2024/07/19 by Benjamin Metha, Sabrina Berger, Metha, Benjamin +1
Engineering · Mathematics · #Astronomical Observations and Instrumentation #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Statistical and numerical algorithms
paper · pdf · doi:10.48550/arxiv.2407.14068
openalex publication_date 2024/07/19 · openalex created_date 2024/09/26 · openalex updated_date 2026/07/28
From the turbulent interstellar medium to the cosmic web, astronomers in many different fields have needed to make sense of spatial data describing our Universe. Through different historical choices for mathematical conventions, many different subfields of spatial data analysis have evolved their own language for analysing structures and quantifying correlation in spatial data. Because of this history, terminology from a myriad of different fields is used, often to describe two data products that are mathematically identical. In this Note, we define and describe the differences and similarities between the power spectrum, the two-point correlation function, the covariance function, the semivariogram, and the structure functions, in an effort to unify the languages used to study spatial correlation. We also highlight under which conditions these data products are useful and describe how the results found using one method can be translated to those found using another, allowing for easier comparison between different subfields' native methods. We hope for this document to be a ``Rosetta Stone" for translating between different statistical approaches, allowing results to be shared between researchers from different backgrounds, facilitating more cross-disciplinary approaches to data analysis.