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arxiv: 2603.05596 · v3 · submitted 2026-03-05 · 🌌 astro-ph.CO

Recognition: 1 theorem link

· Lean Theorem

X-SORTER (X-ray Survey Of meRging clusTErs in Redmapper): X-ray and Spectroscopic Characterization of 12 Optically Selected Galaxy Cluster Merger Candidates

Christopher Hopp , David Wittman , Rodrigo Stancioli , Zhuoran Gao , Faik Bouhrik , Scott Adler
Authors on Pith no claims yet

Pith reviewed 2026-05-15 14:40 UTC · model grok-4.3

classification 🌌 astro-ph.CO
keywords galaxy clustersmerging clustersdark matterX-ray observationsoptical selectionredMaPPerbrightest cluster galaxiesdissociative mergers
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The pith

Optical BCG properties identify post-pericenter galaxy cluster mergers for dark matter studies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether simple optical cuts on brightest cluster galaxy properties in the redMaPPer catalog can reliably pick out merging galaxy clusters that are still near the plane of the sky shortly after first pericenter passage. These systems are valuable because the spatial offsets between the galaxies, the hot gas, and the dark matter halo can constrain the self-interaction cross-section of dark matter. The authors apply three optical criteria—top BCG probability below 0.98, BCG separation of at least 0.95 arcmin, and richness above 120—to select twelve previously unobserved massive clusters, then obtain new XMM-Newton X-ray images and Keck/DEIMOS spectra. Most targets show disturbed X-ray morphologies, and several display clear dissociative signatures with X-ray peaks lying between the two BCGs. Spectroscopy confirms that the BCGs belong to the same cluster and rules out line-of-sight contamination.

Core claim

Application of the optical selection criteria to twelve redMaPPer clusters with no prior XMM or Chandra data yields X-ray images showing that the majority are morphologically disturbed; several exhibit X-ray surface-brightness peaks located between the two brightest cluster galaxies, consistent with post-pericenter dissociative mergers, while Keck spectroscopy verifies cluster membership and excludes foreground or background interlopers.

What carries the argument

Three optical cuts on redMaPPer BCG properties—top BCG probability below 0.98, projected separation at least 0.95 arcmin, and richness lambda greater than 120—used to pre-select candidates for targeted X-ray and spectroscopic follow-up.

If this is right

  • Most clusters passing the optical cuts exhibit disturbed X-ray morphologies indicative of recent dynamical activity.
  • Several systems display X-ray peaks lying between the two BCGs, marking them as clean post-pericenter dissociative mergers.
  • Keck spectra confirm that the selected BCGs reside at the same redshift, eliminating foreground or background contamination as the source of the apparent binarity.
  • The method supplies an efficient route to assemble a statistical sample of mergers suitable for multi-wavelength studies of dark matter and cluster evolution.

Where Pith is reading between the lines

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

  • Extending the same optical pre-selection to the full redMaPPer catalog or future wide-field surveys would increase the number of known clean dissociative mergers available for dark-matter cross-section measurements.
  • Because the cuts rely only on existing optical photometry, they lower the observational cost of finding merger targets compared with blind X-ray or Sunyaev-Zel’dovich surveys.
  • The same BCG-based indicators could be tested on hydrodynamical simulations to quantify the fraction of false positives arising from projection effects or ongoing mergers viewed at different times.

Load-bearing premise

The optical BCG probability, separation, and richness cuts select true post-pericenter mergers rather than projections or non-merging systems.

What would settle it

X-ray imaging and spectroscopy of a larger sample selected by the same three optical cuts would show mostly relaxed, single-peaked morphologies and a high fraction of line-of-sight superpositions instead of disturbed systems with X-ray peaks between BCGs.

Figures

Figures reproduced from arXiv: 2603.05596 by Christopher Hopp, David Wittman, Faik Bouhrik, Rodrigo Stancioli, Scott Adler, Zhuoran Gao.

Figure 1
Figure 1. Figure 1: — Pair plot for the redMaPPer attributes of redshift, richness λ, separation between the top two BCG candidates, and P0 (probability of the top BCG candidate being the actual BCG). This plot includes only clusters with λ > 60 to reduce crowding. Black lines illustrate our cuts, and black points are clusters studied in this paper. the procedure described below) that good merger candidates can be found even … view at source ↗
Figure 2
Figure 2. Figure 2: — Decision tree for X-SORTER targets. The green box on the right is the focus of this paper. tivity. Therefore, our final cut is a visual inspection to rule out this failure mode. To limit the role of subjec￾tive human judgment, we considered no other aspects of redMaPPer performance in this inspection. The resulting target list was evaluated using the deci￾sion tree illustrated in [PITH_FULL_IMAGE:figure… view at source ↗
Figure 3
Figure 3. Figure 3: — X-ray temperature, with self-similar correction E(z)−2/3 , versus richness with scaling relation given in Equation 8. The shaded region gives 1σ uncertainty in the scaling relation by Upsdell et al. (2023) [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: — X-ray luminosity in the 0.5–2.0 keV band, with self￾similar correction E(z)−2 , versus richness with scaling relation given in Equation 10. The shaded region gives 1σ uncertainty in the scaling relation by Upsdell et al. (2023) [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: — [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: — Subclustering analysis of RMJ0003. Bottom panels: spatial distribution of subcluster members, both spectroscopic and photometric, identified within regions defined by the bisector between BCG pairs and a radial distance of 2.5′ from the BCG in each region. Top panels: redshift distribution of the spectroscopic members. The two right panels consider the subcluster associated with BCG 4 to be part of Subcl… view at source ↗
Figure 9
Figure 9. Figure 9: — Redshift distribution of RMJ0109. Top panel: his￾togram of spectroscopic redshifts, combining archival data within 10′ of the cluster with new observations from DEIMOS. The inset highlights galaxies in the redshift range 0.449 ≤ z ≤ 0.470, which are classified as cluster members. Bottom panel: spatial distribu￾tion of those same members. TABLE 12 RMJ0109 BCG Information BCG Probability Redshift r-magc RA… view at source ↗
Figure 10
Figure 10. Figure 10: — Subclustering analysis of RMJ0109. Bottom panels: spatial distribution of subcluster members, both spectroscopic and photometric, identified within regions defined by the bisector between BCG pairs and a radial distance of 2.5′ from the BCG in each region. Top panels: redshift distribution of the spectroscopic members. The two right panels consider the subcluster associated with BCG A to be part of Subc… view at source ↗
Figure 11
Figure 11. Figure 11: — Optical image of RMJ0219. Top panel: 7.5′ by 7.5′ (2.25 by 2.25 Mpc) Pan-STARRS image. Bottom panel: red se￾quence density contours (blue), X-ray surface brightness contours (red), and BCG candidates identified by redMaPPer. a good candidate for further spectroscopic study. Prior to this work, there were only 22 archival redshifts within 10′ of the cluster, the least of any cluster in our sample. We inc… view at source ↗
Figure 13
Figure 13. Figure 13: — Subclustering analysis of RMJ0219. Bottom panel: spatial distribution of subcluster members, both spectroscopic and photometric, identified within regions defined by the bisector be￾tween BCG pairs and a radial distance of 2.5′ from the BCG in each region. Top panel: redshift distribution of the spectroscopic members of those regions. TABLE 15 RMJ0219 Subcluster Properties Subcluster N BCG BCG z Mean z … view at source ↗
Figure 14
Figure 14. Figure 14: — RMJ0219 X-ray features. Top panel: X-ray sur￾face brightness smoothed with a 10′′ kernel and reprojected on an 800 × 800 pixel grid with the extracted 1D profile. The dashed line shows where the profile was extracted across the sharp feature. Center panel: gradient magnitude of the brightness. Bottom panel: residual features after unsharp masking, where an image smoothed with a kernel of 50′′ is subtrac… view at source ↗
Figure 16
Figure 16. Figure 16: — Redshift distribution of RMJ0801. Top panel: his￾togram of spectroscopic redshifts, combining archival data within 10′ of the cluster with new observations from DEIMOS. The inset highlights galaxies in the redshift range 0.485 ≤ z ≤ 0.520, which are classified as cluster members. Bottom panel: spatial distribu￾tion of those same members. The redshift distribution, shown in [PITH_FULL_IMAGE:figures/full… view at source ↗
Figure 17
Figure 17. Figure 17: — Subclustering analysis of RMJ0801. Bottom panels: spatial distribution of subcluster members, both spectroscopic and photometric, identified within regions defined by the bisector between BCG pairs and a radial distance of 2.5′ from the BCG in each region. Top panels: redshift distribution of the spectroscopic members of those regions. The two right panels consider the subcluster associated with BCG A (… view at source ↗
Figure 19
Figure 19. Figure 19: — Redshift distribution of RMJ0829. Top panel: his￾togram of spectroscopic redshifts, combining archival data within 10′ of the cluster with new observations from DEIMOS. The inset highlights galaxies in the redshift range 0.38 ≤ z ≤ 0.41, which are classified as cluster members. Bottom panel: spatial distribution of those same members. ( [PITH_FULL_IMAGE:figures/full_fig_p020_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: — Subclustering analysis of RMJ0829. Bottom panel: spatial distribution of subcluster members, both spectroscopic and photometric, identified within regions defined by the bisector be￾tween BCG pairs and a radial distance of 2.5′ from the BCG in each region. Top panel: redshift distribution of the spectroscopic members of those regions. TABLE 19 RMJ0829 Subcluster Properties Subcluster N BCG BCG z Mean z … view at source ↗
Figure 23
Figure 23. Figure 23: — [PITH_FULL_IMAGE:figures/full_fig_p022_23.png] view at source ↗
Figure 25
Figure 25. Figure 25: — [PITH_FULL_IMAGE:figures/full_fig_p023_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: — Subclustering analysis of RMJ1043. Bottom panel: spatial distribution of subcluster members, both spectroscopic and photometric, identified within regions defined by the bisector be￾tween BCG pairs and a radial distance of 2.5′ from the BCG in each region. Top panel: redshift distribution of the spectroscopic members of those regions. to be in the background. Both luminosity-weighted and unweighted dens… view at source ↗
Figure 27
Figure 27. Figure 27: — Optical image of RMJ1219. Top panel: 6′ by 6′ (2.28 by 2.28 Mpc) Pan-STARRS image. Bottom panel: red sequence density contours (blue), X-ray surface brightness contours (red), and BCG candidates identified by redMaPPer. TABLE 24 RMJ1219 BCG Information BCG Probability Redshift r-magc RA [deg] Dec [deg] 1 0.5124 0.547a 20.07 184.82321 50.90911 2 0.2050 0.555b 20.56 184.79510 50.93541 3 0.1742 0.470b 18.8… view at source ↗
Figure 30
Figure 30. Figure 30: — Distribution of foreground clusters in the full 10′ field of RMJ1219. Contours show the luminosity-weighted density of spectroscopic members in each redshift bin. Individual galaxies are indicated by markers sized proportional to their luminosity [PITH_FULL_IMAGE:figures/full_fig_p026_30.png] view at source ↗
Figure 29
Figure 29. Figure 29: — Redshift distribution of RMJ1219. Top panel: his￾togram of spectroscopic redshifts, combining archival data within 10′ of the cluster with new observations from DEIMOS. The inset highlights galaxies in the redshift range 0.535 ≤ z ≤ 0.566, which are classified as cluster members. Bottom panel: spatial distribu￾tion of those same members. lope. The gradient map (center panel) highlights sharp brightness … view at source ↗
Figure 31
Figure 31. Figure 31: — Subclustering analysis of RMJ1219. Bottom panel: spatial distribution of subcluster members, both spectroscopic and photometric, identified within regions defined by the bisector be￾tween BCG pairs and a radial distance of 2.5′ from the BCG in each region. Top panel: redshift distribution of the spectroscopic members of those regions [PITH_FULL_IMAGE:figures/full_fig_p026_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: — Optical image of RMJ1257. Top panel: 6′ by 6′ (2.22 by 2.22 Mpc) Pan-STARRS image. Bottom panel: red sequence density contours (blue), X-ray surface brightness contours (red), and BCG candidates identified by redMaPPer. 3.3.9. RM J125725.9+365429.4 RMJ1257 is another complicated cluster. The BCGs identified by redMaPPer form roughly along a line ∼400 kpc east of the peak in the X-ray emission, [PITH_FU… view at source ↗
Figure 34
Figure 34. Figure 34: — Subclustering analysis of RMJ1257. Bottom panel: spatial distribution of subcluster members, both spectroscopic and photometric, identified within regions defined by the bisector be￾tween BCG pairs and a radial distance of 2.5′ from each BCG. Top panel: redshift distribution of the spectroscopic members of those regions. slightly exceeds the expectation of 2.44×1044 erg s−1 (Ta￾ble 8). RMJ1257 had the l… view at source ↗
Figure 36
Figure 36. Figure 36: — [PITH_FULL_IMAGE:figures/full_fig_p029_36.png] view at source ↗
Figure 35
Figure 35. Figure 35: — Optical image of RMJ1327. Top panel: 12′ by 12′ (3.84 by 3.84 Mpc) Pan-STARRS image. Bottom panel: red sequence density contours (blue), X-ray surface brightness contours (red), and BCG candidates identified by redMaPPer. 0.9436 to BCG 1 ( [PITH_FULL_IMAGE:figures/full_fig_p029_35.png] view at source ↗
Figure 37
Figure 37. Figure 37: — [PITH_FULL_IMAGE:figures/full_fig_p030_37.png] view at source ↗
Figure 38
Figure 38. Figure 38: — Subclustering analysis of the individual subclusters of RMJ1327. Each panel shows a 5′ field about one of the primary subclusters shown in [PITH_FULL_IMAGE:figures/full_fig_p031_38.png] view at source ↗
Figure 39
Figure 39. Figure 39: — Optical image of RMJ1635. Top panel: 6′ by 6′ (2.10 by 2.10 Mpc) Legacy Survey image. Bottom panel: red sequence density contours (blue), X-ray surface brightness contours (red), and BCG candidates identified by redMaPPer. unsharp-masked (bottom) images. A one-dimensional surface brightness profile extracted across the southern edge of the cluster (marked ‘S’ in the top panel of [PITH_FULL_IMAGE:figure… view at source ↗
Figure 42
Figure 42. Figure 42: — Subclustering analysis of RMJ1635. Bottom panel: spatial distribution of subcluster members, both spectroscopic and photometric, identified within regions defined by the bisector be￾tween BCG pairs and a radial distance of 2.5′ from the BCG in each region. Top panel: redshift distribution of the spectroscopic members of those regions. group. There are multiple peaks in the luminosity den￾sity contours: … view at source ↗
Figure 44
Figure 44. Figure 44: — Redshift distribution of RMJ2321. Top panel: his￾togram of spectroscopic redshifts, combining archival data within 10′ of the cluster with new observations from DEIMOS. The inset highlights galaxies in the redshift range 0.48 ≤ z ≤ 0.51, which are classified as cluster members. Bottom panel: spatial distribution of those same members. difference of 2000 km s−1 between subcluster means (Ta￾ble 33). Subcl… view at source ↗
Figure 45
Figure 45. Figure 45: — Subclustering analysis of RMJ2321. Bottom panel: spatial distribution of subcluster members, both spectroscopic and photometric, identified within regions defined by the bisector between BCG pairs and a radial distance of 2.5’ from the BCG in each region. Top panel: redshift distribution of the spectroscopic members of those regions. TABLE 33 RMJ2321 Subcluster Properties Subcluster N BCG BCG z Mean z σ… view at source ↗
Figure 46
Figure 46. Figure 46: — Distribution of galaxies in RMJ2321 separated by redshift. Contours show the luminosity-weighted density of spec￾troscopic members in each redshift bin. Individual galaxies are indicated by markers sized proportional to their luminosity. spectroscopy focused on Subclusters 2 and 3 would be particularly useful in determining the substructure of the cluster. 4. SUMMARY AND CONCLUSION We have presented X-r… view at source ↗
Figure 47
Figure 47. Figure 47: — Flow diagram summarizing the classification of cluster candidates across the full program. Clusters with archival X-ray data are categorized according to classifications given in Tables 1 to 3 while new designations are assigned to the X-SORTER sample. Binarity classifications are limited to the X-SORTER systems presented in this work. to identify massive, dissociative mergers by requiring low BCG proba… view at source ↗
read the original abstract

Merging galaxy clusters offer a unique probe of dark matter (DM) interactions through the spatial offsets between galaxies, the intracluster medium, and the DM halo. Systems that are binary, near the plane of the sky, and observed shortly after first pericenter provide the cleanest constraints on the DM self-interaction cross-section. The X-SORTER (X-ray Survey Of meRging clusTErs in redMaPPer) program aims to systematically identify such mergers using optical indicators of binarity in the redMaPPer cluster catalog and to follow up promising candidates with X-ray and spectroscopic observations. We select massive clusters where the top redMaPPer brightest cluster galaxy (BCG) probability is below 0.98, the top two BCGs are separated by at least 0.95 arcmin, and the optical richness exceeds lambda = 120. We present XMM and Keck/DEIMOS observations of twelve clusters with no previous XMM-Newton or Chandra archival data meeting these criteria. The X-ray data reveal that most targets are morphologically disturbed, with several clear post-pericenter, dissociative systems exhibiting X-ray peaks between the BCGs. Spectroscopy confirms cluster membership and rules out foreground or background contamination. Together, these results demonstrate that optical BCG properties provide an efficient means of identifying dynamically active clusters, including clean dissociative mergers suitable for detailed, multi-wavelength studies of dark matter and cluster evolution.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces the X-SORTER program, which selects 12 massive galaxy cluster merger candidates from the redMaPPer catalog using optical BCG criteria (top BCG probability <0.98, BCG separation ≥0.95 arcmin, richness λ>120). XMM-Newton X-ray imaging and Keck/DEIMOS spectroscopy are presented for these systems with no prior archival X-ray data, showing morphological disturbance in most targets and clear post-pericenter dissociative signatures in several, with spectroscopy confirming membership and ruling out contamination. The central claim is that these optical BCG properties efficiently identify dynamically active clusters suitable for dark matter and evolution studies.

Significance. If the results hold, the work adds several clean dissociative merger systems to the sample available for multi-wavelength DM self-interaction studies and demonstrates a practical optical pre-selection method. The X-ray and spectroscopic validation of the targets is a concrete contribution. However, the significance of the efficiency claim is limited without quantitative comparison to the parent population.

major comments (2)
  1. [Discussion] Discussion section: The assertion that optical BCG properties 'provide an efficient means of identifying dynamically active clusters' rests on follow-up of the 12 selected systems alone. No control sample of redMaPPer clusters failing the BCG criteria (e.g., top BCG probability ≥0.98 and small separation) is analyzed to measure the increase in merger detection rate or the false-positive rate due to projection effects, so the efficiency and purity of the selection remain unquantified.
  2. [Results] Results section: The X-ray morphological classification relies on qualitative descriptions of disturbance and peak positions without quantitative metrics (e.g., centroid shift, power ratio, or asymmetry parameter) or associated uncertainties, which weakens the ability to objectively rank the sample or compare to literature merger catalogs.
minor comments (2)
  1. [Abstract] The abstract states 'several clear post-pericenter, dissociative systems' but does not specify the exact number or the quantitative thresholds used for this classification.
  2. [Tables] Full data tables listing X-ray properties (e.g., luminosities, temperatures), spectroscopic redshifts with errors, and BCG positions for all 12 clusters are missing, hindering reproducibility and follow-up work.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and detailed comments. We address each major point below, proposing revisions where the manuscript can be strengthened without misrepresenting the presented data.

read point-by-point responses

  1. Referee: [Discussion] Discussion section: The assertion that optical BCG properties 'provide an efficient means of identifying dynamically active clusters' rests on follow-up of the 12 selected systems alone. No control sample of redMaPPer clusters failing the BCG criteria (e.g., top BCG probability ≥0.98 and small separation) is analyzed to measure the increase in merger detection rate or the false-positive rate due to projection effects, so the efficiency and purity of the selection remain unquantified.

    Authors: We agree that the current analysis does not include a control sample, so the efficiency and purity of the BCG-based selection cannot be quantified in a statistical sense. The selection criteria were chosen based on the observed properties of previously known dissociative mergers, and the high incidence of morphological disturbance in the 12 followed-up systems provides qualitative support for their utility. We will revise the discussion section to moderate the efficiency claim, explicitly noting the absence of a control sample and stating that a quantitative assessment of selection efficiency and projection effects is reserved for future work. revision: partial

  2. Referee: [Results] Results section: The X-ray morphological classification relies on qualitative descriptions of disturbance and peak positions without quantitative metrics (e.g., centroid shift, power ratio, or asymmetry parameter) or associated uncertainties, which weakens the ability to objectively rank the sample or compare to literature merger catalogs.

    Authors: We accept this criticism and will strengthen the results section accordingly. In the revised manuscript we will compute and report quantitative X-ray morphological parameters, including centroid shifts and power ratios, together with their uncertainties for each cluster. These metrics will be used to support the morphological classifications and will enable direct comparison with existing merger samples in the literature. revision: yes

Circularity Check

0 steps flagged

No circularity detected in selection and follow-up chain

full rationale

The paper applies fixed optical selection criteria (BCG probability <0.98, separation >=0.95 arcmin, lambda>120) drawn from the external redMaPPer catalog to choose 12 targets, then reports independent XMM imaging and Keck spectroscopy showing morphological disturbance and membership. No equation or claim reduces by construction to a fitted parameter defined from the same data, no self-citation supplies a load-bearing uniqueness theorem, and no ansatz or renaming is smuggled in. The central demonstration rests on new external observations rather than tautological re-expression of the input cuts.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Selection and interpretation rest on the reliability of the redMaPPer catalog for BCG probabilities and richness, plus standard assumptions about merger morphology signatures in X-ray data.

axioms (2)
  • domain assumption The redMaPPer catalog provides accurate BCG probabilities and cluster richness estimates that correlate with dynamical state.
    Invoked in the selection criteria section of the abstract.
  • domain assumption X-ray morphological disturbance and gas-galaxy offsets reliably indicate post-pericenter mergers.
    Used to interpret the X-ray observations as confirming mergers.

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Works this paper leans on

84 extracted references · 84 canonical work pages · 3 internal anchors

  1. [1]

    2020, ApJ, 898, 6, doi: 10.3847/1538-4357/ab929d

    Acebron, A., Zitrin, A., Coe, D., et al. 2020, ApJ, 898, 6, doi: 10.3847/1538-4357/ab929d

    work page doi:10.3847/1538-4357/ab929d 2020

  2. [2]

    P., Machado, R

    Albuquerque, R. P., Machado, R. E. G., & Monteiro-Oliveira, R. 2024, MNRAS, 530, 2146, doi: 10.1093/mnras/stae1004

    work page doi:10.1093/mnras/stae1004 2024

  3. [3]

    F., Argudo-Fern´ andez, M., et al

    Almeida, A., Anderson, S. F., Argudo-Fern´ andez, M., et al. 2023, ApJS, 267, 44, doi: 10.3847/1538-4365/acda98

    work page doi:10.3847/1538-4365/acda98 2023

  4. [4]

    R., Perrott, Y

    Arendt, A. R., Perrott, Y. C., Contreras-Santos, A., et al. 2024, MNRAS, 530, 20, doi: 10.1093/mnras/stae568

    work page doi:10.1093/mnras/stae568 2024

  5. [5]

    Arnaud, K. A. 1996, in Astronomical Society of the Pacific Conference Series, Vol. 101, Astronomical Data Analysis Software and Systems V, ed. G. H. Jacoby & J. Barnes, 17

    work page 1996

  6. [6]

    Arnaud, M., Pointecouteau, E., & Pratt, G. W. 2005, A&A, 441, 893, doi: 10.1051/0004-6361:20052856

    work page doi:10.1051/0004-6361:20052856 2005

  7. [7]

    2002, A&A, 390, 27, doi: 10.1051/0004-6361:20020669 Astropy Collaboration, Robitaille, T

    Arnaud, M., Majerowicz, S., Lumb, D., et al. 2002, A&A, 390, 27, doi: 10.1051/0004-6361:20020669 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: 10.3847/1538-3881/aabc4f Astropy Collaboration,...

    work page doi:10.1051/0004-6361:20020669 2002

  8. [8]

    2009, A&A, 495, 15, doi: 10.1051/0004-6361:200811043

    Boschin, W., Barrena, R., & Girardi, M. 2009, A&A, 495, 15, doi: 10.1051/0004-6361:200811043

    work page doi:10.1051/0004-6361:200811043 2009

  9. [9]

    W., Bonafede, A., et al

    Botteon, A., Shimwell, T. W., Bonafede, A., et al. 2019, A&A, 622, A19, doi: 10.1051/0004-6361/201833861

    work page doi:10.1051/0004-6361/201833861 2019

  10. [10]

    W., Cassano, R., et al

    Botteon, A., Shimwell, T. W., Cassano, R., et al. 2022, A&A, 660, A78, doi: 10.1051/0004-6361/202143020

    work page doi:10.1051/0004-6361/202143020 2022

  11. [11]

    2025, ApJ, 988, 166, doi: 10.3847/1538-4357/ade67c Bradaˇ c, M., Allen, S

    Bouhrik, F., Stancioli, R., & Wittman, D. 2025, ApJ, 988, 166, doi: 10.3847/1538-4357/ade67c Bradaˇ c, M., Allen, S. W., Treu, T., et al. 2008, ApJ, 687, 959, doi: 10.1086/591246

    work page doi:10.3847/1538-4357/ade67c 2025

  12. [12]

    N., Tripp, T

    Burchett, J. N., Tripp, T. M., Wang, Q. D., et al. 2018, MNRAS, 475, 2067, doi: 10.1093/mnras/stx3170

    work page doi:10.1093/mnras/stx3170 2018

  13. [13]

    M., Scaife, A

    Cantwell, T. M., Scaife, A. M. M., Oozeer, N., Wen, Z. L., & Han, J. L. 2016, MNRAS, 458, 1803, doi: 10.1093/mnras/stw419

    work page doi:10.1093/mnras/stw419 2016

  14. [14]

    A., & Read, A

    Carter, J. A., & Read, A. M. 2007, A&A, 464, 1155, doi: 10.1051/0004-6361:20065882

    work page doi:10.1051/0004-6361:20065882 2007

  15. [15]

    2018, ApJ, 859, 159, doi: 10.3847/1538-4357/aabe7b

    Cerny, C., Sharon, K., Andrade-Santos, F., et al. 2018, ApJ, 859, 159, doi: 10.3847/1538-4357/aabe7b

    work page doi:10.3847/1538-4357/aabe7b 2018

  16. [16]

    Y., Joo, H., et al

    Cha, S., Cho, B. Y., Joo, H., et al. 2025, ApJ, 987, L15, doi: 10.3847/2041-8213/add2f0

    work page doi:10.3847/2041-8213/add2f0 2025

  17. [17]

    2025, MNRAS, 539, 981, doi: 10.1093/mnras/staf480

    Chatterjee, S., Pillay, D., Datta, A., et al. 2025, MNRAS, 539, 981, doi: 10.1093/mnras/staf480

    work page doi:10.1093/mnras/staf480 2025

  18. [18]

    2018, ApJ, 863, 145, doi: 10.3847/1538-4357/aad2d3

    Cibirka, N., Acebron, A., Zitrin, A., et al. 2018, ApJ, 863, 145, doi: 10.3847/1538-4357/aad2d3

    work page doi:10.3847/1538-4357/aad2d3 2018

  19. [19]

    H., et al

    Clowe, D., Bradaˇ c, M., Gonzalez, A. H., et al. 2006, ApJ, 648, L109, doi: 10.1086/508162 Dark Energy Survey Collaboration, Abbott, T., Abdalla, F. B., et al. 2016, MNRAS, 460, 1270, doi: 10.1093/mnras/stw641

    work page doi:10.1086/508162 2006

  20. [20]

    S., Schlegel , D

    Dawson, K. S., Schlegel, D. J., Ahn, C. P., et al. 2013, AJ, 145, 10, doi: 10.1088/0004-6256/145/1/10

    work page Pith review doi:10.1088/0004-6256/145/1/10 2013

  21. [21]

    Data Release 1 of the Dark Energy Spectroscopic Instrument

    Dawson, W. A., Wittman, D., Jee, M. J., et al. 2012, ApJ, 747, L42, doi: 10.1088/2041-8205/747/2/L42 DESI Collaboration, Abdul-Karim, M., Adame, A. G., et al. 2025, arXiv e-prints, arXiv:2503.14745, doi: 10.48550/arXiv.2503.14745

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/2041-8205/747/2/l42 2012

  22. [22]

    J., Lang , D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d

    work page doi:10.3847/1538-3881/ab089d 2019

  23. [23]

    Line, J. L. B. 2020, PASA, 37, e037, doi: 10.1017/pasa.2020.29

    work page doi:10.1017/pasa.2020.29 2020

  24. [24]

    R., Cianfaglione, M., & Sembroski, G

    Dutta, A., Peterson, J. R., Cianfaglione, M., & Sembroski, G. 2025, PASP, 137, 064102, doi: 10.1088/1538-3873/adcbd5

    work page doi:10.1088/1538-3873/adcbd5 2025

  25. [25]

    M., Phillips, A

    Faber, S. M., Phillips, A. C., Kibrick, R. I., et al. 2003, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    work page 2003

  26. [26]

    M., Phillips, A

    Series, Vol. 4841, Instrument Design and Performance for Optical/Infrared Ground-based Telescopes, ed. M. Iye & A. F. M. Moorwood, 1657–1669, doi: 10.1117/12.460346

    work page doi:10.1117/12.460346

  27. [27]

    J., Cho, H., et al

    Finner, K., Jee, M. J., Cho, H., et al. 2025, ApJS, 277, 28, doi: 10.3847/1538-4365/adb0b6

    work page doi:10.3847/1538-4365/adb0b6 2025

  28. [28]

    D., Tripp, T

    Ge, C., Wang, Q. D., Tripp, T. M., et al. 2016, MNRAS, 459, 366, doi: 10.1093/mnras/stw599

    work page doi:10.1093/mnras/stw599 2016

  29. [29]

    T., Kale, R., & Wadadekar, Y

    George, L. T., Kale, R., & Wadadekar, Y. 2021, MNRAS, 507, 4487, doi: 10.1093/mnras/stab2309

    work page doi:10.1093/mnras/stab2309 2021

  30. [30]

    M., Brasseur, C

    Ginsburg, A., Sip˝ ocz, B. M., Brasseur, C. E., et al. 2019, AJ, 157, 98, doi: 10.3847/1538-3881/aafc33

    work page doi:10.3847/1538-3881/aafc33 2019

  31. [31]

    A., Wittman, D

    Golovich, N., Dawson, W. A., Wittman, D. M., et al. 2019a, ApJ, 882, 69, doi: 10.3847/1538-4357/ab2f90 —. 2019b, ApJS, 240, 39, doi: 10.3847/1538-4365/aaf88b

    work page doi:10.3847/1538-4357/ab2f90

  32. [32]

    J., Botteon, A., et al

    Groeneveld, C., van Weeren, R. J., Botteon, A., et al. 2025, A&A, 693, A99, doi: 10.1051/0004-6361/202452687

    work page doi:10.1051/0004-6361/202452687 2025

  33. [33]

    A., Koester, B

    Hao, J., McKay, T. A., Koester, B. P., et al. 2010, ApJS, 191, 254, doi: 10.1088/0067-0049/191/2/254

    work page doi:10.1088/0067-0049/191/2/254 2010

  34. [34]

    2015, Science, 347, 1462, doi: 10.1126/science.1261381 39

    Harvey, D., Massey, R., Kitching, T., Taylor, A., & Tittley, E. 2015, Science, 347, 1462, doi: 10.1126/science.1261381 39

    work page doi:10.1126/science.1261381 2015

  35. [35]

    2021, A&A, 654, A68, doi: 10.1051/0004-6361/202039725

    Hoeft, M., Dumba, C., Drabent, A., et al. 2021, A&A, 654, A68, doi: 10.1051/0004-6361/202039725

    work page doi:10.1051/0004-6361/202039725 2021

  36. [36]

    2026, Spectroscopic Redshifts of 1,561 Galaxies in 12 Merging Galaxy Clusters from the X-SORTER Survey, Zenodo, doi: 10.5281/zenodo.18879235 Ivezi´ c,ˇZ., Kahn, S

    Hopp, C., Wittman, D., Stancioli, R., et al. 2026, Spectroscopic Redshifts of 1,561 Galaxies in 12 Merging Galaxy Clusters from the X-SORTER Survey, Zenodo, doi: 10.5281/zenodo.18879235 Ivezi´ c,ˇZ., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111, doi: 10.3847/1538-4357/ab042c

    work page doi:10.5281/zenodo.18879235 2026

  37. [37]

    J., Hughes, J

    Jee, M. J., Hughes, J. P., Menanteau, F., et al. 2014, ApJ, 785, 20, doi: 10.1088/0004-637X/785/1/20

    work page doi:10.1088/0004-637x/785/1/20 2014

  38. [38]

    E., et al

    Kaiser, N., Aussel, H., Burke, B. E., et al. 2002, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference

    work page 2002

  39. [39]

    4836, Survey and Other Telescope Technologies and Discoveries, ed

    Series, Vol. 4836, Survey and Other Telescope Technologies and Discoveries, ed. J. A. Tyson & S. Wolff, 154–164, doi: 10.1117/12.457365

    work page doi:10.1117/12.457365

  40. [40]

    2015, A&A, 579, A92, doi: 10.1051/0004-6361/201525695

    Kale, R., Venturi, T., Giacintucci, S., et al. 2015, A&A, 579, A92, doi: 10.1051/0004-6361/201525695

    work page doi:10.1051/0004-6361/201525695 2015

  41. [41]

    2022, ApJ, 928, 170, doi: 10.3847/1538-4357/ac510e

    Kim, H., Ko, J., Smith, R., et al. 2022, ApJ, 928, 170, doi: 10.3847/1538-4357/ac510e

    work page doi:10.3847/1538-4357/ac510e 2022

  42. [42]

    Y., Peter, A

    Kim, S. Y., Peter, A. H. G., & Wittman, D. 2017, MNRAS, 469, 1414, doi: 10.1093/mnras/stx896

    work page doi:10.1093/mnras/stx896 2017

  43. [43]

    F., Blanc, G

    Kollmeier, J., Anderson, S. F., Blanc, G. A., et al. 2019, in Bulletin of the American Astronomical Society, Vol. 51, 274

    work page 2019

  44. [44]

    2018, ApJ, 863, 102, doi: 10.3847/1538-4357/aad090

    Liu, A., Yu, H., Diaferio, A., et al. 2018, ApJ, 863, 102, doi: 10.3847/1538-4357/aad090

    work page doi:10.3847/1538-4357/aad090 2018

  45. [45]

    2011, , 416, 3017, 10.1111/j.1365-2966.2011.19250.x

    Mann, A. W., & Ebeling, H. 2012, MNRAS, 420, 2120, doi: 10.1111/j.1365-2966.2011.20170.x

    work page doi:10.1111/j.1365-2966.2011.20170.x 2012

  46. [46]

    H., Clowe, D., et al

    Markevitch, M., Gonzalez, A. H., Clowe, D., et al. 2004, ApJ, 606, 819, doi: 10.1086/383178

    work page doi:10.1086/383178 2004

  47. [47]

    2022, MNRAS, 516, 5289, doi: 10.1093/mnras/stac2276

    McDonald, W., Obreschkow, D., & Garratt-Smithson, L. 2022, MNRAS, 516, 5289, doi: 10.1093/mnras/stac2276

    work page doi:10.1093/mnras/stac2276 2022

  48. [48]

    G., & Egret, D., eds

    Ochsenbein, F., Allen, M. G., & Egret, D., eds. 2004, Astronomical Society of the Pacific Conference Series, Vol. 314, Astronomical Data Analysis Software and Systems (ADASS) XIII

    work page 2004

  49. [49]

    W., Bulbul, E., et al

    Paterno-Mahler, R., Randall, S. W., Bulbul, E., et al. 2014, ApJ, 791, 104, doi: 10.1088/0004-637X/791/2/104

    work page doi:10.1088/0004-637x/791/2/104 2014

  50. [50]

    2021, MNRAS, 506, 5389, doi: 10.1093/mnras/stab1965

    Paul, S., Gupta, P., Salunkhe, S., et al. 2021, MNRAS, 506, 5389, doi: 10.1093/mnras/stab1965

    work page doi:10.1093/mnras/stab1965 2021

  51. [51]

    Scikit-learn: Machine Learning in Python

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of Machine Learning Research, 12, 2825, doi: 10.48550/arXiv.1201.0490

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1201.0490 2011

  52. [52]

    S., Turner, D

    Pillay, D. S., Turner, D. J., Hilton, M., et al. 2021, Galaxies, 9, 97, doi: 10.3390/galaxies9040097

    work page doi:10.3390/galaxies9040097 2021

  53. [53]

    2020, The Journal of Open Source Software, 5, 2308, doi: 10.21105/joss.02308

    Prochaska, J., Hennawi, J., Westfall, K., et al. 2020, The Journal of Open Source Software, 5, 2308, doi: 10.21105/joss.02308

    work page doi:10.21105/joss.02308 2020

  54. [54]

    W., Markevitch, M., Clowe, D., Gonzalez, A

    Randall, S. W., Markevitch, M., Clowe, D., Gonzalez, A. H., & Bradaˇ c, M. 2008, ApJ, 679, 1173, doi: 10.1086/587859

    work page doi:10.1086/587859 2008

  55. [55]

    J., Sohn, J., Geller, M

    Rines, K. J., Sohn, J., Geller, M. J., & Diaferio, A. 2022, ApJ, 930, 156, doi: 10.3847/1538-4357/ac67a8

    work page doi:10.3847/1538-4357/ac67a8 2022

  56. [56]

    2019, MNRAS, 488, 3646, doi: 10.1093/mnras/stz1815

    Robertson, A., Harvey, D., Massey, R., et al. 2019, MNRAS, 488, 3646, doi: 10.1093/mnras/stz1815

    work page doi:10.1093/mnras/stz1815 2019

  57. [57]

    2017, MNRAS, 465, 569, doi: 10.1093/mnras/stw2670

    Robertson, A., Massey, R., & Eke, V. 2017, MNRAS, 465, 569, doi: 10.1093/mnras/stw2670

    work page doi:10.1093/mnras/stw2670 2017

  58. [58]

    2020, reproject: Python-based astronomical image reprojection, Astrophysics Source Code Library, record ascl:2011.023

    Robitaille, T., Deil, C., & Ginsburg, A. 2020, reproject: Python-based astronomical image reprojection, Astrophysics Source Code Library, record ascl:2011.023. http://ascl.net/2011.023

    work page 2020

  59. [59]

    S., Rozo, E., Busha, M

    Rykoff, E. S., Rozo, E., Busha, M. T., et al. 2014, ApJ, 785, 104, doi: 10.1088/0004-637X/785/2/104

    work page doi:10.1088/0004-637x/785/2/104 2014

  60. [60]

    S., Rozo, E., Hollowood, D., et al

    Rykoff, E. S., Rozo, E., Hollowood, D., et al. 2016, ApJS, 224, 1, doi: 10.3847/0067-0049/224/1/1

    work page doi:10.3847/0067-0049/224/1/1 2016

  61. [61]

    2023, ApJ, 944, 132, doi: 10.3847/1538-4357/acae9f

    Sarkar, A., Randall, S., Su, Y., et al. 2023, ApJ, 944, 132, doi: 10.3847/1538-4357/acae9f

    work page doi:10.3847/1538-4357/acae9f 2023

  62. [62]

    P., Wittman, D., et al

    Sehgal, N., Hughes, J. P., Wittman, D., et al. 2008, ApJ, 673, 163, doi: 10.1086/523840

    work page doi:10.1086/523840 2008

  63. [63]

    2024, Cookbook for analysis procedures for XMM-Newton EPIC observations of extended objects and the diffuse background, Tech

    Snowden, S., & Kuntz, K. 2024, Cookbook for analysis procedures for XMM-Newton EPIC observations of extended objects and the diffuse background, Tech. report

    work page 2024

  64. [64]

    2015, A&A, 581, A31, doi: 10.1051/0004-6361/201322689

    Soucail, G., Fo¨ ex, G., Pointecouteau, E., Arnaud, M., & Limousin, M. 2015, A&A, 581, A31, doi: 10.1051/0004-6361/201322689

    work page doi:10.1051/0004-6361/201322689 2015

  65. [65]

    2024, ApJ, 966, 49, doi: 10.3847/1538-4357/ad3249

    Stancioli, R., Wittman, D., Finner, K., & Bouhrik, F. 2024, ApJ, 966, 49, doi: 10.3847/1538-4357/ad3249

    work page doi:10.3847/1538-4357/ad3249 2024

  66. [66]

    Stancioli, R., Wittman, D., Finner, K., Bouhrik, F., & Gao, Z. 2026

    work page 2026

  67. [67]

    Stephens, M. A. 1974, Journal of the American statistical Association, 69, 730

    work page 1974

  68. [68]

    2025, A&A, 695, L16, doi: 10.1051/0004-6361/202452581

    Stuardi, C., Botteon, A., Sereno, M., et al. 2025, A&A, 695, L16, doi: 10.1051/0004-6361/202452581

    work page doi:10.1051/0004-6361/202452581 2025

  69. [69]

    2011, ApJ, 736, 21, doi: 10.1088/0004-637X/736/1/21

    Szabo, T., Pierpaoli, E., Dong, F., Pipino, A., & Gunn, J. 2011, ApJ, 736, 21, doi: 10.1088/0004-637X/736/1/21

    work page doi:10.1088/0004-637x/736/1/21 2011

  70. [70]

    Tempel, E., Tuvikene, T., Kipper, R., & Libeskind, N. I. 2017, A&A, 602, A100, doi: 10.1051/0004-6361/201730499

    work page doi:10.1051/0004-6361/201730499 2017

  71. [71]

    G., 2025a, MNRAS.tmp

    Tulin, S., & Yu, H.-B. 2018, Phys. Rep., 730, 1, doi: 10.1016/j.physrep.2017.11.004

    work page doi:10.1016/j.physrep.2017.11.004 2018

  72. [72]

    W., Giles, P

    Upsdell, E. W., Giles, P. A., Romer, A. K., et al. 2023, MNRAS, 522, 5267, doi: 10.1093/mnras/stad1220 van der Walt, S., Sch¨ onberger, J. L., Nunez-Iglesias, J., et al. 2014, PeerJ, 2, e453, doi: 10.7717/peerj.453 van Weeren, R. J., Shimwell, T. W., Botteon, A., et al. 2021, A&A, 651, A115, doi: 10.1051/0004-6361/202039826

    work page doi:10.1093/mnras/stad1220 2023

  73. [73]

    2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2 22

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

    work page doi:10.1038/s41592-019-0686-2 2020

  74. [74]

    The ROSAT All-Sky Survey Bright Source Catalogue

    Voges, W., Aschenbach, B., Boller, T., et al. 1999, A&A, 349, 389, doi: 10.48550/arXiv.astro-ph/9909315

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.astro-ph/9909315 1999

  75. [75]

    L., Han, J

    Wen, Z. L., Han, J. L., & Yuan, Z. S. 2024, MNRAS, 532, 1849, doi: 10.1093/mnras/stae1614

    work page doi:10.1093/mnras/stae1614 2024

  76. [76]

    2019, A&A, 622, A25, doi: 10.1051/0004-6361/201833884

    Wilber, A., Br¨ uggen, M., Bonafede, A., et al. 2019, A&A, 622, A25, doi: 10.1051/0004-6361/201833884

    work page doi:10.1051/0004-6361/201833884 2019

  77. [77]

    Wittman, D., Golovich, N., & Dawson, W. A. 2018, ApJ, 869, 104, doi: 10.3847/1538-4357/aaee77

    work page doi:10.3847/1538-4357/aaee77 2018

  78. [78]

    2025, arXiv e-prints, arXiv:2512.14945, doi: 10.48550/arXiv.2512.14945

    Botteon, A. 2025, arXiv e-prints, arXiv:2512.14945, doi: 10.48550/arXiv.2512.14945

    work page doi:10.48550/arxiv.2512.14945 2025

  79. [79]

    2023, ApJ, 954, 36, doi: 10.3847/1538-4357/acdb73

    Wittman, D., Stancioli, R., Finner, K., et al. 2023, ApJ, 954, 36, doi: 10.3847/1538-4357/acdb73

    work page doi:10.3847/1538-4357/acdb73 2023

  80. [80]

    G., Adelman, J., Anderson, Jr., J

    York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579, doi: 10.1086/301513

    work page doi:10.1086/301513 2000

Showing first 80 references.