Backtracking Bipolar Magnetic Regions to their emergence: Two groups and their implication in the tilt measurements

TL;DR

This work addresses how to robustly identify true BMR emergence on the solar surface by backtracking BMRs detected by AutoTAB to their emergence time $T_e$, revealing two populations: genuine emerging BMRs with significant flux growth and discarded BMRs that show little growth and no clear tilt signature. The authors implement a backtracking algorithm using region masks, differential rotation, and a dual-ratio diagnostic ($r_p$ and $r_f$) to determine $T_e$ for $N\approx 3{,}080$ BMRs out of the AutoTAB set. They show that discarded BMRs do not exhibit a systematic tilt or Joy’s law and can bias statistical studies if not excluded, while emerging BMRs display a robust Joy’s law at emergence in agreement with the thin flux tube model. Overall, the method clarifies the solar-cycle imprint of flux emergence and emphasizes careful population separation to ensure accurate characterization of BMR properties and their predictive value for solar activity.

Abstract

Bipolar Magnetic Regions (BMRs) that appear on the solar photosphere are surface manifestations of the Suns internal magnetic field. With modern observations and continuous data streams, the study of BMRs has moved from manual sunspot catalogs to automated detection and tracking methods. In this work, we present an additional module to the existing BMR tracking algorithm, AutoTAB, that focuses on identifying emerging signatures of BMRs. Specifically, for regions newly detected on the solar disk, this module backtracks the BMRs to their point of emergence. From a total of about 12,000 BMRs identified by AutoTAB, we successfully backtracked 3,080 cases. Within this backtracked sample, we find two distinct populations. One group shows the expected behavior of emerging regions, in which the magnetic flux increases significantly during the emerging phase. The other group consists of BMRs whose flux, however, does not exhibit substantial growth during their evolution, the instances where our algorithm fails to capture the initial emergence of the BMRs. We classify these as discarded BMRs and examine their statistical properties separately. Our analysis shows that these discarded BMRs do not display any preferred tilt angle distribution and do not show systematic latitudinal tilt dependence, in contrast to the trends typically associated with emerging BMRs. This indicates that including such regions in statistical studies of BMR properties can distort or mask the underlying physical characteristics. We therefore emphasise the importance of excluding the discarded population from the whole dataset when analysing the statistical behavior of BMRs.

Figures (7)

• Figure 1: Evolution of two emerging/growing BMRs. (a) to (d) The BMR at the times of initial detection ($T_{e}$), at the middle of the backtracking phase, the starting of our backtracking phase ($T_0$), and at its maximum flux ($T_m$). Note the time sequence: $T_e \rightarrow T_0 \rightarrow T_m$. (e--h) The same time frames but for a different BMR. Numbers in brackets on each panel denote the mean latitude and longitude of the region. The mean latitude and longitude of the region are printed in brackets on each panel. LOS field is saturated at 1.5 kG in all panels. The line in each panel connects the flux-weighted centroids of BMR's poles.
• Figure 2: Examples of the time evolution of four discarded BMRs. The figure format is the same as Figure \ref{['fig:bt_eg']}, but different rows represent the time evolutions of different BMRs that fall in the same discarded BMR group (red points in Figure \ref{['fig:flux_evol']}a) that were discarded in sreedevi25a.
• Figure 3: (a) Flux of BMRs ($\Phi_m$) at the start of the backtracking phase ($T_0$) versus $\Phi_m$ at the time of emergence $T_e$. The inset shows the same but in linear scales. Asterisks, upper triangles, and lower triangles, respectively, represent solar cycles 23, 24, and a part of 25. Red and blue points represent the 'discarded' and 'emerging' BMRs, isolated based on the angle distribution. (b) Histogram of angles measured at the origin with respect to the horizontal axis. The blue/red Gaussian curves represent the two components obtained from the Gaussian Mixture Model (GMM) fit to the angle distribution. Usable BMRs are defined based on the probability of their angles falling into the upper (high-angle) cluster is $> 90\%$. (c--d), Evolution of the flux and the footpoint separation of 'discarded' and 'emerging' BMRs as a function of the normalized time with a bin size of 0.01. The time is normalized by the time period between $T_0$ and $T_e$ for (c), and the total lifetime for (d) of each BMR to bring it within 0 and 1.
• Figure 4: Tilts of discarded BMRs. (a) Distribution and (b) Joy's law of BMR tilt angles at $T_e$. (c) and (d) At $T_0$. (e--f), At $T_m$. Joy's law is computed by binning the tilt values in $5^{\circ}$ latitude intervals and finding the Gaussian mean tilt for each bin. The error bar is the fitted error of the Gaussian mean.
• Figure 5: The same as Figure \ref{['sfig:jlbelow']} but from all BMRs backtracked in our study i.e., without excluding the 'discarded' BMRs.