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Source: paper_references, paper_reference_links, observed 2026-08-01T00:45:02.640642Z
Paper Citation Record · LEDGER
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.
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Source: paper_references, paper_reference_links, observed 2026-08-01T00:45:02.640642Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
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100 of 119 outbound references displayed
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Observation 3fad1cf2-d335-4631-b425-4a23e1e119cd · outbound
Identifying backsplash galaxies using machine learning Probing galaxy evolution in massive clusters using ACT and DES: splashback as a cosmic clock
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Observation f5f4354b-e7b4-4313-9561-bfcfd6b0eaa5 · outbound
Identifying backsplash galaxies using machine learning arXiv e-prints , keywords =
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Observation b96e86a6-015b-4d39-89ae-d82e3fce647a · outbound
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
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
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
Identifying backsplash galaxies using machine learning , keywords =
Reference 6
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Observation 76322730-0cc1-43c7-8039-42015262a2af · outbound
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
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
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
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
Identifying backsplash galaxies using machine learning , keywords =
Reference 12
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Observation 59a89cb3-282d-46e0-bb85-c3c83a38740d · outbound
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
Identifying backsplash galaxies using machine learning Unresolved cited work
Reference 14
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Observation 7716ecb8-13b3-4867-a4fa-e959910db356 · outbound
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
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
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
Identifying backsplash galaxies using machine learning Machine Learning , keywords =
Reference 18
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Observation 791d22f0-c005-495c-add3-b6f6b6bab004 · outbound
Identifying backsplash galaxies using machine learning Unresolved cited work
Reference 19
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Observation bce2341e-4dea-4af2-8024-a05b011f1f50 · outbound
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
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
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
Identifying backsplash galaxies using machine learning Unresolved cited work
Reference 23
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Observation bee3ae14-9300-40c1-bf58-c386c340e24c · outbound
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
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
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
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
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
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
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
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
Identifying backsplash galaxies using machine learning Improving galaxy morphologies for SDSS with Deep Learning
Reference 32
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Observation 8aaad90b-97c2-494e-86cd-aa87db6426e6 · outbound
Identifying backsplash galaxies using machine learning , keywords =
Reference 33
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Observation ff44cee3-848f-4dae-a1ed-593fd3e1a9eb · outbound
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
Identifying backsplash galaxies using machine learning ViCTORIA project: The LOFAR HBA Virgo Cluster Survey
Reference 35
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Observation b6a4e203-9dc8-4d70-b320-80c3a0df28bd · outbound
Identifying backsplash galaxies using machine learning Overview of the Euclid mission
Reference 36
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Observation f6751ddd-c1c5-4572-bcda-5fe18cfa49ac · outbound
Identifying backsplash galaxies using machine learning Unresolved cited work
Reference 37
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Observation 9d6f3c47-3677-4595-a513-e36624a83bdb · outbound
Identifying backsplash galaxies using machine learning Exploring the stellar populations of backsplash galaxies
Reference 38
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Observation bc3a08d1-ba50-48bc-9fbc-0e6b808b29d0 · outbound
Identifying backsplash galaxies using machine learning , keywords =
Reference 39
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Observation f44f2580-f073-4d95-89da-d17385197978 · outbound
Identifying backsplash galaxies using machine learning Virgo Filaments V: Disrupting the Baryon Cycle in the NGC 5364 Galaxy Group
Reference 40
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Observation 943c45de-420c-43d6-8c6a-84664997ebf8 · outbound
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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Observation 1addf14d-3321-4d90-ba47-de397d7c38e1 · outbound
Identifying backsplash galaxies using machine learning , year = 1972, month = aug, volume =
Reference 44
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Observation ca8c4008-b3cf-4c1e-9348-4c80cfb352d8 · outbound
Identifying backsplash galaxies using machine learning The Three Hundred Project: Backsplash galaxies in simulations of clusters
Reference 45
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Observation cc175c1a-4449-42eb-8196-39954db94d08 · outbound
Identifying backsplash galaxies using machine learning The Three Hundred project: Galaxy groups do not survive cluster infall
Reference 46
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Observation 2dd5d869-596f-4e29-8e8b-da6e7f875db8 · outbound
Identifying backsplash galaxies using machine learning Constraining cosmological parameters using the splashback radius of galaxy clusters
Reference 47
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Observation 2b7e2872-c7ef-4fa6-9bd6-39a017c0b559 · outbound
Identifying backsplash galaxies using machine learning Reconsidering the dynamical states of galaxy clusters using PCA and UMAP
Reference 48
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Observation 50688c77-bab9-4e39-81c6-d1931aba2fff · outbound
Identifying backsplash galaxies using machine learning YZiCS: Preprocessing of dark halos in the hydrodynamic zoom-in simulation of clusters
Reference 49
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Observation 439c0c97-2624-4081-9e52-35ff153202b6 · outbound
Identifying backsplash galaxies using machine learning The Open Journal of Astrophysics , keywords =
Reference 50
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Observation ee9669e6-a0a4-4350-8b80-4a60aa30bb45 · outbound
Identifying backsplash galaxies using machine learning GIZMO: A New Class of Accurate, Mesh-Free Hydrodynamic Simulation Methods
Reference 51
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Observation bf26bf6b-a0f4-45cc-80ee-38ff31ee3ba5 · outbound
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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Observation 8cc81c88-672c-4253-97e1-3ef7c856fe80 · outbound
Identifying backsplash galaxies using machine learning Intrinsic Alignment in redMaPPer clusters -- II. Radial alignment of satellites toward cluster centers
Reference 53
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Observation 09063952-dbe7-406f-90ae-c356ec26a3d7 · outbound
Identifying backsplash galaxies using machine learning Unresolved cited work
Reference 54
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Observation 74a83f8d-be3a-4576-aa97-752de20d34fc · outbound
Identifying backsplash galaxies using machine learning GASP IX. Jellyfish galaxies in phase-space: an orbital study of intense ram-pressure stripping in clusters
Reference 55
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Observation 066e309b-e1c9-4fde-a4d3-24b9482449ec · outbound
Identifying backsplash galaxies using machine learning Environmental Dependence of Galaxy Merger Rate in {\Lambda}CDM Universe
Reference 56
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Observation f8fc5b0b-c78e-45e2-acc1-dfc5b8cb38db · outbound
Identifying backsplash galaxies using machine learning The wide-field, multiplexed, spectroscopic facility WEAVE: Survey design, overview, and simulated implementation
Reference 57
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Observation 3ff9f635-728f-454e-8a8d-8b436fce948a · outbound
Identifying backsplash galaxies using machine learning , keywords =
Reference 58
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Observation adaf1d0d-6a0d-4cd8-9afe-e2e716fdd4d9 · outbound
Identifying backsplash galaxies using machine learning The Extended Virgo Cluster Catalog
Reference 59
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Observation 7940120a-070d-4f0d-8e43-a11d30285830 · outbound
Identifying backsplash galaxies using machine learning MultiDark simulations: the story of dark matter halo concentrations and density profiles
Reference 60
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Observation bac5798a-918f-44eb-9135-159e28c2101a · outbound
Identifying backsplash galaxies using machine learning , keywords =
Reference 61
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Observation 6dd764e3-f4eb-4a4e-8f5d-e5d45e31e63d · outbound
Identifying backsplash galaxies using machine learning , keywords =
Reference 62
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Observation ed85c5b7-4c12-4351-bb0d-f590f61e487d · outbound
Identifying backsplash galaxies using machine learning Structure Finding in Cosmological Simulations: The State of Affairs
Reference 63
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Observation a1ef7a4c-7954-4b61-858b-3ae1efd89f22 · outbound
Identifying backsplash galaxies using machine learning The Three Hundred project: shapes and radial alignment of satellite, infalling, and backsplash galaxies
Reference 64
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Observation c3678ea7-83e1-4573-b10b-9cd542c5b67e · outbound
Identifying backsplash galaxies using machine learning Ahf: Amiga's Halo Finder
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Observation 8c1e2dbc-1c90-40ef-90e8-f74e8ea34100 · outbound
Identifying backsplash galaxies using machine learning Cosmic filaments delay quenching inside clusters
Reference 66
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Observation 95e082d4-6c5c-42b1-8c33-98d18c579de2 · outbound
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
Reference 67
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Observation 643ccaab-8243-4612-ac17-3b5c9a50d042 · outbound
Identifying backsplash galaxies using machine learning A Comparison of Methods for Determining the Molecular Content of Model Galaxies
Reference 68
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Observation 905e8c2e-8d79-471e-88cc-9176d3398f19 · outbound
Identifying backsplash galaxies using machine learning Mapping and characterisation of cosmic filaments in galaxy cluster outskirts: strategies and forecasts for observations from simulations
Reference 69
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Observation 05d77e57-78b6-454a-a291-8c83cfb41157 · outbound
Identifying backsplash galaxies using machine learning An inventory of galaxies in cosmic filaments feeding galaxy clusters: galaxy groups, backsplash galaxies, and pristine galaxies
Reference 70
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Observation 7c888137-66ac-448f-a8d5-98fdf2f33d83 · outbound
Identifying backsplash galaxies using machine learning From voids to filaments: environmental transformations of galaxies in the SDSS
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Observation 08fb2548-e752-4639-93d5-43b2899b919e · outbound
Identifying backsplash galaxies using machine learning , keywords =
Reference 72
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Observation 4981b187-ceb1-49a6-a8fd-3754afef3d55 · outbound
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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Observation a6c4944a-ee45-4de9-8f8d-bb9cd7b814d3 · outbound
Identifying backsplash galaxies using machine learning Cosmology and Astrophysics from Relaxed Galaxy Clusters I: Sample Selection
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Observation 842abdfa-77c7-4d64-9a1d-895ad1d6dc5e · outbound
Identifying backsplash galaxies using machine learning Machine Learning to identify ICL and BCG in simulated galaxy clusters
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Observation f324ea6d-40cb-4433-bfc4-cd6b149ee105 · outbound
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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Observation 6a8fafd2-a157-45bd-9a35-ae1a3aea75f5 · outbound
Identifying backsplash galaxies using machine learning 2010 , doi=
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Observation eaa44126-98b2-4069-ae9e-0bc1bb1754bc · outbound
Identifying backsplash galaxies using machine learning Clusters of Galaxies: Probes of Cosmological Structure and Galaxy Evolution , year = 2004, editor =
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Observation 441e426a-6f99-4cf7-8fdc-a1ce9d3392a5 · outbound
Identifying backsplash galaxies using machine learning , keywords =
Reference 80
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Observation 43de786c-5464-4174-86d4-0f1dd531a009 · outbound
Identifying backsplash galaxies using machine learning Galaxy Harassment and the Evolution of Clusters of Galaxies
Reference 81
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Observation c1da87f1-d23b-4632-8259-c21ba25f0fb4 · outbound
Identifying backsplash galaxies using machine learning The splashback radius as a physical halo boundary and the growth of halo mass
Reference 82
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Observation 6013e076-b81c-4623-aac6-ce64995dee5e · outbound
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
Reference 83
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Observation 59acb146-1337-4a10-9f7f-d89c6cef31e7 · outbound
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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Observation d6e34eab-1ad7-4b66-9a88-2d41e6f64928 · outbound
Identifying backsplash galaxies using machine learning The Three Hundred Project: The gas disruption of infalling objects in cluster environments
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Observation 0a29ee47-d60f-4c5f-af73-f22ccc367085 · outbound
Identifying backsplash galaxies using machine learning Galaxy properties in clusters. II. Backsplash Galaxies
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Observation ce0ec242-431a-43f0-ada9-dee847723adf · outbound
Identifying backsplash galaxies using machine learning , keywords =
Reference 88
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Observation ae107987-850a-4193-9d68-964774c1c96c · outbound
Identifying backsplash galaxies using machine learning The IllustrisTNG Simulations: Public Data Release
Reference 89
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Observation 88059d6f-0824-4e43-8e96-0b2f6600f2ef · outbound
Identifying backsplash galaxies using machine learning , keywords =
Reference 90
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Observation 93a9b1c6-2619-488b-8ce4-710fcc25509a · outbound
Identifying backsplash galaxies using machine learning Photometry of 15 Clusters
Reference 91
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Observation f7815b07-0481-4514-ae7f-a94dc642a8f8 · outbound
Identifying backsplash galaxies using machine learning Tracing the Quenching History in Galaxy Clusters in the EAGLE Simulation
Reference 92
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Observation dd7d3166-af57-4890-8bdf-91ec91119e9a · outbound
Identifying backsplash galaxies using machine learning and Varoquaux, G
Reference 93
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Observation 83d2ebab-fc09-4c05-95c9-0313dd77f361 · outbound
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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Observation fe49ac57-d2fe-48e3-b53e-4046d1cefe2a · outbound
Identifying backsplash galaxies using machine learning , keywords =
Reference 95
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Observation 4393dcb9-a4e6-4cd4-b5f3-3821ca4cc0ff · outbound
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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Observation a5b95ae5-e1d6-4392-ad54-5c8831710571 · outbound
Identifying backsplash galaxies using machine learning Planck 2015 results. XIII. Cosmological parameters
Reference 97
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Observation 962204b9-de8d-4499-a238-8f46640975fc · outbound
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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Observation d7130ea8-8293-451e-adbc-a7729d00baea · outbound
Identifying backsplash galaxies using machine learning On the evolution of the HI column density distribution in cosmological simulations
Reference 99
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Observation 9ee14bf5-0c7e-4bbb-8524-3f50b5457d8c · outbound
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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Observation 12fbe75b-6064-4dfb-80e2-0964e461c029 · outbound
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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Observation 91fb67bf-caf7-4c71-808d-80481c9d568e · outbound
Identifying backsplash galaxies using machine learning LoTSS jellyfish galaxies: I. Radio tails in low redshift clusters
Reference 102
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Identifying backsplash galaxies using machine learning , keywords =
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Identifying backsplash galaxies using machine learning CHANCES, the Chilean Cluster Galaxy Evolution Survey: Selection and initial characterisation of clusters and superclusters
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Identifying backsplash galaxies using machine learning Witnessing the Growth of the Nearest Galaxy Cluster: Thermodynamics of the Virgo Cluster Outskirts
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