Comparing First Ionisation Potential bias diagnostics in the solar atmosphere

TL;DR

This study compares different FIP bias diagnostics in the solar atmosphere using Hinode/EIS data, revealing how signal-to-noise thresholds influence the distribution of measured FIP bias values and emphasizing nuanced interpretation.

Contribution

It introduces a comparative analysis of three FIP bias diagnostics and examines the impact of SNR cutoffs on FIP bias measurements in various solar regions.

Findings

Higher SNR cutoffs remove high FIP bias tails.

Median FIP bias remains stable across SNR thresholds.

Different diagnostics show consistent median FIP bias values.

Abstract

Plasma composition in the solar atmosphere differs between the photosphere and corona, producing an observable difference in elemental abundance known as the FIP effect. The FIP effect is characterised by the ratio of low to high FIP elements, giving a number known as the FIP bias. FIP bias values vary between different regions of the solar atmosphere, with typical observed values of $\sim$1 for coronal holes, $\sim$1.5-2 for the quiet Sun, and $\sim$3 for active regions. The Extreme ultraviolet Imaging Spectrometer (EIS) onboard the \emph{Hinode} spacecraft has enabled the widespread use of the Si X/S X line pair as a FIP bias diagnostic, but EIS observes other line pairs that can be used to estimate FIP bias. We consider three FIP bias diagnostics observed by \emph{Hinode}/EIS (Si X/S X, Ca XIV/Ar XIV, and Fe XVI/S XIII), comparing the FIP bias between Quiet Sun and an Active region. We also assume a range of signal-to-noise (SNR) cutoff values for each pixel, finding that while the SNR cutoff affects the number of useable pixels, higher (lower) SNR cutoffs remove (retain) a tail of high FIP bias values within the measured distribution. However, the median value of the FIP bias distribution remains largely unchanged. These results show the importance of a more nuanced view of FIP bias when using this vitally important diagnostic rather than a simplistic one-size-fits-all approach.