Performance of morphological classifiers for galaxy mergers compared to current machine learning methods | Paper Citation Record

Observation 7848e41b-8506-411f-ab58-774fee7a921b · outbound

This paper cites Detection of the 2175{\AA} UV Bump at z>7: Evidence for Rapid Dust Evolution in a Merging Reionisation-Era Galaxy.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Detection of the 2175{\AA} UV Bump at z>7: Evidence for Rapid Dust Evolution in a Merging Reionisation-Era Galaxy

Reference 1

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Observation a72829e0-92ba-48f9-9257-8b8782f390ed · outbound

This paper cites The major merger-active galactic nucleus connection up to the cosmic noon.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The major merger-active galactic nucleus connection up to the cosmic noon

Reference 2

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Observation 24787f25-5747-43b4-bfc4-e129b1ce0a07 · outbound

This paper cites Galaxy morphologies at cosmic noon with JWST: A foundation for exploring gas transport with bars and spiral arms.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Galaxy morphologies at cosmic noon with JWST: A foundation for exploring gas transport with bars and spiral arms

Reference 3

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Observation 0dfcc5c0-7501-4d91-88d9-96e600a921fa · outbound

This paper cites A New Approach to Galaxy Morphology: I. Analysis of the Sloan Digital Sky Survey Early Data Release.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods A New Approach to Galaxy Morphology: I. Analysis of the Sloan Digital Sky Survey Early Data Release

Reference 4

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Observation bccd4471-e043-43a6-9ebd-0bb6994d5242 · outbound

This paper cites Evidence for Multiple Mergers among Ultraluminous IR Galaxies (ULIRGs): Remnants of Compact Groups?.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Evidence for Multiple Mergers among Ultraluminous IR Galaxies (ULIRGs): Remnants of Compact Groups?

Reference 5

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Observation 758480df-b919-47d9-9492-c34bae088613 · outbound

This paper cites Galaxy Zoo: CANDELS Barred Disks and Bar Fractions.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Galaxy Zoo: CANDELS Barred Disks and Bar Fractions

Reference 6

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Observation 2cde6077-4c9f-4677-9ede-157b5df0a704 · outbound

This paper cites Simulations of binary galaxy mergers and the link with fast rotators, slow rotators and kinematically distinct cores.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Simulations of binary galaxy mergers and the link with fast rotators, slow rotators and kinematically distinct cores

Reference 7

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Observation e49c6fe3-0ee8-4f8a-bc8b-ff85c88fe3da · outbound

This paper cites The unorthodox evolution of major merger remnants into star-forming spiral galaxies.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The unorthodox evolution of major merger remnants into star-forming spiral galaxies

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.578746Z

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Observation d931f4f9-974a-4b04-848d-76c83abb0a5e · outbound

This paper cites Galaxy Zoo JWST: Up to 75% of discs are featureless at $3<z<7$.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Galaxy Zoo JWST: Up to 75% of discs are featureless at $3<z<7$

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.254095Z

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Observation 03c4972b-cdf6-48ad-adb5-3f85d1b098d8 · outbound

This paper cites , year = 1996, month = jan, volume =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , year = 1996, month = jan, volume =

Reference 10

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Observation 45b288a6-3c62-42bc-88d6-ccbb04a06e9a · outbound

This paper cites Automated Distant Galaxy Merger Classifications from Space Telescope Images using the Illustris Simulation.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Automated Distant Galaxy Merger Classifications from Space Telescope Images using the Illustris Simulation

Reference 11

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Observation d219bb2e-e12e-43e1-89fb-1a02dab9ffeb · outbound

This paper cites Quasi-Stellar Objects, Ultraluminous IR Galaxies, and Mergers.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Quasi-Stellar Objects, Ultraluminous IR Galaxies, and Mergers

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.322488Z

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Observation 19e18182-1f60-443c-8fe1-6d41fdf4d475 · outbound

This paper cites Properties of galaxies reproduced by a hydrodynamic simulation.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Properties of galaxies reproduced by a hydrodynamic simulation

Reference 13

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Observation 0dfef4c2-1cd1-4b7d-9fb5-b4a6dd17564f · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 14

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Observation ed0e1eeb-42b9-40cd-a7a6-3e99c4388d5d · outbound

This paper cites Star Formation in Galaxies Along the Hubble Sequence.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Star Formation in Galaxies Along the Hubble Sequence

Reference 15

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Observation 6315f1ba-ecb9-4c3c-aa9a-70b0aba2d3f6 · outbound

This paper cites Galactic Stellar and Substellar Initial Mass Function.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Galactic Stellar and Substellar Initial Mass Function

Reference 16

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Observation 48345a03-f0e7-42ba-9c75-cc5a524ff873 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 17

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Observation 161baaba-d746-4f04-817a-d2ca4ff52b3a · outbound

This paper cites Results at z=0.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Results at z=0

Reference 18

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Observation ac56210c-7c30-4f97-a2f8-eab53c2e1c33 · outbound

This paper cites The Journal of Open Source Software , year = 2016, month = oct, volume =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Journal of Open Source Software , year = 2016, month = oct, volume =

Reference 19

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Observation 130a21fe-1bbe-4fa5-8b83-78098a812288 · outbound

This paper cites Galaxy pairs in the Sloan Digital Sky Survey -- XIV. Galaxy mergers do not lie on the Fundamental Metallicity Relation.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Galaxy pairs in the Sloan Digital Sky Survey -- XIV. Galaxy mergers do not lie on the Fundamental Metallicity Relation

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.289917Z

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Observation f0f88b10-b684-4028-ab97-ba3237e66024 · outbound

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

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Planck 2015 results. XIII. Cosmological parameters

Reference 21

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Observation b006d233-d098-45f8-9f4b-cb9e47d06bc1 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 22

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Observation 69aa32cb-71ee-4e9d-af39-faa1b682570c · outbound

This paper cites Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe

Reference 23

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Observation ecc9f14c-61d3-4c33-9c92-fb6792e9f279 · outbound

This paper cites Introducing the Illustris Project: the evolution of galaxy populations across cosmic time.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Introducing the Illustris Project: the evolution of galaxy populations across cosmic time

Reference 24

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Observation e479efa8-ce30-43e4-af1e-e949f23418b9 · outbound

This paper cites The Illustris simulation: the evolving population of black holes across cosmic time.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Illustris simulation: the evolving population of black holes across cosmic time

Reference 25

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Observation 952c38ea-53ca-4985-b0dc-f94e9022d431 · outbound

This paper cites Galaxy Interactions Trigger Rapid Black Hole Growth: an unprecedented view from the Hyper Suprime-Cam Survey.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Galaxy Interactions Trigger Rapid Black Hole Growth: an unprecedented view from the Hyper Suprime-Cam Survey

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.385693Z

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Observation e5f27b19-c080-44f4-9795-4525669cc3ea · outbound

This paper cites RMS asymmetry: a robust metric of galaxy shapes in images with varied depth and resolution.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods RMS asymmetry: a robust metric of galaxy shapes in images with varied depth and resolution

Reference 27

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Observation d4e170f6-dd75-4a6d-a19f-1d6c3443fc8a · outbound

This paper cites Galaxy merger challenge: A comparison study between machine learning-based detection methods.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Galaxy merger challenge: A comparison study between machine learning-based detection methods

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.547084Z

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Observation 3fe8e9bb-a6fc-4160-a655-8fca20c5e8f6 · outbound

This paper cites emcee: The MCMC Hammer.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods emcee: The MCMC Hammer

Reference 29

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Observation fa8c60d4-b79a-484a-81f6-e49644e88454 · outbound

This paper cites The Hyper Suprime-Cam Software Pipeline.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Hyper Suprime-Cam Software Pipeline

Reference 30

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Observation 2261adcc-a35d-4940-996e-e53579474a7f · outbound

This paper cites First results from the IllustrisTNG simulations: A tale of two elements -- chemical evolution of magnesium and europium.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods First results from the IllustrisTNG simulations: A tale of two elements -- chemical evolution of magnesium and europium

Reference 31

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Observation 67f4bd80-861b-4972-be10-e50e9b296c01 · outbound

This paper cites First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies

Reference 32

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Observation 7ed6fc84-b2f8-4406-b389-36a646ff5592 · outbound

This paper cites First results from the IllustrisTNG simulations: matter and galaxy clustering.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods First results from the IllustrisTNG simulations: matter and galaxy clustering

Reference 33

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Observation 80802593-834d-4901-aff4-9f2c69e2c901 · outbound

This paper cites First results from the IllustrisTNG simulations: the galaxy color bimodality.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods First results from the IllustrisTNG simulations: the galaxy color bimodality

Reference 34

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Observation f244c841-a45c-4b53-a717-a78b78dccfa6 · outbound

This paper cites First results from the IllustrisTNG simulations: radio haloes and magnetic fields.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods First results from the IllustrisTNG simulations: radio haloes and magnetic fields

Reference 35

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Observation 84311883-8ab8-47ae-b6aa-6cb387d4ee99 · outbound

This paper cites ERGO-ML I: Inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods ERGO-ML I: Inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.509849Z

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Observation 93402c8d-f836-49dd-abb5-66b97c8e0574 · outbound

This paper cites Communications in Applied Mathematics and Computational Science , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Communications in Applied Mathematics and Computational Science , keywords =

Reference 37

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Observation 2dd138c7-2bd2-4c7e-8a43-1f89172a0506 · outbound

This paper cites The Symmetry, Color, and Morphology of Galaxies.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Symmetry, Color, and Morphology of Galaxies

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.455468Z

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Observation a48f5a07-a531-44e4-ae0c-bd5fc0638ae8 · outbound

This paper cites The Asymmetry of Galaxies: Physical Morphology for Nearby and High Redshift Galaxies.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Asymmetry of Galaxies: Physical Morphology for Nearby and High Redshift Galaxies

Reference 39

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Observation 8e50b7a4-11e8-4420-b57b-3bc8e9c4fae7 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 40

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Observation 1061b566-3f40-401f-a237-a2de712aecf7 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 41

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Observation 5b37a4fb-a90d-439f-8208-9f5f06f7483f · outbound

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

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.270929Z

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Observation 52e3e7f7-db03-464c-b132-db2c5c1cd10b · outbound

This paper cites Dancing in the dark: galactic properties trace spin swings along the cosmic web.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Dancing in the dark: galactic properties trace spin swings along the cosmic web

Reference 43

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Observation 151ff958-83bc-4e05-9046-1a07fa773430 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 44

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Observation d339b84c-360f-4c46-b0a1-4f5344603fc4 · outbound

This paper cites Classifying merger stages with adaptive deep learning and cosmological hydrodynamical simulations.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Classifying merger stages with adaptive deep learning and cosmological hydrodynamical simulations

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.225374Z

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Observation 71091d47-c782-4d2a-93c4-719bcfea791f · outbound

This paper cites Galaxy Mergers in UNIONS -- I: A Simulation-driven Hybrid Deep Learning Ensemble for Pure Galaxy Merger Classification.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Galaxy Mergers in UNIONS -- I: A Simulation-driven Hybrid Deep Learning Ensemble for Pure Galaxy Merger Classification

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.200343Z

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Observation a31b8e1a-d0b3-4af7-afc5-5eda07203b14 · outbound

This paper cites Planck 2013 results. XVI. Cosmological parameters.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Planck 2013 results. XVI. Cosmological parameters

Reference 47

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Observation fe75f4e8-b009-4abc-9473-fdae6fc87610 · outbound

This paper cites The Hyper Suprime-Cam SSP Survey: Overview and Survey Design.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Hyper Suprime-Cam SSP Survey: Overview and Survey Design

Reference 48

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Observation 873e3bad-fa01-4a09-aaa0-786856983605 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 49

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Observation 6d00f7c2-573a-4d1b-84de-198a905851bf · outbound

This paper cites Building Merger Trees from Cosmological N-body Simulations.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Building Merger Trees from Cosmological N-body Simulations

Reference 50

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Observation 22d5b024-591f-4a5b-9d84-b956c79dc9c8 · outbound

This paper cites , year = 1955, month = jan, volume =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , year = 1955, month = jan, volume =

Reference 51

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Observation b808baa2-d974-4c7a-840e-66a15eb922d8 · outbound

This paper cites Radiative Transfer in a Clumpy Universe: II. The Utraviolet Extragalactic Background.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Radiative Transfer in a Clumpy Universe: II. The Utraviolet Extragalactic Background

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation cc6b5f79-3809-49b9-a563-aa325848abd7 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 53

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Observation 4a5a8c72-135d-41e3-9be8-ad71a05566c8 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 54

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Observation da275335-2183-4a16-bc94-eae267ff1fd7 · outbound

This paper cites Initial Conditions for Large Cosmological Simulations.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Initial Conditions for Large Cosmological Simulations

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.508058Z

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Observation e3aa9e34-949e-47e4-824b-cac798e0c9fc · outbound

This paper cites Seven-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Interpretation.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Seven-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Cosmological Interpretation

Reference 56

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Observation 6c78e1d6-7143-45a3-883d-421c0f1c66d8 · outbound

This paper cites Cosmological Hydrodynamics with Adaptive Mesh Refinement: a new high resolution code called RAMSES.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Cosmological Hydrodynamics with Adaptive Mesh Refinement: a new high resolution code called RAMSES

Reference 57

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Observation 685b5043-d925-4320-9534-d6feaa33ea57 · outbound

This paper cites Using transfer learning to detect galaxy mergers.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Using transfer learning to detect galaxy mergers

Reference 58

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This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 59

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Observation 1d27af9a-2dde-4853-8795-e2f19ea2d5a1 · outbound

This paper cites Morphological signatures of mergers in the TNG50 simulation and the Kilo-Degree Survey: the merger fraction from dwarfs to Milky Way-like galaxies.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Morphological signatures of mergers in the TNG50 simulation and the Kilo-Degree Survey: the merger fraction from dwarfs to Milky Way-like galaxies

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.596757Z

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Observation beffd50b-eb67-427f-b795-4f335fd6b4c2 · outbound

This paper cites North Ecliptic Pole merging galaxy catalogue.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods North Ecliptic Pole merging galaxy catalogue

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.441063Z

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Observation e7697e5d-8c1f-43f7-bdc7-0f46d451fb15 · outbound

This paper cites LSST: from Science Drivers to Reference Design and Anticipated Data Products.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods LSST: from Science Drivers to Reference Design and Anticipated Data Products

Reference 62

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Observation 66c842e7-342c-4976-9bff-5e5c42e5a4ed · outbound

This paper cites Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?

Reference 63

Resolution: metadata mismatch; local_arxiv, observed 2026-07-13T04:49:20.418532Z

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Observation beee3e72-c059-4ea7-b4bd-32d1bcac5a78 · outbound

This paper cites Galaxy evolution in the post-merger regime. III -- The triggering of active galactic nuclei peaks immediately after coalescence.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Galaxy evolution in the post-merger regime. III -- The triggering of active galactic nuclei peaks immediately after coalescence

Reference 64

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Observation 32e2a3a1-267c-4620-afd7-1684808ec9f8 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 65

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation b192bb21-a8c3-4d77-9c6d-56d08b130c79 · outbound

This paper cites Overview of the Euclid mission.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Overview of the Euclid mission

Reference 66

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Observation 34ea20b2-573a-4e72-835c-0724cf687437 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 67

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Observation 371a8653-8991-4770-8fc5-8cf18019df72 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 02910006-2d78-4bbf-8e39-dc5e0e3b4ecc · outbound

This paper cites Effect of galaxy mergers on star formation rates.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Effect of galaxy mergers on star formation rates

Reference 69

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Observation e7cac22c-366f-48d8-a606-1e825e468363 · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 70

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 275b6c2d-7750-42b2-a387-7eaef981c55a · outbound

This paper cites Toward First-Principle Simulations of Galaxy Formation: II. Shock-Induced Starburst at a Collision Interface During the First Encounter of Interacting Galaxies.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Toward First-Principle Simulations of Galaxy Formation: II. Shock-Induced Starburst at a Collision Interface During the First Encounter of Interacting Galaxies

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.184151Z

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Observation e932051f-beb2-4ceb-8085-76834d121657 · outbound

This paper cites The optical morphologies of galaxies in the IllustrisTNG simulation: a comparison to Pan-STARRS observations.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The optical morphologies of galaxies in the IllustrisTNG simulation: a comparison to Pan-STARRS observations

Reference 72

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 5a6af9dc-ff19-4cd4-bc89-8b539e0f63ab · 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.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

Reference 73

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 317a25d0-65a6-414c-b7ce-a0a544707620 · outbound

This paper cites and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and

Reference 74

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation c4af4f3b-f339-4191-8171-cb53a743498f · outbound

This paper cites The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package

Reference 75

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation c881002d-5741-4777-b505-739e83b45058 · outbound

This paper cites Astropy: A Community Python Package for Astronomy.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Astropy: A Community Python Package for Astronomy

Reference 76

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation c7e810f6-5995-4e02-8428-44d3e89f3ef0 · outbound

This paper cites Constraining the major merger history of $z \sim 3-9$ galaxies using JADES: dominant in-situ star formation.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Constraining the major merger history of $z \sim 3-9$ galaxies using JADES: dominant in-situ star formation

Reference 77

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 3d2cb06b-2d14-4d09-86aa-0eb3aa8e61e2 · outbound

This paper cites Dynamically Close Galaxy Pairs and Merger Rate Evolution in the CNOC2 Redshift Survey.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Dynamically Close Galaxy Pairs and Merger Rate Evolution in the CNOC2 Redshift Survey

Reference 78

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation c0dc207f-2654-431a-9739-4d557cba40b9 · outbound

This paper cites A direct measurement of galaxy major and minor merger rates and stellar mass accretion histories at $z < 3$ using galaxy pairs in the REFINE survey.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods A direct measurement of galaxy major and minor merger rates and stellar mass accretion histories at $z < 3$ using galaxy pairs in the REFINE survey

Reference 79

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.333046Z

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Observation e551529b-028a-4a94-a82b-d57ff48526dd · outbound

This paper cites The ALHAMBRA survey: Accurate merger fractions by PDF analysis of photometric close pairs.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The ALHAMBRA survey: Accurate merger fractions by PDF analysis of photometric close pairs

Reference 80

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.411069Z

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Observation f3d916ed-9976-41a5-ae2a-3c3f02ac4a3d · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 81

Resolution: verified exact; doi, observed 2026-07-13T04:49:20.355560Z

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Observation abf309a0-9c95-4682-9b9f-cb13125e332d · outbound

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Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Unresolved cited work

Reference 82

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 34094ad7-3058-42a5-8551-2c0bb9da02f1 · outbound

This paper cites The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The fourth data release of the Kilo-Degree Survey: ugri imaging and nine-band optical-IR photometry over 1000 square degrees

Reference 83

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation c573a9dd-044f-418e-88d5-4171fd491a34 · outbound

This paper cites Bayesian photometric redshift estimation.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Bayesian photometric redshift estimation

Reference 84

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 527be963-a304-4464-b997-c67946edb369 · outbound

This paper cites The Messenger , year = 2013, month = dec, volume =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Messenger , year = 2013, month = dec, volume =

Reference 85

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 4afc0607-37b8-41ff-adcd-b38f040fc4d7 · outbound

This paper cites KiDS+VIKING-450: A new combined optical & near-IR dataset for cosmology and astrophysics.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods KiDS+VIKING-450: A new combined optical & near-IR dataset for cosmology and astrophysics

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.350918Z

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Observation cd8da55a-30b6-4194-9d75-379c1afb2009 · outbound

This paper cites Galaxy And Mass Assembly (GAMA): end of survey report and data release 2.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Galaxy And Mass Assembly (GAMA): end of survey report and data release 2

Reference 87

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 1b17885e-8c13-4b54-957a-46661d0adf6d · outbound

This paper cites Beyond Spheroids and Discs: Classifications of CANDELS Galaxy Structure at 1.4 < z < 2 via Principal Component Analysis.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Beyond Spheroids and Discs: Classifications of CANDELS Galaxy Structure at 1.4 < z < 2 via Principal Component Analysis

Reference 88

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.362088Z

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Observation a3d241d7-6c85-4ec1-8dc8-45da4e3ffcb8 · outbound

This paper cites The Evolution of Galaxy Structure over Cosmic Time.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Evolution of Galaxy Structure over Cosmic Time

Reference 89

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation a5f41f02-fda7-4003-ad02-6d57c1ddeb66 · outbound

This paper cites New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods New Image Statistics for Detecting Disturbed Galaxy Morphologies at High Redshift

Reference 90

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation dbff0f60-fd46-43a7-8861-30bb4a0be2d6 · outbound

This paper cites High Energy, Optical, and Infrared Detectors for Astronomy V , year = 2012, editor =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods High Energy, Optical, and Infrared Detectors for Astronomy V , year = 2012, editor =

Reference 91

Resolution: verified exact; doi, observed 2026-07-13T04:49:20.344323Z

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Observation 8c975a25-d909-4775-aec6-63c72e3e232d · outbound

This paper cites , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods , keywords =

Reference 92

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation f2349eb7-8614-464f-96ae-08bf8d1054c9 · outbound

This paper cites A New Non-Parametric Approach to Galaxy Morphological Classification.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods A New Non-Parametric Approach to Galaxy Morphological Classification

Reference 93

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 2038af3d-4428-435b-9b36-cad57ac06667 · outbound

This paper cites an unresolved cited work.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Unresolved cited work

Reference 94

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 483f5ac3-09ec-43f4-b938-a1756c41bef6 · outbound

This paper cites Structural and Photometric Classification of Galaxies - I. Calibration Based on a Nearby Galaxy Sample.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Structural and Photometric Classification of Galaxies - I. Calibration Based on a Nearby Galaxy Sample

Reference 95

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation 0572b216-f28c-4809-9711-e7a06f067de2 · outbound

This paper cites The Relationship Between Stellar Light Distributions of Galaxies and their Formation Histories.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods The Relationship Between Stellar Light Distributions of Galaxies and their Formation Histories

Reference 96

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation f9bbe65e-0c54-484c-bd11-a0dd6a944ac9 · outbound

This paper cites Describing the Nonuniversal Galaxy Merger Timescales in IllustrisTNG: Effects of Host Halo Mass, Baryons, and Sample Selection.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Describing the Nonuniversal Galaxy Merger Timescales in IllustrisTNG: Effects of Host Halo Mass, Baryons, and Sample Selection

Reference 97

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation f3983fce-d8c1-4564-afe5-129d96b04181 · outbound

This paper cites Euclid Quick Data Release (Q1). First Euclid statistical study of galaxy mergers and their connection to active galactic nuclei.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods Euclid Quick Data Release (Q1). First Euclid statistical study of galaxy mergers and their connection to active galactic nuclei

Reference 98

Resolution: unresolved; no resolver link, observed 2026-07-13T04:41:12.165957Z

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Observation cc240766-c5ef-4866-9cb2-23095f56c6fa · outbound

This paper cites arXiv e-prints , keywords =.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods arXiv e-prints , keywords =

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-07-13T04:49:20.352456Z

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Observation 555079c4-c897-47a0-99e4-574851062d1a · outbound

This paper cites MaNGA AGN dwarf galaxies (MAD). III. The role of mergers and environment in active galactic nucleus activity in dwarf galaxies.

Performance of morphological classifiers for galaxy mergers compared to current machine learning methods MaNGA AGN dwarf galaxies (MAD). III. The role of mergers and environment in active galactic nucleus activity in dwarf galaxies

Reference 100

Resolution: verified exact; local_arxiv, observed 2026-07-13T04:49:20.236259Z

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