KAN-LSTM-Transformer Neural Networks, MFV and Cosmological Parameters

arXiv:2607.06959 · astro-ph.CO · Submitted 2026-07-08 · Read on arXiv

Jiang Zhang, Yan-dong Chen

astro-ph.CO

Submitted: 2026-07-08

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 34× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Attention Mechanism — 12× in this paper · explained in 3 episodes
• Most Frequent Value — 11× in this paper
• Ablation — 7× in this paper · explained in 1 episode
• Consistency — 6× in this paper · explained in 3 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Activation Function — 5× in this paper · explained in 1 episode
• Long Short-Term Memory — 5× in this paper
• Core — 4× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Ensemble Learning — 2× in this paper · explained in 2 episodes
• Hubble Tension — 2× in this paper · explained in 9 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Parameter Efficiency — 2× in this paper · explained in 3 episodes
• Physics-Guided Transformer — 2× in this paper
• Symbolic Regression — 2× in this paper · explained in 5 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Abundance — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Gaussian Processes (GPs) — 1× in this paper · explained in 2 episodes
• Initial Data — 1× in this paper · explained in 2 episodes
• Kolmogorov-Arnold Networks (KAN) — 1× in this paper · explained in 2 episodes
• Metallicity — 1× in this paper · explained in 3 episodes
• Neutron-capture elements — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes

Sources

• Cosmology with the Large Synoptic Survey Telescope: an Overview
• Role of Future SNIa Data from Rubin LSST in Reinvestigating Cosmological Models
• Reconstructing the Hubble parameter with future Gravitational Wave missions using Machine Learning
• A possible late-time transition of $M_B$ inferred via neural networks
• Neural network reconstruction of scalar-tensor cosmology
• Fast Radio Bursts and Artificial Neural Networks: a cosmological-model-independent estimation of the Hubble Constant
• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
• JWST Observations Reject Unrecognized Crowding of Cepheid Photometry as an Explanation for the Hubble Tension at 8 sigma Confidence
• Systematics in the Cepheid and TRGB Distance Scales: Metallicity Sensitivity of the Wesenheit Leavitt Law
• Calibration of the Tip of the Red Giant Branch (TRGB)
• Progress in Direct Measurements of the Hubble Constant
• DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations
• New Hubble Space Telescope Discoveries of Type Ia Supernovae at z > 1: Narrowing Constraints on the Early Behavior of Dark Energy
• The Complete Light-curve Sample of Spectroscopically Confirmed Type Ia Supernovae from Pan-STARRS1 and Cosmological Constraints from The Combined Pantheon Sample
• CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
• Machine Learning for Observational Cosmology
• KAN: Kolmogorov-Arnold Networks
• Attention Is All You Need
• Galaxy-Galaxy Strong Lensing with U-Net (GGSL-UNet). I. Extracting 2-Dimensional Information from Multi-Band Images in Ground and Space Observations
• Advancing Cosmological Parameter Estimation and Hubble Parameter Reconstruction with Long Short-Term Memory and Efficient-Kolmogorov-Arnold Networks

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