Extracting causality from spectroscopy
K. Fujita, K. Nakayama, Y. Fujiki, T. Kato, H. Suito, H. Higuchi, T. Sato · 2025
Read Extracting causality from spectroscopy by K. Fujita, K. Nakayama, Y. Fujiki, T. Kato, H. Suito, H. Higuchi, T. Sato (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12722227.txt.
Description
Spectroscopy reads the light or electrons from a material, but reading causes from those spectra is far harder. Fujita and co-authors present a protocol for that task in this 2025 paper. Cause here means a directed link: one state produces or shapes another. Machine learning offers tools, yet no settled principle guides their use for causal claims.
Their protocol applies a statistical method that learns a linear causal model from data that is not bell-shaped. The test case is a layered superconductor with a kagome lattice, measured by a photoemission method that resolves place. The analysis tied surface coverage of one element to the intensity and position of spectral lines and to the background. Finding causal order inside spectra could sharpen how materials data are read.
Catalog record
| CREATOR | K. Fujita, K. Nakayama, Y. Fujiki, T. Kato, H. Suito, H. Higuchi, T. Sato |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 60 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |