Pith. sign in

REVIEW 3 major objections 5 minor 118 references

Mining for Protoclusters at $z\sim4$ from Photometric Datasets with Deep Learning

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read A point-cloud network trained on mock galaxies finds 121 protocluster candidates at $z\approx4$ using photometry alone.

desk verdict Promising new ML method for z~4 protocluster search, but the headline performance claims are internally inconsistent and rest on a non-independent simulation evaluation. read the letter →

arxiv 2411.11956 v1 pith:FLXMEP6L submitted 2024-11-18 astro-ph.GA astro-ph.IM

Yoshihiro Takeda , Nobunari Kashikawa , Kei Ito , Jun Toshikawa , Rieko Momose , Kent Fujiwara , Yongming Liang , Rikako Ishimoto
show 4 more authors
Takehiro Yoshioka Junya Arita Mariko Kubo Hisakazu Uchiyama
This is my paper · ORCID
classification astro-ph.GAastro-ph.IM
keywords protoclustersz≈4deeplearningpointcloudsphotometricredshiftsLyman-breakgalaxiesHSC-SSPgalaxyquenching
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

Protoclusters at $z\approx4$—the overdense regions that evolve into today's galaxy clusters—are rare and hard to identify because photometric redshifts smear member galaxies along the line of sight. This paper claims that a deep-learning model called PCFNet can spot them using only optical broad-band photometry by treating each galaxy as a point in a cloud and feeding the network the sky distribution, $i$-band magnitude, $(g-i)$ color, and a full redshift probability distribution. On mock data from the PCcone light cone, PCFNet recovers about five times more protocluster member candidates than conventional surface-density methods at comparable purity (recall $=7.5\pm0.2\%$, precision $=44\pm1\%$), and it preferentially finds the less-massive progenitors ($M_\mathrm{halo}^{z=0}=10^{14}$–$10^{14.5}\,M_\odot$) that older searches miss. Applied to $\sim17\,\mathrm{deg}^2$ of HSC-SSP Deep/UltraDeep imaging, it returns 121 protocluster candidates at $z\approx4$ whose members are brighter in rest-UV than field galaxies. If the claim holds, statistically meaningful protocluster samples at $z\approx4$ become accessible from imaging alone, without spectroscopy.

What carries the argument

PCFNet is a point-cloud classifier: it takes the set of dropout-selected galaxies within a $5'$ radius of a target galaxy as an unordered point cloud, expands each point into 16 features, and processes local neighborhoods with EdgeConv 'skipDG' blocks before a global max-pooling layer and a classifier output a membership probability. The line-of-sight information comes from a Mixture Density Network (MDN) that turns $g,r,i$ magnitudes into a three-Gaussian redshift probability density, so the network can exploit the full multi-peaked redshift uncertainty rather than a single photometric redshift. The grouping stage uses persistent homology peak detection on the significance map to assemble galaxies into protocluster candidates. The training and evaluation data are dropout-selected galaxies from PCcone, a Millennium Simulation plus L-GALAXIES light cone with HSC-SSP-matched depths; the protocluster labels come from merger-tree-defined core galaxies and members within $5.5\,\mathrm{cMpc}$.

What would settle it

A spectroscopic redshift survey of the 121 candidates' members is the direct falsifier: if the fraction of galaxies sharing the core's redshift is statistically indistinguishable from the field, PCFNet's membership probability is not tracing real protocluster structure. A cheaper check is comparing the angular correlation function of $g$-dropouts in PCcone and HSC-SSP on $5'$ scales, since a mismatch would invalidate the training distribution.

Watch

Extended reading notes

Core claim

The central claim is that protocluster membership at $z\approx4$ can be predicted per galaxy from photometric data alone, and that a model trained on a realistic mock light cone transfers to real survey data. PCFNet assigns each galaxy a membership probability; thresholding at $2.5\sigma$ and grouping the candidates with persistent homology yields per-protocluster completeness $10.9\pm0.8\%$ and purity $69\pm4\%$ in simulation, while a conventional surface-density search reaches comparable purity only at much lower completeness. The same pipeline applied to HSC-SSP Deep/UltraDeep data finds 121 protocluster candidates over $\sim17.6\,\mathrm{deg}^2$, and reaches protoclusters that will become only $10^{14}$–$10^{14.5}\,M_\odot$ halos by $z=0$, not just Coma-like superclusters. On the observed candidates, member galaxies show a rest-UV bright-end excess after correcting for the model's brightness-dependent selection bias, which the paper interprets as early, enhanced star formation in protocluster environments.

Load-bearing premise

PCcone, the semi-analytic Millennium-Simulation light cone, reproduces the real $z\approx4$ dropout galaxy population closely enough that a network trained on it can recognize protoclusters in HSC-SSP photometry; the paper acknowledges the semi-analytic model does not perfectly match the actual universe.

Editorial extensions

If this is right

  • The method removes spectroscopy as a prerequisite for building a $z\approx4$ protocluster sample; inference runs on a single GPU in minutes to hours, so the same network can be applied to LSST, Euclid, and Roman data.
  • The 121 HSC-SSP candidates give a detection density of $6.9\,\mathrm{deg}^{-2}$, about 3.8 times higher than the earlier surface-density search, yielding a larger and less massive-biased sample for follow-up.
  • The selection reaches protoclusters destined to become $M_\mathrm{halo}^{z=0}\sim10^{14}\,M_\odot$ groups, where supernova feedback and galactic winds may dominate, so environmental-effect studies can extend below the Coma-like mass scale.
  • In the simulation, the fraction of protocluster cores associated with quiescent satellites rises with both the $z\approx4$ halo mass and the accumulated $z=0$ halo mass, giving a quantitative prediction for future observations.
  • PCFNet is not tied to one dropout color: retraining on other dropout selections should extend the search to $z\approx2$–$8$, as the paper notes.

Reading between the lines

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

  • If the mock-to-real transfer holds, running PCFNet on the wider HSC-SSP Wide layer or LSST should yield thousands of $z\approx4$ protocluster candidates, turning the current small-sample studies into population statistics.
  • The six-fold recall gap between bright ($i<24.5$) and faint members implies a strong selection function on any luminosity or mass measurement from a learned photometric finder; the simulation-based correction used in the paper is a template future searches will need.
  • A sharper physical test would be to train the same network on a hydrodynamic light-cone simulation and compare the recovered candidates: overlap of the 121 candidates would show the detections trace real overdensity rather than the semi-analytic model's particular galaxy-halo assignment.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents PCFNet, a point-cloud deep-learning classifier that assigns each g-dropout galaxy a probability of being a member of a z~4 protocluster, using sky coordinates, i-band magnitude, (g-i) color, and an MDN-derived redshift PDF for neighbors within 5'. PCFNet is trained on PCcone, a Millennium Simulation plus L-GALAXIES light cone, with labels defined from merger-tree-derived protocluster members (halo mass > 10^14 Msun at z=0). The authors report per-galaxy precision/recall values of 7.5% and 44% (with the labels reversed in the abstract relative to Section 4.1), a PR AUC of about 0.28, protocluster-level completeness/purity of 10.9%/69%, and a factor 7 +/- 2 increase in detected lower-mass descendant halos compared with the 2DBM baseline. Applied to about 17.6 deg^2 of HSC-SSP Deep/UltraDeep, the method yields 121 unconfirmed protocluster candidates, 63% of which match Toshikawa et al. (2024) within 8'. The paper also discusses rest-UV brightness and quenching trends in both simulation and observation.

Significance. If the headline numbers survive independent evaluation, this would be a useful advance: PCFNet is a flexible, publicly coded method that treats protoclusters as point clouds and uses full photo-z PDFs rather than a single redshift, and it is plausibly applicable to other dropout samples and to Euclid, LSST, and Roman. The baselines (2DBM, 3DBM) are reasonable, the labels are defined externally from merger trees rather than by the network, and the paper is transparent about the semi-analytic model's limitations and the unconfirmed status of its observed candidates. The 121-candidate catalog and the low-mass comparison (23 versus 1 at M_halo^z=0 = 10^14-10^14.5 Msun) would be of broad interest to protocluster searches. However, the evaluation's non-independence from the training set and the internal inconsistency in the headline precision/recall numbers currently prevent the central claim from being accepted at face value.

major comments (3)
  1. [Abstract and Section 4.1] The headline numbers are internally inconsistent. The abstract states 'recall = 7.5 +/- 0.2%, precision = 44 +/- 1%', while Section 4.1 states 'the precision and recall of PCFNet are 7.5 +/- 0.2% and 44 +/- 1%, respectively'; one of these has the labels swapped. The same section reports 2DBM precision/recall of 1.5 +/- 0.1% and 38 +/- 2%, then says PCFNet's recall at the equivalent precision is 'approximately 11 times the recall of the 2DBM'; numerically 16/1.5 ~ 10.7, so the comparison appears to be with 2DBM's precision, not its recall. The abstract's 'five times more protocluster member candidates' is also not supported by the quoted recall values (44% versus 38% is a factor of 1.16); it may refer to precision (7.5/1.5 = 5) or to the halo-mass comparison in Section 6.1, but the sentence as written is misleading. These are the central quantitative claims and must be corrected and made mutually consistent.
  2. [Section 2.1, Section 4.1, Figure 6, Figure 11] The evaluation is not independent of the training data. Section 2.1 discloses that 11,264 of 82,245 Deep-layer and 10,345 of 77,986 UltraDeep-layer evaluation galaxies originate from the same galaxies used in training, and all light cones are drawn from the same Millennium Simulation box. The authors argue that the leakage risk is 'slight' because coordinates and resampled magnitudes differ, but the underlying dark-matter density field, the galaxy IDs, the halo memberships, and therefore the spatial configurations of protocluster members are shared. The PR curve in Figure 6, the precision/recall values in Section 4.1, and the descendant-mass comparison in Figure 11 are all computed on this partly in-sample evaluation, so the claimed gains over 2DBM could be inflated by memorization of specific overdensities. Please provide a clean evaluation, for example by training on light cones from one simulation box and testing on light cones from a different box or from a disjoint spatial region with no shared galaxy IDs, or by reporting all metrics restricted to the non-shared evaluation galaxies (70,981 Deep and 67,641 UltraDeep).
  3. [Section 5.2 and Section 2.2] The transfer of PCFNet to real data is not yet demonstrated. The network is trained purely on PCcone, and Section 2 states that the semi-analytic model 'does not perfectly reproduce the actual universe'; Figure 1 also shows a bright-end excess in the HSC-SSP magnitude distributions relative to PCcone. The only external checks in Section 5.2 are spatial matching to Toshikawa et al. (2024) (63% within 8') and the authors' own statement that the candidates are unconfirmed. To support the abstract's claim that PCFNet can be applied to future surveys, the paper should validate the model on spectroscopically confirmed z~4 protoclusters or on an independent overdensity-selected sample, or otherwise clearly frame the 121 candidates as a predicted catalog requiring follow-up rather than as a validation of the method.
minor comments (5)
  1. [Section 4.1 footnote] The footnote says 'Precision represents how completely selected, and Recall does how purely selected', which is the reverse of the standard definitions; please correct it, as it contributes to the headline-number confusion.
  2. [Section 2.1 and Section 2.2] There are typos: 'Simualtion Data' should be 'Simulation Data' and 'Outliners' should be 'Outliers'.
  3. [Section 5.2 and Table 5] The Deep and UltraDeep fields overlap on the sky (for example, COSMOS is listed as both Deep and UltraDeep), and the table entries sum to 121; please state explicitly whether the 121 candidates are merged across layers and whether 'unique' excludes objects detected in both layers.
  4. [Equation (13) and Section 5.2] The normalization of sigma_prob uses the mean and standard deviation of the evaluation data, which are then applied to HSC observations; please justify this choice given the known depth and number-density differences between Deep and UltraDeep, or explain how the domain shift is handled.
  5. [Section 4.2] The sentence introducing the grouping threshold as 'the minimum number of protocluster members, i.e., gamma N_th in case of lowest completeness' is unclear and should be rewritten for precision.

Circularity Check

1 steps flagged · score 4.0 of 10

Evaluation light cones share 13.7% of their galaxies with the training light cones, so the headline five-times-recall and low-mass-progenitor gains are measured on a non-independent evaluation set.

  1. fitted input called prediction [Section 2.1, 'Simualtion Data – PCcone', data-split paragraph]
    "We note that the same galaxies may appear in training and validation/evaluation data because light cones are made from the same simulation boxes on Millennium Simulation. However, the input information (e.g., coordinates or magnitudes; see Sec 3.2) varies because the direction of the line of sight and the resampled magnitudes differ, and the possibility of critical overtraining or leakage is slight."

    The headline performance claims (Sec. 4.1: recall = 7.5 +- 0.2%, precision = 44 +- 1%, five-fold and eleven-fold recall gains over 2DBM; Sec. 6.1: 7 +- 2 times more M_halo^z=0 < 10^15 Msun progenitors, and 23 versus 1 low-mass protoclusters) are computed on the four evaluation light cones. A large fraction of the evaluation galaxies (11264/82245 Deep, 10345/77986 UltraDeep) are the same simulated galaxies that appear in the 15 training light cones, so the network has already seen their halo environment and member/nonmember labels during training; only coordinates and resampled magnitudes differ.

full rationale

The derivation chain is not circular in the pure self-definitional sense: protocluster labels are independently defined from merger trees and descendant halo masses (Sec. 2.3), the 2DBM/3DBM baselines are independent methods, and the MDN redshift estimator is compared with EAZY on simulated truth. The dominant circularity risk is the training/evaluation overlap within PCcone. Because the four evaluation light cones are cut from the same Millennium Simulation box as the 15 training cones and share 11,264 (Deep) and 10,345 (UltraDeep) individual galaxies, the simulation-side precision/recall, PR AUC, and descendant-halo-mass distributions are not fully external validations. This directly undercuts the abstract's claim that PCFNet 'detects five times more protocluster member candidates' and the Sec. 6.1 claim that it detects 7 +- 2 times more low-mass progenitors. The observational application to HSC-SSP is an independent input, but it lacks spectroscopic ground truth; the only cross-check is spatial matching to Toshikawa et al. (2024), which is a consistency check rather than an external performance benchmark. Other weaknesses of the paper, such as the semi-analytic model not perfectly reproducing the real universe and the absence of spectroscopic confirmation for the 121 candidates, are correctness risks rather than circularity. Overall, the central simulation-based performance claim is partially circular because the evaluation set is not independent of the training set by construction, but the labels and baseline comparisons retain independent content, so the score is moderate rather than severe.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

Most of the load-bearing content is supplied by PCcone and by the merger-tree definition of protoclusters. The free parameters are threshold choices and neural network hyperparameters, not physical constants. No new physical entities are introduced. The main unverified input is the fidelity of the semi-analytic mock to the real z~4 universe, on which both the simulation metrics and the interpretation of the 121 candidates rest.

free parameters (6)
  • Protocluster member threshold N_th = 5
    A protocluster is defined as a region with corrected member count greater than 5 in Section 2.3; the authors state results are insensitive to 3 through 7, but the chosen value sets all training labels.
  • Detection significance threshold sigma_th = 2.5
    Chosen in Section 4.2 to balance completeness and purity; the 121 observational candidates and all catalog numbers depend on this threshold.
  • MDN mixture components K = 3
    Number of Gaussian components in the redshift probability density function in Section 3.2.1; the authors report K=5 gives no change, but K=3 is used throughout.
  • PCFNet training hyperparameters = lr=0.001, batch size 512, dropout p=0.3
    Neural network optimization choices selected by the authors in Section 3.2.3; they are not derived from data but affect the learned features and the reported performance.
  • Protocluster search aperture = r=1.8 arcmin on a 1 arcmin grid
    Aperture used for surface density peak detection and grouping in Section 3.2.4; the peak-finding and membership assignment are sensitive to this choice.
  • Mask correction cubic function = not given numerically
    A cubic function is fit to the ratio of member probabilities with and without masked regions in Section 5.2, then applied to observational data to correct for masked areas.
assumptions (6)
  • domain assumption PCcone, built from the Millennium Simulation and L-GALAXIES, accurately reproduces the z~4 g-dropout galaxy population and HSC-SSP photometry.
    The entire training set is generated from PCcone in Section 2; the paper states it is the best available light-cone model for this purpose while acknowledging it does not perfectly reproduce the actual universe.
  • domain assumption Merger-tree main progenitor identification correctly defines protocluster cores and member galaxies at z=4.
    Section 2.3 labels galaxies by tracing merger trees through the Millennium Simulation; if the tree tracing or the chosen halo mass threshold is wrong, the training labels are wrong.
  • domain assumption The g-dropout selection of Eq. 1 isolates a representative z~4 galaxy population.
    Section 2.2 uses the Lyman-break criterion and keeps 2.9% low-redshift contaminants; the paper relies on this contamination being small and well-modeled.
  • domain assumption The MDN redshift probability density functions from g, r, i magnitudes contain enough line-of-sight information for PCFNet to separate protocluster members from foreground and background galaxies.
    Section 3.2.1 estimates line-of-sight distances from three bands; the reported RMS(δz)=0.045 is small in redshift but still corresponds to large comoving distances, so the point-cloud input is uncertain.
  • domain assumption HSC-SSP photometric calibration, flags, and masking corrections preserve the feature distribution seen in the simulation.
    Section 5.1 applies many flags and magnitude offsets to match the mocks; any residual mismatch between observed and simulated photometry changes the network's predicted probabilities.
  • domain assumption The overlap of galaxies between training and evaluation light cones has a negligible effect on measured performance.
    Section 2.1 asserts the leakage is slight because input features vary, but the paper does not demonstrate this with a fully disjoint test set; about 14% of evaluation galaxies originate from the same simulated galaxies as training.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Mining for Protoclusters at $z\sim4$ from Photometric Datasets with Deep Learning." pith.science (2026). https://pith.science/paper/FLXMEP6L

@misc{pith2026241111956,
  author       = {Pith},
  title        = {Pith review of: Mining for Protoclusters at $z\sim4$ from Photometric Datasets with Deep Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FLXMEP6L}},
  note         = {Machine review of arXiv:2411.11956}
}
abstract

Protoclusters are high-$z$ overdense regions that will evolve into clusters of galaxies by $z=0$, making them ideal for studying galaxy evolution expected to be accelerated by environmental effects. However, it has been challenging to identify protoclusters beyond $z=3$ only by photometry due to large redshift uncertainties, hindering statistical study. To tackle the issue, we develop a new deep-learning-based protocluster detection model, PCFNet, which considers a protocluster as a point cloud. To detect protoclusters at $z\sim4$ using only optical broad-band photometry, we train and evaluate PCFNet with mock $g$-dropout galaxies based on the N-body simulation with the semi-analytic model. We use the sky distribution, $i$-band magnitude, $(g-i)$ color, and the redshift probability density function surrounding a target galaxy on the sky. PCFNet achieves to detect five times more protocluster member candidates while maintaining high purity (recall $=7.5\pm0.2$%, precision $=44\pm1$%) than conventional methods. Moreover, PCFNet is able to detect more progenitors ($M_\mathrm{halo}^{z=0}=10^{14-14.5}\,M_\odot$) that are less massive than supermassive clusters like the Coma cluster. We apply PCFNet to the observational photometric dataset of the HSC-SSP Deep/UltraDeep layer ($\sim17\mathrm{\,deg^2}$) and detect $121$ protocluster candidates at $z\sim4$. We find the rest-UV luminosities of our protocluster member candidates are brighter than those of field galaxies, which is consistent with previous studies. Additionally, the quenching of satellite galaxies depends on both the core galaxy's halo mass at $z\sim4$ and accumulated mass until $z=0$ in the simulation. PCFNet is very flexible and can find protoclusters at other redshifts or in future extensive surveys by Euclid, LSST, and Roman.

Figures

Figures reproduced from arXiv: 2411.11956 by the authors.

Figure 1
Figure 1. The g, r, i-bands magnitude distribution of g-dropout galaxies at z ∼ 4 in the simulation (PCcone; light blue) and the observational (HSC-SSP; yellow) data. The circle and square represent the Deep and UltraDeep layers, respectively. The error bars of each point indicate Poisson errors. 3.0 3.5 4.0 4.5 Redshift 0 2 4 N u m b e r D e n sit y [c M p c ¡ 3 ] £10 4 0.0 0.25 0.5 0.75 1.0 Normalized Density [PITH_FULL_IM… view at source ↗
Figure 2
Figure 2. Number density as a function of redshift for g-dropout galaxies in the simulation data. The right vertical axis represents the normalized density. We use a point cloud-based deep learning model to deal with the galaxy distribution. A point cloud is a set of points represented by vectors, and the vectors are generally de￾scribed in a three-dimensional space with additional informa￾tion. In the field of information sc… view at source ↗
Figure 4
Figure 4. The MDN predictions zbest vs. true distances ztrue in the simulation data. The color of each point represents the number of galaxies. The g-dropout galaxies in the HSC-SSP UltraDeep layer with known spectroscopic redshifts are marked with red stars. in the HSC-SSP Deep layer4 retrieved from HSC-SSP specz table. This table is composed of zCOSMOS DR3 (Lilly et al. 2009), UDSz (Bradshaw et al. 2013; McLure et al. 2013)… view at source ↗
Figures from the paper (12 more)
Figure 5
Figure 5. Figure 5: The architecture of PCFNet. The round squares represent each layer, and the numbers at the bottom show the input and output dimensions of the features. The squares represent the data at each stage, and the dimensions of the matrix are written inside. Prior is a single￾…
Figure 6
Figure 6. Figure 6: PR curve for protocluster member detection at z ∼ 4. The horizontal and vertical axes represent the recall and precision, respectively. The solid orange line represents the PR curve of PCFNet, the blue line represents that of the surface number density￾based model (2DB…
Figure 7
Figure 7. Figure 7: Detection performance per protoclusters at z ∼ 4. The horizontal and vertical axes represent completeness and purity, re￾spectively. The solid lines connecting the dots show the transition of the protocluster detection performance when the threshold for detecting proto…
Figure 8
Figure 8. Figure 8: The ratio of the protocluster member probabilities of the mock data with (pmask) and without (p) masked regions. The hori￾zontal axis represents the percentage of the mask within the field of view (within a 5 ′ radius), and the vertical axis represents the ratio to the…
Figure 9
Figure 9. Figure 9: Sky distribution of the predicted probabilities of protocluster member galaxies and protocluster candidates at z ∼ 4 in the Deep and UltraDeep layers. Each point represents a galaxy, and the color indicates the probability of being a protocluster member galaxy predicte…
Figure 10
Figure 10. Figure 10: The distributions of the number (left) and the maximal significance (right) of members of protocluster candidates at z ∼ 4 in HSC-SSP Deep and UltraDeep layers. 13.0 13.5 14.0 14.5 15.0 15.5 16.0 log (Mz = 0 halo =M ¯ ) 0 10 20 30 Protocluster Count PCFNet 2DBM Actual…
Figure 11
Figure 11. Figure 11: Distribution of halo mass of protoclusters at z = 0 in the simulation data. The solid lines represent the distribution of maximum halo masses at z = 0 of the protocluster candidates de￾tected by each model, with orange and blue histograms representing PCFNet and 2DBM,…
Figure 12
Figure 12. Figure 12: (left) Distribution of i-band magnitude of protocluster member galaxies that were detected by PCFNet (green; Detected), non￾member field galaxies (blue; Field), and actual protocluster member galaxies (pink; Actual) at z ∼ 4 in PCcone. (right) Similar to the left, but…
Figure 13
Figure 13. Figure 13: Comparison of PR curves between different brightness (left: i ≤ 24.5, right: i > 24.5) in PCcone. The orange, blue, and yellow lines represent the PR curve of PCFNet, the surface number density-based model (2DBM), and the integrated probability density-based model (3D…
Figure 14
Figure 14. Figure 14: Distribution of the stellar mass, SFR, and sSFR of protocluster members at z ∼ 4 in the simulation data. The legend of colors is the same as the left panel of [PITH_FULL_IMAGE:figures/full_fig_p016_14.png]
Figure 15
Figure 15. Figure 15: CWQ fraction as functions of halo masses at z = 0, 4 in the simulation data. The error bars indicate Poisson error in each bin. Note that the fraction of core galaxies associated with QGs relative to the halo mass at z = 0 inclines more gradually with error than the o…
Figure 16
Figure 16. Figure 16: Two-dimensional distribution of halo masses at z = 0 and z = 4 of CWQs in the simulation data. The color of the background represents the CWQ fraction. The white dots represent the CWQs. cone, and if restricted core galaxies whose halo masses at z ∼ 4 are more massive…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

118 extracted references · 6 canonical work pages

  1. [1]

    2020, ApJS, 249, 3, doi: 10.3847/1538-4365/ab929e

    Ahumada, R., Allende Prieto, C., Almeida, A., et al. 2020, ApJS, 249, 3, doi: 10.3847/1538-4365/ab929e

    doi:10.3847/1538-4365/ab929e 2020

  2. [2]

    2018, PASJ, 70, S8, doi: 10.1093/pasj/psx081

    Aihara, H., Armstrong, R., Bickerton, S., et al. 2018, PASJ, 70, S8, doi: 10.1093/pasj/psx081

    doi:10.1093/pasj/psx081 2018

  3. [3]

    2022, PASJ, 74, 247, doi: 10.1093/pasj/psab122

    Aihara, H., AlSayyad, Y ., Ando, M., et al. 2022, PASJ, 74, 247, doi: 10.1093/pasj/psab122

    doi:10.1093/pasj/psab122 2022

  4. [4]

    2022, arXiv e-prints, arXiv:2211.12346, doi: 10.48550/arXiv.2211.12346

    Anagnostidis, S., Thomsen, A., Kacprzak, T., et al. 2022, arXiv e-prints, arXiv:2211.12346, doi: 10.48550/arXiv.2211.12346

    arXiv doi:10.48550/arxiv.2211.12346 2022

  5. [5]

    2021, A&A, 650, A90, doi: 10.1051/0004-6361/202039675

    Ansari, Z., Agnello, A., & Gall, C. 2021, A&A, 650, A90, doi: 10.1051/0004-6361/202039675

    work page doi:10.1051/0004-6361/202039675 2021

  6. [6]

    C., Sodré, Laerte, J., Overzier, R

    Araya-Araya, P., Vicentin, M. C., Sodré, Laerte, J., Overzier, R. A., & Cuevas, H. 2021, MNRAS, 504, 5054, doi: 10.1093/mnras/stab1133 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/15...

    doi:10.1093/mnras/stab1133 2021

  7. [7]

    P., Jankowiak, M., et al

    Bingham, E., Chen, J. P., Jankowiak, M., et al. 2019, J. Mach. Learn. Res., 20, 28:1. http://jmlr.org/papers/v20/18-403.html

    2019

  8. [8]

    J., Almaini, O., Hartley, W

    Bradshaw, E. J., Almaini, O., Hartley, W. G., et al. 2013, MNRAS, 433, 194, doi: 10.1093/mnras/stt715

    doi:10.1093/mnras/stt715 2013

Show all 118 references
  1. [9]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503, doi: 10.1086/591786

    2008 doi

  2. [10]

    2021, MNRAS, 502, 4558, doi: 10.1093/mnras/staa4037

    Calvi, R., Dannerbauer, H., Arrabal Haro, P., et al. 2021, MNRAS, 502, 4558, doi: 10.1093/mnras/staa4037

    2021 doi

  3. [11]

    L., Hu, E

    Capak, P., Cowie, L. L., Hu, E. M., et al. 2004, AJ, 127, 180, doi: 10.1086/380611

    2004 doi

  4. [12]

    L., Riechers, D., Scoville, N

    Capak, P. L., Riechers, D., Scoville, N. Z., et al. 2011, Nature, 470, 233, doi: 10.1038/nature09681

    2011 doi

  5. [13]

    C., McLeod, D

    Carnall, A. C., McLeod, D. J., McLure, R. J., et al. 2023, MNRAS, 520, 3974, doi: 10.1093/mnras/stad369

    2023 doi

  6. [14]

    2022, A&A, 662, A115, doi: 10.1051/0004-6361/202243348

    Castellano, M., Pentericci, L., Cupani, G., et al. 2022, A&A, 662, A115, doi: 10.1051/0004-6361/202243348

    2022 doi

  7. [15]

    2023, MNRAS, doi: 10.1093/mnras/stad1868

    Chen, X., Zhu, F., Gaines, S., & Padmanabhan, N. 2023, MNRAS, doi: 10.1093/mnras/stad1868

    2023 doi

  8. [16]

    2013, ApJ, 779, 127, doi: 10.1088/0004-637X/779/2/127

    Chiang, Y .-K., Overzier, R., & Gebhardt, K. 2013, ApJ, 779, 127, doi: 10.1088/0004-637X/779/2/127

    2013 doi

  9. [17]

    A., Gebhardt, K., & Henriques, B

    Chiang, Y .-K., Overzier, R. A., Gebhardt, K., & Henriques, B. 2017, ApJL, 844, L23, doi: 10.3847/2041-8213/aa7e7b

    2017 doi

  10. [18]

    L., Blanton, M

    Coil, A. L., Blanton, M. R., Burles, S. M., et al. 2011, ApJ, 741, 8, doi: 10.1088/0004-637X/741/1/8

    2011 doi

  11. [19]

    A., Jackson, C., et al

    Colless, M., Peterson, B. A., Jackson, C., et al. 2003, arXiv e-prints, astro, doi: 10.48550/arXiv.astro-ph/0306581

    2003 doi arXiv

  12. [20]

    Conroy, C., & Gunn, J. E. 2010, ApJ, 712, 833, doi: 10.1088/0004-637X/712/2/833

    2010 doi

  13. [21]

    E., & White, M

    Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486, doi: 10.1088/0004-637X/699/1/486

    2009 doi

  14. [22]

    A., Hatch, N

    Cooke, E. A., Hatch, N. A., Rettura, A., et al. 2015, MNRAS, 452, 2318, doi: 10.1093/mnras/stv1413

    2015 doi

  15. [23]

    J., Moustakas, J., Blanton, M

    Cool, R. J., Moustakas, J., Blanton, M. R., et al. 2013, ApJ, 767, 118, doi: 10.1088/0004-637X/767/2/118

    2013 doi

  16. [24]

    C., Aird, J

    Cooper, M. C., Aird, J. A., Coil, A. L., et al. 2011, ApJS, 193, 14, doi: 10.1088/0067-0049/193/1/14

    2011 doi

  17. [26]

    M., Newman, J., et al

    Davis, M., Faber, S. M., Newman, J., et al. 2003, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    2003

  18. [27]

    4834, Discoveries and Research Prospects from 6- to 10-Meter-Class Telescopes II, ed

    Series, V ol. 4834, Discoveries and Research Prospects from 6- to 10-Meter-Class Telescopes II, ed. P. Guhathakurta, 161–172, doi: 10.1117/12.457897 De Lucia, G., & Blaizot, J. 2007, MNRAS, 375, 2, doi: 10.1111/j.1365-2966.2006.11287.x Di Matteo, T., Springel, V ., & Hernquist...

    2007

  19. [28]

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

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

    1980 doi

  20. [30]

    Endsley, R., & Stark, D. P. 2022, MNRAS, 511, 6042, doi: 10.1093/mnras/stac524

    2022 doi

  21. [32]

    2008, ApJS, 176, 1, doi: 10.1086/527321 Ganeshaiah Veena, P., Lilow, R., & Nusser, A

    Furusawa, H., Kosugi, G., Akiyama, M., et al. 2008, ApJS, 176, 1, doi: 10.1086/527321 Ganeshaiah Veena, P., Lilow, R., & Nusser, A. 2023, MNRAS, 522, 5291, doi: 10.1093/mnras/stad1222

    2008 doi

  22. [33]

    2014, A&A, 562, A23, doi: 10.1051/0004-6361/201322790

    Garilli, B., Guzzo, L., Scodeggio, M., et al. 2014, A&A, 562, A23, doi: 10.1051/0004-6361/201322790

    2014 doi

  23. [34]

    2012, in 2012 IEEE conference on computer vision and pattern recognition, IEEE, 3354–3361

    Geiger, A., Lenz, P., & Urtasun, R. 2012, in 2012 IEEE conference on computer vision and pattern recognition, IEEE, 3354–3361

    2012

  24. [35]

    Glorot, X., Bordes, A., & Bengio, Y . 2011, in Proceedings of the fourteenth international conference on artificial intelligence and statistics, JMLR Workshop and Conference Proceedings, 315–323 Gottlöber, S., Klypin, A., & Kravtsov, A. V . 2001, ApJ, 546, 223, doi: 10.1086/318248

    2011 doi

  25. [36]

    2019, ApJ, 883, 142, doi: 10.3847/1538-4357/ab2cd5

    Harikane, Y ., Ouchi, M., Ono, Y ., et al. 2019, ApJ, 883, 142, doi: 10.3847/1538-4357/ab2cd5

    2019 doi

  26. [37]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    2020 doi

  27. [38]

    2023, ApJL, 955, L2, doi: 10.3847/2041-8213/acf57c

    Hashimoto, T., Álvarez-Márquez, J., Fudamoto, Y ., et al. 2023, ApJL, 955, L2, doi: 10.3847/2041-8213/acf57c

    2023 doi

  28. [39]

    2018, ApJ, 858, 77, doi: 10.3847/1538-4357/aabacf

    Hasinger, G., Capak, P., Salvato, M., et al. 2018, ApJ, 858, 77, doi: 10.3847/1538-4357/aabacf

    2018 doi

  29. [40]

    A., Kurk, J

    Hatch, N. A., Kurk, J. D., Pentericci, L., et al. 2011, MNRAS, 415, 2993, doi: 10.1111/j.1365-2966.2011.18735.x

    2011

  30. [41]

    M., Sun, F., Woodrum, C., et al

    Helton, J. M., Sun, F., Woodrum, C., et al. 2024, ApJ, 962, 124, doi: 10.3847/1538-4357/ad0da7

    2024 doi

  31. [42]

    Henriques, B. M. B., White, S. D. M., Thomas, P. A., et al. 2015, MNRAS, 451, 2663, doi: 10.1093/mnras/stv705

    2015 doi

  32. [43]

    F., Cox, T

    Hopkins, P. F., Cox, T. J., Kereš, D., & Hernquist, L. 2008, ApJS, 175, 390, doi: 10.1086/524363

    2008 doi

  33. [44]

    2021, Nature Astronomy, 5, 485, doi: 10.1038/s41550-020-01291-y

    Hu, W., Wang, J., Infante, L., et al. 2021, Nature Astronomy, 5, 485, doi: 10.1038/s41550-020-01291-y

    2021 doi

  34. [45]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    2007 doi

  35. [46]

    2022, MNRAS, 515, 4065, doi: 10.1093/mnras/stac2055

    Inoue, S., Si, X., Okamoto, T., & Nishigaki, M. 2022, MNRAS, 515, 4065, doi: 10.1093/mnras/stac2055

    2022 doi

  36. [47]

    2016, ApJ, 822, 5, doi: 10.3847/0004-637X/822/1/5

    Ishigaki, M., Ouchi, M., & Harikane, Y . 2016, ApJ, 822, 5, doi: 10.3847/0004-637X/822/1/5

    2016 doi

  37. [48]

    2020, ApJ, 899, 5, doi: 10.3847/1538-4357/aba269

    Ito, K., Kashikawa, N., Toshikawa, J., et al. 2020, ApJ, 899, 5, doi: 10.3847/1538-4357/aba269

    2020 doi

  38. [49]

    2021, ApJ, 916, 35, doi: 10.3847/1538-4357/abfc50

    Ito, K., Kashikawa, N., Tanaka, M., et al. 2021, ApJ, 916, 35, doi: 10.3847/1538-4357/abfc50

    2021 doi

  39. [50]

    2023, ApJL, 945, L9, doi: 10.3847/2041-8213/acb49b Ivezi´c, Ž., Kahn, S

    Ito, K., Tanaka, M., Valentino, F., et al. 2023, ApJL, 945, L9, doi: 10.3847/2041-8213/acb49b Ivezi´c, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c

    2023 doi

  40. [51]

    H., Saunders, W., Colless, M., et al

    Jones, D. H., Saunders, W., Colless, M., et al. 2004, MNRAS, 355, 747, doi: 10.1111/j.1365-2966.2004.08353.x

    2004

  41. [52]

    H., Read, M

    Jones, D. H., Read, M. A., Saunders, W., et al. 2009, MNRAS, 399, 683, doi: 10.1111/j.1365-2966.2009.15338.x

    2009

  42. [53]

    S., Daddi, E., Bournaud, F., et al

    Kalita, B. S., Daddi, E., Bournaud, F., et al. 2022, A&A, 666, A44, doi: 10.1051/0004-6361/202243100 Kereš, D., Katz, N., Weinberg, D. H., & Davé, R. 2005, MNRAS, 363, 2, doi: 10.1111/j.1365-2966.2005.09451.x

    2022

  43. [54]

    P., & Ba, J

    Kingma, D. P., & Ba, J. 2014, arXiv e-prints, arXiv:1412.6980, doi: 10.48550/arXiv.1412.6980

    2014 doi arXiv

  44. [55]

    D., Faber, S

    Kocevski, D. D., Faber, S. M., Mozena, M., et al. 2012, ApJ, 744, 148, doi: 10.1088/0004-637X/744/2/148

    2012 doi

  45. [56]

    2012, ApJL, 746, L22, doi: 10.1088/2041-8205/746/2/L22

    Koss, M., Mushotzky, R., Treister, E., et al. 2012, ApJL, 746, L22, doi: 10.1088/2041-8205/746/2/L22

    2012 doi

  46. [57]

    2013, MNRAS, 428, 1551, doi: 10.1093/mnras/sts133

    Koyama, Y ., Kodama, T., Tadaki, K.-i., et al. 2013, MNRAS, 428, 1551, doi: 10.1093/mnras/sts133

    2013 doi

  47. [58]

    2018, ApJ, 867, 1, doi: 10.3847/1538-4357/aae3e8

    Kubo, M., Tanaka, M., Yabe, K., et al. 2018, ApJ, 867, 1, doi: 10.3847/1538-4357/aae3e8

    2018 doi

  48. [59]

    2019, ApJ, 887, 214, doi: 10.3847/1538-4357/ab5a80

    Kubo, M., Toshikawa, J., Kashikawa, N., et al. 2019, ApJ, 887, 214, doi: 10.3847/1538-4357/ab5a80

    2019 doi

  49. [60]

    2022, ApJ, 935, 89, doi: 10.3847/1538-4357/ac7f2d

    Kubo, M., Umehata, H., Matsuda, Y ., et al. 2022, ApJ, 935, 89, doi: 10.3847/1538-4357/ac7f2d

    2022 doi

  50. [61]

    1993, MNRAS, 262, 627, doi: 10.1093/mnras/262.3.627

    Lacey, C., & Cole, S. 1993, MNRAS, 262, 627, doi: 10.1093/mnras/262.3.627

    1993 doi

  51. [62]

    L., Finkelstein, S

    Larson, R. L., Finkelstein, S. L., Hutchison, T. A., et al. 2022, ApJ, 930, 104, doi: 10.3847/1538-4357/ac5dbd Le Fèvre, O., Cassata, P., Cucciati, O., et al. 2013, A&A, 559, A14, doi: 10.1051/0004-6361/201322179

    2022 doi

  52. [63]

    Lebigot, E. O. 2016, Uncertainties: a Python package for calculations with uncertainties. http://pythonhosted.org/uncertainties/

    2016

  53. [64]

    2006, in Astronomical Society of the Pacific Conference Series, V ol

    Lemson, G., & Springel, V . 2006, in Astronomical Society of the Pacific Conference Series, V ol. 351, Astronomical Data Analysis Software and Systems XV , ed. C. Gabriel, C. Arviset, D. Ponz, & S. Enrique, 212

    2006

  54. [65]

    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

    2002

  55. [66]

    J., Le Brun, V ., Maier, C., et al

    Lilly, S. J., Le Brun, V ., Maier, C., et al. 2009, ApJS, 184, 218, doi: 10.1088/0067-0049/184/2/218

    2009 doi

  56. [67]

    2021, MNRAS, 501, 1803, doi: 10.1093/mnras/staa3693

    Lim, S., Scott, D., Babul, A., et al. 2021, MNRAS, 501, 1803, doi: 10.1093/mnras/staa3693

    2021 doi

  57. [68]

    2024, arXiv e-prints, arXiv:2402.17819, doi: 10.48550/arXiv.2402.17819 DEEP LEARNING FOR PROTOCLUSTERS 21

    Lim, S., Tacchella, S., Schaye, J., et al. 2024, arXiv e-prints, arXiv:2402.17819, doi: 10.48550/arXiv.2402.17819 DEEP LEARNING FOR PROTOCLUSTERS 21

    2024 doi arXiv

  58. [69]

    K., Driver, S

    Liske, J., Baldry, I. K., Driver, S. P., et al. 2015, MNRAS, 452, 2087, doi: 10.1093/mnras/stv1436 maintainers, T., & contributors. 2016, TorchVision: PyTorch’s Computer Vision library, https://github.com/pytorch/vision, GitHub

    2015 doi

  59. [70]

    C., Muzzin, A., Marchesini, D., et al

    Marsan, Z. C., Muzzin, A., Marchesini, D., et al. 2022, ApJ, 924, 25, doi: 10.3847/1538-4357/ac312a

    2022 doi

  60. [71]

    C., Stern, D

    Masters, D. C., Stern, D. K., Cohen, J. G., et al. 2017, ApJ, 841, 111, doi: 10.3847/1538-4357/aa6f08 —. 2019, ApJ, 877, 81, doi: 10.3847/1538-4357/ab184d

    2017 doi

  61. [72]

    2022, ApJ, 926, 37, doi: 10.3847/1538-4357/ac2b9f

    McConachie, I., Wilson, G., Forrest, B., et al. 2022, ApJ, 926, 37, doi: 10.3847/1538-4357/ac2b9f

    2022 doi

  62. [73]

    J., Pearce, H

    McLure, R. J., Pearce, H. J., Dunlop, J. S., et al. 2013, MNRAS, 428, 1088, doi: 10.1093/mnras/sts092

    2013 doi

  63. [74]

    A., et al

    Mechtley, M., Jahnke, K., Windhorst, R. A., et al. 2016, ApJ, 830, 156, doi: 10.3847/0004-637X/830/2/156

    2016 doi

  64. [75]

    B., Chapman, S

    Miller, T. B., Chapman, S. C., Aravena, M., et al. 2018, Nature, 556, 469, doi: 10.1038/s41586-018-0025-2

    2018 doi

  65. [76]

    G., Brammer, G

    Momcheva, I. G., Brammer, G. B., van Dokkum, P. G., et al. 2016, ApJS, 225, 27, doi: 10.3847/0067-0049/225/2/27

    2016 doi

  66. [77]

    2023, ApJL, 947, L24, doi: 10.3847/2041-8213/acb99e

    Morishita, T., Roberts-Borsani, G., Treu, T., et al. 2023, ApJL, 947, L24, doi: 10.3847/2041-8213/acb99e

    2023 doi

  67. [78]

    I., Hatch, N

    Muldrew, S. I., Hatch, N. A., & Cooke, E. A. 2015, MNRAS, 452, 2528, doi: 10.1093/mnras/stv1449 —. 2018, MNRAS, 473, 2335, doi: 10.1093/mnras/stx2454

    2015 doi

  68. [79]

    A., Cooper, M

    Newman, J. A., Cooper, M. C., Davis, M., et al. 2013, ApJS, 208, 5, doi: 10.1088/0067-0049/208/1/5

    2013 doi

  69. [80]

    J., Hsieh, B.-C., Tanaka, M., & Takata, T

    Nishizawa, A. J., Hsieh, B.-C., Tanaka, M., & Takata, T. 2020, arXiv e-prints, arXiv:2003.01511, doi: 10.48550/arXiv.2003.01511

    2020 doi arXiv

  70. [81]

    2008, MNRAS, 390, 1326, doi: 10.1111/j.1365-2966.2008.13763.x

    Ocvirk, P., Pichon, C., & Teyssier, R. 2008, MNRAS, 390, 1326, doi: 10.1111/j.1365-2966.2008.13763.x

    2008

  71. [82]

    B., & Gunn, J

    Oke, J. B., & Gunn, J. E. 1983, ApJ, 266, 713, doi: 10.1086/160817

    1983 doi

  72. [83]

    J., Balogh, M

    Old, L. J., Balogh, M. L., van der Burg, R. F. J., et al. 2020, MNRAS, 493, 5987, doi: 10.1093/mnras/staa579

    2020 doi

  73. [84]

    2018, PASJ, 70, S10, doi: 10.1093/pasj/psx103

    Ono, Y ., Ouchi, M., Harikane, Y ., et al. 2018, PASJ, 70, S10, doi: 10.1093/pasj/psx103

    2018 doi

  74. [85]

    2018, PASJ, 70, S31, doi: 10.1093/pasj/psx092

    Onoue, M., Kashikawa, N., Uchiyama, H., et al. 2018, PASJ, 70, S31, doi: 10.1093/pasj/psx092

    2018 doi

  75. [86]

    J., Dunne, L., et al

    Oteo, I., Ivison, R. J., Dunne, L., et al. 2018, ApJ, 856, 72, doi: 10.3847/1538-4357/aaa1f1

    2018 doi

  76. [87]

    2019, BAAS, 51, 180, doi: 10.48550/arXiv.1903.04980 Pâris, I., Petitjean, P., Aubourg, É., et al

    Overzier, R., & Kashikawa, N. 2019, BAAS, 51, 180, doi: 10.48550/arXiv.1903.04980 Pâris, I., Petitjean, P., Aubourg, É., et al. 2018, A&A, 613, A51, doi: 10.1051/0004-6361/201732445

    2019 doi arXiv

  77. [88]

    2019, arXiv e-prints, arXiv:1912.01703, doi: 10.48550/arXiv.1912.01703

    Paszke, A., Gross, S., Massa, F., et al. 2019, arXiv e-prints, arXiv:1912.01703, doi: 10.48550/arXiv.1912.01703

    2019 doi arXiv

  78. [89]

    2011, Journal of Machine Learning Research, 12, 2825 Planck Collaboration, Ade, P

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of Machine Learning Research, 12, 2825 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2014, A&A, 571, A16, doi: 10.1051/0004-6361/201321591

    2011 doi

  79. [90]

    Postman, M., & Geller, M. J. 1984, ApJ, 281, 95, doi: 10.1086/162078

    1984 doi

  80. [91]

    R., Su, H., Mo, K., & Guibas, L

    Qi, C. R., Su, H., Mo, K., & Guibas, L. J. 2016, arXiv e-prints, arXiv:1612.00593, doi: 10.48550/arXiv.1612.00593

    2016 doi arXiv

  81. [92]

    2023, ApJ, 950, 191, doi: 10.3847/1538-4357/accb91

    Remus, R.-S., Dolag, K., & Dannerbauer, H. 2023, ApJ, 950, 191, doi: 10.3847/1538-4357/accb91

    2023 doi

  82. [93]

    A., Kartaltepe, J

    Shah, E. A., Kartaltepe, J. S., Magagnoli, C. T., et al. 2020, ApJ, 904, 107, doi: 10.3847/1538-4357/abbf59

    2020 doi

  83. [94]

    A., Lemaux, B., Forrest, B., et al

    Shah, E. A., Lemaux, B., Forrest, B., et al. 2024, MNRAS, 529, 873, doi: 10.1093/mnras/stae519

    2024 doi

  84. [95]

    Shimakawa, R., Koyama, Y ., Röttgering, H. J. A., et al. 2018, MNRAS, 481, 5630, doi: 10.1093/mnras/sty2618

    2018 doi

  85. [96]

    B., Jin, S., Magdis, G

    Sillassen, N. B., Jin, S., Magdis, G. E., et al. 2022, A&A, 665, L7, doi: 10.1051/0004-6361/202244661

    2022 doi

  86. [97]

    D., Kashino, D., Sanders, D., et al

    Silverman, J. D., Kashino, D., Sanders, D., et al. 2015, ApJS, 220, 12, doi: 10.1088/0067-0049/220/1/12

    2015 doi

  87. [98]

    E., Whitaker, K

    Skelton, R. E., Whitaker, K. E., Momcheva, I. G., et al. 2014, ApJS, 214, 24, doi: 10.1088/0067-0049/214/2/24

    2014 doi

  88. [99]

    S., & White, S

    Springel, V ., Frenk, C. S., & White, S. D. M. 2006, Nature, 440, 1137, doi: 10.1038/nature04805

    2006 doi

  89. [100]

    C., Forrest, B., et al

    Staab, P., Lemaux, B. C., Forrest, B., et al. 2024, MNRAS, 528, 6934, doi: 10.1093/mnras/stae301

    2024 doi

  90. [101]

    Adelberger, K. L. 1996, ApJL, 462, L17, doi: 10.1086/310029

    1996 doi

  91. [102]

    Straatman, C. M. S., van der Wel, A., Bezanson, R., et al. 2018, ApJS, 239, 27, doi: 10.3847/1538-4365/aae37a

    2018 doi

  92. [103]

    T., Daddi, E., et al

    Strazzullo, V ., Coogan, R. T., Daddi, E., et al. 2018, ApJ, 862, 64, doi: 10.3847/1538-4357/aacd10

    2018 doi

  93. [104]

    2023, arXiv e-prints, arXiv:2311.11569, doi: 10.48550/arXiv.2311.11569

    Tanaka, M., Onodera, M., Shimakawa, R., et al. 2023, arXiv e-prints, arXiv:2311.11569, doi: 10.48550/arXiv.2311.11569

    2023 doi arXiv

  94. [105]

    2020, findpeaks is for the detection of peaks and valleys in a 1D vector and 2D array (image)., 2.3.1

    Taskesen, E. 2020, findpeaks is for the detection of peaks and valleys in a 1D vector and 2D array (image)., 2.3.1. https://erdogant.github.io/findpeaks

    2020

  95. [106]

    2005, ApJ, 621, 673, doi: 10.1086/426932

    Thomas, D., Maraston, C., Bender, R., & Mendes de Oliveira, C. 2005, ApJ, 621, 673, doi: 10.1086/426932

    2005 doi

  96. [107]

    2012, ApJ, 750, 137, doi: 10.1088/0004-637X/750/2/137

    Toshikawa, J., Kashikawa, N., Ota, K., et al. 2012, ApJ, 750, 137, doi: 10.1088/0004-637X/750/2/137

    2012 doi

  97. [108]

    2018, PASJ, 70, S12, doi: 10.1093/pasj/psx102

    Toshikawa, J., Uchiyama, H., Kashikawa, N., et al. 2018, PASJ, 70, S12, doi: 10.1093/pasj/psx102

    2018 doi

  98. [109]

    2024, MNRAS, 527, 6276, doi: 10.1093/mnras/stad3162

    Toshikawa, J., Wuyts, S., Kashikawa, N., et al. 2024, MNRAS, 527, 6276, doi: 10.1093/mnras/stad3162

    2024 doi

  99. [110]

    D., Stiavelli, M., et al

    Trenti, M., Bradley, L. D., Stiavelli, M., et al. 2012, ApJ, 746, 55, doi: 10.1088/0004-637X/746/1/55

    2012 doi

  100. [111]

    2018, PASJ, 70, S32, doi: 10.1093/pasj/psx112

    Uchiyama, H., Toshikawa, J., Kashikawa, N., et al. 2018, PASJ, 70, S32, doi: 10.1093/pasj/psx112

    2018 doi

  101. [112]

    2020, ApJ, 905, 125, doi: 10.3847/1538-4357/abc47b 22 T AKEDA ET AL

    Uchiyama, H., Akiyama, M., Toshikawa, J., et al. 2020, ApJ, 905, 125, doi: 10.3847/1538-4357/abc47b 22 T AKEDA ET AL

    2020 doi

  102. [113]

    Valentino, F., Brammer, G., Gould, K. M. L., et al. 2023, ApJ, 947, 20, doi: 10.3847/1538-4357/acbefa van der Burg, R. F. J., Rudnick, G., Balogh, M. L., et al. 2020, A&A, 638, A112, doi: 10.1051/0004-6361/202037754

    2023 doi

  103. [114]

    E., et al

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

    2020 doi

  104. [115]

    2012, Nature, 486, 233, doi: 10.1038/nature11073

    Walter, F., Decarli, R., Carilli, C., et al. 2012, Nature, 486, 233, doi: 10.1038/nature11073

    2012 doi

  105. [116]

    F., et al

    Wang, F., Yang, J., Hennawi, J. F., et al. 2024, ApJL, 962, L11, doi: 10.3847/2041-8213/ad20ef

    2024 doi

  106. [117]

    2018a, ApJL, 867, L29, doi: 10.3847/2041-8213/aaeb2c

    Wang, T., Elbaz, D., Daddi, E., et al. 2018a, ApJL, 867, L29, doi: 10.3847/2041-8213/aaeb2c

    doi

  107. [118]

    2018b, arXiv e-prints, arXiv:1801.07829, doi: 10.48550/arXiv.1801.07829 Wes McKinney

    Wang, Y ., Sun, Y ., Liu, Z., et al. 2018b, arXiv e-prints, arXiv:1801.07829, doi: 10.48550/arXiv.1801.07829 Wes McKinney. 2010, in Proceedings of the 9th Python in Science Conference, ed. Stéfan van der Walt & Jarrod Millman, 56 – 61, doi: 10.25080/Majora-92bf1922-00a

    2010 doi arXiv

  108. [119]

    F., & Kragh Jespersen, C

    Wu, J. F., & Kragh Jespersen, C. 2023, arXiv e-prints, arXiv:2306.12327, doi: 10.48550/arXiv.2306.12327

    2023 doi arXiv

  109. [120]

    2023, MNRAS, 522, 4748, doi: 10.1093/mnras/stad1290

    Wu, Z., Xiao, L., Xiao, X., et al. 2023, MNRAS, 522, 4748, doi: 10.1093/mnras/stad1290

    2023 doi

  110. [121]

    2022, MNRAS, 509, 4037, doi: 10.1093/mnras/stab3092

    Yajima, H., Abe, M., Khochfar, S., et al. 2022, MNRAS, 509, 4037, doi: 10.1093/mnras/stab3092

    2022 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.