Estimating redshift distributions of Fermi blazars with multiple machine-learning models

arXiv:2609.27397 · astro-ph.HE, astro-ph.IM · Submitted 2026-09-23 · Read on arXiv

astro-ph.HE, astro-ph.IM

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• AGN — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Activation Function — 2× in this paper · explained in 1 episode
• Observational Selection Effects — 2× in this paper · explained in 2 episodes
• Uncertainty Quantification — 2× in this paper · explained in 8 episodes
• Active Galactic Nuclei (AGN) — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Ensemble Learning — 1× in this paper · explained in 2 episodes
• Fourth Catalog of Active Galactic Nuclei — 1× in this paper
• Gaussian Mixture Model (GMM) — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Rational Quadratic — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes

Sources

• Fermi Large Area Telescope Fourth Source Catalog Data Release 4 (4FGL-DR4)
• Gamma-ray Bursts as distance indicators through a machine learning approach
• Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

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