi:10.1051/0004-6361/202349031 2024

  39. [40]

    A., Montes, M., et al

    Kluge, M., Hatch, N. A., Montes, M., et al. 2025, A&A, 697, A13, doi: 10.1051/0004-6361/202450772

    doi:10.1051/0004-6361/202450772 2025

  40. [41]

    V., Valenzuela, O., & Prada, F

    Klypin, A., Kravtsov, A. V., Valenzuela, O., & Prada, F. 1999, ApJ, 522, 82, doi: 10.1086/307643

    doi:10.1086/307643 1999

  41. [42]

    2014, Experimental Astronomy, 38, 213, doi: 10.1007/s10686-014-9414-1

    Kosyra, R., G¨ ossl, C., Hopp, U., et al. 2014, Experimental Astronomy, 38, 213, doi: 10.1007/s10686-014-9414-1

    work page doi:10.1007/s10686-014-9414-1 2014

  42. [43]

    M., Stebbins, A., Annis, J., et al

    Kubo, J. M., Stebbins, A., Annis, J., et al. 2007, ApJ, 671, 1466, doi: 10.1086/523101

    doi:10.1086/523101 2007

  43. [44]

    2011, arXiv e-prints, arXiv:1110.3193, doi: 10.48550/arXiv.1110.3193

    Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv e-prints, arXiv:1110.3193, doi: 10.48550/arXiv.1110.3193

    Pith/arXiv arXiv doi:10.48550/arxiv.1110.3193 2011

  44. [45]

    2002, MNRAS, 334, 673, doi: 10.1046/j.1365-8711.2002.05558.x

    Lewis, I., Balogh, M., De Propris, R., et al. 2002, MNRAS, 334, 673, doi: 10.1046/j.1365-8711.2002.05558.x

    arXiv 2002

  45. [46]

    J., & Stanford, S

    Lin, Y.-T., Mohr, J. J., & Stanford, S. A. 2004, ApJ, 610, 745, doi: 10.1086/421714

    doi:10.1086/421714 2004

  46. [47]

    G., Dolag, K., & Aghanim, N

    Malavasi, N., Sorce, J. G., Dolag, K., & Aghanim, N. 2023, A&A, 675, A76, doi: 10.1051/0004-6361/202245777

    work page doi:10.1051/0004-6361/202245777 2023

  47. [48]

    R., Habas, R., Poulain, M., et al

    Marleau, F. R., Habas, R., Poulain, M., et al. 2021, A&A, 654, A105, doi: 10.1051/0004-6361/202141432

    doi:10.1051/0004-6361/202141432 2021

  48. [49]

    R., Cuillandre, J

    Marleau, F. R., Cuillandre, J. C., Cantiello, M., et al. 2025a, A&A, 697, A12, doi: 10.1051/0004-6361/202450799

    doi:10.1051/0004-6361/202450799

  49. [50]

    R., Habas, R., Carollo, D., et al

    Marleau, F. R., Habas, R., Carollo, D., et al. 2025b, arXiv e-prints, arXiv:2503.15335, doi: 10.48550/arXiv.2503.15335

    Pith/arXiv arXiv doi:10.48550/arxiv.2503.15335

  50. [51]

    Mateo, M. L. 1998, ARA&A, 36, 435, doi: 10.1146/annurev.astro.36.1.435

    doi:10.1146/annurev.astro.36.1.435 1998

  51. [52]

    2012, MNRAS, 421, 2384, doi: 10.1111/j.1365-2966.2012.20470.x

    Menci, N., Fiore, F., & Lamastra, A. 2012, MNRAS, 421, 2384, doi: 10.1111/j.1365-2966.2012.20470.x

    arXiv 2012

  52. [53]

    2003, ApJ, 587, 605, doi: 10.1086/368305

    Mobasher, B., Colless, M., Carter, D., et al. 2003, ApJ, 587, 605, doi: 10.1086/368305

    work page doi:10.1086/368305 2003

  53. [54]

    1999, ApJL, 524, L19, doi: 10.1086/312287

    Moore, B., Ghigna, S., Governato, F., et al. 1999, ApJL, 524, L19, doi: 10.1086/312287

    doi:10.1086/312287 1999

  54. [55]

    P., Naab, T., & White, S

    Moster, B. P., Naab, T., & White, S. D. M. 2013, MNRAS, 428, 3121, doi: 10.1093/mnras/sts261 —. 2018, MNRAS, 477, 1822, doi: 10.1093/mnras/sty655

    doi:10.1093/mnras/sts261 2013

  55. [56]

    P., Somerville, R

    Moster, B. P., Somerville, R. S., Maulbetsch, C., et al. 2010, ApJ, 710, 903, doi: 10.1088/0004-637X/710/2/903 M¨ uller, O., Jerjen, H., & Binggeli, B. 2017, A&A, 597, A7, doi: 10.1051/0004-6361/201628921

    doi:10.1088/0004-637x/710/2/903 2010

  56. [57]

    Negri, A., Dalla Vecchia, C., Aguerri, J. A. L., & Bah´ e, Y. 2022, MNRAS, 515, 2121, doi: 10.1093/mnras/stac1481

    doi:10.1093/mnras/stac1481 2022

  57. [58]

    Y., Ho, L

    Peng, C. Y., Ho, L. C., Impey, C. D., & Rix, H.-W. 2010, AJ, 139, 2097, doi: 10.1088/0004-6256/139/6/2097

    doi:10.1088/0004-6256/139/6/2097 2010

  58. [59]

    2018, MNRAS, 473, 4077, doi: 10.1093/mnras/stx2656

    Pillepich, A., Springel, V., Nelson, D., et al. 2018, MNRAS, 473, 4077, doi: 10.1093/mnras/stx2656

    doi:10.1093/mnras/stx2656 2018

  59. [60]

    2025, MNRAS, 542, 170, doi: 10.1093/mnras/staf1186 Rom´ an, J., Trujillo, I., & Montes, M

    Queirolo, G., Seitz, S., Riffeser, A., et al. 2025, MNRAS, 542, 170, doi: 10.1093/mnras/staf1186 Rom´ an, J., Trujillo, I., & Montes, M. 2020, A&A, 644, A42, doi: 10.1051/0004-6361/201936111 Galaxy Luminosity Function of the Coma Cluster25

    work page doi:10.1093/mnras/staf1186 2025

  60. [61]

    2024, Supermassive black hole spin evolution in cosmological simulations, Ludwig-Maximilians-Universit¨ at M¨ unchen

    Sala, L. 2024, Supermassive black hole spin evolution in cosmological simulations, Ludwig-Maximilians-Universit¨ at M¨ unchen. http://nbn-resolving.de/urn:nbn:de:bvb:19-341219

    2024

  61. [62]

    2023, Supermassive black hole spin evolution in cosmological simulations with OpenGadget3

    Sala, L., Valentini, M., Biffi, V., & Dolag, K. 2023, Supermassive black hole spin evolution in cosmological simulations with OpenGadget3. https://arxiv.org/abs/2312.07657

    Pith/arXiv arXiv 2023

  62. [63]

    1976, ApJ, 203, 297, doi: 10.1086/154079

    Schechter, P. 1976, ApJ, 203, 297, doi: 10.1086/154079

    doi:10.1086/154079 1976

  63. [64]

    F., & Finkbeiner, D

    Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103, doi: 10.1088/0004-637X/737/2/103

    doi:10.1088/0004-637x/737/2/103 2011

  64. [65]

    A., Dolag, K., Remus, R

    Seidel, B. A., Dolag, K., Remus, R. S., et al. 2024, arXiv e-prints, arXiv:2412.08708, doi: 10.48550/arXiv.2412.08708

    work page doi:10.48550/arxiv.2412.08708 2024

  65. [66]

    Sorce, J. G. 2018, MNRAS, 478, 5199, doi: 10.1093/mnras/sty1631

    doi:10.1093/mnras/sty1631 2018

  66. [67]

    G., Gottl¨ ober, S., Yepes, G., et al

    Sorce, J. G., Gottl¨ ober, S., Yepes, G., et al. 2015, MNRAS, 455, 2078, doi: 10.1093/mnras/stv2407

    doi:10.1093/mnras/stv2407 2015

  67. [68]

    Speagle, J. S. 2020, MNRAS, 493, 3132, doi: 10.1093/mnras/staa278

    doi:10.1093/mnras/staa278 2020

  68. [69]

    K., Dolag, K., Hirschmann, M., Prieto, M

    Steinborn, L. K., Dolag, K., Hirschmann, M., Prieto, M. A., & Remus, R.-S. 2015, MNRAS, 448, 1504, doi: 10.1093/mnras/stv072

    doi:10.1093/mnras/stv072 2015

  69. [70]

    F., & Rood, H

    Struble, M. F., & Rood, H. J. 1999, ApJS, 125, 35, doi: 10.1086/313274 S´ ersic, J. L. 1968, Atlas de Galaxias Australes The Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, The Astronomical Journal, 156, 123, doi: 10.3847/1538-3881/aabc4f

    doi:10.1086/313274 1999

  70. [71]

    1998, MNRAS, 293, 71, doi: 10.1046/j.1365-8711.1998.01125.x van der Walt, S., Colbert, S

    Trentham, N. 1998, MNRAS, 293, 71, doi: 10.1046/j.1365-8711.1998.01125.x van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, CSE, 13, 22, doi: 10.1109/MCSE.2011.37 van Dokkum, P. G., Abraham, R., Merritt, A., et al. 2015, ApJL, 798, L45, doi: 10.1088/2041-8205/798/2/L45

    arXiv 1998

  71. [72]

    2017, A&A, 608, A142, doi: 10.1051/0004-6361/201730696 —

    Venhola, A., Peletier, R., Laurikainen, E., et al. 2017, A&A, 608, A142, doi: 10.1051/0004-6361/201730696 —. 2019, A&A, 625, A143, doi: 10.1051/0004-6361/201935231

    doi:10.1051/0004-6361/201730696 2017

  72. [73]

    2021, scipy/scipy: SciPy 1.6.3, v1.6.3, Zenodo, doi: 10.5281/zenodo.4718897

    Virtanen, P., Gommers, R., Burovski, E., et al. 2021, scipy/scipy: SciPy 1.6.3, v1.6.3, Zenodo, doi: 10.5281/zenodo.4718897

    work page doi:10.5281/zenodo.4718897 2021

  73. [74]

    1977, ApJ, 216, 214, doi: 10.1086/155464

    Visvanathan, N., & Sandage, A. 1977, ApJ, 216, 214, doi: 10.1086/155464

    doi:10.1086/155464 1977

  74. [75]

    J., Quadri, R

    Williams, R. J., Quadri, R. F., Franx, M., van Dokkum, P., & Labb´ e, I. 2009, ApJ, 691, 1879, doi: 10.1088/0004-637X/691/2/1879

    doi:10.1088/0004-637x/691/2/1879 2009

  75. [76]

    Willmer, C. N. A. 2018, The Astrophysical Journal Supplement Series, 236, 47, doi: 10.3847/1538-4365/aabfdf

    doi:10.3847/1538-4365/aabfdf 2018

  76. [77]

    2017, MNRAS, 470, 1512, doi: 10.1093/mnras/stx1229

    Wittmann, C., Lisker, T., Ambachew Tilahun, L., et al. 2017, MNRAS, 470, 1512, doi: 10.1093/mnras/stx1229

    doi:10.1093/mnras/stx1229 2017

  77. [78]

    H., Robotham, A

    Wright, A. H., Robotham, A. S. G., Driver, S. P., et al. 2017, MNRAS, 470, 283, doi: 10.1093/mnras/stx1149

    doi:10.1093/mnras/stx1149 2017

  78. [79]

    Wright, E. L. 2006, PASP, 118, 1711, doi: 10.1086/510102

    doi:10.1086/510102 2006

  79. [80]

    2016, ApJS, 225, 11, doi: 10.3847/0067-0049/225/1/11

    Yagi, M., Koda, J., Komiyama, Y., & Yamanoi, H. 2016, ApJS, 225, 11, doi: 10.3847/0067-0049/225/1/11

    doi:10.3847/0067-0049/225/1/11 2016

  80. [81]

    2012, AJ, 144, 40, doi: 10.1088/0004-6256/144/2/40

    Yamanoi, H., Komiyama, Y., Yagi, M., et al. 2012, AJ, 144, 40, doi: 10.1088/0004-6256/144/2/40

    work page doi:10.1088/0004-6256/144/2/40 2012

Showing first 80 references.

This paper was first reviewed by deepseek-v4-flash on August 4, 2026.