Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample | Paper Citation Record

Observation 694b8794-5cf4-4d3e-9ef7-53709a2d59f7 · outbound

This paper cites Constraining ΩM and Dark Energy with Gamma-Ray Bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraining ΩM and Dark Energy with Gamma-Ray Bursts

Reference 1

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Observation 2405c05e-e3a1-425d-b3e2-b9cb9534f3e0 · outbound

This paper cites A new method optimized to use gamma-ray bursts as cosmic rulers.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample A new method optimized to use gamma-ray bursts as cosmic rulers

Reference 2

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Observation 3be8ca8a-347d-4131-844b-39a6b5c870dd · outbound

This paper cites Gamma-Ray Bursts: New Rulers to Measure the Universe.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Gamma-Ray Bursts: New Rulers to Measure the Universe

Reference 3

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Observation 8dd75ce4-bbc1-4282-b969-5c8305a653f9 · outbound

This paper cites Gamma-ray bursts as standard candles to constrain the cosmological parameters.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Gamma-ray bursts as standard candles to constrain the cosmological parameters

Reference 4

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Observation aaefb809-9389-469a-b6c6-ee6b9843d5ea · outbound

This paper cites Calibration of gamma-ray burst luminosity indicators.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Calibration of gamma-ray burst luminosity indicators

Reference 5

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Observation 71a5bb47-9793-4fa3-a487-59e7087a93bc · outbound

This paper cites Gamma-Ray Burst Hubble Diagram to z = 4.5.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Gamma-Ray Burst Hubble Diagram to z = 4.5

Reference 6

Resolution: verified exact; doi, observed 2026-08-07T05:09:17.492970Z

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Observation 1e4b35cb-7bcd-469b-b705-885836735eca · outbound

This paper cites The Hubble Diagram to Redshift >6 from 69 Gamma-Ray Bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample The Hubble Diagram to Redshift >6 from 69 Gamma-Ray Bursts

Reference 7

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Observation 200a3bee-c583-49df-980c-f35ec3a27974 · outbound

This paper cites Constraining the cosmological parameters and transition redshift with gamma-ray bursts and supernovae.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraining the cosmological parameters and transition redshift with gamma-ray bursts and supernovae

Reference 8

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Observation 7f4f1ab4-f221-434e-904d-985aadb5d382 · outbound

This paper cites Can Gamma-Ray Bursts Be Used to Measure Cosmology? A Further Analysis.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Can Gamma-Ray Bursts Be Used to Measure Cosmology? A Further Analysis

Reference 9

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Observation fb922639-437e-41ea-97f0-9c036eea8255 · outbound

This paper cites A Cosmology-Independent Calibration of Gamma-Ray Burst Luminosity Relations and the Hubble Diagram.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample A Cosmology-Independent Calibration of Gamma-Ray Burst Luminosity Relations and the Hubble Diagram

Reference 10

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Observation 82936d9c-d7c1-415c-8994-97fedb447ff1 · outbound

This paper cites Addressing the circularity problem in the Ep-Eiso correlation of gamma-ray bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Addressing the circularity problem in the Ep-Eiso correlation of gamma-ray bursts

Reference 11

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Observation 5b92f4fc-8f0c-4082-837b-9a39c978edc2 · outbound

This paper cites Intrinsic spectra and energetics of BeppoSAX Gamma-Ray Bursts with known redshifts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Intrinsic spectra and energetics of BeppoSAX Gamma-Ray Bursts with known redshifts

Reference 12

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Observation e210c5f8-8f5a-48e1-ac30-7a884b81b4d9 · outbound

This paper cites Cosmography by GRBs: Gamma Ray Bursts as possible distance indicators.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Cosmography by GRBs: Gamma Ray Bursts as possible distance indicators

Reference 14

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Observation 6fad474b-4191-4f19-8e4e-5e318080eda8 · outbound

This paper cites Cosmology with gamma-ray bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Cosmology with gamma-ray bursts

Reference 15

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Observation 2f66b4f4-aa6f-4fe3-b208-509f1f8c9c84 · outbound

This paper cites Cosmology with gamma-ray bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Cosmology with gamma-ray bursts

Reference 16

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Observation f80a1e12-1fb3-4177-9ac6-fd9cb3f9f089 · outbound

This paper cites Constraints on cosmological models and reconstructing the acceleration history of the Universe with gamma-ray burst distance indicators.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraints on cosmological models and reconstructing the acceleration history of the Universe with gamma-ray burst distance indicators

Reference 17

Resolution: verified exact; doi, observed 2026-08-07T05:09:16.210827Z

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Observation 90133ce2-cc24-41fd-8071-dc906a6e19d2 · outbound

This paper cites Constraints on the generalized Chaplygin gas model including gamma-ray bursts via a Markov Chain Monte Carlo approach.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraints on the generalized Chaplygin gas model including gamma-ray bursts via a Markov Chain Monte Carlo approach

Reference 18

Resolution: verified exact; doi, observed 2026-08-07T05:09:15.872044Z

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Observation 743399bc-a702-4a3b-be63-25d001bc90cf · outbound

This paper cites Reconstructing the cosmic expansion history up to redshift z = 6.29 with the calibrated gamma-ray bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Reconstructing the cosmic expansion history up to redshift z = 6.29 with the calibrated gamma-ray bursts

Reference 19

Resolution: verified exact; doi, observed 2026-08-07T05:09:15.655316Z

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Observation ca493109-1ba5-4c2c-834c-ab1c09f893ad · outbound

This paper cites Observational constraints on cosmological models with the updated long gamma-ray bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Observational constraints on cosmological models with the updated long gamma-ray bursts

Reference 20

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Observation 64c53580-e6bb-4764-b166-925180a0a395 · outbound

This paper cites Intermediate redshift calibration of gamma-ray bursts and cosmic constraints in non-flat cosmology.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Intermediate redshift calibration of gamma-ray bursts and cosmic constraints in non-flat cosmology

Reference 21

Resolution: verified exact; doi, observed 2026-08-07T05:09:15.275461Z

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Observation 3647a509-cd26-4e77-ba9d-577101e24a7f · outbound

This paper cites Improving sampling and calibration of gamma-ray bursts as distance indicators.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Improving sampling and calibration of gamma-ray bursts as distance indicators

Reference 22

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Observation 49798864-2f16-4f44-8da9-82675c3e2326 · outbound

This paper cites Measuring dark energy with the Eiso - Ep correlation of gamma-ray bursts using model-independent methods.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Measuring dark energy with the Eiso - Ep correlation of gamma-ray bursts using model-independent methods

Reference 23

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Observation 48956f5f-b38d-4f9c-b5f6-13e969c4a177 · outbound

This paper cites Calibration of Gamma-Ray Burst Luminosity Correlations Using Gravitational Waves as Standard Sirens.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Calibration of Gamma-Ray Burst Luminosity Correlations Using Gravitational Waves as Standard Sirens

Reference 24

Resolution: verified exact; doi, observed 2026-08-07T05:05:42.955268Z

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Observation a9026174-fc62-4bf2-a48c-19b4460c46a6 · outbound

This paper cites Redshift evolution of the Amati relation: Calibrated results from the Hubble diagram of quasars at high redshifts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Redshift evolution of the Amati relation: Calibrated results from the Hubble diagram of quasars at high redshifts

Reference 25

Resolution: verified exact; doi, observed 2026-08-07T05:05:42.941424Z

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Observation 861bae90-58c8-46f1-993e-973bfc5ac152 · outbound

This paper cites Calibration of Luminosity Correlations of Gamma-Ray Bursts Using Quasars.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Calibration of Luminosity Correlations of Gamma-Ray Bursts Using Quasars

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.928243Z

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Observation e621fd5d-ce98-4863-a0e0-1f8739c22373 · outbound

This paper cites Low redshift calibration of the Amati relation using galaxy clusters.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Low redshift calibration of the Amati relation using galaxy clusters

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Observation 1afd65e6-63bf-4acd-8c4d-8a556052ba16 · outbound

This paper cites Measuring the cosmological parameters with the E p,i-Eiso correlation of gamma-ray bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Measuring the cosmological parameters with the E p,i-Eiso correlation of gamma-ray bursts

Reference 28

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Observation 8765bd56-7781-4c70-a604-cceb01abb0bd · outbound

This paper cites Standardizing Platinum Dainotti-correlated gamma-ray bursts, and using them with standardized Amati-correlated gamma-ray bursts to constrain cosmological model parameters.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Standardizing Platinum Dainotti-correlated gamma-ray bursts, and using them with standardized Amati-correlated gamma-ray bursts to constrain cosmological model parameters

Reference 29

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Observation c030a1f9-8f78-485c-a621-7f4824b3f202 · outbound

This paper cites Standardizing Dainotti-correlated gamma-ray bursts, and using them with standardized Amati- correlated gamma-ray bursts to constrain cosmological model parameters.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Standardizing Dainotti-correlated gamma-ray bursts, and using them with standardized Amati- correlated gamma-ray bursts to constrain cosmological model parameters

Reference 30

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Observation cacf41f1-4af9-47b8-b3ff-7c27bf6bd566 · outbound

This paper cites Constraints on cosmological parameters from gamma-ray burst peak photon energy and bolometric fluence measurements and other data.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraints on cosmological parameters from gamma-ray burst peak photon energy and bolometric fluence measurements and other data

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Observation e0eaba1a-aa49-45eb-ad18-65e79ab52fba · outbound

This paper cites Do gamma-ray burst measurements provide a useful test of cosmological models? J.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Do gamma-ray burst measurements provide a useful test of cosmological models? J

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Observation ef09fabd-1650-4660-9a9e-b1c53cd3a800 · outbound

This paper cites Using lower redshift, non-CMB, data to constrain the Hubble constant and other cosmological parameters.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Using lower redshift, non-CMB, data to constrain the Hubble constant and other cosmological parameters

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Observation ff356f38-7dff-4302-8d79-43eea1dfe676 · outbound

This paper cites Do high redshift QSOs and GRBs corroborate JWST?Phys.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Do high redshift QSOs and GRBs corroborate JWST?Phys

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Observation f4b9e96f-7f4e-41af-bbe6-d98d9287fcfb · outbound

This paper cites Towards a new model-independent calibration of Gamma-Ray Bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Towards a new model-independent calibration of Gamma-Ray Bursts

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Observation d5c45fde-ead4-4427-8a6f-5a159dbc97fe · outbound

This paper cites Detection of gamma-ray burst Amati relation based on Hubble data set and Pantheon+ samples.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Detection of gamma-ray burst Amati relation based on Hubble data set and Pantheon+ samples

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Observation 4b1614d4-3dc7-478f-b614-928ce85c93a1 · outbound

This paper cites Measuring cosmological parameters with a luminosity-time correlation of gamma-ray bursts.Mon.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Measuring cosmological parameters with a luminosity-time correlation of gamma-ray bursts.Mon

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Observation 88be7fe7-3488-454b-bac3-dc93459f0a87 · outbound

This paper cites Standardizing the gamma-ray burst as a standard candle and applying it to cosmological probes: Constraints on the two-component dark energy model.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Standardizing the gamma-ray burst as a standard candle and applying it to cosmological probes: Constraints on the two-component dark energy model

Reference 38

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Observation 628cae12-2c5f-48a6-8410-adc37d9fa447 · outbound

This paper cites The Improved Amati Correlations from Gaussian Copula.Astrophys.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample The Improved Amati Correlations from Gaussian Copula.Astrophys

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Observation 69660b49-e195-4836-9a11-65d0c39721c2 · outbound

This paper cites Gamma-Ray Burst Constraints on Cosmological Models from the Improved Amati Correlation.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Gamma-Ray Burst Constraints on Cosmological Models from the Improved Amati Correlation

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Observation d36ecf2e-8099-4e80-8d6b-ed64ac6a207a · outbound

This paper cites Testing Non-Coincident f (Q)-gravity with DESI DR2 BAO and GRBs.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Testing Non-Coincident f (Q)-gravity with DESI DR2 BAO and GRBs

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Observation 0d2ccdc6-e8f3-4174-bfc5-d9ae933784f8 · outbound

This paper cites Radio Plateaus in Gamma-Ray Burst Afterglows and Their Application in Cosmology.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Radio Plateaus in Gamma-Ray Burst Afterglows and Their Application in Cosmology

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Observation 313ebfa5-4fb2-46ad-bd84-9ac0bf693e11 · outbound

This paper cites High-redshift cosmology by Gamma-Ray Bursts: An overview.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample High-redshift cosmology by Gamma-Ray Bursts: An overview

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Observation 579da6f9-d94d-4d53-bf1f-2554b3c20e8b · outbound

This paper cites The Observed Luminosity Correlations of Gamma-Ray Bursts and Their Applications.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample The Observed Luminosity Correlations of Gamma-Ray Bursts and Their Applications

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Observation 0ba54445-28a3-4ac4-9d88-3e3b36f30a84 · outbound

This paper cites Cosmology-Independent Distance Moduli of 42 Gamma-Ray Bursts between Redshift of 1.44 and 6.60.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Cosmology-Independent Distance Moduli of 42 Gamma-Ray Bursts between Redshift of 1.44 and 6.60

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Observation 235ff05e-a3a0-4a5f-9de9-8846b8f8e1c7 · outbound

This paper cites An updated gamma-ray bursts Hubble diagram.Mon.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample An updated gamma-ray bursts Hubble diagram.Mon

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Observation dec6cde9-9cab-4ddb-80c9-137864148af7 · outbound

This paper cites A cosmographic calibration of the Ep,i - Eiso (Amati) relation for GRBs.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample A cosmographic calibration of the Ep,i - Eiso (Amati) relation for GRBs

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Observation 1505a76c-218e-445d-a45f-4062fe9bc55a · outbound

This paper cites Calibration of GRB Luminosity Relations with Cosmography.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Calibration of GRB Luminosity Relations with Cosmography

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.665857Z

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

Observation e0316796-810d-4246-a2ea-197188dbc246 · outbound

This paper cites Cosmological models and gamma-ray bursts calibrated by using Padé method.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Cosmological models and gamma-ray bursts calibrated by using Padé method

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Observation 06828237-5c70-41d5-8963-ad3311c609f0 · outbound

This paper cites New measurements of Ωm from gamma-ray bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample New measurements of Ωm from gamma-ray bursts

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Observation 8225993e-5b2e-428f-8ff2-6e667cb4b6ab · outbound

This paper cites B.; Zaninoni, E.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample B.; Zaninoni, E

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Observation 22466989-c275-4ac0-968b-2cb3b3085d80 · outbound

This paper cites Reconstruction of dark energy and expansion dynamics using Gaussian processes.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Reconstruction of dark energy and expansion dynamics using Gaussian processes

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Observation 17f223a0-9ff3-4a99-a7f0-70ed50ab6306 · outbound

This paper cites Constraints on the Cosmological Parameters with Three-Parameter Correlation of Gamma-Ray Bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraints on the Cosmological Parameters with Three-Parameter Correlation of Gamma-Ray Bursts

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Observation b9ee0bbe-142c-4ec8-be46-d6aa9e9c93af · outbound

This paper cites Calibrating Gamma-Ray Bursts by Using a Gaussian Process with Type Ia Supernovae.Astrophys.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Calibrating Gamma-Ray Bursts by Using a Gaussian Process with Type Ia Supernovae.Astrophys

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Observation 1e65e9d4-db60-4061-9714-6478b0ae13fb · outbound

This paper cites Cosmography via Gaussian process with gamma ray bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Cosmography via Gaussian process with gamma ray bursts

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.608111Z

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Observation 56739576-3d24-46f1-8a5a-7fc1720e6b61 · outbound

This paper cites Testing the Phenomenological Interacting Dark Energy Model with Gamma-Ray Bursts and Pantheon+ type Ia Supernovae.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Testing the Phenomenological Interacting Dark Energy Model with Gamma-Ray Bursts and Pantheon+ type Ia Supernovae

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.594916Z

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Observation 4afe8689-31cf-4bac-a800-d76652d1fd38 · outbound

This paper cites Constraining the emergent dark energy models with observational data at intermediate redshift.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraining the emergent dark energy models with observational data at intermediate redshift

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.581617Z

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Observation 323fcd9a-d48c-4a3c-9978-d82a30217ab4 · outbound

This paper cites Constraints from Fermi observations of long gamma-ray bursts on cosmological parameters.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraints from Fermi observations of long gamma-ray bursts on cosmological parameters

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.461800Z

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Observation 50420a70-534d-4efa-b5aa-98a0940cd8c0 · outbound

This paper cites Constraints on cosmological models with gamma-ray bursts in cosmology-independent way.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraints on cosmological models with gamma-ray bursts in cosmology-independent way

Reference 60

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Observation 59a8a932-438f-459d-8726-1bd7351a755a · outbound

This paper cites Using H(z) data as a probe of the concordance model.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Using H(z) data as a probe of the concordance model

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.469383Z

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Observation 57ee25aa-d725-4e2b-8844-a6f81fd15883 · outbound

This paper cites An Improved Method to Measure the Cosmic Curvature.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample An Improved Method to Measure the Cosmic Curvature

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Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.472886Z

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Observation b179c8fb-d23e-4dc7-8531-19583eeb1409 · outbound

This paper cites Testing the fidelity of Gaussian processes for cosmography.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Testing the fidelity of Gaussian processes for cosmography

Reference 63

Resolution: verified exact; doi, observed 2026-08-07T05:05:42.533027Z

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Observation 7ee7a811-8677-4eb2-af06-b1cc527fb910 · outbound

This paper cites Model-independent calibrations of gamma-ray bursts using machine learning.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Model-independent calibrations of gamma-ray bursts using machine learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.480608Z

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Observation bdf79d94-4a47-413c-8cc8-1b1a706603bb · outbound

This paper cites Measuring the Hubble constant with cosmic chronometers: A machine learning approach.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Measuring the Hubble constant with cosmic chronometers: A machine learning approach

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.510747Z

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Observation 440ff213-d20c-46ed-86f4-4a59b598890e · outbound

This paper cites Model-independent gamma-ray bursts constraints on cosmological models using machine learning.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Model-independent gamma-ray bursts constraints on cosmological models using machine learning

Reference 66

Resolution: verified exact; doi, observed 2026-08-07T05:05:42.497976Z

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

Observation cfb87391-9837-4e67-bb40-f4568194052b · outbound

This paper cites The Pantheon+ Analysis: The Full Data Set and Light-curve Release.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample The Pantheon+ Analysis: The Full Data Set and Light-curve Release

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.492908Z

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Observation 2e2d2d4e-0913-4a58-8303-c58067d7d448 · outbound

This paper cites Estimating Cosmological Parameters and Reconstructing Hubble Constant with Artificial Neural Networks: A Test with covariance matrix and mock H(z).

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Estimating Cosmological Parameters and Reconstructing Hubble Constant with Artificial Neural Networks: A Test with covariance matrix and mock H(z)

Reference 68

Resolution: verified exact; doi, observed 2026-08-07T05:05:42.474609Z

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Observation bd7d8670-7cda-4b76-a144-a56d914b7133 · outbound

This paper cites an unresolved cited work.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Unresolved cited work

Reference 69

Resolution: verified exact; doi, observed 2026-08-07T05:05:42.914063Z

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Observation efcdae4d-dd8a-4975-904a-d4283386b9e8 · outbound

This paper cites Do high redshift QSOs and GRBs corroborate JWST?.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Do high redshift QSOs and GRBs corroborate JWST?

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.504730Z

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Observation 3ba5aa91-bf53-4647-9b4e-2436424e65b9 · outbound

This paper cites A deep learning approach to cosmological dark energy models.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample A deep learning approach to cosmological dark energy models

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.388225Z

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Observation 40b32f7e-d8e1-4487-8831-8fd019fa69d4 · outbound

This paper cites Neural network reconstruction of late-time cosmology and null tests.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Neural network reconstruction of late-time cosmology and null tests

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.500850Z

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Observation d3ec992b-36a4-49a2-831e-9217910c41e9 · outbound

This paper cites Constraining the Hubble Constant with a Simulated Full Covariance Matrix Using Neural Networks.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraining the Hubble Constant with a Simulated Full Covariance Matrix Using Neural Networks

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.360109Z

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Observation 23afd6b4-f892-49ff-ab8e-8b1b51bfe0f3 · outbound

This paper cites Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with a Hubble Parameter and SNe Ia.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with a Hubble Parameter and SNe Ia

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.521461Z

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Observation 61cdc28b-9049-4b7c-bd79-715d217df9ca · outbound

This paper cites Neural network reconstructions for the Hubble parameter, growth rate and distance modulus.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Neural network reconstructions for the Hubble parameter, growth rate and distance modulus

Reference 75

Resolution: verified exact; doi, observed 2026-08-07T05:05:42.374556Z

Source-reported events for the cited work

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Observation 03d7f2eb-90e5-469d-b20f-234589735f85 · outbound

This paper cites LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring Its Applications.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring Its Applications

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.262477Z

Source-reported events for the cited work

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Observation d9bd4eeb-1215-41e8-b1f8-53af3d1fa218 · outbound

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

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Model-independent calibration of Gamma-Ray Bursts with neural networks

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.532842Z

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Observation 0fcaede6-349e-4518-9256-a7d840128e93 · outbound

This paper cites A Nonparametric Reconstruction of the Hubble Parameter H(z) Based on Radial Basis Function Neural Networks.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample A Nonparametric Reconstruction of the Hubble Parameter H(z) Based on Radial Basis Function Neural Networks

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.525277Z

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Observation d89b473b-0338-45af-9ca0-b1d59f52de2c · outbound

This paper cites A Fundamental Plane for Long Gamma-Ray Bursts with X-Ray Plateaus.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample A Fundamental Plane for Long Gamma-Ray Bursts with X-Ray Plateaus

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Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.540525Z

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Observation d089274a-d03e-44a6-a41b-6db35c234115 · outbound

This paper cites Gamma-ray bursts calibrated from the observational H(z) data in artificial neural network framework.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Gamma-ray bursts calibrated from the observational H(z) data in artificial neural network framework

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.544319Z

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Observation 26dc0b53-6314-4cfd-bb3b-e50070d30483 · outbound

This paper cites A time-luminosity correlation forγ-ray bursts in the X-rays.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample A time-luminosity correlation forγ-ray bursts in the X-rays

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.537009Z

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Observation 08257dce-28d5-4263-b320-56831aac01f9 · outbound

This paper cites E iso-Ep correlation of gamma-ray bursts: Calibration and cosmological applications.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample E iso-Ep correlation of gamma-ray bursts: Calibration and cosmological applications

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.552223Z

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Observation a68edf46-dba6-46c8-af83-3340950e96c6 · outbound

This paper cites Neural networks and standard cosmography with newly calibrated high redshift GRB observations.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Neural networks and standard cosmography with newly calibrated high redshift GRB observations

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.203210Z

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Observation ff03f966-9c2b-4347-96ea-33dc095cdf2e · outbound

This paper cites Testing the standardizability of, and deriving cosmological constraints from, a new Amati-correlated gamma-ray burst data compilation.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Testing the standardizability of, and deriving cosmological constraints from, a new Amati-correlated gamma-ray burst data compilation

Reference 84

Resolution: verified exact; doi, observed 2026-08-07T05:05:42.225988Z

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Observation 3e7d16ec-5e81-4119-9443-c428fdda228f · outbound

This paper cites Reconstructing the Hubble diagram of gamma-ray bursts using deep learning.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Reconstructing the Hubble diagram of gamma-ray bursts using deep learning

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.175080Z

Source-reported events for the cited work

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Observation a31be0ce-9823-4b48-a80b-f0c8d982541f · outbound

This paper cites Learning representations by back-propagating errors.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Learning representations by back-propagating errors

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.568015Z

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Observation 3bca329b-6d35-4a9f-8400-439bcd47587f · outbound

This paper cites Model-independently Calibrating the Luminosity Correlations of Gamma-Ray Bursts Using Deep Learning.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Model-independently Calibrating the Luminosity Correlations of Gamma-Ray Bursts Using Deep Learning

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.188772Z

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Observation ea92b51a-bab1-4eb4-8e38-4290a27bc94c · outbound

This paper cites Dropout as a Bayesian Approximation: Appendix.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Dropout as a Bayesian Approximation: Appendix

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.576156Z

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Observation c7dce690-e93c-445f-82a6-c8f13e431a15 · outbound

This paper cites Dropout: A Simple Way to Prevent Neural Networks from Overftting.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Dropout: A Simple Way to Prevent Neural Networks from Overftting

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.580878Z

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Observation 8066fc61-4bd9-4b95-a53f-cbf3ed6c3802 · outbound

This paper cites Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.571994Z

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Observation 48aedb92-c27a-46dc-a091-789d3610b154 · outbound

This paper cites emcee: The MCMC Hammer.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample emcee: The MCMC Hammer

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.589384Z

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Observation 543af2a0-6dcd-4d40-a37b-60bfb1756d79 · outbound

This paper cites Dust Extinction Curves and Lyα Forest Flux Deficits for Use in Modeling Gamma-Ray Burst Afterglows and All Other Extragalactic Point Sources.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Dust Extinction Curves and Lyα Forest Flux Deficits for Use in Modeling Gamma-Ray Burst Afterglows and All Other Extragalactic Point Sources

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.100066Z

Source-reported events for the cited work

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Observation 9be3f240-9b94-4bd5-bf5e-1b13534fb794 · outbound

This paper cites Constraints on cosmological models from quasars calibrated with type Ia supernova by a Gaussian process.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Constraints on cosmological models from quasars calibrated with type Ia supernova by a Gaussian process

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.122827Z

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Observation 5bc620e4-2696-4430-a416-0b7e6dfbcede · outbound

This paper cites Testing dark energy models with gamma-ray bursts calibrated from the observational H(z) data through a Gaussian process.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Testing dark energy models with gamma-ray bursts calibrated from the observational H(z) data through a Gaussian process

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.600574Z

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Observation d0e42729-09b9-4d1a-9748-773b9d4064dc · outbound

This paper cites Prospects of high redshift constraints on dark energy models with the Ep,i-Eiso correlation in long gamma ray bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Prospects of high redshift constraints on dark energy models with the Ep,i-Eiso correlation in long gamma ray bursts

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.067453Z

Source-reported events for the cited work

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Observation 8da5befb-5a94-4866-86ba-f4382f1a647f · outbound

This paper cites Measuring Cosmological Parameters with Gamma Ray Bursts.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Measuring Cosmological Parameters with Gamma Ray Bursts

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Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.596882Z

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Observation f58d09e1-98d5-4fac-ae5b-8e7bb88d0e7b · outbound

This paper cites Model-independent distance calibration of high-redshift gamma-ray bursts and constrain on the ΛCDM model.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Model-independent distance calibration of high-redshift gamma-ray bursts and constrain on the ΛCDM model

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.044143Z

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 950c0c4f-4a42-4a24-9632-d7901c5a17ee · outbound

This paper cites Model-independent Constraints on Cosmic Curvature and Opacity.Astrophys.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Model-independent Constraints on Cosmic Curvature and Opacity.Astrophys

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Resolution: verified exact; doi, observed 2026-08-07T05:05:42.030603Z

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Observation 96c8c9b2-bd60-48c9-bec7-105488651664 · outbound

This paper cites Gamma rays bursts: A viable cosmological probe? J.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Gamma rays bursts: A viable cosmological probe? J

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Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.608444Z

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Observation 38155c3e-b792-4864-88c8-167abb926ad7 · outbound

This paper cites Exploring the Expansion History of the Universe.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Exploring the Expansion History of the Universe

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.624634Z

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Observation 412bb255-802c-413b-a86d-7371c4b1655a · outbound

This paper cites Improved constraints on the expansion rate of the Universe up to z ~1.1 from the spectroscopic evolution of cosmic chronometers.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Improved constraints on the expansion rate of the Universe up to z ~1.1 from the spectroscopic evolution of cosmic chronometers

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Resolution: unresolved; no resolver link, observed 2026-08-07T05:05:41.628144Z

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Observation 14d9de9b-1668-4fcd-bc53-1ac127687a63 · outbound

This paper cites Accelerating Universes with Scaling Dark Matter.

Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample Accelerating Universes with Scaling Dark Matter

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