Anisotropic cosmology using observational datasets: exploring via machine learning approaches | Paper Citation Record

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Riess et al., Observational evidence from supernovae for an ac- celerating universe and a cosmological constant, Astron

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Nojiri, S.D

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Matsumoto, S

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Nojiri, S.D

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Nojiri, S.D

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Nicolis, R

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Deruelle, L

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Bahamonde, M

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Chen, J.B

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Jamil, D

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4394c40a-3588-4874-90df-edaf8c7d21e2 · outbound

This paper cites an unresolved cited work.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-06T13:05:39.763533Z

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Observation 31df4dab-bbd9-4c7d-95e1-4bcf2d4bd633 · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Lucie-Smith et al., Deep learning insights into cosmological structure formation, Phys

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Observation 0a34cb53-6c47-4f21-8683-6519a03f84ab · outbound

This paper cites Vilardi, S.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Vilardi, S

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Observation be419ed6-5c08-4c64-84cc-095b7a03e0fe · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Bennett et al., First-year Wilkinson microwave anisotropy probe (WMAP) observations: Preliminary maps and basic results, Astrophys

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Observation 35175a64-87de-4cbb-93c0-8e8ee7fa68cc · outbound

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Observation f7a55e11-c969-4636-ba12-fa26bb9ec022 · outbound

This paper cites Singh, B.K.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Singh, B.K

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:05:39.931113Z

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Observation e26edf03-4049-46b8-b267-4d07ee747d5c · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-06T13:05:39.706237Z

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Observation ba554157-1bd1-4950-a7ca-c94ffc52b89e · outbound

This paper cites Nojiri, S.D.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Nojiri, S.D

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Observation a991df0d-4992-453f-a036-ed48e6b1a59c · outbound

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Observation 52e53739-76f6-449b-b337-908b4330b75f · outbound

This paper cites Ghaffari et al., Tsallis holographic dark energy in the Brans–Dicke cosmology, Eur.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Ghaffari et al., Tsallis holographic dark energy in the Brans–Dicke cosmology, Eur

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Observation 8fb61a8a-757f-42b3-92ed-71d5f60a1b5d · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-06T13:05:39.667304Z

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Observation 5de4b605-34dc-4d63-a9c9-913b5aaed7cd · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-06T13:05:39.656998Z

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Observation 28facba4-db29-4bf0-8996-a13e1dbfc876 · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Moresco, Raising the bar: new constraints on the Hubble parameter with cosmic chronometers at z ∼ 2, Mon

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Observation ad5d245c-2e43-466e-afa5-23ba69c68677 · outbound

This paper cites Sharov, V.O.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Sharov, V.O

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Observation c4ca3b3c-5409-4b1c-b664-2815b5a255a8 · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Beutler et al., The 6dF Galaxy Survey: baryon acoustic oscillations and the local Hubble constant, Mon

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Observation 82d6fec7-3ba0-4b30-8a2e-e1b9e4334e4b · outbound

This paper cites Anderson et al., The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: baryon acoustic oscillations in the Data Releases 10 and 11 Galaxy samples, Mon.

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Observation 9e60fdb4-3857-4c50-b220-35cc2d4f4907 · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Padmanabhan et al., A 2% distance to z = 0.35 by reconstructing baryon acoustic oscillations - I: Methods and application to the Sloan Digital Sky Survey, Mon

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Observation eaad710d-b760-4763-baef-791e9edc0467 · outbound

This paper cites Blake et al., The WiggleZ Dark Energy Survey: joint measurements of the expansion and growth history at z <1, Mon.

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Observation 5d7ce9ce-a67c-485b-8b6b-5981639d509a · outbound

This paper cites Farooq, B.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Farooq, B

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Observation 246c9b01-7a15-49d2-89d3-13b5714a8220 · outbound

This paper cites Colgain, M.M.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Colgain, M.M

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Observation 6855997c-d63f-4013-9b5b-29b50fb5b63c · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Luongo et al., Larger H0 values in the CMB dipole direction Phys

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Observation 73b7c4cf-529b-4194-b302-beca0badb0b6 · outbound

This paper cites Percival et al., Baryon acoustic oscillations in the Sloan Digital Sky Survey Data Release 7 galaxy sample, Mon.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Percival et al., Baryon acoustic oscillations in the Sloan Digital Sky Survey Data Release 7 galaxy sample, Mon

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Observation ff2f361b-87b8-47a4-a3e0-babf2019fe88 · outbound

This paper cites Blake et al., The WiggleZ Dark Energy Survey: mapping the distance- redshift relation with baryon acoustic oscillations, Mon.

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Observation 0186453a-a47f-45bc-94df-f219d6fc8d30 · outbound

This paper cites Wiley, T.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Wiley, T

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Observation 269184ed-59d1-4232-a29e-d8770b1858c8 · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches The Role of Machine Learning in the Next Decade of Cosmology

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Observation f66d7b03-fc53-4986-9d6e-f1a9ef35114c · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Giostri et al., New constraints from SN Ia and BAO/CMB, J

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Observation 98600222-e19d-475c-9c8f-55fe8bda1adb · outbound

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Observation 9f2a48ce-ccdc-4fb3-9ba9-1cb3985f9c7f · outbound

This paper cites Ishida, R.R.R.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Ishida, R.R.R

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Observation 410bd59e-f937-4da5-9959-f2a7bf814ccb · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Hinshaw et al., Nine-year Wilkinson Microwave Anisotropy Probe (WMAP) observations: cosmological parameter results, Astrophys

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Observation 3325162c-f9fe-4b63-a72c-5ba080f25468 · outbound

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Observation f2f67a60-1447-4259-9b96-282cf52ce55c · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-06T13:05:38.705066Z

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Observation a57e0e59-8736-4142-bc04-ff8836ffd885 · outbound

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Observation 27fd1061-9ed3-4024-8348-5447b1297bf1 · outbound

This paper cites an unresolved cited work.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-06T13:05:38.214476Z

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Observation 8629bea6-e36f-481d-94ab-b73a6d80f79b · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Unresolved cited work

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Observation 260c9753-45b1-4ea6-be0a-90aff15f52bb · outbound

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Observation 35011987-3edb-417c-a8af-6b52eed40872 · outbound

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Observation b457bf08-c8fd-43e2-9fdd-ee5a8e4e4d8b · outbound

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Anisotropic cosmology using observational datasets: exploring via machine learning approaches Mamon, K

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:05:37.496656Z

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Observation 599207c6-75e9-4dc7-b0d7-8229b78b1976 · outbound

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Observation 9c4960d3-9dae-45ef-97c6-126400019409 · outbound

This paper cites Bhardwaj, A.K.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Bhardwaj, A.K

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Observation 7479a969-d82b-4a4b-9500-87bb793abfc1 · outbound

This paper cites Carbonell, R.S.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Carbonell, R.S

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Observation 7ec47122-8dd5-4f76-97f2-063e1ab817cc · outbound

This paper cites Rumelhart, G.E.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Rumelhart, G.E

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Observation 40afec62-5bfc-45f6-932b-55b9eadbf34b · outbound

This paper cites Geron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly Media, Inc.

Anisotropic cosmology using observational datasets: exploring via machine learning approaches Geron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly Media, Inc

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