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Paper Citation Record · LEDGER

Identifying backsplash galaxies using machine learning

As of 5 August 2026, this Paper Citation Record lists 100 of 119 outbound references and 0 inbound Pith citation observations for arXiv:2607.26136.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.26136 v1

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measured 100 of 119 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

100 of 119 outbound references displayed

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Outbound references

Observation 3fad1cf2-d335-4631-b425-4a23e1e119cd · outbound

This paper cites Probing galaxy evolution in massive clusters using ACT and DES: splashback as a cosmic clock.

Identifying backsplash galaxies using machine learning Probing galaxy evolution in massive clusters using ACT and DES: splashback as a cosmic clock

Reference 1

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Observation f5f4354b-e7b4-4313-9561-bfcfd6b0eaa5 · outbound

This paper cites arXiv e-prints , keywords =.

Identifying backsplash galaxies using machine learning arXiv e-prints , keywords =

Reference 2

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Observation b96e86a6-015b-4d39-89ae-d82e3fce647a · outbound

This paper cites Effect of local and large-scale environments on nuclear activity and star formation.

Identifying backsplash galaxies using machine learning Effect of local and large-scale environments on nuclear activity and star formation

Reference 3

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Observation e20889a3-cae0-4b36-b6c7-d4b2053377bf · outbound

This paper cites The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package.

Identifying backsplash galaxies using machine learning The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

Reference 4

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Observation b1c641f5-a67f-4f41-8c77-22cc183fc849 · outbound

This paper cites Why does the environmental influence on group and cluster galaxies extend beyond the virial radius?.

Identifying backsplash galaxies using machine learning Why does the environmental influence on group and cluster galaxies extend beyond the virial radius?

Reference 5

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Observation 49757ecd-9a67-4a8f-8878-f8583191a71b · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

Reference 6

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Observation 76322730-0cc1-43c7-8039-42015262a2af · outbound

This paper cites Differential Galaxy Evolution in Cluster and Field Galaxies at z=0.3.

Identifying backsplash galaxies using machine learning Differential Galaxy Evolution in Cluster and Field Galaxies at z=0.3

Reference 7

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Observation becd80a9-f2bd-4257-a15c-cd95f01fc2df · outbound

This paper cites The Origin of Star Formation Gradients in Rich Galaxy Clusters.

Identifying backsplash galaxies using machine learning The Origin of Star Formation Gradients in Rich Galaxy Clusters

Reference 8

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Observation feb14e1a-5d92-4425-bc4f-4cf28589f1b4 · outbound

This paper cites GASP II. A MUSE view of extreme ram-pressure stripping along the line of sight: kinematics of the jellyfish galaxy JO201.

Identifying backsplash galaxies using machine learning GASP II. A MUSE view of extreme ram-pressure stripping along the line of sight: kinematics of the jellyfish galaxy JO201

Reference 10

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Observation 9e6d2cb8-77bd-42a1-b0f8-36b39d3ba397 · outbound

This paper cites Accretion of Galaxy Groups into Galaxy Clusters.

Identifying backsplash galaxies using machine learning Accretion of Galaxy Groups into Galaxy Clusters

Reference 11

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Observation 1b9775c2-64a8-43b2-acf0-b3ec43bfb52b · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

Reference 12

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Observation 59a89cb3-282d-46e0-bb85-c3c83a38740d · outbound

This paper cites Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG.

Identifying backsplash galaxies using machine learning Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG

Reference 13

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Observation fdb70cb1-8b4e-4b19-970a-fcd5e5a5fcaa · outbound

This paper cites an unresolved cited work.

Identifying backsplash galaxies using machine learning Unresolved cited work

Reference 14

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Observation 7716ecb8-13b3-4867-a4fa-e959910db356 · outbound

This paper cites There and back again: understanding the critical properties of backsplash galaxies.

Identifying backsplash galaxies using machine learning There and back again: understanding the critical properties of backsplash galaxies

Reference 15

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Observation 2d719472-f182-4943-a874-30358f7d881c · outbound

This paper cites The GALEX Ultraviolet Virgo Cluster Survey (GUViCS). IV: The role of the cluster environment on galaxy evolution.

Identifying backsplash galaxies using machine learning The GALEX Ultraviolet Virgo Cluster Survey (GUViCS). IV: The role of the cluster environment on galaxy evolution

Reference 16

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Observation 85d4bd7b-d51d-4171-b3a8-327de1bc7601 · outbound

This paper cites A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE) XV. The Halpha luminosity function of the Virgo cluster.

Identifying backsplash galaxies using machine learning A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE) XV. The Halpha luminosity function of the Virgo cluster

Reference 17

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Observation 9340fd72-151d-4870-8e8c-a5402228bd25 · outbound

This paper cites Machine Learning , keywords =.

Identifying backsplash galaxies using machine learning Machine Learning , keywords =

Reference 18

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Observation 791d22f0-c005-495c-add3-b6f6b6bab004 · outbound

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Identifying backsplash galaxies using machine learning Unresolved cited work

Reference 19

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Observation bce2341e-4dea-4af2-8024-a05b011f1f50 · outbound

This paper cites The Atlas3D project - XV. Benchmark for early-type galaxies scaling relations from 260 dynamical models: mass-to-light ratio, dark matter, Fundamental Plane and Mass Plane.

Identifying backsplash galaxies using machine learning The Atlas3D project - XV. Benchmark for early-type galaxies scaling relations from 260 dynamical models: mass-to-light ratio, dark matter, Fundamental Plane and Mass Plane

Reference 20

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Observation 1412af07-a412-4a5a-8263-497b9ca53767 · outbound

This paper cites A Physically Motivated Framework to Compare Merger Timescales of Isolated Low- and High-Mass Galaxy Pairs Across Cosmic Time.

Identifying backsplash galaxies using machine learning A Physically Motivated Framework to Compare Merger Timescales of Isolated Low- and High-Mass Galaxy Pairs Across Cosmic Time

Reference 21

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Observation ca2e4ca6-674b-470c-b804-8010354b5eea · outbound

This paper cites The GOGREEN Survey: Evidence of an excess of quiescent disks in clusters at $1.0<z<1.4$.

Identifying backsplash galaxies using machine learning The GOGREEN Survey: Evidence of an excess of quiescent disks in clusters at $1.0<z<1.4$

Reference 22

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Observation 68cde874-1e12-4ed9-a0ad-5ec833f74dbc · outbound

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Identifying backsplash galaxies using machine learning Unresolved cited work

Reference 23

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Observation bee3ae14-9300-40c1-bf58-c386c340e24c · outbound

This paper cites Dark matter halo properties of intermediate-z star-forming galaxies.

Identifying backsplash galaxies using machine learning Dark matter halo properties of intermediate-z star-forming galaxies

Reference 24

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Observation 2755791f-06fa-41fa-86af-fa563fa5c6f6 · outbound

This paper cites Semi-Analytic Galaxies - I. Synthesis of environmental and star-forming regulation mechanisms.

Identifying backsplash galaxies using machine learning Semi-Analytic Galaxies - I. Synthesis of environmental and star-forming regulation mechanisms

Reference 25

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Observation e3ae4604-46a9-484e-818b-e8460ee74866 · outbound

This paper cites The Three Hundred project: a large catalogue of theoretically modelled galaxy clusters for cosmological and astrophysical applications.

Identifying backsplash galaxies using machine learning The Three Hundred project: a large catalogue of theoretically modelled galaxy clusters for cosmological and astrophysical applications

Reference 26

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Observation 7f8aa904-9d1f-4704-b477-910aab2492cc · outbound

This paper cites \textsc{The Three Hundred} project: The \textsc{Gizmo-Simba} run.

Identifying backsplash galaxies using machine learning \textsc{The Three Hundred} project: The \textsc{Gizmo-Simba} run

Reference 27

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Observation 1d378b2a-6ca3-45c1-b50d-3b133899c292 · outbound

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Identifying backsplash galaxies using machine learning MUFASA: Galaxy Formation Simulations With Meshless Hydrodynamics

Reference 28

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Observation ddd8b6a7-011c-4c3f-8211-4f1a5c501bbd · outbound

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Identifying backsplash galaxies using machine learning Simba: Cosmological Simulations with Black Hole Growth and Feedback

Reference 29

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Observation 54ab7dca-4c8a-407d-8dbd-2ec09382eb45 · outbound

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Identifying backsplash galaxies using machine learning ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques

Reference 30

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Observation 79743ef5-5cc7-4607-95d3-0a4d453557fd · outbound

This paper cites Clusters' far-reaching influence on narrow-angle tail radio galaxies.

Identifying backsplash galaxies using machine learning Clusters' far-reaching influence on narrow-angle tail radio galaxies

Reference 31

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Observation 33b21b0e-fd78-4fa3-a9dc-1feb7b5387eb · outbound

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Identifying backsplash galaxies using machine learning Improving galaxy morphologies for SDSS with Deep Learning

Reference 32

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Identifying backsplash galaxies using machine learning , keywords =

Reference 33

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Observation ff44cee3-848f-4dae-a1ed-593fd3e1a9eb · outbound

This paper cites The IMACS Cluster Building Survey: II. Spectral Evolution of Galaxies in the Epoch of Cluster Assembly.

Identifying backsplash galaxies using machine learning The IMACS Cluster Building Survey: II. Spectral Evolution of Galaxies in the Epoch of Cluster Assembly

Reference 34

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Observation 25d19bb7-3b78-4e98-896b-9cda30e2a234 · outbound

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Identifying backsplash galaxies using machine learning ViCTORIA project: The LOFAR HBA Virgo Cluster Survey

Reference 35

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Identifying backsplash galaxies using machine learning Overview of the Euclid mission

Reference 36

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Identifying backsplash galaxies using machine learning Unresolved cited work

Reference 37

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Identifying backsplash galaxies using machine learning Exploring the stellar populations of backsplash galaxies

Reference 38

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Identifying backsplash galaxies using machine learning , keywords =

Reference 39

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source=arxiv_source observed=2026-08-01T00:44:55.582106Z digest=sha256:3389d4a2000b95cf6200fe135f6493b98133fd06982010aa751aa08a6dec3bf3

Observation f44f2580-f073-4d95-89da-d17385197978 · outbound

This paper cites Virgo Filaments V: Disrupting the Baryon Cycle in the NGC 5364 Galaxy Group.

Identifying backsplash galaxies using machine learning Virgo Filaments V: Disrupting the Baryon Cycle in the NGC 5364 Galaxy Group

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source=arxiv_source observed=2026-08-01T00:44:55.654980Z digest=sha256:8420eaeee41e0ee953fda3e2ca8b0901cfbc46e8bde39e2112562841eebf3257

Observation 943c45de-420c-43d6-8c6a-84664997ebf8 · outbound

This paper cites The miniJPAS survey: The role of group environment in quenching the star formation.

Identifying backsplash galaxies using machine learning The miniJPAS survey: The role of group environment in quenching the star formation

Reference 43

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source=arxiv_source observed=2026-08-01T00:44:55.982091Z digest=sha256:7b63c34ff19d2adb5eba9cd1ba827d9e4d0ddae6a96a28d367f38f3785c3a781

Observation 1addf14d-3321-4d90-ba47-de397d7c38e1 · outbound

This paper cites , year = 1972, month = aug, volume =.

Identifying backsplash galaxies using machine learning , year = 1972, month = aug, volume =

Reference 44

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source=arxiv_source observed=2026-08-01T00:44:56.093801Z digest=sha256:7b79ad5b0c5594331123f347cc574827d2d824037d09870be523cd4987a75cd5

Observation ca8c4008-b3cf-4c1e-9348-4c80cfb352d8 · outbound

This paper cites The Three Hundred Project: Backsplash galaxies in simulations of clusters.

Identifying backsplash galaxies using machine learning The Three Hundred Project: Backsplash galaxies in simulations of clusters

Reference 45

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source=arxiv_source observed=2026-08-01T00:44:56.156855Z digest=sha256:2bee0871803a1ab82095c39298b9124ebfa3dbb0e6ee32319c9ddc673e224b1e

Observation cc175c1a-4449-42eb-8196-39954db94d08 · outbound

This paper cites The Three Hundred project: Galaxy groups do not survive cluster infall.

Identifying backsplash galaxies using machine learning The Three Hundred project: Galaxy groups do not survive cluster infall

Reference 46

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source=arxiv_source observed=2026-08-01T00:44:56.245206Z digest=sha256:12f0e011f9a5d0c5c96b12bab4057d407ac6f44fb30295de6b378601ad3e9d0d

Observation 2dd5d869-596f-4e29-8e8b-da6e7f875db8 · outbound

This paper cites Constraining cosmological parameters using the splashback radius of galaxy clusters.

Identifying backsplash galaxies using machine learning Constraining cosmological parameters using the splashback radius of galaxy clusters

Reference 47

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source=arxiv_source observed=2026-08-01T00:44:56.347405Z digest=sha256:e4576cb4b6afc7c28fa92aaee1293328df7d965c9e2c5a43e390667f1427069c

Observation 2b7e2872-c7ef-4fa6-9bd6-39a017c0b559 · outbound

This paper cites Reconsidering the dynamical states of galaxy clusters using PCA and UMAP.

Identifying backsplash galaxies using machine learning Reconsidering the dynamical states of galaxy clusters using PCA and UMAP

Reference 48

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source=arxiv_source observed=2026-08-01T00:44:56.414529Z digest=sha256:03b8d2634b78ae22f9e2cc4a9098141f15de3292baa33b23ec329250beb2c145

Observation 50688c77-bab9-4e39-81c6-d1931aba2fff · outbound

This paper cites YZiCS: Preprocessing of dark halos in the hydrodynamic zoom-in simulation of clusters.

Identifying backsplash galaxies using machine learning YZiCS: Preprocessing of dark halos in the hydrodynamic zoom-in simulation of clusters

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source=arxiv_source observed=2026-08-01T00:44:56.467859Z digest=sha256:8c5c5c58c144253fd90682df47a86f696ad0826dedbb9990ec007fdfabe4b796

Observation 439c0c97-2624-4081-9e52-35ff153202b6 · outbound

This paper cites The Open Journal of Astrophysics , keywords =.

Identifying backsplash galaxies using machine learning The Open Journal of Astrophysics , keywords =

Reference 50

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source=arxiv_source observed=2026-08-01T00:44:56.524975Z digest=sha256:d65a7ea78dc7cee31bb3297c5d79a495496a4ce5642c3aa8e8473be83af99b84

Observation ee9669e6-a0a4-4350-8b80-4a60aa30bb45 · outbound

This paper cites GIZMO: A New Class of Accurate, Mesh-Free Hydrodynamic Simulation Methods.

Identifying backsplash galaxies using machine learning GIZMO: A New Class of Accurate, Mesh-Free Hydrodynamic Simulation Methods

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source=arxiv_source observed=2026-08-01T00:44:56.571006Z digest=sha256:dc80457bedda0b14c7031fe4af56cb24c8d5e8b13f927bf2d95004cd9d2b9cfc

Observation bf26bf6b-a0f4-45cc-80ee-38ff31ee3ba5 · outbound

This paper cites The Three Hundred project: connection between star formation quenching and dynamical evolution in and around simulated galaxy clusters.

Identifying backsplash galaxies using machine learning The Three Hundred project: connection between star formation quenching and dynamical evolution in and around simulated galaxy clusters

Reference 52

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no resolver link, observed 2026-08-01T00:44:56.709894Z

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source=arxiv_source observed=2026-08-01T00:44:56.709894Z digest=sha256:c3cba9e385afbca6943e9fc4b316147464161eb36cec03b827650d369de9fde5

Observation 8cc81c88-672c-4253-97e1-3ef7c856fe80 · outbound

This paper cites Intrinsic Alignment in redMaPPer clusters -- II. Radial alignment of satellites toward cluster centers.

Identifying backsplash galaxies using machine learning Intrinsic Alignment in redMaPPer clusters -- II. Radial alignment of satellites toward cluster centers

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no resolver link, observed 2026-08-01T00:44:56.856668Z

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source=arxiv_source observed=2026-08-01T00:44:56.856668Z digest=sha256:4a8932e4b5053f9b143bc976985744468b62154e8e94f9a22dc77f7324f77584

Observation 09063952-dbe7-406f-90ae-c356ec26a3d7 · outbound

This paper cites an unresolved cited work.

Identifying backsplash galaxies using machine learning Unresolved cited work

Reference 54

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source=arxiv_source observed=2026-08-01T00:44:56.987427Z digest=sha256:79ebacccef8854d5fb1e6d8739d40815ba2b0250c5d25eea613ee59d30862362

Observation 74a83f8d-be3a-4576-aa97-752de20d34fc · outbound

This paper cites GASP IX. Jellyfish galaxies in phase-space: an orbital study of intense ram-pressure stripping in clusters.

Identifying backsplash galaxies using machine learning GASP IX. Jellyfish galaxies in phase-space: an orbital study of intense ram-pressure stripping in clusters

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no resolver link, observed 2026-08-01T00:44:57.089060Z

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source=arxiv_source observed=2026-08-01T00:44:57.089060Z digest=sha256:cd157e37d2d16d8baf82fa95c95132d5739366730bcecbe74a36fc4eb6c19f7c

Observation 066e309b-e1c9-4fde-a4d3-24b9482449ec · outbound

This paper cites Environmental Dependence of Galaxy Merger Rate in {\Lambda}CDM Universe.

Identifying backsplash galaxies using machine learning Environmental Dependence of Galaxy Merger Rate in {\Lambda}CDM Universe

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source=arxiv_source observed=2026-08-01T00:44:57.148459Z digest=sha256:0134b9f56ab7c031970422f845e16efde428b8d4f3e14c65d6f9439b05e18dbb

Observation f8fc5b0b-c78e-45e2-acc1-dfc5b8cb38db · outbound

This paper cites The wide-field, multiplexed, spectroscopic facility WEAVE: Survey design, overview, and simulated implementation.

Identifying backsplash galaxies using machine learning The wide-field, multiplexed, spectroscopic facility WEAVE: Survey design, overview, and simulated implementation

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source=arxiv_source observed=2026-08-01T00:44:57.223657Z digest=sha256:a53c80cfe44c9a047d4b231b6027b17736395148fd39a3dec0f693c530150f1f

Observation 3ff9f635-728f-454e-8a8d-8b436fce948a · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

Reference 58

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source=arxiv_source observed=2026-08-01T00:44:57.227276Z digest=sha256:5e50906f12251d1a17762f3816fbdc577c070dc3d504106ba5ff1ac4b6456113

Observation adaf1d0d-6a0d-4cd8-9afe-e2e716fdd4d9 · outbound

This paper cites The Extended Virgo Cluster Catalog.

Identifying backsplash galaxies using machine learning The Extended Virgo Cluster Catalog

Reference 59

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source=arxiv_source observed=2026-08-01T00:44:57.234321Z digest=sha256:ed82d50f0b716fc4da449b36dc5b8938982fed49865baf13edee150448a1bb22

Observation 7940120a-070d-4f0d-8e43-a11d30285830 · outbound

This paper cites MultiDark simulations: the story of dark matter halo concentrations and density profiles.

Identifying backsplash galaxies using machine learning MultiDark simulations: the story of dark matter halo concentrations and density profiles

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source=arxiv_source observed=2026-08-01T00:44:57.348483Z digest=sha256:72be1f7208b5fc9ab0a29c98d4725df0d683a3e0e13320ff65df29dc020c6770

Observation bac5798a-918f-44eb-9135-159e28c2101a · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

Reference 61

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source=arxiv_source observed=2026-08-01T00:44:57.483881Z digest=sha256:65c27d6201e8bc6f522e8fa696ac8c03ea635e62d77875048c1c9dc8ebfa1768

Observation 6dd764e3-f4eb-4a4e-8f5d-e5d45e31e63d · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

Reference 62

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source=arxiv_source observed=2026-08-01T00:44:57.573850Z digest=sha256:5171ada372c19323b4298ef0ecc0e3f7ff47fb5257a24899735b6380d94f37a7

Observation ed85c5b7-4c12-4351-bb0d-f590f61e487d · outbound

This paper cites Structure Finding in Cosmological Simulations: The State of Affairs.

Identifying backsplash galaxies using machine learning Structure Finding in Cosmological Simulations: The State of Affairs

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no resolver link, observed 2026-08-01T00:44:57.649729Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-01T00:44:57.649729Z digest=sha256:1fefb7170df75b000756999ab7a4bc46a0362e81ec9ac6636e5534b486bbf633

Observation a1ef7a4c-7954-4b61-858b-3ae1efd89f22 · outbound

This paper cites The Three Hundred project: shapes and radial alignment of satellite, infalling, and backsplash galaxies.

Identifying backsplash galaxies using machine learning The Three Hundred project: shapes and radial alignment of satellite, infalling, and backsplash galaxies

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source=arxiv_source observed=2026-08-01T00:44:57.777493Z digest=sha256:07d7294f8a2dc3f0ec0cab211cd4e7a172feb160af026906ed997938a3fcb3b4

Observation c3678ea7-83e1-4573-b10b-9cd542c5b67e · outbound

This paper cites Ahf: Amiga's Halo Finder.

Identifying backsplash galaxies using machine learning Ahf: Amiga's Halo Finder

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source=arxiv_source observed=2026-08-01T00:44:57.964768Z digest=sha256:8c9c5727051e767b38dd9800200d81b8d04f62d2f14214f8ee4ca32d274863c1

Observation 8c1e2dbc-1c90-40ef-90e8-f74e8ea34100 · outbound

This paper cites Cosmic filaments delay quenching inside clusters.

Identifying backsplash galaxies using machine learning Cosmic filaments delay quenching inside clusters

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source=arxiv_source observed=2026-08-01T00:44:58.100425Z digest=sha256:a604597c61bce58dbd0739702d9c62b524ad2e471b9ed94880afe3438932caac

Observation 95e082d4-6c5c-42b1-8c33-98d18c579de2 · outbound

This paper cites The clustering of X-ray AGN at 0.5 < z < 4.5: host galaxies dictate dark matter halo mass.

Identifying backsplash galaxies using machine learning The clustering of X-ray AGN at 0.5 < z < 4.5: host galaxies dictate dark matter halo mass

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source=arxiv_source observed=2026-08-01T00:44:58.256514Z digest=sha256:35c78fe0e8b244a76b4f4aaacbd28acb52169a601df3842aa5b4376b7ee59435

Observation 643ccaab-8243-4612-ac17-3b5c9a50d042 · outbound

This paper cites A Comparison of Methods for Determining the Molecular Content of Model Galaxies.

Identifying backsplash galaxies using machine learning A Comparison of Methods for Determining the Molecular Content of Model Galaxies

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source=arxiv_source observed=2026-08-01T00:44:58.431712Z digest=sha256:72352e57e8d3291b3c2dc5d6e527b5a0dadc59fb43f22f9ee65f22d3b337862d

Observation 905e8c2e-8d79-471e-88cc-9176d3398f19 · outbound

This paper cites Mapping and characterisation of cosmic filaments in galaxy cluster outskirts: strategies and forecasts for observations from simulations.

Identifying backsplash galaxies using machine learning Mapping and characterisation of cosmic filaments in galaxy cluster outskirts: strategies and forecasts for observations from simulations

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source=arxiv_source observed=2026-08-01T00:44:58.563788Z digest=sha256:f6857a683d3091b4d363ca85a8a4e0e733ce8a01b929da8dc6a8c5a5a999f4dc

Observation 05d77e57-78b6-454a-a291-8c83cfb41157 · outbound

This paper cites An inventory of galaxies in cosmic filaments feeding galaxy clusters: galaxy groups, backsplash galaxies, and pristine galaxies.

Identifying backsplash galaxies using machine learning An inventory of galaxies in cosmic filaments feeding galaxy clusters: galaxy groups, backsplash galaxies, and pristine galaxies

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source=arxiv_source observed=2026-08-01T00:44:58.729513Z digest=sha256:05cc667be702e2397752503e66d2b246887acc8eb725d0de4ad66afce65bb1a2

Observation 7c888137-66ac-448f-a8d5-98fdf2f33d83 · outbound

This paper cites From voids to filaments: environmental transformations of galaxies in the SDSS.

Identifying backsplash galaxies using machine learning From voids to filaments: environmental transformations of galaxies in the SDSS

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source=arxiv_source observed=2026-08-01T00:44:58.905564Z digest=sha256:90956039e483691ccd8ce79522158bbb5708906355d5ce94f07da207d739f860

Observation 08fb2548-e752-4639-93d5-43b2899b919e · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

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source=arxiv_source observed=2026-08-01T00:44:59.064928Z digest=sha256:03d16a912d80241c3404afdbd27d5366a106de30a6727f47c3bbaab44198017e

Observation 4981b187-ceb1-49a6-a8fd-3754afef3d55 · outbound

This paper cites The origin of HI-deficiency in galaxies on the outskirts of the Virgo cluster. I. How far can galaxies bounce out of clusters?.

Identifying backsplash galaxies using machine learning The origin of HI-deficiency in galaxies on the outskirts of the Virgo cluster. I. How far can galaxies bounce out of clusters?

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source=arxiv_source observed=2026-08-01T00:44:59.177757Z digest=sha256:4164217ed3d7ce7b2a51b63e436cf2635f47072fe274d44bc7b425d09fc0f05e

Observation a6c4944a-ee45-4de9-8f8d-bb9cd7b814d3 · outbound

This paper cites Cosmology and Astrophysics from Relaxed Galaxy Clusters I: Sample Selection.

Identifying backsplash galaxies using machine learning Cosmology and Astrophysics from Relaxed Galaxy Clusters I: Sample Selection

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source=arxiv_source observed=2026-08-01T00:44:59.303087Z digest=sha256:982fba7f5c0a912d99b3e391329f5a7673e8d6b616e6950e358653c0a914e977

Observation 842abdfa-77c7-4d64-9a1d-895ad1d6dc5e · outbound

This paper cites Machine Learning to identify ICL and BCG in simulated galaxy clusters.

Identifying backsplash galaxies using machine learning Machine Learning to identify ICL and BCG in simulated galaxy clusters

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source=arxiv_source observed=2026-08-01T00:44:59.463147Z digest=sha256:343abb31f1f22159fe61eb1020425952b74c286574f374f9d30d5863801f854c

Observation f324ea6d-40cb-4433-bfc4-cd6b149ee105 · outbound

This paper cites Reconstructing Orbits of Galaxies in Extreme Regions (ROGER) III: galaxy evolution patterns in projected phase space around massive X-ray clusters.

Identifying backsplash galaxies using machine learning Reconstructing Orbits of Galaxies in Extreme Regions (ROGER) III: galaxy evolution patterns in projected phase space around massive X-ray clusters

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source=arxiv_source observed=2026-08-01T00:44:59.574931Z digest=sha256:c8bb602905060d795db5f94ac39b6ff974bf1422a4957b1121265b07f1ba0a14

Observation 6a8fafd2-a157-45bd-9a35-ae1a3aea75f5 · outbound

This paper cites 2010 , doi=.

Identifying backsplash galaxies using machine learning 2010 , doi=

Reference 78

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source=arxiv_source observed=2026-08-01T00:44:59.806106Z digest=sha256:cdd4c4a2ae3f3eedb53db692f24e74c34bec9477339a12dee2412349dc00f7a4

Observation eaa44126-98b2-4069-ae9e-0bc1bb1754bc · outbound

This paper cites Clusters of Galaxies: Probes of Cosmological Structure and Galaxy Evolution , year = 2004, editor =.

Identifying backsplash galaxies using machine learning Clusters of Galaxies: Probes of Cosmological Structure and Galaxy Evolution , year = 2004, editor =

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no resolver link, observed 2026-08-01T00:44:59.915326Z

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source=arxiv_source observed=2026-08-01T00:44:59.915326Z digest=sha256:684270f7c4c9cf37652bf406a35e03fa620b2b3b71973ab11899eed39e172977

Observation 441e426a-6f99-4cf7-8fdc-a1ce9d3392a5 · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

Reference 80

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source=arxiv_source observed=2026-08-01T00:45:00.046089Z digest=sha256:18f9a890ca1d956cf1c9b417f406cf961007c55fd41b800afa645f8eb129ad75

Observation 43de786c-5464-4174-86d4-0f1dd531a009 · outbound

This paper cites Galaxy Harassment and the Evolution of Clusters of Galaxies.

Identifying backsplash galaxies using machine learning Galaxy Harassment and the Evolution of Clusters of Galaxies

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source=arxiv_source observed=2026-08-01T00:45:00.149515Z digest=sha256:bcedad5d3dee9893de95f9a9b666f763883c660829d90422cf861a375402c06e

Observation c1da87f1-d23b-4632-8259-c21ba25f0fb4 · outbound

This paper cites The splashback radius as a physical halo boundary and the growth of halo mass.

Identifying backsplash galaxies using machine learning The splashback radius as a physical halo boundary and the growth of halo mass

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source=arxiv_source observed=2026-08-01T00:45:00.331689Z digest=sha256:e15a2420f202489dda4d8b7187307b5779d24a284d7d26c29e84aa1b5af229cc

Observation 6013e076-b81c-4623-aac6-ce64995dee5e · outbound

This paper cites A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE): XVI. The ubiquity of truncated star-forming disks across the Virgo cluster environment.

Identifying backsplash galaxies using machine learning A Virgo Environmental Survey Tracing Ionised Gas Emission (VESTIGE): XVI. The ubiquity of truncated star-forming disks across the Virgo cluster environment

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source=arxiv_source observed=2026-08-01T00:45:00.435088Z digest=sha256:eab21c8070a41770378374e005096256426cd303b6fe998a1f2179ede0aa8876

Observation 59acb146-1337-4a10-9f7f-d89c6cef31e7 · outbound

This paper cites Deep galaxy stellar mass functions as a function of star formation rate in the Virgo cluster environment.

Identifying backsplash galaxies using machine learning Deep galaxy stellar mass functions as a function of star formation rate in the Virgo cluster environment

Reference 84

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source=arxiv_source observed=2026-08-01T00:45:00.585700Z digest=sha256:a62cbfc189ef5cc92ab9465f591fce4150765638059ddad8842477f3e4f259ae

Observation d6e34eab-1ad7-4b66-9a88-2d41e6f64928 · outbound

This paper cites The Three Hundred Project: The gas disruption of infalling objects in cluster environments.

Identifying backsplash galaxies using machine learning The Three Hundred Project: The gas disruption of infalling objects in cluster environments

Reference 85

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source=arxiv_source observed=2026-08-01T00:45:00.694644Z digest=sha256:5ff347079a4d2326148ca2195ef6cf9275ade889f4748ccb0a7dbd7d97267eaa

Observation 0a29ee47-d60f-4c5f-af73-f22ccc367085 · outbound

This paper cites Galaxy properties in clusters. II. Backsplash Galaxies.

Identifying backsplash galaxies using machine learning Galaxy properties in clusters. II. Backsplash Galaxies

Reference 87

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source=arxiv_source observed=2026-08-01T00:45:00.952804Z digest=sha256:fae2e9b2ee282fdb6cab2627a9a05f8ace33e6e3240cf8957796ebb837167c92

Observation ce0ec242-431a-43f0-ada9-dee847723adf · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

Reference 88

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source=arxiv_source observed=2026-08-01T00:45:01.116800Z digest=sha256:08b56de406f55b88086f5869b84b28e14a4979574958cc31c0c6f18f9453c8d8

Observation ae107987-850a-4193-9d68-964774c1c96c · outbound

This paper cites The IllustrisTNG Simulations: Public Data Release.

Identifying backsplash galaxies using machine learning The IllustrisTNG Simulations: Public Data Release

Reference 89

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source=arxiv_source observed=2026-08-01T00:45:01.251099Z digest=sha256:c6dc9207d65dad91fd03247f50b16d84621f1a6e59984854f4f967e94b55a6ec

Observation 88059d6f-0824-4e43-8e96-0b2f6600f2ef · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

Reference 90

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source=arxiv_source observed=2026-08-01T00:45:01.333573Z digest=sha256:a6618d0b6dda2dfcb1b1dead569fc8117863628f04c6b8cec56c4687b8bc8928

Observation 93a9b1c6-2619-488b-8ce4-710fcc25509a · outbound

This paper cites Photometry of 15 Clusters.

Identifying backsplash galaxies using machine learning Photometry of 15 Clusters

Reference 91

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source=arxiv_source observed=2026-08-01T00:45:01.438419Z digest=sha256:4b83cb541d0d17f112cb453ad230a51aaa153fe292894d0ace433621de9f7094

Observation f7815b07-0481-4514-ae7f-a94dc642a8f8 · outbound

This paper cites Tracing the Quenching History in Galaxy Clusters in the EAGLE Simulation.

Identifying backsplash galaxies using machine learning Tracing the Quenching History in Galaxy Clusters in the EAGLE Simulation

Reference 92

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source=arxiv_source observed=2026-08-01T00:45:01.555363Z digest=sha256:f533234551342816db98ced2de1076d4423aa220cc8f0ebcde30ecd6f37913fa

Observation dd7d3166-af57-4890-8bdf-91ec91119e9a · outbound

This paper cites and Varoquaux, G.

Identifying backsplash galaxies using machine learning and Varoquaux, G

Reference 93

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source=arxiv_source observed=2026-08-01T00:45:01.654906Z digest=sha256:148de62438b80646fb44ed4638837c2907b99f718509107d51ed95aaef181edf

Observation 83d2ebab-fc09-4c05-95c9-0313dd77f361 · outbound

This paper cites Mass and environment as drivers of galaxy evolution in SDSS and zCOSMOS and the origin of the Schechter function.

Identifying backsplash galaxies using machine learning Mass and environment as drivers of galaxy evolution in SDSS and zCOSMOS and the origin of the Schechter function

Reference 94

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source=arxiv_source observed=2026-08-01T00:45:01.746318Z digest=sha256:6f1dbabe818a20f22d4ab311979d44f5ee13ed517e54902a0b9d1c9d92020916

Observation fe49ac57-d2fe-48e3-b53e-4046d1cefe2a · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

Reference 95

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source=arxiv_source observed=2026-08-01T00:45:01.844910Z digest=sha256:f8f830cc2f7e27c2d7e94cd0e4e8aca0694d1a2b70fcdc17fac32223b4775c23

Observation 4393dcb9-a4e6-4cd4-b5f3-3821ca4cc0ff · outbound

This paper cites On the quenching of star formation in observed and simulated central galaxies: Evidence for the role of integrated AGN feedback.

Identifying backsplash galaxies using machine learning On the quenching of star formation in observed and simulated central galaxies: Evidence for the role of integrated AGN feedback

Reference 96

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source=arxiv_source observed=2026-08-01T00:45:01.899527Z digest=sha256:48bf43ec77a44b4b44a1cce4dadf6a82f37cf87a34ef97713544b6020698c123

Observation a5b95ae5-e1d6-4392-ad54-5c8831710571 · outbound

This paper cites Planck 2015 results. XIII. Cosmological parameters.

Identifying backsplash galaxies using machine learning Planck 2015 results. XIII. Cosmological parameters

Reference 97

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source=arxiv_source observed=2026-08-01T00:45:01.971255Z digest=sha256:1e3ef1cdbe89b8a117ab55899f8136d60c5452440601a579b5066ec147f8f782

Observation 962204b9-de8d-4499-a238-8f46640975fc · outbound

This paper cites Dependency of halo concentration on mass, redshift and fossilness in Magneticum hydrodynamic simulations.

Identifying backsplash galaxies using machine learning Dependency of halo concentration on mass, redshift and fossilness in Magneticum hydrodynamic simulations

Reference 98

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source=arxiv_source observed=2026-08-01T00:45:02.058787Z digest=sha256:3c41a93cd70ac39ec9d31e9ef8700e091734c551b6dd4ffd2d3cbd38587fb921

Observation d7130ea8-8293-451e-adbc-a7729d00baea · outbound

This paper cites On the evolution of the HI column density distribution in cosmological simulations.

Identifying backsplash galaxies using machine learning On the evolution of the HI column density distribution in cosmological simulations

Reference 99

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source=arxiv_source observed=2026-08-01T00:45:02.177837Z digest=sha256:1ad59eac8d289b3694def3d502c8cb01bb4e2d5061859f22dabd9b5786b18116

Observation 9ee14bf5-0c7e-4bbb-8524-3f50b5457d8c · outbound

This paper cites Phase-space Analysis in the Group and Cluster environment: Time since Infall and Tidal Mass Loss.

Identifying backsplash galaxies using machine learning Phase-space Analysis in the Group and Cluster environment: Time since Infall and Tidal Mass Loss

Reference 100

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source=arxiv_source observed=2026-08-01T00:45:02.283452Z digest=sha256:928b26016ef67a43da9b50a795d950ebbe3f75dbc6d57dfab6c44d88d9b511f8

Observation 12fbe75b-6064-4dfb-80e2-0964e461c029 · outbound

This paper cites Ram pressure stripping candidates in the Coma Cluster: Evidence for enhanced star formation.

Identifying backsplash galaxies using machine learning Ram pressure stripping candidates in the Coma Cluster: Evidence for enhanced star formation

Reference 101

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source=arxiv_source observed=2026-08-01T00:45:02.371592Z digest=sha256:b5dcdbf8e2ebfff20e151ea9a9aaecf4eaceb271f813cfb0d78a4fa2676c50ac

Observation 91fb67bf-caf7-4c71-808d-80481c9d568e · outbound

This paper cites LoTSS jellyfish galaxies: I. Radio tails in low redshift clusters.

Identifying backsplash galaxies using machine learning LoTSS jellyfish galaxies: I. Radio tails in low redshift clusters

Reference 102

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source=arxiv_source observed=2026-08-01T00:45:02.468815Z digest=sha256:2ae5e67cf80d991a1306d5319569ce54c5d72dd5209102151a702333be7b3d61

Observation f943d3fa-5c74-455e-bf15-dcc48662fbc6 · outbound

This paper cites , keywords =.

Identifying backsplash galaxies using machine learning , keywords =

Reference 103

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source=arxiv_source observed=2026-08-01T00:45:02.574431Z digest=sha256:2a8e215bc0a494e09ed6741249a59ad067d2dadc93fb54c171cc558ef5420280

Observation c7a0f5a6-f9a5-4bc4-9f94-336d4f241e85 · outbound

This paper cites CHANCES, the Chilean Cluster Galaxy Evolution Survey: Selection and initial characterisation of clusters and superclusters.

Identifying backsplash galaxies using machine learning CHANCES, the Chilean Cluster Galaxy Evolution Survey: Selection and initial characterisation of clusters and superclusters

Reference 104

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source=arxiv_source observed=2026-08-01T00:45:02.598286Z digest=sha256:bafecafc6d38f2fc32d62d769c69dcf21ad7e5bbc5cd475c28e8cd4cc416dbad

Observation 37950f28-05ca-4d05-bb44-f21f3b35a968 · outbound

This paper cites Witnessing the Growth of the Nearest Galaxy Cluster: Thermodynamics of the Virgo Cluster Outskirts.

Identifying backsplash galaxies using machine learning Witnessing the Growth of the Nearest Galaxy Cluster: Thermodynamics of the Virgo Cluster Outskirts

Reference 105

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source=arxiv_source observed=2026-08-01T00:45:02.640642Z digest=sha256:b1e60e6b8c11285b43a16c8ce4b1982a8e8ad8ea1df7903d33ef9f456c0574de

Pith citing papers

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