Not-quite-transcendental Functions for Logarithmic Interpolation of Tabulated Data

Abstract From tabulated nuclear and degenerate equations of state to photon and neutrino opacities and nuclear reaction rates, tabulated data is ubiquitous in computational astrophysics. The dynamic range that must be covered by these tables typically spans many orders of magnitude. Here we present a novel strategy for accurately and performantly interpolating tabulated data that spans these large dynamic ranges. We demonstrate the efficacy of this strategy in tabulated lookups for nuclear and terrestrial equations of state. We show that this strategy is a faster drop-in replacement for linear interpolation of logarithmic grids.

Authors

Publication Details

Journal
The Astrophysical Journal Supplement Series
Published
2025-04-01
DOI
https://doi.org/10.3847/1538-4365/adbbd4
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Not-quite-transcendental Functions for Logarithmic Interpolation of Tabulated Data

P. Hammond, Jonah M Miller, Jacob Fields, Brandon L. Barker
The Astrophysical Journal Supplement Series
Model Reduction and Neural Networks
article

Not-quite-transcendental Functions for Logarithmic Interpolation of Tabulated Data

P. Hammond, Jonah M Miller, Jacob Fields, Brandon L. Barker
article en

Abstract

Abstract From tabulated nuclear and degenerate equations of state to photon and neutrino opacities and nuclear reaction rates, tabulated data is ubiquitous in computational astrophysics. The dynamic range that must be covered by these tables typically spans many orders of magnitude. Here we present a novel strategy for accurately and performantly interpolating tabulated data that spans these large dynamic ranges. We demonstrate the efficacy of this strategy in tabulated lookups for nuclear and terrestrial equations of state. We show that this strategy is a faster drop-in replacement for linear interpolation of logarithmic grids.

The Astrophysical Journal Supplement SeriesVol. 277(2)
Openalex Percentile: Top 94%
Model Reduction and Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.