Paper Citation Record · LEDGER

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning

As of 15 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2501.01942.

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

pith.paper-citation-record.v1
2501.01942 v2

Coverage vector

measured 19 of 19 reference resolution

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External citation measurements

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Outbound references

Observation ee380917-4b37-4865-a0a2-c0036b1bb2c3 · outbound

This paper cites 2020, ApJS, 249, 3, doi:10.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning 2020, ApJS, 249, 3, doi:10

Reference 1

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Observation 6fa9c845-0a56-4157-b892-c8dc2b05d4ed · outbound

This paper cites 2021, A&A, 653, A82, doi:.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning 2021, A&A, 653, A82, doi:

Reference 3

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Observation 640be954-5be8-42e1-9f66-b0d861dde321 · outbound

This paper cites 2016, MNRAS, 462, 4240, doi:.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning 2016, MNRAS, 462, 4240, doi:

Reference 4

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Observation 060e3429-08f5-4dc8-8374-c19a441c60e6 · outbound

This paper cites G., Sun, Y ., Davey, N., et al.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning G., Sun, Y ., Davey, N., et al

Reference 5

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Observation 61b507b9-31f3-450f-bc82-9e9ca92de1de · outbound

This paper cites A., & Lahav, O.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning A., & Lahav, O

Reference 6

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Observation 8efa56ba-a2ee-4f7b-b6f3-d1448cd0eb3c · outbound

This paper cites The DESI Experiment Part I: Science,Targeting, and Survey Design.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning The DESI Experiment Part I: Science,Targeting, and Survey Design

Reference 7

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Observation 69b2d0fc-96f4-4f89-9146-87ed3767e0d1 · outbound

This paper cites Pooling Methods in Deep Neural Networks, a Review.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning Pooling Methods in Deep Neural Networks, a Review

Reference 8

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Observation bd6b32f4-f7dd-4108-b956-2dfe91637115 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning Deep Residual Learning for Image Recognition

Reference 9

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Observation 4877fc81-98f6-4f9f-9280-8e1d5b430dad · outbound

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Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning Unresolved cited work

Reference 10

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Observation e8613d0d-c3c6-43fe-96eb-dc55311e8f25 · outbound

This paper cites L., Wright, A.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning L., Wright, A

Reference 11

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Observation 81bbc1ed-c109-480b-be20-c3a3c7fc8d24 · outbound

This paper cites 1998, Proc.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning 1998, Proc

Reference 12

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Observation 6f264ee7-3de1-4ce5-ae2c-94d17f5654f0 · outbound

This paper cites LSST Science Book, Version 2.0.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning LSST Science Book, Version 2.0

Reference 13

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Observation a1bbc759-6566-41a8-b736-1c486fe694e7 · outbound

This paper cites B., & Lahav, O.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning B., & Lahav, O

Reference 15

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Observation d027b240-c32f-4c61-b6f3-6ca2bf091de5 · outbound

This paper cites H., Cañameras, R., et al.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning H., Cañameras, R., et al

Reference 16

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Observation 98832a23-d9c4-43b9-9b21-4eb8e5397ce5 · outbound

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Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning Unresolved cited work

Reference 17

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Observation 4e938abf-4da8-41f2-8a17-cbd22d5ed395 · outbound

This paper cites R., et al.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning R., et al

Reference 18

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Observation 92937711-2eda-466c-b1c7-67f7448380d3 · outbound

This paper cites E., et al.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning E., et al

Reference 19

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Observation 9d6b0da0-d98a-4a94-84f7-70b938b76ae4 · outbound

This paper cites 2021, Monthly Notices of the Royal Astronomi- cal Society, 509, 2289, doi: 10.1093/mnras/stab3165 Li, R., Napolitano, N.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning 2021, Monthly Notices of the Royal Astronomi- cal Society, 509, 2289, doi: 10.1093/mnras/stab3165 Li, R., Napolitano, N

Reference 2278

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Observation ebd02913-cc2f-4125-bffd-6be5af4919f3 · outbound

This paper cites 1996, in Neural Networks.

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning 1996, in Neural Networks

Reference 2825

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Pith citing papers

No inbound Pith citation observations are available.