Diagnosing solar wind origins using in-situ measurements in the inner heliosphere
MMNRAS , 000–000 (2018) Preprint 21 November 2018 Compiled using MNRAS L A TEX style file v3.0
Diagnosing solar wind origins using in-situ measurementsin the inner heliosphere
D. Stansby, (cid:63) T. S. Horbury, and L. Matteini Department of Physics, Imperial College London, London, SW7 2AZ, UK LESIA, Observatoire de Paris, Universit´e PSL, CNRS, Sorbonne Universit´e,Univ. Paris Diderot, Sorbonne Paris Cit´e, 5 place Jules Janssen, 92195 Meudon, France
21 November 2018
ABSTRACT
Robustly identifying the solar sources of individual packets of solar wind measured ininterplanetary space remains an open problem. We set out to see if this problem iseasier to tackle using solar wind measurements closer to the Sun than 1 AU, wherethe mixing and dynamical interaction of different solar wind streams is reduced. Usingmeasurements from the Helios mission, we examined how the proton core tempera-ture anisotropy and cross helicity varied with distance. At 0.3 AU there are two clearlyseparated anisotropic and isotropic populations of solar wind, that are not distinguish-able at 1 AU. The anisotropic population is always Alfv´enic and spans a wide rangeof speeds. In contrast the isotropic population has slow speeds, and contains a mixof Alfv´enic wind with constant mass fluxes, and non-Alfv´enic wind with large andhighly varying mass fluxes. We split the in-situ measurements into three categoriesaccording these observations, and suggest that these categories correspond to windthat originated in the core of coronal holes, in or near active regions or the edges ofcoronal holes, and as small transients form streamers or pseudostreamers. Althoughour method by itself is simplistic, it provides a new tool that can be used in combina-tion with other methods for identifying the sources of solar wind measured by ParkerSolar Probe and Solar Orbiter.
Key words:
Sun: heliosphere – solar wind.
The solar wind is a continuous flow of plasma, travellingaway from the Sun to fill interplanetary space. Althoughwell studied by both in-situ and remote sensing instruments,robustly determining the solar origin of all the solar windmeasured in-situ by spacecraft is still an open problem. Ex-cluding large ejecta, the solar wind has traditionally beenclassified according to the average speed of protons, whichmake up ∼ v > ∼
500 km/s) is open field lines rooted in coronal holes on thesurface of the sun (Krieger et al. 1973; Sheeley et al. 1976;Cranmer 2009), and that it is comprised of a slowly varyingbulk speed superposed with shorter timescale Alfv´enic ve-locity fluctuations above this background level (Belcher &Davis 1971; Thieme et al. 1989; Matteini et al. 2015). TheAlfv´enic fluctuations are believed to be generated close tothe surface of the Sun where the wind is sub-Alfv´enic, andthen propagate outwards into the heliosphere on long-lived (cid:63)
E-mail: [email protected] open magnetic field lines (Belcher & Davis 1971; Cranmer& van Ballegooijen 2005). As well as the global dynamics,the local kinetic properties of fast solar wind are also wellknown: the protons in fast solar wind have large temperatureanisotropies and a low plasma beta in the inner heliosphere(Marsch et al. 1982b, 2004; Matteini et al. 2007). In addition,alpha particles, which constitute 1% – 5% of the solar windby ion number density, exhibit large magnetic field aligneddrifts relative to the protons in the fast solar wind (Marschet al. 1982a; Steinberg et al. 1996).In contrast to the fast wind, the plasma properties ofthe slow solar wind ( v < ∼
500 km/s) are much more vari-able (Schwenn 2007), and although it must have a number ofdifferent solar sources (Abbo et al. 2016), it is not clear howthese sources contribute to the different parts of the slow so-lar wind measured in-situ. In general there are two possiblegeneration mechanisms for the slow wind: it can flow con-tinuously on magnetic field lines that maintain a connectionfrom the base of the corona to the heliosphere, in a similarmanner to the fast solar wind (Wang & Sheeley 1990; Cran-mer et al. 2007; Wang 2010), or can be released transientlyfrom closed magnetic field lines undergoing interchange re- c (cid:13) a r X i v : . [ a s t r o - ph . S R ] N ov D. Stansby et al. connection with the open magnetic field lines that connectto the heliosphere (Sheeley et al. 1997; Einaudi et al. 2001;Rouillard et al. 2010; Higginson et al. 2017).During initial analysis of Helios data, Marsch et al.(1981) discovered a portion of slow solar wind measured at0.3 AU that, apart from its speed, had the same proper-ties of fast solar wind: large proton-alpha drift speeds, largeproton core temperature anisotropies, and highly Alfv´enicwave activity. In addition, Roberts et al. (1987) describedan 80 day interval in the Helios data where the purestAlfv´enic fluctuations were in slow solar wind. The strongAlfv´enic wave activity during these periods implies that thewind was released on open magnetic field lines, allowing theAlfv´en waves to freely propagate outwards from the coronato the point of measurement. More recently the presence ofan “Alfv´enic slow wind” has been studied statistically us-ing data taken at 1 AU, and independent of solar activitythe slow solar wind is comprised of both non-Alfv´enic andAlfv´enic components (D’Amicis & Bruno 2015). These re-sults hint that some slow solar wind has exactly the samesource (and therefore properties) as fast solar wind, but justhappens to be released at a slower speed.As well as protons and alphas, much less abundantheavy ions are measured in the solar wind, which can beused as a more direct proxy for solar source than the protonspeed. As the solar wind travels away from the sun it effec-tively becomes collision-less within a few solar radii (Hund-hausen et al. 1968). This means that ions are no longer ableto gain or lose electrons through electron-ion collisions, andthe fraction of different charge states becomes frozen in. Ioncharge state ratios therefore act as a tracer of the plasmaproperties at the freezing in point. The most commonly usedratios are O /O and C /C , which are positively cor-related with the electron temperature at the freezing in point(Hundhausen et al. 1968; Bochsler 2007; Landi et al. 2012).Low charge state ratios are present in wind that originatesin coronal holes, which have relatively low electron temper-atures, and high charge state ratios are present in streamerbelt plasma that has relatively high electron temperatures.This information can therefore be used to distinguish be-tween coronal hole and non-coronal hole solar wind (Geisset al. 1995; Zhao et al. 2009). As an example, the Alfv´enicslow wind identified by D’Amicis & Bruno (2015) had simi-lar low charge state ratios to the fast solar wind, indicatingit had similar solar origins (D’Amicis et al. 2016), further re-inforcing the need to go beyond classifying solar wind basedsolely on the average proton speed.Although the Helios mission was equipped with an in-strument for measuring heavy ions in the solar wind (Rosen-bauer et al. 1981), it is believed that the data from thisinstrument has been lost. However, the case studies ofD’Amicis & Bruno (2015) and D’Amicis et al. (2016) hintthat when heavy ion measurements are not available theAlfv´enicity of the solar wind fluctuations may be a more re-liable proxy for solar wind origin than speed. The problemwith using Alfv´enicity as a categorisation variable is thatthe solar wind becomes systematically less Alfv´enic with dis-tance (Roberts et al. 1987; Bruno et al. 2007; Iovieno et al.2016), due to both small scale turbulent evolution and largescale velocity shears and interaction regions (Bruno et al.2006). This means not all solar wind that started off Alfv´enicnear the sun is still Alfv´enic when it is measured at 1 AU. In this paper we mitigate this problem by using the uniqueHelios data, with measurements of solar wind plasma from0.3 AU to 1 AU, to link properties measured in-situ, that areonly observable at distances < The data used here were measured by the twin Helios space-craft, which were operational during the late 1970s and early1980s. Both spacecraft had an electrostatic analyser for mea-suring the ion distribution function at 40.5 second cadence(Schwenn et al. 1975), and two different fluxgate magne-tometers for measuring the magnetic field (Musmann et al.1975; Scearce et al. 1975). Here we use a re-analysis of theion distribution functions which fitted a bi-Maxwellian tothe proton core population present in the experimentallymeasured ion distribution functions. This dataset providesthe proton core number density ( n p ), velocity ( v p ), temper-atures parallel ( T p (cid:107) ) and perpendicular ( T p ⊥ ) to the mag-netic field, and corresponding magnetic field values ( B ) ata maximum cadence of 40.5 seconds. For more details onthe fitting procedure and access to the data see Stansbyet al. (2018a). Note that all the data presented in this ar-ticle are parameters of the proton core population, and notnumerical moments of the overall ion distribution. The to-tal temperature was calculated as T p = (cid:0) T p ⊥ + T p (cid:107) (cid:1) / T p ⊥ /T p (cid:107) , the parallel plasmabeta as β p = 2 µ n p k B T p (cid:107) / | B | , and the Alfv´en speed as v A = | B | / √ n p m p µ .To avoid contamination of very large transients all of theintervals listed as coronal mass ejections by Liu et al. (2005)were removed from the dataset before further analysis. Thestate of the Sun undergoes an 11 year solar cycle, oscillatingbetween solar minimum and solar maximum. Because thehighest quality Helios data were taken early in the mission,only data taken in the years 1974 – 1978 inclusive (duringthe solar minimum between cycles 20 and 21) were used. In order to classify the solar wind as Alfv´enic or non-Alfv´enic, the cross helicity was calculated in every 20 minuteinterval where at least 10 velocity and magnetic field datapoints were available. The cross helicity is defined as σ c = 2 (cid:104) v · b (cid:105) (cid:10) | v | + | b | (cid:11) (1)where v = v p − v p are the proton velocity fluctuations inthe wave frame, v p is the local Alfv´en wave phase velocity, b = v A ( B / | B | ) is the magnetic field in velocity units, and (cid:104)(cid:105) indicates a time average over all points in a 20 minute inter-val (Bruno & Carbone 2013). v p was calculated using themethod given by Sonnerup et al. (1987), which finds the local MNRAS , 000–000 (2018) nner heliosphere solar wind categorisation de-Hoffman Teller frame of reference in which (cid:10) | v × b | (cid:11) isminimised; by construction, this is the value of v p for whichthe absolute value of σ c is maximised. Although a plasmawith Alfv´en waves propagating in opposite directions canhave low values of σ c , in this paper “Alfv´enic” is specificallyreserved to denote a plasma where Alfv´en waves propagatepredominantly in only one direction.The magnitude of σ c indicates whether the fluctua-tions in the plasma are predominantly uni-directional Alfv´enwaves ( | σ c | ≈
1) or not ( | σ c | < σ c determines the direction of wave propagation withrespect to the local magnetic field. Because Alfv´en wavesin the solar wind almost always travel away from the Sun(Gosling et al. 2009), the sign of σ c is a reliable proxy forthe magnetic polarity of the solar wind. Heavy ion charge state data measured at 1 AU is commonlyused to diagnose the solar origin of solar wind. Unfortunatelythere is no heavy ion data available from the Helios mission,so instead proton specific entropy was used as a proxy. Thespecific entropy argument is easily calculated from the pro-ton distribution parameters and given by S p = T p n α − p (2)where α is the polytropic index of the fluid. Here α wastaken to be 1.5, the value used by Pagel et al. (2004) andStakhiv et al. (2016) who studied correlations between en-tropy and composition, and whose results are used in section4.2 to make an indirect link between proton temperatureanisotropy and heavy ion charge states using S p as an inter-mediate variable. Figure 1 shows the distribution of the solar wind in the β p (cid:107) − T p ⊥ /T p (cid:107) plane measured by Helios at heliocentric dis-tances of 0.3 – 0.4 AU and 0.9 – 1.0 AU, and split intoslow and fast wind using a simple cut in speed. The dis-tribution at 0.3 AU has previously been presented for anindividual high speed stream by Matteini et al. (2007). Thetop right panel of figure 1 shows that the ∼ T p ⊥ /T p (cid:107) = 1. In contrast, at 1 AU the slow solarwind is distributed around T p ⊥ /T p (cid:107) = 1, where it is thoughtto be maintained during transit by a combination of colli-sions and kinetic instabilities that are active when β p (cid:107) ≥ T p T p | v p | < 500 km/s | v p | > 500 km/s p T p T p p Figure 1.
Distribution of data in the β p (cid:107) - T p ⊥ /T p (cid:107) plane. Con-tours are interpolated from 2D histogram counts with 40 bins log-arithmically spaced along each axis and normalised to the max-imum bin count. Left panels shows slow solar wind and rightpanels show fast solar wind. Top panels show the distribution at0.3 – 0.4 AU and bottom panels show 0.9 – 1.0 AU. T p ( K) T p ( K ) T p / T p = 1 T p / T p = 3 Figure 2.
Joint probability distribution of parallel and perpen-dicular proton temperatures at 0.3 – 0.4 AU. Histogram values arebin counts normalised to the maximum bin value. In this param-eter space lines of constant temperature anisotropy are diagonalwith a gradient of 1, with two examples shown for reference.MNRAS , 000–000 (2018)
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At 0.3 AU the majority of the slow solar wind is alsospread around T p ⊥ /T p (cid:107) = 1, but there is a significant frac-tion with T p ⊥ /T p (cid:107) > β p (cid:28) T p ⊥ and T p (cid:107) for allmeasurements between 0.3 AU and 0.4 AU. In this param-eter space two populations are clearly distinct: one centredaround T p ⊥ /T p (cid:107) = 1 and one centred around T p ⊥ /T p (cid:107) = 3.All fast solar wind occupies the anisotropic population, butthe slow wind is split between the two populations (as shownin figure 1).As well as being anisotropic, the fast solar wind is filledby anti-sunward propagating Alfv´en waves. This is a state-ment about the global dynamics of the plasma, in contrast tothe local kinetic properties given by the parallel and perpen-dicular temperatures, and is a consequence of the wind beingreleased on long-lived open field lines. To investigate whetherthe anisotropic wind as a whole has the same Alfv´enic prop-erty as the fast solar wind, figure 3 shows the joint probabil-ity distribution of temperature anisotropy and cross helicityat both 0.3 AU and 1 AU. The distribution of temperatureanisotropy is clearly bi-modal at 0.3 AU with a minimumat T p ⊥ /T p (cid:107) = 1 .
7, a feature that is not observable at 1 AUdue to the 0.3 AU anisotropic component becoming moreisotropic with radial distance (as a result of adiabatic evolu-tion). The distribution stops being clearly bi-modal at radialdistances greater than around 0.8 AU (not shown). In therest of this paper data taken at 0.3 AU to 0.4 AU are pre-sented, but the qualitative properties discussed are presentat all radial distances from 0.3 AU to 0.8 AU.It is clear from figure 3 that the fraction of solar windthat is Alfv´enic is much higher at 0.3 AU ( ∼ ∼ | σ c | > • An anisotropic, Alfv´enic population • An isotropic, Alfv´enic population • An isotropic, non-Alfv´enic populationThe split in anisotropy was chosen to be the saddle in be-tween the two populations at T p ⊥ /T p (cid:107) = 1 .
7, and the splitin cross-helicity was chosen at the edge of the Alfv´enic pop-ulation at | σ c | = 0 .
8. These boundaries are shown in figure3. At 0.3 AU - 0.4 AU, ∼
80% of the wind was Alfv´enic, splitequally between isotropic and anisotropic, and the remain-ing 20% was non-Alfv´enic.With this classification in mind, the top panel of figure4 shows the radial velocity distribution of the solar wind ineach category. Both isotropic populations consist primarilyof solar wind with speeds less than 500 km/s, whereas theanisotropic population spans a wide range of speeds from300 km/s – 700 km/s. In fact, at 0.3 – 0.4 AU Helios mea-sured slightly more anisotropic solar wind below 500 km/sthan above. This reinforces the idea that the concept of fastand slow solar winds breaks down at intermediate speeds | c | T p T p | c | T p T p Figure 3.
2D histograms of temperature anisotropy against abso-lute cross helicity (main panels) with adjoining 1D histograms oftemperature anisotropy (right panels) and cross helicity (top pan-els). 2D bin counts are normalised to the maximum bin count. 1Dhistograms are linearly scaled. The horizontal and vertical dashedlines show the partitioning of the parameter space into three dis-tinct regions; see text in section 3.1 for more details. Percentagesindicate the fraction of data points in each of the three regions. where wind can have properties similar to either the veryslow or very fast wind, and again suggests that some slowwind may have the same origin as fast wind. Another knownproperty of the fast solar wind is that the radial mass fluxdoes not depend on speed (Feldman et al. 1978; Wang 2010).To investigate whether this is true for the anisotropic windas a whole, the main panel of figure 4 shows radial fluxas a function of radial velocity for each of the three cate-
MNRAS , 000–000 (2018) nner heliosphere solar wind categorisation
200 300 400 500 600 700 800 v pr (km/s) n v r r ( s s r ) Isotropic, non-AlfvénicIsotropic, AlfvénicAnisotropic, Alfvénic0.3 - 0.4 AU
Figure 4.
Contours of the 2D histograms of radial number den-sity flux against radial velocity (centre panel) with adjoining 1Dhistograms of radial velocity (top panel) and radial number den-sity flux (right panel) from data taken at 0.3 – 0.4 AU. Differ-ent colours represent the three different categories of solar winddefined statistically in the top panel of figure 3. Contours areinterpolated from a 2D histogram and plotted at levels of (0.3,0.5, 0.7, 0.9) times the maximum bin value. 1D histograms arelinearly scaled. gories. The anisotropic wind has a constant flux that doesnot depend on speed; when evaluated at 1 AU this averageflux is ∼ × cm − s − , which agrees well with inde-pendent measurements made at 1 AU and beyond (Phillipset al. 1995; Goldstein et al. 1996; Wang 2010). The isotropicAlfv´enic wind has a slightly higher flux, whereas the non-Alfv´enic wind has widely ranging fluxes varying up to 1 to4 times the base value of the anisotropic wind, suggesting adistinct physical origin.To investigate the link between the three categories andtheir source regions on the Sun we also looked at the depen-dence of temperature anisotropy on proton specific entropy(used later as a proxy for composition). Figure 5 shows thejoint distribution of temperature anisotropy, and solar windspeed or proton specific entropy. At 0.3 AU, low speed wind(200 km/s – 300 km/s) is all isotropic, and at high speeds(450 km/s – 700 km/s) all of the wind is anisotropic, how-ever, at intermediate speeds (300km/s – 450 km/s) the windis spread between the two different states of T ⊥ /T (cid:107) . In con-trast the variation between temperature anisotropy and spe-cific entropy is slightly smoother; isotropic wind correspondsexclusively to low entropy and anisotropic wind exclusivelyto high entropy, with a continuous variation in between. Thisresult is used only as a correlation, and we do not claimthat there is any causal relationship between entropy andtemperature anisotropy. In section 4.2 we discuss how thiscorrelation can be used as an intermediate step to infer thecompositional properties of our three different categories.
200 400 600 v pr (km/s) T p T p T p / n p ( K cm ) Figure 5.
Joint probability distributions of temperatureanisotropy (y–axis) against radial velocity (x–axis, LH panel) andproton specific entropy (x–axis, RH panel). 1D histograms for thex-axis variables are shown above the 2D histograms on a linearscale.
We have shown that at 0.3 AU it is possible to distinguishbetween three types of solar wind based on the statisticsof proton temperature anisotropy and Alfv´enicity. To un-derstand the spatial distribution of each population withinthe solar wind, figure 6 shows the time-series measurementsmade by Helios 1 inside 0.5 AU during its first perihelionpass.The bi-modal nature of proton temperature anisotropyis clear, even within the un-averaged and noisy 40.5 sec-ond cadence measurements, and the wind remained in oneanisotropy state for days at a time. In contrast, within theisotropic category the Alfv´enic and non-Alfv´enic sub cate-gories are well mixed and interspersed within each other.During some isotropic periods (e.g. around 1975-03-09)the non-Alfv´enic wind is sub-dominant and appears to beembedded in the Alfv´enic wind, whereas at other times(e.g. around 1975-03-30) there appears to be an approxi-mately even mix of Alfv´enic and non-Alfv´enic wind.The transition from isotropic to anisotropic wind wassharp, and always occurred at the leading edge of high speedstreams. It is known that composition and entropy undergosharp changes at the leading edge of high speed streams(Wimmer-Schweingruber et al. 1997; Lazarus et al. 2003;Crooker & McPherron 2012), but figure 6 demonstratesthat a coincident temperature anisotropy boundary is alsopresent. The sharp increases in temperature anisotropy weredriven by increases in T ⊥ whilst T (cid:107) stayed roughly con-stant across the boundaries (not shown). The transition fromanisotropic back to isotropic was also sharp, caused by sharpdecreases in T ⊥ , and occurred inside the rarefaction edge ofhigh speed streams. The sudden drop in T ⊥ , which caused acoincident drop in total temperature and therefore a drop inspecific entropy, was not correlated with changes in the crosshelicity. The only other observable change in the plasma andmagnetic field data are magnetic field fluctuations that lookqualitatively different on either side of the boundary (notshown). The time-series gives a clear visual demonstration MNRAS , 000–000 (2018)
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AnisotropicIso, AlfvénicIso, non-Alfvénic 200400600800 v p r ( k m / s ) T p / T p Date (YYYY-MM-DD) | c | c > 0 c < 0 Figure 6.
Timeseries data from the first perihelion pass of Helios 1, illustrating the categorisation displayed statistically in the top panelof figure 3. Top panel shows the categorisation. Second panel shows the radial proton speed, coloured by the categorisation. Third panelshows full resolution proton temperature anisotropy (light blue) and 20 minute averaged values (dark black). The line at T p ⊥ /T p (cid:107) = 1 . | σ c | = 0 . that it is impossible to cut the velocity in a single placeto separate different types of wind, but clear bi-modalitymakes a cut in temperature anisotropy easy. We re-iteratethat performing this separation is only possible at heliocen-tric distances < We now use the observations made in section 3 to link ourthree categories of solar wind to their solar sources. A sum-mary of the conclusions drawn in this section is given intable 1.
It has long been known that wind originating on open fieldlines rooted inside large coronal holes forms the fast so-lar wind (Krieger et al. 1973; Sheeley et al. 1976; Cranmer2009). Remote sensing measurements also show pronouncedtemperature anisotropies present above coronal holes whilstthe solar wind is still near to the Sun (Kohl et al. 1997;Cranmer et al. 2008). Because the anisotropic category is theonly one with high speeds (figure 4), we infer that wind pro-duced in the core of coronal holes belongs to our anisotropiccategory. The spatial distribution of anisotropic wind, withslower speeds always occurring in the rarefaction edges ofhigh speed streams (figure 6) shows that rarefaction duringtransit is responsible for the relatively low speed of someanisotropic wind (Pizzo 1991). The reason slower speeds arenot observable at the leading edge of high speed streams is because by 0.3 AU they have already been accelerated by thefaster wind to form a co-rotating interaction region (Burlaga1974; Pizzo 1991; McGregor et al. 2011; Richardson 2018).Note that we have chosen to distinguish between theedges and the core of coronal holes; at the edge of coronalholes the magnetic field lines typically undergo large sepa-rations as a function of height in the corona, which has theeffect of reducing both the wind speed (Levine et al. 1977;Wang & Sheeley 1991; Cranmer et al. 2007; Pinto et al. 2016)and charge state ratios (Wang et al. 2009). In the next twosections further evidence is used to predict which one of ourthree categories coronal hole edge wind is part of.
At distances beyond 1 AU the proton specific entropy isanti-correlated with the O /O charge state ratio (Pagelet al. 2004). In addition observations at 1 AU show thatspecific entropy is anti-correlated with the C /C chargestate ratio (Stakhiv et al. 2016). We have shown in figure 5that entropy has a monotonic dependence on proton tem-perature anisotropy (but note again that this is not neces-sarily a causal relationship). Linking this newly observed re-lationship to the inferred relationship between entropy andcharge state ratios suggests that anisotropic wind has lowO /O and C /C charge state ratios, and isotropicwind has high charge state ratios. As well as being relatedstatistically, the sharp boundaries between anisotropic windand isotropic wind mimic the locations of sharp compositionboundaries found in other studies (see section 3.2 for a dis-cussion). This backs up the statistical inference derived be-tween proton temperature anisotropy and heavy ion chargestates. MNRAS , 000–000 (2018) nner heliosphere solar wind categorisation Table 1.
Properties of our three categories of solar wind near the Sun at solar minimum. The first 6 rows show properties directlymeasured by Helios at distances 0.3 AU – 0.4 AU in this study. The bottom 4 rows show inferred properties. See section 4 for moredetails. Isotropic non-Alfv´enic Isotropic Alfv´enic AnisotropicFraction at 0.3 AU – 0.4 AU 21.6 % 37.4% 39.0 %Speed 200 km/s – 500 km/s 200 km/s – 500 km/s 300 km/s – 700 km/s T p (cid:107) T p ⊥ /O High High LowC /C High High LowSolar source(s) Small scale transients Active regions, coronal hole edges Coronal hole cores
Using specific entropy as a bridge between anisotropyand composition therefore corroborates our previous con-clusion that anisotropic wind originates in the core of largecoronal holes, which are known to emit wind with low chargestate ratios (Geiss et al. 1995; Wang et al. 2009). This al-lows us to infer that both categories of isotropic wind do notoriginate in the core of coronal holes, but may originate atcoronal hole edges or outside coronal holes. This again agreeswith remote sensing measurements that show reduced tem-perature anisotropies near the edges of coronal holes whencompared to the core of coronal holes (Susino et al. 2008).We therefore suggest that the ‘isotropic’ wind forms whatis commonly thought of as the ‘slow solar wind’. There area number of theories as to the origin of the slow solar wind(Abbo et al. 2016); in the next section we assign possibletheories to either the Alfv´enic or non-Alfv´enic categories ofthe isotropic wind.
Solar wind with a high Alfv´enicity implies a steady staterelease of plasma on open field lines. This hypothesis is sup-ported by the relatively constant mass flux in the Alfv´enicisotropic wind (see figure 4). This means any Alfv´enic windmust have been released on field lines that remained openfor at least the 20 minute resolution of the cross-helicity cal-culated from in-situ data. Areas of long lasting open field onthe Sun can be split into the core of coronal holes (alreadycategorised), edges of coronal holes, and active regions. Re-mote sensing measurements have shown that active regionoutflows have high coronal electron temperatures (Neuge-bauer et al. 2002; Brooks & Warren 2012), contain openfield lines allowing plasma to escape into the heliosphere(Slemzin et al. 2013), and can supply mass fluxes similarto those measured in-situ (Brooks et al. 2015). We there-fore conclude active region wind is most consistent with theisotropic-Alfv´enic category, along with the wind from theedges of coronal holes which also contain long lasting openmagnetic fields and has similar properties.Finally, we predict that the non-Alfv´enic wind is consis-tent with the final known type of slow solar wind, typicallycalled “number density structures” or “blobs”, which havebeen detected both remotely (e.g. Sheeley et al. 1997; Viall &Vourlidas 2015; DeForest et al. 2018) and in-situ (e.g. Kepko & Spence 2003; Sanchez-Diaz et al. 2017; Stansby & Hor-bury 2018). These are non steady state transient structureswith a high density but similar speed as the surroundingslow wind, and therefore have enhanced mass fluxes relativeto the background wind. This property is exactly what wehave measured for the non-Alfv´enic wind (figure 4), backingup our final categorisation.A summary of our mapping of possible solar sources toin-situ solar wind categories is given in table 1.
Recently several authors have also attempted to categorisesources of the solar wind using in-situ observations, choos-ing the categories of coronal mass ejection wind, coronal holewind, and interstream wind (for a summary, see Neugebaueret al. 2016). In this paper we have deliberately removedcoronal mass ejection wind from our dataset, and have usedproton temperature anisotropy as the only variable separat-ing coronal hole wind (anisotropic) and interstream wind(isotropic).Zhao et al. (2009) used only the O /O ratio and so-lar wind speed measured at 1 AU. This method is limited bythe slow cadence (1 hour) of charge state ratio measurementsavailable, but has the advantage that the O /O ratio isknown to be directly related to plasma properties near theSun. Because the distribution of heavy ion charge states isonly clearly bimodal at solar minimum (Zurbuchen et al.2002), it is not clear if this method works well during solarmaximum conditions. Because our method also assumes abimodal distribution of charge state ratios, it is not clear ifit is still applicable during solar maximum conditions either.Xu & Borovsky (2015) picked in-situ measurements,manually categorised specific intervals of the measurements,and then tried to find boundaries in a multi-dimensional pa-rameter space that reliably split the data into the assumedcategories. These boundaries could then be applied to otherintervals where the categorisation is unknown. This methodis practical and pragmatic for rapidly categorising solar windsources, but the boundaries between categories are some-what arbitrary and do not necessarily directly relate to thedifferent physics of solar wind formation at each solar windsource. The advantage of the Xu & Borovsky (2015) method MNRAS , 000–000 (2018)
D. Stansby et al. is that it only uses single point measurements of solar windprotons and magnetic fields, so the cadence at which it canbe applied is limited only by that of the in-situ measure-ments. In contrast our method is limited to a 20 minutecadence, which is in practice limited by the number of 40.5second cadence of proton measurements needed to reliablycalculate σ c .Other authors have backmapped solar wind measuredat 1 AU to try and determine the exact location on theSun from which it originated (e.g. Neugebauer et al. 1998;Fu et al. 2015; Fazakerley et al. 2016; Peleikis et al. 2017;Zhao et al. 2017). This method assumes that the solar windtravels along magnetic field lines between the Sun’s surfaceand magnetic field source surface at 2.5 r s (solar radii), whichare computed using a potential field source surface model,and then travels radially and at a constant speed to the in-situ observer. This method has the advantage of drawing adirect link by trying to predict the exact solar wind sourcelocation of in-situ measurements. Although it is successful inidentifying sources on very large time scales of ∼ days, it iscurrently not possible to probe smaller scales, and does nottake into account dynamical processing that occurs duringtransit between the Sun and the in-situ observer. We have presented an attempt to map in-situ measurementsof solar wind to their sources, using properties of the solarwind observable at 0.3 AU that are un-observable at 1 AUdue to dynamical interactions during transit. We find thatthe solar wind can be split into three categories (summarisedin table 1), based on in-situ measurements of proton tem-perature anisotropy and Alfv´enicity (section 3), and sortpossible solar origins of the solar wind into these three cate-gories (section 4). Although many other methods have beendeveloped to attempt the same goal of solar source categori-sation (section 5), the lack of in-situ composition and remotesensing data available during the Helios era (1974 – 1984)restricted our ability to use these more modern techniques.However, in the near future we will have access to simultane-ous in-situ measurements of protons in the inner heliosphere,in-situ measurements of solar wind composition, and a widerange of remote sensing data. We finish by describing howour new categorisation scheme can be applied to data fromupcoming missions to the inner heliosphere.Parker Solar Probe (PSP, Fox et al. 2016) will makein-situ measurements of the solar wind at heliocentric dis-tances inside 0.3 AU, and the first comprehensive in-situsolar wind measurements inside 1 AU since Helios. Protonand magnetic field measurements made by PSP will allow usto perform the categorisation scheme outlined in this paper.Advances in modelling and remote sensing since the Heliosera mean that it will then be possible to backmap the solarwind measured by PSP to a predicted source location on theSun. If our categorisation is correct, the three categories ofin-situ solar wind will backmap to their respective inferredsolar sources.Solar Orbiter (SO, M¨uller et al. 2013) will provide thefirst solar wind composition measurements between 0.3 AUand 1 AU. This will allow us to directly test the correla- tion between temperature anisotropy and charge state ra-tios, without having to bridge the gap by using proton spe-cific entropy as an intermediate variable. If our categorisa-tion is correct, isotropic wind will clearly correspond to highcharge state ratios, and anisotropic wind will clearly corre-spond to low charge state ratios. In addition SO will carryon board remote sensing instruments that are designed totarget the predicted solar sources of in-situ measurements,making backmapping wind to its source even more accuratethan using remote sensing instruments at 1 AU.
ACKNOWLEDGEMENTS
D. Stansby is supported by STFC studentshipST/N504336/1, and thanks Trevor Bowen, Allan Mac-Neil, Denise Perrone and Alexis Roulliard for helpfuldiscussions. T. S. Horbury is supported by STFC grantST/N000692/1. This work was supported by the Pro-gramme National PNST of CNRS/INSU co-funded byCNES.Data were retrieved using HelioPy v0.5.3 (Stansby et al.2018b) and processed using astropy v3.0.3 (The AstropyCollaboration et al. 2018). Figures were produced usingMatplotlib v2.2.2 (Hunter 2007; Droettboom et al. 2018).Code to reproduce the figures presented in thispaper is available at https://github.com/dstansby/publication-code . REFERENCES
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