Helioseismology of Pre-Emerging Active Regions I: Overview, Data, and Target Selection Criteria

TL;DR

This study investigates whether pre-emergence signatures of solar active regions can be detected using local helioseismology, aiming to understand flux emergence mechanisms and set observational constraints for comparison with simulations.

Contribution

It introduces a comprehensive data selection and analysis framework for detecting pre-emergence signatures, including new methods for potential-field calculation and bias mitigation.

Findings

No definitive pre-emergence helioseismic signatures identified

Developed new algorithms for data matching and bias correction

Established a large, well-characterized sample for future analysis

Abstract

This first paper in a series describes the design of a study testing whether pre-appearance signatures of solar magnetic active regions were detectable using various tools of local helioseismology. The ultimate goal is to understand flux-emergence mechanisms by setting observational constraints on pre-appearance subsurface changes, for comparison with results from simulation efforts. This first paper provides details of the data selection and preparation of the samples, each containing over 100 members, of two populations: regions on the Sun that produced a numbered NOAA active region, and a "control" sample of areas that did not. The seismology is performed on data from the GONG network; accompanying magnetic data from SOHO/MDI are used for co-temporal analysis of the surface magnetic field. Samples are drawn from 2001 -- 2007, and each target is analyzed for 27.7 hr prior to an objectively determined time of emergence. The results of two analysis approaches are published separately: one based on averages of the seismology- and magnetic-derived signals over the samples, another based on Discriminant Analysis of these signals, for a statistical test of detectable differences between the two populations. We include here descriptions of a new potential-field calculation approach and the algorithm for matching sample distributions over multiple variables. We describe known sources of bias and the approaches used to mitigate them. We also describe unexpected bias sources uncovered during the course of the study and include a discussion of refinements that should be included in future work on this topic.