Paper Citation Record · LEDGER

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning

As of 18 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 1 inbound Pith citation observation for arXiv:1908.02765.

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

pith.paper-citation-record.v1
1908.02765 v2

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Outbound references

Observation 0f692b2b-2f9c-4852-b828-1dfda0d46275 · outbound

This paper cites G., Tanvir, N.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning G., Tanvir, N

Reference 1

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Observation f7569776-22a7-489e-90b0-7c02a85aa7b2 · outbound

This paper cites E., Springel, V ., White, S.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning E., Springel, V ., White, S

Reference 2

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This paper cites E., Mantz, A., Allen, S.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning E., Mantz, A., Allen, S

Reference 3

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This paper cites J., Kay, S.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning J., Kay, S

Reference 4

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Unresolved cited work

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Unresolved cited work

Reference 6

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Unresolved cited work

Reference 7

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This paper cites H., Mohammed, I., & Lovisari, L.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning H., Mohammed, I., & Lovisari, L

Reference 8

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Observation 35b2dc45-5b1e-4499-a234-8d4b6726053a · outbound

This paper cites 2001, Mach.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning 2001, Mach

Reference 9

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Observation f9d536b4-5096-4ea1-897e-16f6bc829b7d · outbound

This paper cites A., & Tsai, J.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning A., & Tsai, J

Reference 10

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Observation 3762da8a-37b1-46da-a2b0-0be1410d443b · outbound

This paper cites Prediction of galaxy halo masses in SDSS DR7 via a machine learning approach.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Prediction of galaxy halo masses in SDSS DR7 via a machine learning approach

Reference 11

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Observation bbfad067-653b-424f-916b-31e11d2fc53a · outbound

This paper cites E., Ridl, J., et al.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning E., Ridl, J., et al

Reference 12

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Observation 7ada1197-8bfc-4148-9970-e26668dff8aa · outbound

This paper cites Multiwavelength cluster mass estimates and machine learning.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Multiwavelength cluster mass estimates and machine learning

Reference 13

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This paper cites Dark Energy Survey Year 1 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Dark Energy Survey Year 1 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing

Reference 14

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This paper cites P., Bocquet, S., Schrabback, T., et al.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning P., Bocquet, S., Schrabback, T., et al

Reference 15

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Observation 30ee9034-38f6-49e0-aafa-e998fa213573 · outbound

This paper cites 2009, MNRAS, 399, 497.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning 2009, MNRAS, 399, 497

Reference 16

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This paper cites M., Beck, A.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning M., Beck, A

Reference 17

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Observation 2d528231-2ee3-491c-b7be-0ed8241a7177 · outbound

This paper cites 2016, MNRAS, 463, 1797.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning 2016, MNRAS, 463, 1797

Reference 18

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Observation 381ce9b0-5ee4-4b05-8649-8b90b51eb78a · outbound

This paper cites 2011, A&A, 526, A79.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning 2011, A&A, 526, A79

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This paper cites 2019, A&A, 621, A40.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning 2019, A&A, 621, A40

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning 2019, A&A, 621, A39

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Unresolved cited work

Reference 22

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Unresolved cited work

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This paper cites 2019, A&A, 621, A41.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning 2019, A&A, 621, A41

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This paper cites A., Puchwein, E., Shen, S., & Sijacki, D.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning A., Puchwein, E., Shen, S., & Sijacki, D

Reference 25

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Observation b8634aa0-cb9f-4b05-a7e1-c9f8d68b929e · outbound

This paper cites 2017, MNRAS, 465, 3361.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning 2017, MNRAS, 465, 3361

Reference 26

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This paper cites KiDS+VIKING-450: Cosmic shear tomography with optical+infrared data.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning KiDS+VIKING-450: Cosmic shear tomography with optical+infrared data

Reference 27

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This paper cites A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters

Reference 28

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This paper cites 2015, MNRAS, 449, 685.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning 2015, MNRAS, 449, 685

Reference 29

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This paper cites E., & Kennard, R.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning E., & Kennard, R

Reference 30

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Unresolved cited work

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This paper cites KiDS+VIKING-450 and DES-Y1 combined: Cosmology with cosmic shear.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning KiDS+VIKING-450 and DES-Y1 combined: Cosmology with cosmic shear

Reference 32

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This paper cites R., Muldrew, S.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning R., Muldrew, S

Reference 33

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This paper cites M., Dunkley, J., et al.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning M., Dunkley, J., et al

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning V ., & Borgani, S

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning V ., Vikhlinin, A., & Nagai, D

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This paper cites T., Kravtsov, A.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning T., Kravtsov, A

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This paper cites T., Nagai, D., & Nelson, K.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning T., Nagai, D., & Nelson, K

Reference 38

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning M., Primack, J., & Madau, P

Reference 39

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning R., Jones, C., et al

Reference 40

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This paper cites New constraints on the mass bias of galaxy clusters from the power spectra of the thermal Sunyaev-Zeldovich effect and cosmic shear.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning New constraints on the mass bias of galaxy clusters from the power spectra of the thermal Sunyaev-Zeldovich effect and cosmic shear

Reference 41

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This paper cites The Fundamental Relation between Halo Mass and Galaxy Group Properties.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning The Fundamental Relation between Halo Mass and Galaxy Group Properties

Reference 42

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This paper cites B., Allen, S.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning B., Allen, S

Reference 43

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This paper cites P., Smith, G.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning P., Smith, G

Reference 44

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Unresolved cited work

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This paper cites eROSITA Science Book: Mapping the Structure of the Energetic Universe.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning eROSITA Science Book: Mapping the Structure of the Energetic Universe

Reference 46

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This paper cites J., Fabricant, D.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning J., Fabricant, D

Reference 47

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Unresolved cited work

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This paper cites Cluster Cosmology with the Velocity Distribution Function of the HeCS-SZ Sample.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Cluster Cosmology with the Velocity Distribution Function of the HeCS-SZ Sample

Reference 49

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This paper cites J., et al.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning J., et al

Reference 50

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Unresolved cited work

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning Unresolved cited work

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This paper cites H., Porciani, C., Borm, K., & Merloni, A.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning H., Porciani, C., Borm, K., & Merloni, A

Reference 53

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This paper cites W., Arnaud, M., Biviano, A., et al.

Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning W., Arnaud, M., Biviano, A., et al

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning W., Croston, J

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Observation 1aa11c65-b0e9-49ac-a752-59f794f9670d · outbound

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Observation b99dd773-4c33-43cb-8769-9a48696f277f · outbound

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Observation ed084e14-acfe-4375-a55c-27ce51a5f6db · outbound

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Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning 2013, AstRv, 8, 40

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Observation 9fbe8bf7-7b6e-468c-83bf-7a4aef47952d · outbound

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Resolution: unresolved; raw_fallback, observed 2026-08-14T14:41:20.321001Z

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Observation 79954671-952e-4132-8d11-8538269adc9a · outbound

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Resolution: unresolved; raw_fallback, observed 2026-08-14T14:41:20.295605Z

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Observation 6fbf9a25-1281-495b-b402-e2786344e29e · outbound

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Observation 94e05e30-4e7d-4673-8109-a00094646de1 · outbound

This paper cites K., Dolag, K., Comerford, J.

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Observation e53141fb-09f0-4742-bf4a-6fec2c848144 · outbound

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Observation dfea7f03-45fa-4f7e-9028-1e6a10f4bea6 · outbound

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Observation 0edd4de7-e491-4aa6-91ce-65b44535afd6 · outbound

This paper cites an unresolved cited work.

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Resolution: unresolved; raw_fallback, observed 2026-08-14T14:41:20.222438Z

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Observation 9c834a86-28ce-4b25-b5ef-89b55d0fa881 · outbound

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Observation 77396707-6789-4466-bfb2-975f762d7ac4 · outbound

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Observation de8ba6ab-8c63-4190-8991-7efa08f93a18 · inbound

Tighter Dark Matter Constraints from the Projected Mass Method: A Neural Network Enhanced Method for Galaxy Groups and Clusters cites this paper.

Tighter Dark Matter Constraints from the Projected Mass Method: A Neural Network Enhanced Method for Galaxy Groups and Clusters Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning

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Resolution: unresolved; no resolver link, observed 2026-08-01T23:43:57.600085Z

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