Learning the Universe: The Structure of Dust Attenuation Curves in Galaxy Simulations
astro-ph.GA, astro-ph.CO, astro-ph.IM
Submitted: 2026-06-08
Updated: 2026-06-08
Comments: This is a Learning the Universe publication. Submitted to Apj, 31 pages, 17 Figures
Journal ref: ApJ 1008 218 (2026)
Code: https://github.com/heal-research/pyoperon
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Symbolic Regression and Differentiable Fits in Beyond the Standard Model Physics
- Symbolic Emulators for Cosmology: Accelerating Cosmological Analyses Without Sacrificing Precision
- Contemporary Symbolic Regression Methods and their Relative Performance
- Massive Galaxies Form Early and Gray: Stellar Assembly and Dust Attenuation at $\mathbf{z>3.5}$ from CAPERS
- Investigating the ultraviolet and infrared radiation through the turbulent life of molecular clouds
- Sym-EFT: Accelerating Effective Field Theory of Large Scale Structure with Symbolic Regression
- REBELS-IFU: Dust attenuation curves of 12 massive galaxies at $z\simeq7$
- Big, Dusty Galaxies in Blue Jay: Insights into the Relationship Between Morphology and Dust Attenuation at Cosmic Noon
- Dust attenuation evolution in $z \sim 2$-$12$ JWST galaxies
- Information-Ordered Bottlenecks for Adaptive Semantic Compression
- Adam: A Method for Stochastic Optimization
- Constraining dark matter halo profiles with symbolic regression
- Symbolically regressing dark matter halo profiles using weak lensing
- Polycyclic Aromatic Hydrocarbon and the Ultraviolet Extinction Bump at the Cosmic Dawn
- The Growth of Dust in Galaxies in the First Billion Years with Applications to Blue Monsters
- Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours
- Dust and Grain Size Evolution in Galaxy Simulations: What Matters and What Does Not
- A first empirical derivation of the average dust attenuation law at 2<z<7
- CALIMA: On-the-fly dust and PAH evolution for radiation-hydrodynamics galaxy formation simulations
- Exploring Multi-view Symbolic Regression methods in physical sciences
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