Learning from galactic rotation curves: a neural network approach | Paper Citation Record

Observation 03a5b05f-faa9-4dbe-9549-aa93b8abe808 · outbound

This paper cites an unresolved cited work.

Learning from galactic rotation curves: a neural network approach Unresolved cited work

Reference 1

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Observation cfd9be44-5a17-48c1-9c4c-eb5d0d0db372 · outbound

This paper cites Planck 2018 results. VI. Cosmological parameters.

Learning from galactic rotation curves: a neural network approach Planck 2018 results. VI. Cosmological parameters

Reference 2

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Observation b7f44bbe-69e2-47c5-8a8c-a517c925e66d · outbound

This paper cites LSST: from Science Drivers to Reference Design and Anticipated Data Products.

Learning from galactic rotation curves: a neural network approach LSST: from Science Drivers to Reference Design and Anticipated Data Products

Reference 3

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Observation 5b7722cf-26c6-4343-baf6-1f12017b4698 · outbound

This paper cites Snowmass 2021 CMB-S4 White Paper.

Learning from galactic rotation curves: a neural network approach Snowmass 2021 CMB-S4 White Paper

Reference 4

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Observation 9230ac14-068d-42a9-9afc-7e7317cb90d7 · outbound

This paper cites DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations.

Learning from galactic rotation curves: a neural network approach DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations

Reference 5

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Observation 9c2893b3-8181-4a5e-a102-9ff9139e8428 · outbound

This paper cites A high-bias, low-variance introduction to Machine Learning for physicists.

Learning from galactic rotation curves: a neural network approach A high-bias, low-variance introduction to Machine Learning for physicists

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Observation df3b1d83-59c5-4b70-bc83-cbe4c92c9246 · outbound

This paper cites Alzubaidi et al.,Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,J Big Data8(2021), 53 https://doi.org/10.1186/s40537-021-00444-8.

Learning from galactic rotation curves: a neural network approach Alzubaidi et al.,Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,J Big Data8(2021), 53 https://doi.org/10.1186/s40537-021-00444-8

Reference 7

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Observation 86c04394-f988-459d-9597-6ad681393b47 · outbound

This paper cites Attention Is All You Need.

Learning from galactic rotation curves: a neural network approach Attention Is All You Need

Reference 8

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Observation 7eba63a7-f8f8-402c-bd61-cb1fbddc979f · outbound

This paper cites Graff, F.

Learning from galactic rotation curves: a neural network approach Graff, F

Reference 9

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

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

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 8733480f-4350-4286-8229-6ed6f63f6ed7 · outbound

This paper cites ECoPANN: A Framework for Estimating Cosmological Parameters using Artificial Neural Networks.

Learning from galactic rotation curves: a neural network approach ECoPANN: A Framework for Estimating Cosmological Parameters using Artificial Neural Networks

Reference 11

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Observation 9948e147-7e5a-4d9d-82b8-a8df3d2895e4 · outbound

This paper cites Cosmological Parameter Estimation and Inference using Deep Summaries.

Learning from galactic rotation curves: a neural network approach Cosmological Parameter Estimation and Inference using Deep Summaries

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-11T22:21:37.184178Z

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Observation 97f4f255-9974-4f5a-a70a-2cb9109dd077 · outbound

This paper cites Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters.

Learning from galactic rotation curves: a neural network approach Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-11T22:21:37.124864Z

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Observation e31186a7-5193-44a8-8f9c-70cab23dd1dc · outbound

This paper cites Gmez-Vargas, R.

Learning from galactic rotation curves: a neural network approach Gmez-Vargas, R

Reference 14

Resolution: verified exact; doi, observed 2026-08-11T22:21:35.569386Z

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Observation 15fd08f3-2e19-45fb-9600-02381d1d2643 · outbound

This paper cites ParamANN: A Neural Network to Estimate Cosmological Parameters for $\Lambda$CDM Universe Using Hubble Measurements.

Learning from galactic rotation curves: a neural network approach ParamANN: A Neural Network to Estimate Cosmological Parameters for $\Lambda$CDM Universe Using Hubble Measurements

Reference 15

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Observation 2ad19d4b-ecd9-4dfd-bb72-12dc5951b420 · outbound

This paper cites Estimation of Full Sky Power Spectrum between Intermediate to Large Angular Scales from Partial Sky CMB Anisotropies using Artificial Neural Network.

Learning from galactic rotation curves: a neural network approach Estimation of Full Sky Power Spectrum between Intermediate to Large Angular Scales from Partial Sky CMB Anisotropies using Artificial Neural Network

Reference 16

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Observation 617c44d8-c526-487f-baa2-aa97e0607faa · outbound

This paper cites Reconstruction of full sky CMB $\bf{E}$ and $\bf{B}$ modes spectra removing $\bf{E}$-to-$\bf{B}$ leakage from partial sky using deep learning.

Learning from galactic rotation curves: a neural network approach Reconstruction of full sky CMB $\bf{E}$ and $\bf{B}$ modes spectra removing $\bf{E}$-to-$\bf{B}$ leakage from partial sky using deep learning

Reference 17

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Observation 68226099-ecca-4cc1-b9d6-0232ecec3f93 · outbound

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

Learning from galactic rotation curves: a neural network approach LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring its Applications

Reference 18

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Observation 96f1ae0d-f0bb-4c99-9d02-f3efd7031383 · outbound

This paper cites Data-driven modeling of rotation curves with artificial neural networks.

Learning from galactic rotation curves: a neural network approach Data-driven modeling of rotation curves with artificial neural networks

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-11T22:21:36.930112Z

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Observation fb4432e2-4d37-4226-9872-5e3928da42a4 · outbound

This paper cites Accurate and Unbiased Reconstruction of CMB B Mode using Deep Learning.

Learning from galactic rotation curves: a neural network approach Accurate and Unbiased Reconstruction of CMB B Mode using Deep Learning

Reference 20

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Observation 2ce276a8-9d24-4c14-a2ce-86d3765393f7 · outbound

This paper cites Extracting Axion String Network Parameters from Simulated CMB Birefringence Maps using Convolutional Neural Networks.

Learning from galactic rotation curves: a neural network approach Extracting Axion String Network Parameters from Simulated CMB Birefringence Maps using Convolutional Neural Networks

Reference 21

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Observation 1756abe9-2210-401f-b93a-a9aacb640e36 · outbound

This paper cites Signatures of warm dark matter in the cosmological density fields extracted using Machine Learning.

Learning from galactic rotation curves: a neural network approach Signatures of warm dark matter in the cosmological density fields extracted using Machine Learning

Reference 22

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Observation 8885b588-3006-4432-b5e7-40608d9afc28 · outbound

This paper cites Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks.

Learning from galactic rotation curves: a neural network approach Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks

Reference 23

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Observation 14475ecb-4dfc-4781-8870-43b9111aaec3 · outbound

This paper cites Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders.

Learning from galactic rotation curves: a neural network approach Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders

Reference 24

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Observation a3c1ada9-b10e-4e64-9220-eb9ee04eafb1 · outbound

This paper cites SPARC: Mass Models for 175 Disk Galaxies with Spitzer Photometry and Accurate Rotation Curves.

Learning from galactic rotation curves: a neural network approach SPARC: Mass Models for 175 Disk Galaxies with Spitzer Photometry and Accurate Rotation Curves

Reference 25

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Observation 2500f74b-b287-4ec9-a79a-0d55eb440a2e · outbound

This paper cites The distribution of dark matter in galaxies.

Learning from galactic rotation curves: a neural network approach The distribution of dark matter in galaxies

Reference 26

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Observation 023dbd23-807d-49d7-9ddf-15b5e558f0d2 · outbound

This paper cites An Introduction to Particle Dark Matter.

Learning from galactic rotation curves: a neural network approach An Introduction to Particle Dark Matter

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Observation 01d73dda-4736-4b1f-9779-a0e2baedcacb · outbound

This paper cites Cosmic Structure as the Quantum Interference of a Coherent Dark Wave.

Learning from galactic rotation curves: a neural network approach Cosmic Structure as the Quantum Interference of a Coherent Dark Wave

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Observation 2ace8b14-d31f-4f3b-b4ef-409b7a50e434 · outbound

This paper cites Ultra-Light Dark Matter.

Learning from galactic rotation curves: a neural network approach Ultra-Light Dark Matter

Reference 29

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Observation 7c31dd0d-7e20-44b8-92bf-297717dfcba7 · outbound

This paper cites Wave Dark Matter.

Learning from galactic rotation curves: a neural network approach Wave Dark Matter

Reference 30

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Observation b27eef94-7318-43a3-b941-5e1e7d5153a7 · outbound

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Learning from galactic rotation curves: a neural network approach Unresolved cited work

Reference 31

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Observation 7d8fde4e-704d-4444-95be-a1d33d10d71e · outbound

This paper cites an unresolved cited work.

Learning from galactic rotation curves: a neural network approach Unresolved cited work

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Observation 398e4383-5ac8-442c-bceb-ecf166e5dcdf · outbound

This paper cites Rotation curves of high-resolution LSB and SPARC galaxies with fuzzy and multistate (ultra-light boson) scalar field dark matter.

Learning from galactic rotation curves: a neural network approach Rotation curves of high-resolution LSB and SPARC galaxies with fuzzy and multistate (ultra-light boson) scalar field dark matter

Reference 33

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Observation f6ae6710-1979-4608-916f-732e113def37 · outbound

This paper cites Self-Interacting Superfluid Dark Matter Droplets.

Learning from galactic rotation curves: a neural network approach Self-Interacting Superfluid Dark Matter Droplets

Reference 34

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Observation 83cd88c6-9357-4b01-9485-6f66d0e7bece · outbound

This paper cites Dark matter profiles of SPARC galaxies: a challenge to fuzzy dark matter.

Learning from galactic rotation curves: a neural network approach Dark matter profiles of SPARC galaxies: a challenge to fuzzy dark matter

Reference 35

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Observation 0bf49152-c9ef-44ca-8971-042b5760dcff · outbound

This paper cites Confronting fuzzy dark matter with the rotation curves of nearby dwarf irregular galaxies.

Learning from galactic rotation curves: a neural network approach Confronting fuzzy dark matter with the rotation curves of nearby dwarf irregular galaxies

Reference 36

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This paper cites The Structure of Cold Dark Matter Halos.

Learning from galactic rotation curves: a neural network approach The Structure of Cold Dark Matter Halos

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This paper cites Constraints on the mass and self-coupling of Ultra-Light Scalar Field Dark Matter using observational limits on galactic central mass.

Learning from galactic rotation curves: a neural network approach Constraints on the mass and self-coupling of Ultra-Light Scalar Field Dark Matter using observational limits on galactic central mass

Reference 38

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This paper cites Self-interactions of ULDM to the rescue?.

Learning from galactic rotation curves: a neural network approach Self-interactions of ULDM to the rescue?

Reference 39

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Observation cae28b19-3d44-447b-91c7-e5c02720e00b · outbound

This paper cites Galactic rotation curves versus ultralight dark matter: A systematic comparison with SPARC data.

Learning from galactic rotation curves: a neural network approach Galactic rotation curves versus ultralight dark matter: A systematic comparison with SPARC data

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This paper cites Galaxy rotation curves in modified gravity models.

Learning from galactic rotation curves: a neural network approach Galaxy rotation curves in modified gravity models

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Observation ebc986ed-cfc9-4e5e-9e03-8e058ced3b15 · outbound

This paper cites Phenomenology of renormalization group improved gravity from the kinematics of SPARC galaxies.

Learning from galactic rotation curves: a neural network approach Phenomenology of renormalization group improved gravity from the kinematics of SPARC galaxies

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Observation 632cd2cb-acaa-47b6-87d7-3173da859010 · outbound

This paper cites Bounding the Cosmological Constant using Galactic Rotation Curves from the SPARC Dataset.

Learning from galactic rotation curves: a neural network approach Bounding the Cosmological Constant using Galactic Rotation Curves from the SPARC Dataset

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Observation cd27a8d7-a69b-4e37-b136-d48f62af070f · outbound

This paper cites Strang,Linear Algebra and Learning from Data, Wellesley Cambrige Press, 2018 [ISBN: 978-0-6921-9638-0].

Learning from galactic rotation curves: a neural network approach Strang,Linear Algebra and Learning from Data, Wellesley Cambrige Press, 2018 [ISBN: 978-0-6921-9638-0]

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This paper cites Hornik, M.

Learning from galactic rotation curves: a neural network approach Hornik, M

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Learning from galactic rotation curves: a neural network approach Unresolved cited work

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Observation 0b3bb573-457f-4fa4-a2bc-2982cc5dfe99 · outbound

This paper cites Mhaskar, Q.

Learning from galactic rotation curves: a neural network approach Mhaskar, Q

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This paper cites Efficient shallow learning as an alternative to deep learning.

Learning from galactic rotation curves: a neural network approach Efficient shallow learning as an alternative to deep learning

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Learning from galactic rotation curves: a neural network approach Unresolved cited work

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This paper cites ERGO-ML I: Inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks.

Learning from galactic rotation curves: a neural network approach ERGO-ML I: Inferring the assembly histories of IllustrisTNG galaxies from integral observable properties via invertible neural networks

Reference 50

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This paper cites Adam: A Method for Stochastic Optimization.

Learning from galactic rotation curves: a neural network approach Adam: A Method for Stochastic Optimization

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This paper cites Srivastava, G.

Learning from galactic rotation curves: a neural network approach Srivastava, G

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Learning from galactic rotation curves: a neural network approach Unresolved cited work

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This paper cites Kendall and Y.

Learning from galactic rotation curves: a neural network approach Kendall and Y

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:21:37.594641Z

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Observation b62b35c5-1c17-4294-998e-c0ab5938bded · outbound

This paper cites Cosmological constraints with deep learning from KiDS-450 weak lensing maps.

Learning from galactic rotation curves: a neural network approach Cosmological constraints with deep learning from KiDS-450 weak lensing maps

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This paper cites Fast Bayesian gravitational wave parameter estimation using convolutional neural networks.

Learning from galactic rotation curves: a neural network approach Fast Bayesian gravitational wave parameter estimation using convolutional neural networks

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This paper cites Machine Learning and Cosmology.

Learning from galactic rotation curves: a neural network approach Machine Learning and Cosmology

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Observation d9fca0af-e748-4b58-b6a4-10d96e9644a4 · outbound

This paper cites Snowmass2021 Theory Frontier White Paper: Data-Driven Cosmology.

Learning from galactic rotation curves: a neural network approach Snowmass2021 Theory Frontier White Paper: Data-Driven Cosmology

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Observation 5012e2d1-8f28-4400-8a40-8e0ea67d8593 · outbound

This paper cites Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks.

Learning from galactic rotation curves: a neural network approach Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks

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Learning from galactic rotation curves: a neural network approach Unresolved cited work

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This paper cites Cosmological Inference using Gravitational Waves and Normalising Flows.

Learning from galactic rotation curves: a neural network approach Cosmological Inference using Gravitational Waves and Normalising Flows

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This paper cites Tuning neural posterior estimation for gravitational wave inference.

Learning from galactic rotation curves: a neural network approach Tuning neural posterior estimation for gravitational wave inference

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This paper cites Di Valentinoet al.[CosmoVerse Network],The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics,Phys.

Learning from galactic rotation curves: a neural network approach Di Valentinoet al.[CosmoVerse Network],The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics,Phys

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