Paper Citation Record · LEDGER

Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1910.03636.

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pith.paper-citation-record.v1
1910.03636 v5

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measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Outbound references

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Pith citing papers

Observation d82a20ac-10e2-40c8-a795-b91175ecb673 · inbound

Model-independent calibration of Gamma-Ray Bursts with neural networks cites this paper.

Model-independent calibration of Gamma-Ray Bursts with neural networks Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:55:44.270502Z

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Observation fb567919-ab97-4bd9-88d0-00b743d88962 · inbound

Non-parametric reconstructions of cosmic curvature: current constraints and forecasts cites this paper.

Non-parametric reconstructions of cosmic curvature: current constraints and forecasts Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 45

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Observation a3dc8530-3015-4458-8642-06f50550afef · inbound

Learning from galactic rotation curves: a neural network approach cites this paper.

Learning from galactic rotation curves: a neural network approach Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 10

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Observation b4b9dca2-9d8b-47dd-a845-b76577b75680 · inbound

In search of an interaction in the dark sector through Gaussian Process and ANN approaches cites this paper.

In search of an interaction in the dark sector through Gaussian Process and ANN approaches Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T23:39:45.739550Z

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Observation 53efe5bc-aa1c-4e45-aa8c-eec3c89e173c · inbound

Cosmo-Learn: code for learning cosmology using different methods and mock data cites this paper.

Cosmo-Learn: code for learning cosmology using different methods and mock data Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 70

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Observation f23dfa1a-349c-41e5-8b92-54748b03dd9b · inbound

Testing General Relativity on Galactic Scales via DESI-BAO and Strong Lensing: Circumventing Assumptions on the Hubble Constant, Sound Horizon, and Dark Energy cites this paper.

Testing General Relativity on Galactic Scales via DESI-BAO and Strong Lensing: Circumventing Assumptions on the Hubble Constant, Sound Horizon, and Dark Energy Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 67

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Observation e3e179aa-3202-45c4-a056-8995c0cfd63a · inbound

Model-independent test of the cosmic distance duality relation with recent observational data cites this paper.

Model-independent test of the cosmic distance duality relation with recent observational data Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:13:04.258133Z

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Observation 267ee6f0-90cc-4666-8b8d-800ec321d881 · inbound

Testing $\Lambda$CDM with ANN-Reconstructed Expansion History from Cosmic Chronometers cites this paper.

Testing $\Lambda$CDM with ANN-Reconstructed Expansion History from Cosmic Chronometers Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 46

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Observation fe90cd39-21b6-4889-ba63-1d24a171bca0 · inbound

Constraints on the baryon density from fast radio bursts using a non-parametric reconstruction of the Hubble parameter cites this paper.

Constraints on the baryon density from fast radio bursts using a non-parametric reconstruction of the Hubble parameter Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:00:35.778723Z

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Observation 639159f6-42d1-444a-b434-feb622509c8d · inbound

KAN-LSTM-Transformer Neural Networks, MFV and Cosmological Parameters cites this paper.

KAN-LSTM-Transformer Neural Networks, MFV and Cosmological Parameters Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 94

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b02e5f2f-00d2-4815-a0db-7c211dc7b275 · inbound

Breaking the Dark Sector Degeneracy with Nonparametric Expansion--Growth Reconstruction cites this paper.

Breaking the Dark Sector Degeneracy with Nonparametric Expansion--Growth Reconstruction Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia

Reference 73

Resolution: unresolved; no resolver link, observed 2026-07-14T03:00:21.146194Z

Source-reported events for the cited work

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