Category: UKSP Nugget

114. Hidden Coronal Loop Strands within Hi-C 2.1 Data

Author: Thomas Williams and Robert W. Walsh at the University of Central Lancashire.

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Introduction

Observational investigations of coronal loop structure have been undertaken since the 1940s [1]; however, due to insufficient spatial resolution of current and previous instrumentation, the definitive widths of these fundamental structures have not been fully resolved. Recent high-resolution data from NASA’s Interface Region Imaging Spectrometer (IRIS; [2]) and the High-resolution Coronal imager (Hi-C; [3]) have led to coronal loop width studies in unprecedented detail.

Hi-C Observations

Recent work with 17.2 nm observations investigates loops from five regions within the field of view of the latest Hi-C (2.1) flight [4,5]. As with [6], coronal strand widths of ~513 km were determined for four of the five regions analysed. In the final region, which exhibits low emission, low density loops, much narrower coronal strands are found of ~388 km width, placing those structures below the width of a single AIA pixel. The fact that these strands are above the resolution limit of Hi-C (220-340 km [5]) suggests that Hi-C may be beginning to resolve a key spatial scale of coronal loops.

Notably, [4] also find example structures that may not be fully resolved within the Hi-C data. These relate to smaller ‘bumps’ or turning points in the cross-section intensity profiles that are larger than the observational error bars but do not constitute a completely isolated strand. Could these be the result of projection effects of overlapping structures along the integrated line of sight for this optically thin plasma, or are they the result of further structures beneath even the resolving abilities of Hi-C?

Method

To answer this, a selection of slices is taken from the Hi-C 2.1 field-of-view (Figure 1) where these non-Gaussian shaped structures are seen. Following the method of [4], each slice is time-averaged for ~60 s and summed across a width of three-pixels to increase the signal-to-noise ratio. Cubic spline interpolation is then employed to generate and subtract the background intensity of each slice. To estimate the number of strands hidden within the cross-sectional profiles, a non-linear least-squares curve fitting method is employed to fit a number of Gaussians to the observed intensity profile. The correct number of Gaussian profiles fitted to each cross-sectional slice is determined by using the Akaike Information Criteria (AIC; [8]) model selection method. AIC evaluates how well a fit is supported by the data by rewarding a fit for the accuracy relative to the original Hi-C data, but punishes each fit as the complexity increases i.e. as the number of Gaussians fitted increases. The use of AIC helps mitigate the potential for over(under)-fitting the Hi-C data as the number of Gaussians fitted is taken as the model best supported by the data.

Analysis

A total of 183 Gaussian profiles are fitted to twenty-four Hi-C cross sectional slices, the positions of which are shown in Figure 1. A closer view of four sample slices shown in Figure 2 and their cross-sections are displayed in Figure 3. In the cross-section plots, the original intensity (blue) is compared to the best AIC-determined fit (red) and the Gaussian profiles (grey) that generate said fit. Overall, the observed intensity is well reproduced though there are minor discrepancies that can be observed (e.g. Slice 2: positions 5’’ and 13’’). These could be eradicated by fitting more Gaussian profiles, however AIC determines that additional Gaussians are not supported by the Hi-C data.

The full-width at half-maximum (FWHM) of the 183 Gaussian profiles are collated into occurrence frequency plots (Figure 4) with the same spatial binning as [4] along with their 1-σ errors returned from the curve fitting method. As with [4], the most frequent spatial width of the measured strands is ~500 km, whilst the majority lie between 200 – 800 km, yielding a similar median (645 km) to previous 19.3 nm Hi-C width measurements [9].

Furthermore, ~21% of widths exceed 1000 km whilst ~32% of the strands studied are at the SDO/AIA resolving scale of 600-1000 km. From this, ~47% of the strands are beneath the resolving scale of SDO/AIA. The  Hi-C strand widths obtained reveal the presence of numerous strands (~32% of the 183 Gaussian widths) whose FWHMs are beneath the most frequent strand widths seen previously [~513 km; 4] Similarly, ~17% are below an AIA pixel width of 435 km. Comparatively then, only ~6% of the strands are actually at the smallest scale at which Hi-C can resolve structures [220-340 km, 5].

Conclusion

This work outlines a follow-up analysis to [4], in which non-Gaussian shaped loop profiles not fully resolved within the Hi-C data were found. Employing a nonlinear least-squares curve-fitting method, a total of 183 Gaussian profiles are fitted to these partially resolved Hi-C structures. The fact that (i) the FWHM of these Gaussians are  at the same spatial scales as previous high-resolution findings [4,6,9] and (ii) ~94% of strand widths measured are above the Hi-C resolving scale (220-340 km) provides strong evidence that structures with non-Gaussian distributions are likely the result of overlapping structures along the integrated line of sight rather than the result of an amalgamation of strands beneath even the resolving capabilities of instruments like Hi-C.

This work has been published in The Astrophysical Journal and a full-text version can be found here.

References

  • [1] Bray, R. J., Cram, L. E., Durrant, C., Loughhead, R. E. 1991, Plasma Loops in the Solar Corona (Cambridge: Cambridge University Press)
  • [2] De Pontieu, B., Title, A. M., Lemen, J. R., et al. 2014, SoPh, 289, 2733
  • [3] Kobayashi, K., Cirtain, J., Winebarger, A. R., et al. 2014, SoPh, 289, 4393
  • [4] Williams, T., Walsh, R. W., Winebarger, A. R., et al. 2020, ApJ, 892, 134
  • [5] Rachmeler, A. L., Winebarger, A. R., Savage, A. L., et al. 2019, SoPh, 294, 174
  • [6] Aschwanden, M. J., Peter, H. 2017 ApJ, 840, 4
  • [7] Morgan, H., Druckmuller, M. 2014, SoPh, 289, 2945
  • [8] Akaike, H. 1974, ITAC, 19, 716
  • [9] Brooks, D. H., Warren, H. P., Ugarte-Urra, I.,

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113. Probing small-scale solar magnetic fields

Author: Mykola Gordovskyy and Philippa Browning (University of Manchester), Sergiy Shelyag (Deakin University), Vsevolod Lozitsky (Kyiv Taras Shevchenko University).

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What is known about small scale structure of photospheric magnetic fields?

The magnetic field in the solar photosphere is very inhomogeneous, and has fine structure with spatial scales of about 10km (see [1,2] and references therein for a review). Therefore, magnetograms produced by instruments, such as SDO/HMI or Hinode/SOT, show not the actual magnetic field, but the field averaged over a volume of around 100x100km (spatial resolution of an instrument) x500km (thickness of the photosphere) (Figure 1). To complicate things further, the observed magnetic field is weighted by a function of depth, or the contribution function. Since different spectral lines used for magnetic field measurements have different magnetic field sensitivities and different contribution functions, the maps produced by different magnetographs can differ substantially, particularly in active regions, where the magnetic field is expected to be complex.

To a first approximation, the small-scale magnetic field structure can be described using the so-called two-component model, where photospheric magnetic flux is carried by thin fluxtubes with field strength Breal and the filling factor α (α represents the fraction of volume penetrated by Breal). Hence, the observed magnetic field is

Bobs = α Breal.

Although the two-component approximation sounds like an oversimplification, in fact, it is not very far from the reality. Simulations show that the magneto-convective collapse results in two populations of magnetic field [3]: strong magnetic elements with kG strength (usually concentrated at the photospheric network boundaries) and weak ambient field (Figure 2).

Why is this important?

The difference between the observed and real distributions of magnetic field in the photosphere can affect all sorts of measurements. For instance, in terms of the two-component model, ignoring the filling factor α while estimating the magnetic energy density or the Poynting flux in the photosphere would result in them being underestimated by factor of 1/α. Similarly, the current density would be underestimated by factor of about α-1.5.

Can we do anything about it?

There are a few different ways to evaluate the “real” magnetic field (or the filling factor). Firstly, the classical Magnetic Line Ratio (MLR) method based on the comparison of the Zeeman Effect in spectral lines with different magnetic field sensitivities (see [4] for an in-depth review).

Recently, Gordovskyy et al. [5,6] have developed an alternative method for diagnostics of unresolved field: the Stokes V Width (SVW) method. It links the filling factor with the width of the Stokes V component in some classical magnetometric spectral lines, such as Fe I 5247 Å and 6301 Å. An important advantage of this new method is that, unlike MLR, it requires only one spectral line. The SVW method has been tested using the magnetoconvection models of the photosphere and appears to be, generally, as reliable as the classical MLR method (Figure 3). Comparison of these methods applied to different spectral lines show that MLR is usually more reliable for lower values of Bobs (typically, up to about 500G), while SVW is more reliable for higher Bobs values.

Can we do better?

The methods discussed above cannot properly account for the temperature and velocity variations, which affect spectral line profiles and, hence, the reliability of both the MLR and SVW methods. Stokes inversion yields much better quality [e.g. 7]. The idea of the Stokes inversion approach is to find the line-of-sight distribution of thermodynamic parameters, LOS velocity, magnetic field components, and filling factors (for B and VLOS) providing the best fit to the observed Stokes components of selected spectral lines. However, this approach is computationally expensive, and usually applied only to relatively small patches of the solar surface (order of 100×100 arcsec or so). Therefore, the Stokes inversion can be used for more accurate analysis of smaller areas, while MLR and SVW can be used for fast “on-the-fly” analysis of large areas (a big active region or the whole solar disk) or for calibration of large-area magnetograms.

Can we do even better?

The methods discussed above can only evaluate Bobs or α. To find the sizes and shapes of small-scale magnetic elements, we need direct high-resolution observations.

During the last decade the spatial resolution of solar optical observations have improved greatly, currently reaching ~0.1arcsec (70km) [e.g. 8]. The Daniel K. Inouye Solar Telescope (DKIST), which is entering operation this year, and the planned European Solar Telescope (EST) will push this boundary even further: DKIST spatial resolution will be 35km, while EST is expected to resolve scales as small as 20-25km. These two instruments are likely to be game-changers, finally revealing the fine structure of solar magnetic fields.

References

  • [1] Frazier, E.N. & Stenflo, J.O., 1972, Solar Phys., 27, 330.
  • [2] de Wijn, A.G., Stenflo, J.O., Solanki, S.K. & Tsuneta, S., 2009, SSRv, 144, 275.
  • [3] Vogler, A., Shelyag, S., Schussler, M., Cattaneo, F. et al., 2005, A&A, 429, 335..
  • [4] Smitha H.N. & Solanki S.K., 2017, A&A, 608, A111.
  • [5] Gordovskyy M., Shelyag, S., Browning P.K. & Lozitsky V.G., 2018, A&A, 619, A164.
  • [6] Gordovskyy M., Shelyag, S., Browning P.K. & Lozitsky V.G., 2020, A&A, 633, A136.
  • [7] Kobel, P., Solanki, S.K. & Borrero, J.M., 2011, A&A, 531, A112.
  • [8] Keys, P.H., Reid, A., Mathioudakis, M., Shelyag, S., Henriques, V.M.J. et al., 2020, A&A, 633, A60.

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112. Particle acceleration and transport in CME eruptions

Author: Qian Xia and Valentina Zharkova at Northumbria University, Newcastle.

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Introduction

Coronal mass ejections (CMEs) are explosive solar events that involve enormous ejections of plasma and magnetic flux, which drive interplanetary dynamics. CMEs are often associated with a filament channel, in the form of a twisted flux rope or sheared arcade, that stores the required large amount of free magnetic energy. The structures can be destabilised by reconnection or by an ideal process (e.g., torus or kink instability) leading to consequent eruption.

In the magnetic breakout model [1], the energy buildup in the filament channel deforms a coronal nullpoint above the system. It forms the breakout current sheet (CS), as shown in Fig. (1). This CS eventually reconnects, removing the flux overlaying the filament channel, disrupting the force balance, and triggering the eruption onset. A generic vertical flare CS forms beneath the erupting filament and reconnects and drives explosive CME acceleration.

Observations show that a significant fraction of the total released magnetic energy is transferred to high-energy electrons and ions. Energetic electrons in flares can be observed through bremsstrahlung hard X-ray, as illustrated in Fig. (2), and gyrosynchrotron microwave emission from the solar corona and chromosphere. At the same time, a fraction of energetic particles escapes to interplanetary space as solar energetic particles that can be detected by in situ observations. The strong particle energisation during solar flares may be driven by various mechanisms associated with a magnetic reconnection process [3]. By studying particle energisation in different breakout and flare CSs occurring during a CME’s evolution, we can understand the energy release by magnetic field restructuring in these events and the energy transfer to energetic particles, and, thus, determine the properties of the high-energy particles produced.

Numerical approaches

Current computing power is unable to resolve the particle characteristic scale (e.g., proton gyroradius ~ 102 m) in an observable hydrodynamic domain (flare current sheets ~ 106m) [3]. Due to the kinetic effects of particle acceleration, many kinetic simulation codes have been developed, such as hybrid and particle-in-cell approaches. The simulation domains are simplified and restricted to the most interesting area, such as the magnetic reconnection sites or the shock fronts, targeting a single specific process. On the other hand, the test-particle approach implements passively moving particles into magnetohydrodynamic (MHD) simulations (their motion would not change the electromagnetic fields). It allows scientists to access the larger hydrodynamic scale. In this nugget, we outline recent progress with this method, which explores multiple particle acceleration regions simultaneously in a single CME eruption model.

Results and discussion

The ideal MHD code, ARMS, is adaptively refined. The non-uniform grid size becomes smaller near the discontinuities (such as current sheets, shocks) due to the steeper gradients (Fig. 3). This ultra-high-resolution code can produce fine structures, such as magnetic islands in the breakout and flare current sheets.

A large number of test particles are initialised randomly in the green regions of Fig. 4 [5]. The particle acceleration sites identified by the most accelerated particles include the single X-nullpoint, the magnetic islands, and the flare loop-top regions, which are consistent with previous localised kinetic studies. Furthermore, particle re-acceleration in different regions (the blue line in Fig. 4 (3) and (6)) is shown for the first time.

When we look at the particle distributions, we first notice that the protons and electrons are ejected from the X-nullpoint asymmetrically, consistent with the previous kinetic (particle-in-cell) results [6]. After the particles are accelerated, the flare current sheets are more efficient at accelerating particles than the breakout current sheets. What is the reason behind them? Can they contribute to different acceleration mechanisms?

To answer these questions, we adopt the particle drift equations and the fluid description of magnetic field energy changes. These analyses ignore the single-particle motions and instead, focus on the macro scale. For example, the betatron acceleration is related to the change of magnetic field strength, and the first-order Fermi acceleration is related to the shortening of magnetic field lines. The results indicate that different mechanisms dominate different acceleration sites (e.g., 1st-order Fermi acceleration is important in the magnetic islands, the compression of the magnetic field does the trick in the flare loop top, etc.). The amplitudes of the energisation terms explain the different efficiency of acceleration sources. If we look into the change of particle energy distributions, we find that the peak of the distribution starts higher than the loop top and then moves downwards to the flare loop. The transition is consistent with the hard X-ray emissions in the impulsive phase of an X8.2 (a giant explosion) flare event matching the standard CME eruption model. On the other hand, the decrease of particle acceleration efficiency in the decaying phase of the flare is accompanied by the fading of the magnetic guiding field after the impulsive phase.

The numerical studies presented have calculated particle acceleration for a “realistic” CME eruption system rather than a preassumed current sheet. The combination of MHD and test-particle simulation assists the prediction of maximum energy gains of particles in different magnetic configurations, and different phases of the flares. Our model could distinguish the different particle energization mechanisms operating on these macro-scales.

References

  • [1] Antiochos, S. K., Dahlburg, R. B., & Klimchuk, J. A. 1994, ApJL, 420, L41
  • [2] Karpen, J. T., Antiochos, S. K., & DeVore, C. R. 2012, ApJ, 760, 81
  • [3] Zharkova, V. V., Arzner, K., Benz, A. O., et al. 2011, SSRv, 159, 357
  • [4] Masson, S., Antiochos, S. K., DeVore, C.R. 2013, ApJ, 771, 82
  • [5] Xia, Q., Dahlin, J. T., Zharkova, V. V., Antiochos, S. K. 2020, ApJ, 894, 2
  • [6] Siversky, T. V., & Zharkova, V. V. 2009, J. Plasma Phys., 75, 619

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111. Increasing occurrence of inverted magnetic fields from 0.3 to 1 au

Author: Allan Macneil, Mathew Owens, Mike Lockwood, Matthew Lang, Sarah Bentley (University of Reading) and Robert Wicks (University of Northumbria)

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Introduction

Local inversions in the heliospheric magnetic field (HMF) are observed at a range of solar distances and latitudes by in situ solar wind spacecraft. Figure 1 shows a schematic example of inverted and uninverted magnetic field.

Recent observations of numerous rapid, Alfvénic inversions (known as ‘switchbacks’) by the new Parker Solar Probe (PSP) mission at distances down to 0.16 au have led to renewed interest in this phenomenon [2, 3]. Finding the origins of these inversions, and specifically whether they are formed at the Sun or through in-transit processes, is of particular interest. Knowledge of some solar origin for these inversions could help to reveal processes occurring in the corona which contribute to the production of the solar wind, such as jets and interchange reconnection [4, 5]. In this nugget, we describe results which put constraints on the origins of HMF inversions by quantifying the change in inversion occurrence as a function of distance r, as measured by the Helios 1 spacecraft.

Helios Observations of Inverted Field

Helios 1 observed the low-latitude solar wind and HMF over distances of 0.3 to 1 au, over several orbits from 1974 to 1981. For this data set, we classify 40 s cadence magnetic field samples as corresponding to either inverted or uninverted HMF. This is done by combining the magnetic field polarity (defined relative to the nominal Parker spiral direction) with the beam direction of the suprathermal electron strahl (which traces an anti-sunward path along the field). Once all valid data has been classified, we bin the samples into bins of r for analysis.

Evolution of Relative Inverted Field Occurrence

For our binned data, we calculate the fractions of all valid samples which correspond to inverted and uninverted HMF. These results are plotted against r in Figure 2.

The occurrence of inverted HMF increases between 0.3 and 1 au, at the expense of uninverted HMF. The relative number of inverted HMF samples increases by a factor of around 4. This result implies that inverted HMF over this distance range is primarily created through some driving process in the heliosphere. If most inversions formed at the Sun, then we would expect inverted HMF occurrence to instead drop-off with r, as the inversions gradually decay.

Direction of Magnetic Field Deflection

Field and plasma properties associated with inversions can provide evidence as to what mechanisms are driving the creation of inverted HMF. We calculate the azimuthal ‘deflection angle’, ΔϕP, of each sampled magnetic field vector away from the nominal Parker spiral direction. The strahl beam direction is used again to remove magnetic sector dependence, such that |ΔϕP| is 0° when the field is unperturbed, and |ΔϕP| > 90° indicates that the field has been deflected to the point of inversion.

Figure 3 shows that the distribution of ΔϕP gradually broadens with r. This supports the above interpretation that the increase in inverted HMF is driven by the gradual deflection of the field away from the nominal Parker spiral direction, as more samples exceed |ΔϕP| = 90°.

Generation of Inversions

In Figure 4 we show some simple schematics of possible processes, adapted from suggestions in [6], which could generate inverted magnetic fields in the heliosphere.

Panels a to c show that the action of convecting plasma in the solar wind can drive inversions into the field. The angle between the background Parker spiral field and the radial propagation of these elements means that inversions can only be generated through a deflection in the positive ΔϕP direction. Meanwhile, inversions created by waves and turbulence can result from deflection of the field in either direction. Comparison of the wings of the distributions in Figure 3 at the positive and negative extremes reveals that there is no strong bias towards inversion through either clockwise or anti-clockwise deflection. Thus, of the presented processes, only waves and turbulence are consistent with our observations. We note that the schematics here do not account for more complex possible effects, such as the interaction between stream shears and turbulence [7] or the expansion of inverted structures [8].

Conclusions

We have shown that the occurrence of inverted HMF gradually increases over the distance range 0.3 to 1 au. This indicates that most of these inversions are being actively driven into the HMF, instead of being a remnant of some process at the Sun. Analysis of the azimuthal deflection angle of inverted HMF suggests that waves and turbulence may be the dominant process in creating these inversions. While these results demonstrate that in situ driving of inversions takes place, they do not rule out that inversions may also be generated by processes at the Sun. This is particularly true for the frequent near-Sun switchbacks observed by PSP. These results raise an interesting question: as the switchbacks which dominate the PSP encounters become common on approaching the Sun, at what distance does the occurrence of inverted HMF start to (presumably) increase?

References

  • [1] Macneil, A. R., et al., MNRAS 494 3 (2020)
  • [2] Bale, S. D., et al., Nature 576 7786 (2019)
  • [3] Kasper, J. C., et al., Nature 576 7786 (2019)
  • [4] Horbury, T. S., Matteini, L., and Stansby, D., MNRAS 478 2 (2018)
  • [5] Crooker, N. U., et al. JGR: Space Phys 109 A3 (2004)
  • [6] Lockwood, M., Owens, M. J., and Macneil, A. R., Sol Phys 294 6 (2019)
  • [7] Landi, S., Hellinger, P., and Velli, M., GRL 33 14 (2006)
  • [8] Jokipii,i J. R., Kota, J., GRL 16 L1 (1989)

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110. Flare/CME Cartoons

Author: Hugh Hudson, Nicolina Chrysaphi, and Norman Gray at the University of Glasgow.

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Introduction

A “Grand Archive of Solar Flare Cartoons” has long existed on the Web [1], but without updates within the past decade because of the unfortunate loss of a password, and because the original quite primitive HTML made it hard to access new items. Now, announced here for the first time, a brand-new Archive [2] at last replaces it! The new version contains almost 400 entries (see Figure 1 for some examples), each with a new or good-as-new refurbished descriptive blurb, usually containing links that let the user hop around seeking that exactly perfect concept (which almost never exists, alas). The blurb links to the original source.

Note that some mission creep has occurred: originally inspired only by the venerable and too-often-cited CSHKP model, the Archive has gone far beyond that in an effort to capture lateral thinking and more physically relevant items. Note please that we capitalize Archive in an effort to clothe our pretty lightweight subject with some gravitas. We would not describe these toons as funny ha-ha, at least not by intent.

Often the inspiration for an important bit of science appears first as a sketch on a bit of crumpled paper, perhaps in a bar somewhere like the Eagle Pub in Cambridge (UK). We think that a good cartoon represents a sort of intuitive interpolation formula, in that it captures some crucial new aspect of the science and allows extrapolations of that idea into some useful further direction or other. Note that many of the cartoons in the Archive do not actually do that very well. These often suggest the possibility (probability?) of obsolescence as a matter of course. In fact, one could argue that perpetuating even the most brilliant cartoon may actually serve to stifle innovation and lead to a stale cartoon-chasing style of research.

Of what use is such an Archive?

Does the Archive really serve any useful purpose, or does it merely ossify outdated concepts of little generality? Both, we think. We offer this Archive mainly as an educational matter for the benefit of the Archivist really, but many eager users of the old Archive [1] have (if faintly) praised it. The typical comment notes last-minute deadline pressure for writing a presentation or a proposal. The Archivist has in fact sometimes sat glumly through seminar presentations that seemed to consist mainly of cartoons, and has no statistical basis for judging the success rate for any of the proposal efforts.

Access to the Archive

A recent example (shown in Figure 2) shows how a cartoon can neatly suggest a specific physical mechanism within a global structure. This one also pops up in the segment of the thumbnails view of the Archive shown in Figure 1, clickable in its direct form though not here. In addition to this thumbnails view, the Archive also offers various list options; the chronological list view starts in 1905. The Archive embraces half a dozen varieties of cartoon, but tries to avoid snapshots of numerical simulations wherever possible. Though, of course, a good simulation really just fleshes out somebody’s idea of the important physics.

Contributions

The Archive continues to grow gradually as further brilliant ideas appear (or sometimes, just as the graphics get better). A successful new entry must satisfy at least one basic requirement: it needs to have appeared in a regular journal with a better-than-average impact factor. If you have a really new and interesting cartoon in such a state that does not currently appear on the Archive, please email the Archive Accessions Department directly. Note again that the Archive does not presently include intentionally funny items.

References

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109. Kink oscillations of sigmoid coronal loops

Author: Norbert Magyar and Valery M. Nakariakov at the University of Warwick.

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Introduction

In solar physics, coronal loops have long been in the spotlight. As building blocks of the closed solar corona, understanding their structure and evolution is akin to understanding the coronal heating problem. Moreover, oscillations detected in loops can serve as natural diagnostic probes of their physical properties through coronal seismology, a field in which the properties of coronal plasmas are inferred from observed wave properties and wave theory. Since the first observation of kink oscillations of coronal loops and the first application of coronal seismology [2], models have continuously improved to account for effects such as loop curvature, density stratification, loop cross-sectional variations, cooling, elliptic cross-sections, and so on. A frequently observed property of loops is non-planarity, i.e. exhibiting a helical or sigmoid shape [3]. The effects of coronal loop helicity on their standing kink oscillations were previously investigated analytically, but only through their effect on the local stratified equilibrium density [4]. Here, for the first time, we simulate kink oscillations of sigmoid coronal loops and investigate the ability of coronal seismology to assess the sigmoidity.

Numerical model

Our 3D numerical model consists of a background coronal plasma in a hydrostatic equilibrium in which we embed a coronal loop of higher density. The magnetic field is a force-free dipole with constant ⍺ parameter, adapted from [5]. The ⍺ parameter controls the helicity of the field lines (according to ∇ X B = ⍺B ). We add a higher density loop by tracing a single magnetic field line, and then using it as a central axis to construct a tube. The origin of this single field line, which varies depending on ⍺, is chosen in order to maximise the sigmoidity of the resulting loop while keeping it in the simulation domain. See Figure 1 for an example of a sigmoid flux tube.

The pulse is a horizontally polarised velocity perturbation varying sinusoidally along the loop, which aims to excite a fundamental standing kink mode. However, note that this initial perturbation probably does not coincide with the eigenfunction of the fundamental kink, which is not known. Therefore, while preferentially exciting the fundamental kink mode, other modes are also excited to a small degree, including leaky waves. The resulting kink oscillation of the loop is shown in Figure 2.

The ideal MHD equations are solved in a 3D rectangular domain using MPI-AMRVAC 2.0 [6], with a finite-volume approach. We applied a splitting strategy for the magnetic field, with the time-independent force-free magnetic field considered as a background field. Thus we only solve for the (nonlinear) perturbed magnetic field components. For this, we used the newly-implemented HLLD solver adapted for magnetic field decomposition described in [7].

Results

Analysis of the oscillation properties is based on synthetic 171 Å intensity and Doppler shift images with the lines of sight corresponding to the coordinate axes. After measuring the oscillation properties, we proceed to infer magnetic field estimates seismologically by calculating the theoretical kink period. We do this using the WKB approximation, as the kink speed varies along the loop. From this the magnetic field intensity is determined using the measured oscillation period, loop length, and estimations of the density.

The results are shown in Figure 3. We have considered a range of an order of magnitude for the precision to which the average internal density can be determined, while the density ratio (internal to external density) is taken to range from 1.5 to 10. In the simulation, the average density ratio is close to 2. Here we assume that the measurements of the length of the loop and of the oscillation period are exact.

For the simulation with no sigmoidity, despite the measured and theoretically calculated periods being close to each other, the seismologically predicted magnetic field value is lower than the average value. This can be understood in the following way: as the displacement amplitude of the fundamental mode has a maximum near the apex, the oscillation period is more sensitive to the weaker magnetic field near the apex rather than near footpoints. With increasing sigmoidity however, the predicted magnetic field shows an increasing trend with respect to the average value. This observation might allow for the seismological determination of the sigmoidity of a coronal loop, if some other method to determine the average magnetic field is available, such as force-free extrapolations. In this sense, the free magnetic energy in a coronal loop could be estimated seismologically.

Conclusions

We propose that the dependence of the magnetic field estimate on the loop sigmoidity could be exploited seismologically in order to measure the non-potentiality, i.e. the free magnetic energy in coronal loops. However, for this method to work, the determination of the average magnetic field along the loop is needed, as well as an accurate measurement of the density along the loop. The external/internal density ratio only weakly impacts the results. On the other hand, we demonstrated the robustness of the seismological method, even when applied to non-planar or sigmoid coronal loops. For all values of sigmoidity considered, the estimation of the magnetic field is within the extremal magnetic field values measured in the loop, despite considering an order of magnitude accuracy for the average density determination.

References

  • [1] Magyar, N. & Nakariakov, V. M., ApJL 894 L23 (2020)
  • [2] Nakariakov, V. M. & Ofman, L., A&A, 372, L53 (2001)
  • [3] Aschwanden, M. J. et al., ApJ, 756, 124 (2012)
  • [4] Ruderman, M. S. & Scott, A., A&A, 529, A33 (2011)
  • [5] Cuperman, S. et al., A&A, 216, 265 (1989)
  • [6] Xia, C. et al., ApJS, 234, 30 (2018)
  • [7] Guo, X. et al., JCP, 327, 543 (2016)

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108. Cool and hot emission in a recurring active region jet

Author: Sargam M. Mulay (now University of Glasgow) Giulio Del Zanna, and Helen Mason at the University of Cambridge.

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Introduction

Solar jets are small scale energetic events that eject collimated plasma in the solar atmosphere. The appearance of jets in different magnetic environments (quiet sun, active region, and coronal holes) and their association with various explosive solar events (surges, nonthermal type-III radio burst, hard X-ray emission, and solar flares, etc.) make them most interesting candidates for space-weather studies.

Due to the dynamic nature of jets, they are difficult to observe simultaneously using imaging instruments and spectrographs. We were fortunate to find a simultaneous observation in the existing database. We carried out a comprehensive analysis of the cool (log T [K] < 5.7 (0.5 MK)) and hot (log T [K] > 6.0 (1.3 MK)) components of recurring active region jets (AR jets) using both spectroscopic observations from the Interface Region Imaging Spectrograph (IRIS; [3]) and imaging observations from the slit-jaw imager (IRIS/SJI), the Solar Dynamics Observatory Atmospheric Imaging Assembly (AIA; [8]) and the Hinode X-ray Telescope (XRT; [7]). A direct comparison of cool and hot plasma in AR jets has been carried out for the first time and the study provided important clues about their thermal structure.

Overview and kinematics

The homologous and recurrent AR jets were observed on July 10, 2015 (from 07:29 to 08:35 UT). They originated from the eastern boundary of active region NOAA 12381 and the footpoint of the jet was embedded in the penumbra of the trailing sunspot. A fine, multi-threaded structure of the jet “spire” was observed in multithermal AIA and SJI channels (see the movie in Fig. 1). Here, we measured the physical parameters for the AR jet-4 (observed during 08:19:07 – 08:30:27 UT) using Si IV 1402.77 Å and O IV 1401.16 Å lines.

The wavelength calibration was carried out using the photospheric O I 1355.6 Å line. Non-Gaussian Si IV line profiles (with a narrow core and broad wings) were seen at various pixels and the intensities were calculated by summing the total intensity under the line profile. An intensity raster map was created (see panel (a) of Fig. 2) and the Doppler velocities were obtained for the spire (green boxed region, -32±6 km/s, blue-shift) and footpoint region of the jet (yellow boxed region, 13±4 km/s, red-shift) (see panel (b) of Fig. 2). By considering that a single Gaussian is a good approximation for the core of the line, the Si IV line was fitted with single Gaussian and the nonthermal velocities (see panel (c) of Fig. 2) were measured at similar locations (spire – 69±6 and footpoint – 53±14 km/s).

Temperature structure of the jet

The temperature of the jet spire and footpoint regions (green and yellow boxed regions) was obtained by performing differential emission measure (DEM) analysis in the temperature interval 4.1<log T [K]<7.0. In order to combine the spectra from the IRIS (Si IV (log T [K] = 4.9) and O IV (log T [K] = 5.15) lines) and the images from the AIA (94, 131, 171, 193, 211, and 335 Å channels) in the DEM analysis, we substantially modified the xrt_dem_iterative2.pro routine [12]. This is the first time that such an analysis has been carried out, and the IRIS lines provided a better constraint on the lower temperatures (log T [K] < 5.4) for the DEMs.

The coalignment of AIA images with IRIS raster images was tricky for a number of reasons such as different exposure times, the sensitivity of the AIA channels to a broad range of temperatures, the temporal and spatial resolution of the IRIS slit, etc. The coalignment method given by [4] was followed and the time-averaged images for each AIA channel were obtained (see panels (a)-(d) of Fig. 3). The CHIANTI atomic database ([2], [6]), contribution functions of the Si IV and O IV lines, electron number densities from O IV lines (spire – 2.0×1010 and footpoint – 7.6×1010 cm-3) and the photospheric abundances by [1] were used in this analysis.

A best-fit DEM for the footpoint is shown in panel (e) of Fig. 3. By randomly varying the input intensities by 20%, the uncertainties on the DEM were obtained and they are plotted as 50% (blue), 80% (red), and 95% (yellow) of the solutions closest to best-fit DEM. The DEM curve shows strong cool emission in the footpoint along with hot emission which peaked at log T [K] = 6.5 with peak DEM of 7×1021 cm-5 K-1. The total EM (9.7×1031 cm-5) was obtained by integrating DEM over the temperature interval, 4.1 < log T [K] < 7.0. Further, we compared this AIA+IRIS DEM with the DEM that we obtained using only AIA EUV images (see panel (f) of Fig. 3). Because of the lower sensitivity of AIA EUV channels for log T [K] < 5.2, the DEMs were calculated for the temperature interval, 5.2 < log T [K] < 7.0. The DEM curve shows a similar peak temperature log T [K] = 6.5 as that obtained for AIA+IRIS DEM but a slightly higher peak DEM of 1.1×1022 cm-5 K-1 was obtained for the AIA DEM. The total EM (3.1×1028 cm-5) calculated for the AIA DEM in the temperature range 5.2 < log T [K] < 7.0 was found to be almost three orders of magnitude lower than that obtained for AIA+IRIS DEM. Both DEM curves fall sharply as there is no constraint on the higher temperatures (log T [K] > 6.6) of DEMs.

In order to get a reliable estimate of higher temperatures (log T [K] > 6.2), we estimated Fe XVIII 93.932 Å emission (see panels (f) of Fig. 2 and (d) of Fig. 3) from the AIA 94 Å channel using the empirical formula (I(Fe XVIII (93.93 Å)) = I(94 Å) – I(211 Å)/120 – I(171 Å)/450) given by [5]. The images show a clear indication of Fe XVIII emission at the footpoint and their comparison with simultaneous SJI images show the existence of cool plasma (log T [K] = 4.9-5.1) at the same location (see panels (d)-(f) of Fig.… continue to the full article

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107. Forced reconnection unveiled in the large-scale corona

Author: Gerry Doyle (Armagh); Abhishek Srivastava, Sudheer Mishra, Bhola Dwivedi & Dipankar Banerjee (India), Petr Jelı́nek & Pradeep Kayshap (Czech Republic); Tanmoy Samanta & Hui Tian (China); Vaibhav Pant (Belgium)

Introduction

Magnetic reconnection is a physical process that may yield various phenomena in astrophysical and laboratory plasma, e.g., energetic flares, geomagnetic substorms, tokamak disruptions, controlled fusion experiments, etc [1,2]. In the high temperature astrophysical plasmas of solar and stellar coronae, it is basically defined as the self-organization and relaxation of complex and twisted magnetic fields leading to the liberation of stored magnetic energy [2]. In the magnetically dominated solar corona, magnetic reconnection is one of the key physical processes to heat its atmosphere, and also to generate various space weather candidates (e.g., flares, prominence eruption, and coronal mass ejections), which may influence the Earth’s outer atmosphere, its satellite and communication systems, power systems etc [3,4,5,6]. The variety of exact physical conditions of the magnetic reconnection region are still poorly known despite several novel discoveries both in theory and observations in the astrophysical and laboratory plasmas. Some known burning issues that need to be explored in a more deterministic manner are the formation of current sheets and their morphology, appropriate reconnection rate, establishment of natural diffusion regions and their physical properties, etc to understand exactly the role of reconnection in various exotic plasma processes.

Forced Reconnection is Unveiled in the Large-Scale Solar Corona

Using multi-wavelength observations of the solar corona from the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) on 12 May 2012, we establish directly that forced reconnection at a considerably high rate occur locally in its magnetized plasma (Fig. 1; [7]). It was triggered in the corona when two oppositely directed magnetic field lines forming an X-point are perturbed by an external disturbance (prominence motion in the present case). This type of reconnection has only been reported in theory [8,9], and has never been directly observed in the Sun’s large-scale corona.

Figure 1 displays SDO/AIA imaging observations on 3rd May 2012 at 14:10:08 UT, which show the formation of a temporary X-point and a forced reconnection region. In the righthand panel (“a”) we have the composite image of AIA 171Å, 304Å showing the off-limb region. In the top middle panel (“b”) we have a zoomed view of the region of interest as shown by the dotted-black box in panel “a”. In the top left panel (“c”) we have the difference image map of AIA 171Å, which clearly display the formation of the temporary X-point and the set of the inflowing and outflowing plasma in the large-scale corona exhibiting the reconnection. This is forced reconnection which is driven externally by an impulse generated by a prominence.

The Differential Emission Measure (DEM) map for coronal temperature (shown in the bottom left panel “d”) display the formation of the current sheet and an instance of the ongoing forced reconnection. The modelling of current sheet initialized by the observed initial conditions of the forced reconnection (shown in the middle bottom panel “e”) exhibits an important result that the implementation of the external driver increases the rate of the reconnection even when the resistivity required for creating a normal diffusion region decreases. The detailed description of these first observational results are published in the Astrophysical Journal [7].

Conclusion

In conclusion, the dynamical corona may be episodically subjected to the rapid forced reconnection driven by external perturbations. It can help significantly in the energy release and in the evolution of the eruptive phenomena. These first observational clues to the forced reconnection can also be extended to the laboratory plasma, where it can be employed to constrain the behaviour of the diffusive plasma, and to generate the energy.

References

  • [1] Yamada, M., Kulsrud, R. & Ji, H., Magnetic reconnection, Rev. Mod. Phys. 82, 603-664 (2010)
  • [2] Priest, E.R. & Forbes, T.G. Reconnection of Magnetic Fields: Magnetohydrodynamics and Collision less Theory and Observations (Cambridge Univ. Press, 2007).
  • [3] Cargill, P.J. & Klimchuk, J.A. Nano-flare heating of the corona revisited. Astrophys. J. 605, 911-920 (2004).
  • [4] Klimchuk, J.A. Key aspects of coronal heating. Phil. Trans. R. Soc. A. 373, 20140256 (2015). 11.
  • [5] Shibata, K. & Magara, T. 2011, Solar Flares: Magnetohydrodynamic Processes; Living Reviews in Solar Physics, 8, 6.
  • [6] Schwenn, R. Space weather: The solar perspective. Liv. Rev. Sol. Phys. 3, article id. 2, 72 pp (2006).
  • [7] Srivastava, A.K., Mishra, Sudheer K., Jelinek, P., Samanta, T., Tian, H.; Pant, Vaibhav, Kayshap, P., Banerjee,D., Doyle, J.G., Dwivedi, B.N., On the observations of the forced reconnection in the solar corona, 2019, ApJ 887 2.
  • [8] Jain, R., Browning, P. & Kusano, K., Solar coronal heating by forced magnetic reconnection: Multiple reconnection events. Phys. Plasmas 12, 012904-012904-12 (2005).
  • [9] Potter, M., Browning, P., & Gordovskyy, M., Forced magnetic reconnection and plasmoid coalescence. I. Magnetohydrodynamic simulations, Astro. Astrophys. 623, A 15(2018).

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106. A new procedure for detecting periodicities within complex coronal arcades

Author: Farhad Allian, Rekha Jain (The University of Sheffield) and Bradley W. Hindman (University of Colorado Boulder)

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Introduction & Motivation

Amongst the most fascinating features of the dynamic solar atmosphere lie the complex curved loops of illuminatingly hot plasma that are traced by the magnetic field lines. These loops form waveguides that support various magnetohydrodynamic waves and are typically observed to undulate back and forth in the plane-of-the-sky. Distinctively, oscillations generated by impulsive events, such as solar flares, have displacement amplitudes that suddenly grow and diminish within a few wave-periods [1]. Conversely, oscillations in the absence of an obvious driver are observed to oscillate for longer without any significant decay, and with displacement amplitudes on the order of SDO/AIA’s pixel size.

Alongside appropriate mathematical models, coronal loop oscillations remain an indispensable tool to indirectly probe the solar atmosphere. However, such seismological inversions rely on time-series analyses of a clear oscillating loop with well-defined amplitude boundaries. This leads to the unavoidable question: how can we analyse EUV images of coronal loop oscillations with a poor background contrast?

Observations

Utilising the high spatial and temporal resolutions of SDO/AIA, we examined EUV imagery of coronal loop oscillations embedded within a complex arcade on 27th January 2014 [2]. This arcade belonged to a multi-polar active region, which exhibited two consecutive M-class flares prior to the initiation of large-amplitude oscillations. Surprisingly, despite the flaring activities being situated next to each other, the second flare did not restart any additional large-amplitude oscillations.

In association with the first flare, we observed a small wavefront propagating away from the solar limb and throughout the arcade. By carefully tracking the wavefront, we estimated an initial projected speed of about 40 km/s. After its initial stage of propagation, the wavefront was obscured by a bundle of loops in the line-of-sight and so further tracking was not possible. This coincidence with the wavefront and the first flare proved difficult to distinctively determine the likely exciter for these large-amplitude oscillations.

Moreover, we investigated the oscillations manifested along two slits placed perpendicular to the apparent arcade, as shown in Figure 1. The bright loop featured in the time-distance image of Slit 1 undergoes a clear decaying large-amplitude oscillation with a periodicity of approximately 13 minutes. However, within this slit and even more so in Slit 2, exists the presence of an overlapping multitude of faint oscillations, perhaps with different periodicities. In such cases, the oscillatory parameters cannot be estimated with fidelity.

The 2D Autocorrelation Function: Observed & Synthetic Signals

The regularly sampled intensity variations recorded by AIA allows us to use our newly developed analysis procedure which employs spatio-temporal autocorrelations. The main benefit of our procedure is that it reveals the periodicities that remain hidden in the traditional time–distance analysis. In general, the autocorrelation describes the degree of similarity of a given input with itself and as a result, this transformation reveals any periodic structure that is already present in the data. For applications to complex loop systems viewed in time-distance images, as in Figure 1, our procedure simply requires a temporal high-pass filter in order to sharpen the oscillatory features. Thereafter, we generate the autocorrelation as a function of time lag (minutes) and spatial offset (Mm) for our two slits.

The autocorrelation function for the time-distance image of Slit 1 is shown in Figure 2. The maximum correlation occurs at the zero lag and offset as expected, which is used as the reference point of recurring structures. Noticeably, the focal points of this image are a series of X-like structures in addition to the near-vertical streaks. In order to understand these structures, we generated synthetic signals that consist of a bright oscillating loop embedded in a background of faint, dispersed oscillations, shown in Figure 3. We immediately see that the X-like features are due to the bright oscillating loop correlating with itself. Moreover, the slopes passing through the centers of the Xs arise due to a time shift of the faint background loops along the slit, possibly due to a moving driver or the observed wavefront. The bottom panel of Figure 3 demonstrates the dependency on the relative brightness contrast between the bright loop and the faint phase-shifted background within the autocorrelation. As a result, for time-distance images wherein a bright loop is not distinguished well, the 2D autocorrelation still reveals the dominant periodic structures as a series of strongly correlated slopes.

Conclusions

  • We have introduced and developed a novel image analysis procedure based on 2D autocorrelations that can be utilised in complex loop systems to reveal the periodicity of the faint background.
  • We have demonstrated that the autocorrelation of a bright loop is revealed as a series of Xs, whereas the faint background oscillations are revealed as a series of phase-shifted slopes. From our observations, we have successfully extracted the dominant periodicity of the bright loop at 13 minutes, as well as the periodicity of the background at 10 minutes. Moreover, the gradient of the background within the 2D autocorrelation corresponds to the group velocity of the wavefield across the slit.
  • Importantly, our method has the salutary feature that it can be successfully applied to coronal arcades for which a typical time-series fitting method would fail due to the poor image contrast, for example, in complex loop systems or when seeking for small-amplitude oscillations. As a result, our procedure can be used to constrain the seismological inversions that are necessary to understand the local plasma conditions of solar coronal arcades.
  • Acknowledgments

    F.A. acknowledges the STFC (UK) studentship. We are grateful for the use of SDO/AIA and GOES data. This research has made use of SunPy, an open-source and free community-developed solar data analysis Python package [3].

    References

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    105. Transient inverse-FIP effect observed during a solar flare

    Author: Deborah Baker, Lidia van Driel-Gesztelyi, David Long (UCL-MSSL, UK) and David H. Brooks (George Mason University, USA).

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    Introduction

    The elemental composition of astrophysical plasma and its variations are crucial to our understanding of the physical conditions and processes occurring within the plasma. Elemental abundance variations of solar and stellar coronae have mainly been linked to the surface temperatures of stars [1] and recent EUV spectroscopic observations of the Sun demonstrate that magnetic activity also has a major role to play [e.g. 2-5].

    Coronae of the Sun and solar-type stars are over-abundant in elements with low first ionization potential (FIP effect), while cooler M dwarfs, which have large starspots and produce giant flares, have coronae either depleted of such elements or enhanced in high-FIP elements (inverse FIP or IFIP effect). Plasma fractionation takes place in stellar chromospheres where low-FIP elements are ionized and high-FIP elements are neutral.

    For the first time, Hinode/EIS observed the IFIP effect on the Sun in highly localized patches near large sunspots during flares [6]. Since then, IFIP plasma has been observed in only eight active regions. In this nugget, we feature the most striking Hinode/EIS observations showing the spatial distribution and temporal evolution of IFIP plasma in AR 11429 during the decay phase of an M-class flare. This work was published in [7].

    Hinode/EIS Observations

    Hinode/EIS observed AR 11429 when operating in an autonomous observing mode during a Major Flare Watch campaign on 6 March 2012. The M2.2 flare triggered a high cadence response study and Hinode/EIS rastered the active region from the time of the flare’s peak to the end of the decay phase (Figure 1).

    We have selected a series of observations that highlight the rapid and extreme evolution of plasma composition. Figure 2 is composed of Ar XIV intensity maps (top panel) and line ratio composition maps of high-FIP Ar XIV to low-FIP Ca XIV (bottom panel). When the flare is at peak intensity (12:38 UT), only FIP effect is evident throughout the active region. Minutes into the decay phase (12:47 UT and 12:56 UT), IFIP plasma appears at the footpoints of bright flare loops within the active region while the loop tops exhibit enhanced FIP composition. By the time the soft X-ray intensity has returned to preflare levels (13:23 UT), the plasma at the eastern footpoints has evolved to photospheric plasma (ratio ~1) whereas IFIP composition still persists at the western footpoints. At the same time, the FIP effect has weakened along the loops. Hinode/EIS observed these extremes in plasma evolution in less than one hour after the decay phase of the flare. Seven hours later there was no IFIP plasma detected within the active region.

    What is special about the location of the IFIP patches?

    Distinct IFIP patches occurred at very particular locations within the unusually complex magnetic configuration of AR 11429. Figure 3 displays an SDO/HMI continuum image during the flare’s decay phase at 12:47 UT overplotted with contours of IFIP plasma (green) and flare ribbons (orange). The IFIP patches are located in the highly sheared emerging flux over coalescing umbrae that are crossed by flare ribbons as indicated by the intersection of the contours. When the active region first rotated onto the solar disk, it was a mature sunspot group containing several common penumbra. For several days, major flux emergence of highly sheared field took place with 2–3 major bipoles still in emergence at the time of the Hinode/EIS observations on 6 March.

    During the evolution of the active region, flux approached and collided with a pre-existing spot, forcing the coalescence of the smaller flux fragments into a growing, strongly coherent umbra surrounded by a common penumbra. Such field represents different strands of highly sheared field – evidenced by the presence of magnetic tongues [8] – that are converging towards each other to form sunspots, and therefore meet below the photosphere/chromosphere in the location of the coalescing umbrae. This is highly suggestive of subsurface/sub-chromospheric magnetic reconnection. Such reconnection leads to increased fast-mode wave flux from below the region of plasma fractionation in the chromosphere.

    Our interpretation of the observations is consistent with the ponderomotive fractionation model for the creation of IFIP plasma [9]. The model invokes the ponderomotive force exerted by Alfvén waves when they refract from the high density gradient in the chromosphere. This gives rise to ion–neutral separation in the chromospheres of the Sun and other stars. The direction of the ponderomotive force determines whether low FIP elements become enhanced or depleted in stellar coronae. Alfvén waves originating in the corona produce the FIP effect and waves of sub-chromospheric origin create the IFIP effect. Sunspots are preferential locations for upward traveling acoustic waves to mode convert as the plasma β = 1 layer occurs at lower heights within the photosphere. Therefore the increased wave flux generated by the subsurface reconnection at coalescing umbrae will in turn preferentially create IFIP plasma above the umbrae. The IFIP plasma is only observed when the flare ribbons cross the umbrae and the IFIP plasma is evaporated into the flare loops. The flare reveals the IFIP plasma but does not create it.

    Conclusions

    We have shown that IFIP plasma is observed for a short time during the decay phase of a moderate flare in very particular locations within the unusually complex magnetic configuration of AR 11429. These highly localized regions of IFIP plasma appear over coalescing umbrae crossed by flare ribbons. We argue that the highly unusual plasma composition was created by increased fast mode wave flux that was generated by subsurface reconnection of the coalescing umbrae. According to the Laming fractionation model, fast mode waves coming from below the fractionation region of the chromosphere means that the ponderomotive force is directed downward so that low-FIP elements are depleted from the chromospheric plasma. The plasma is then evaporated into the corona in the flaring loops where it is observed by Hinode/EIS. This has implications for understanding the coronal composition of M dwarfs. The spatially resolved observations on the Sun may provide clues to the processes on M-stars which have IFIP-dominated coronae all the time, not only during large flares.… continue to the full article

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