Abstract
The structure and dynamics of the solar corona evolve with the Sun’s magnetic cycle, yet how this variability manifests in the disk-integrated, Sun-as-a-star observables used in stellar activity studies remains poorly constrained. We compile 18 full-disk spectroscopic mosaic scans made by Hinode/EIS spanning 2013–2024, covering solar cycle 24 and the rise of solar cycle 25, and probe coronal plasma variability through the integrated and spatially resolved intensity, Doppler velocity, and non-thermal velocity of log T6.2 plasma in active regions and the quiet Sun. Disk-integrated coronal intensity is strongly correlated with the solar cycle, consistent with stellar observations. No clear solar-cycle variation is found in the distributions of coronal Doppler or non-thermal velocity in either active regions or the quiet Sun, though their total intensities do track the cycle. Active region intensity per unit solid angle shows a moderate correlation with solar cycle. Taken together, these results support the hypothesis that Sun-as-a-star coronal intensity variability across the solar cycle is driven primarily by the changing fraction of the disk occupied by active regions, rather than by changes in the log T6.2 plasma properties of those regions. The well-established correlation between upflowing plasma and elevated non-thermal line broadening in active regions persists throughout the cycle, implying that the underlying kinematic properties of active region plasma are insensitive to the global magnetic field configuration, a result with direct implications for the interpretation of coronal activity cycles on solar-like stars.
1 Introduction
The solar corona is structured by the Sun’s magnetic field, which evolves over the 11-year solar cycle. During solar minimum, the global magnetic field is dominated by a low-order, largely axisymmetric dipolar component, giving rise to long-lived polar coronal holes and an enhanced fraction of open magnetic flux. In contrast, solar maximum is characterised by a far more complex and multipolar topology driven by the frequent emergence of active regions at low and mid-latitudes, which disrupts the large-scale dipole and redistributes open flux across the solar surface (e.g., Mackay and Yeates, 2012; Babcock, 1961). These shifts in magnetic topology govern the global behaviour of coronal plasma, dictating the distribution of open versus closed field lines and, consequently, the global balance of plasma confinement and outflows. Extreme ultraviolet (EUV) spectroscopy provides direct diagnostics of these dynamics. Doppler shifts trace bulk motions along the line of sight, while excess line broadening (often expressed as a non-thermal velocity) can reflect unresolved wave activity, turbulence, or multi-component flows (e.g., Doschek et al., 2008). In spatially resolved solar observations, coronal holes are commonly associated with blue-shifted emission indicative of outflows (e.g., Hassler et al., 1999), whereas active regions show a mix of red-shifts in cooling loops and blueshifts at footpoints and peripheries (e.g., Tian et al., 2021). Non-thermal broadening is also frequently enhanced in regions exhibiting upflows, although the physical drivers are not understood, in part due to instrumental limitations (e.g., Doschek, 2012).
Stellar spectra are spatially unresolved and combine emission from multiple magnetic structures (e.g., Kowalski, 2024), so stellar atmospheric dynamics cannot be studied with solar-like spatial detail. Sun-as-a-star observations, where emission from the solar disk is spatially integrated (as in stellar spectra) but where the spatially-resolved contributors to the emission are known, can be useful in understanding how disk-integrated spectra represent the atmospheres of stars (Toriumi, 2026). Toriumi et al. (2020), for example, showed that integrated multi-wavelength emission retains information on the presence and evolution of active regions, plages, and coronal loops. One complication is that stellar activity studies usually use chromospheric diagnostics such as Ca II H&K (e.g., Costes et al., 2021), whereas resolved solar coronal studies often use optically thin EUV lines (e.g., McKevitt et al., 2026a). Nevertheless, on long timescales chromospheric and coronal diagnostics broadly trace the same underlying cycle in stars, and solar magnetic flux correlates with atmospheric emission from the chromosphere to the corona (Toriumi and Airapetian, 2022).
In these solar-stellar contexts solar atmospheric irradiance is typically measured using full-disk spatially-resolved broadband imaging, given the availability of such data from the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory (SDO/AIA; Pesnell et al., 2012; Lemen et al., 2012). The Extreme ultraviolet Variability Experiment on SDO (EVE; Woods et al., 2012) provides full-disk Sun-as-a-star spectra, but cannot isolate contributions from specific solar regions. No observatory currently provides sustained, high-cadence, spatially-resolved full-disk spectral diagnostics of the solar atmosphere (Ugarte-Urra et al., 2023). Small-field-of-view spectrographs such as the EUV Imaging Spectrometer onboard Hinode (Hinode/EIS; Kosugi et al., 2007; Culhane et al., 2007) provide unmatched plasma diagnostics, but typically cover less than 10% of the full solar disk so can miss large-scale coupling and distant destabilisation (Schrijver et al., 2013; Schrijver and Higgins, 2015). Therefore full-disk spectroscopic studies require mosaics assembled from sequences of individual observations.
Coronal activity cycles are known on stars (e.g., Hempelmann et al., 2006; Robrade et al., 2012). The ‘Sun as an X-ray star’ series (e.g., Orlando et al., 2000) used solar differential emission measure (DEM) analysis from the Soft X-ray Telescope on Yohkoh (Tsuneta et al., 1991; Ogawara et al., 1991) to infer the fraction of stellar disks occupied by active regions (filling factor). Orlando et al. (2001) found that active region filling factor is the main cause of solar-cycle emission-measure-variability. Also, Morgan and Taroyan (2017) used broadband SDO/AIA imaging to show that solar-cycle-related variability in full-disk emission is driven by the presence of active regions, but that active regions themselves display no clear solar-cycle-related variability in average temperatures, emission measures, or magnetic field strength. Such work supports the hypothesis that stellar coronal activity cycles are caused by active region filling factor. However, approaching this with spectroscopic data, rather than broadband imaging, would prove useful in challenging the hypothesis further.
To this end, we compile and analyse a set of 18 spatially resolved full-disk spectroscopic mosaics taken between 2013 and 2024 by Hinode/EIS, processed using the EISMaps pipeline (McKevitt et al., 2026a), and perform an analysis of the variability of log 6.2 plasma. We also release full-disk maps of intensity, Doppler velocity, and non-thermal velocity for log 4.7–7.1 plasma, which were processed but whose analysis fell outside the scope of this study.
2 Observations and data reduction
Hinode/EIS performs different full-disk mosaic observations using both it is narrow slits (e.g., Brooks et al., 2015), and it is slots1 (e.g., Warren et al., 2014). In this study we use 18 full-disk spectroscopic narrow-slit mosaic scans, taken between 2013 and 2024, for which Level-1 data are available2. To enable the full-disk scan to be completed within a typical 48-h Hinode observing window, the widest 2 arcsec slit is used and the scan step size is set to 4 arcsec (sparse rastering). The plate scale of 1 arcsec results in spectrally-resolved measurements of plasma in pixels of 4 arcsec (X) 1 arcsec (Y).
2.1 Spectroscopy
Due to the time taken to make the full disk scan, the exposure time for each observation is limited, resulting in only a few emission lines possessing resolved spectra with a signal-to-noise (SNR) ratio sufficient to derive reasonable non-thermal velocity values from fitted emission lines. In this study we fit and analyse the strong Fe XII 195.119 Å. We find the other coronal lines, Fe XI 188.216 Å and Fe XIII 202.044 Å, to be sufficiently strong to derive reliable non-thermal velocity values across the disk, while all lines captured in the Hinode/EIS observations show intensity and Doppler velocity values with signal to noise ratios sufficient to analyse solar-cycle trends.
We assemble a Sun-as-a-star spectrum for each disk by summing the spatially-resolved spectra after correcting for the various instrument effects, as discussed in detail in the Appendix. We also make spatially-resolved full-disk maps of intensity, velocity, and non-thermal velocity. To do this, we fit Gaussian profiles to the spectra using the MPFIT algorithm (Markwardt, 2009) implemented in the EIS Python Analysis Code (EISPAC; Weberg et al., 2023). In the case of Fe XII 195.119, we use two-component Gaussian fits to account for the blended Fe XII 195.179 Å line. Other lines are fitted with multi-component Gaussian templates of EISPAC where blends are present. The Doppler velocity was calculated from the fitted line centroid , rest wavelength , and speed of light using . We normalise measured values for their position on disk using , where is the angle between the line of sight and the normal to the solar surface. The excess broadening (non-thermal velocity; ) was calculated usingwhere and refer to the observed and instrumental full width at half maximum values respectively, and where is the thermal velocity calculated using the peak-ionisation-fraction temperature of the ion. We take the instrumental width from eis_slit_width in the EIS software tree in Solarsoft. Additionally, we correct intensity measurements using the radiometric calibration of Del Zanna et al. (2025), and individual rasters making up the full disk map are assembled using the method of McKevitt et al. (2026a)3. We make the processed FITS files for the disks available4.
2.2 Solar features
We use Space-weather HMI Active Region Patches (SHARP; Bobra et al., 2014) from the Helioseismic and Magnetic Imager onboard SDO (SDO/HMI; Scherrer et al., 2012), to locate active regions. We use multi-wavelength intensity imaging from SDO/AIA and the neural network method of Jarolim et al. (2021) to locate coronal holes. We then define quiet Sun, for the purpose of this study, as pixels on the disk not captured by active region or coronal hole masking. For our spatially-resolved measurements, we additionally exclude pixels with heliocentric angle 85°, where is the angle between the observer’s line of sight and the local solar surface normal. This corresponds to pixels lying within 5° of the solar limb on the visible disk, where the large Doppler cosine correction amplifies artifacts. We do not consider the fitted parameters in coronal holes in our analysis given their low intensity further reduces signal-to-noise and their susceptibility to interference from other effects (see e.g., scatter light effects discussed by Young and Viall, 2022).
3 Results
3.1 Variability of plasma distributions
Figure 1 shows measurements of log 6.2 coronal plasma from the first disk in the series with maps of coronal intensity, velocity, and non-thermal velocity on the left, and 2D histograms of velocity versus intensity and non-thermal velocity for the full disk, quiet Sun, and active regions on the right. Our histograms use bins of 50 erg/s/ /sr in intensity, 0.56 km/s in velocity, and 0.60 km/s in non-thermal velocity. Figure 2 shows the corresponding distributions for all 18 disks, represented by 75% histogram-density contours coloured by solar-cycle phase. We take the peak of solar cycle 24 as April 2014, the start of solar cycle 25 as December 2019, and the peak of solar cycle 25 as October 2024 (Clette and Lefèvre, 2015). These contours capture the core plasma distributions well. The only excluded feature in this first disk is a sparse tail of high full-disk non-thermal velocities in upflowing plasma, which may be associated with active regions. Given the low pixel counts in this tail and possible velocity systematics from instrumental spectral drift (see Appendix), we do not consider it further in this study.
FIGURE 1
FIGURE 2
3.2 Doppler velocity and intensity
The distribution of the full disk plasma shows that the spread of velocities remains consistent throughout the solar cycle. The primary variation is a shift in peak intensity; during solar maximum, the distribution peaks at higher intensities but the velocity centroid remains stable at 0 km/s. The quiet sun shows a similar distribution to the full disk, though with a reduced peak intensity. Active regions display a strong dependence on intensity. At the highest intensities, Doppler velocities are tightly clustered around 0 km/s and even slightly red-shifted. However, as intensity decreases, the distribution skews significantly towards upflowing plasma. This relationship appears largely consistent across the solar cycle.
3.3 Doppler and non-thermal velocities
For the full disk, the distribution is symmetric around zero Doppler velocity, with no clearly distinguishable variations with the solar cycle. We find the same to be largely true for the quiet Sun distribution, but note some disks near to solar maximum show slightly lower peak non-thermal velocities. In active regions, stronger non-thermal velocities are correlated with upflowing plasma. There is no clear variation in this relationship with respect to the solar cycle.
3.4 Disk-integrated variability
To consider our results in a Sun-as-a-star context, we show the integrated spectra around Fe XII 195.119 at different cycle phases in the left panels of Figure 3. The lines display similar profiles, but their total intensity varies with the solar cycle, with stronger emission near solar maximum. The top-right panel shows the Sun-as-a-star full-disk, quiet-Sun, and active-region line intensities with the solar cycle together with sunspot number, where all three are strongly positively correlated with the cycle (r = 0.98, 0.97, and 0.87). The bottom-right panel shows active-region intensity normalised by active-region solid angle, coloured by total active-region solid angle. Missing pixels in some disks were corrected by assigning them the mean intensity of the same feature type elsewhere on the disk, to ensure all disks had the same number of contributing pixels. We find a moderate correlation between active-region intensity per solid angle and the solar cycle (r = 0.52), with higher values during the rise of cycle 25.
FIGURE 3
4 Discussion
This study finds that disk-integrated coronal EUV intensity tracks the solar cycle closely (r = 0.98; Figure 3), and that the distributions of Doppler and non-thermal velocity within the magnetic structures responsible for that emission show no clear modulation across the same interval. This strong cycle dependence at the integrated level, and near-invariance at the per-structure level, supports the hypothesis that active region filling factor is the primary driver of coronal intensity variability, rather than any change in the spectroscopic characteristics of the regions themselves. We note, however, that spectroscopic characteristics such as composition and thermal structure were not covered in this study. Orlando et al. (2001) reached a similar conclusion using analysis of soft X-ray DEM variability between the maximum and the minimum of solar cycle 22. Morgan and Taroyan (2017) subsequently supported this result using SDO/AIA broadband imaging across most of cycle 24 and some of cycle 25, demonstrating that the average temperature, emission measure, and photospheric magnetic field strength of individual active regions showed no systematic cycle-phase dependence. The spectroscopic analysis we present is sensitive to plasma motions and wave activity in a way that broadband imaging and DEM analysis is not, and so further supports the filling factor hypothesis.
The persistence of the well-known anti-correlation between upflows and non-thermal broadening in active regions throughout the cycle implies that the mechanism(s) responsible (e.g., impulsive footpoint heating, unresolved Alfvénic wave activity, or reconnection-driven outflow) operate whenever the magnetic topology of an active region is present, regardless of the global solar magnetic field. We caution, however, that our sample size of active region plasma at solar minimum is limited. The moderate correlation between active region intensity normalised by solid angle with the solar cycle (r = 0.52; Figure 3) is therefore curious. A pure filling-factor model would predict no residual correlation once solid angle is accounted for. The fact that one remains at moderate significance suggests that either the typical active region near solar maximum carries higher intrinsic intensity, perhaps reflecting higher photospheric flux and more complex magnetic geometry, or that diffuse emission from the outskirts of active regions, not fully captured by the SHARP-based masking, contaminates the solid angle normalisation. The former would be consistent with the known dependence of coronal luminosity on total unsigned magnetic flux, which depends not only on the area occupied by magnetic structures but also on their field strength (Pevtsov et al., 2003). In that sense, normalising only by active region solid angle would not necessarily remove all solar-cycle-dependent variability. Distinguishing between enhanced unsigned magnetic flux per unit active region area near solar maximum and contamination from diffuse emission at active region outskirts would require more directly comparing the spectroscopic dataset against HMI magnetic flux, a step that falls outside the scope of the present analysis.
There is some small variation seen with solar cycle in the peak non-thermal velocity values in the quiet Sun plasma distributions, where solar maximum is associated with slightly lower non-thermal velocities. This could be due to stronger emission reducing artificial enhancers of non-thermal velocity such as signal-to-noise ratio and stray light effects. However, intensity versus non-thermal velocity histograms would be required to properly consider this, something outside the scope of this study. The next-generation high-throughput SOLAR-C/EUVST spectrometer will enable such measurements to a much higher precision (McKevitt et al., 2026b). The quiet-Sun distributions appear to show variability resembling that of the full disk. We find this logical given the majority of the disk in all cases is quiet Sun plasma. However, we note that the active region masking technique we apply may also influence the quiet Sun distributions, where diffuse or extended emission from nearby active regions is considered as quiet Sun plasma.
The original motivation for assembling this dataset was to connect spatially resolved coronal spectroscopy to Sun-as-a-star observables, and in particular to ask whether any solar-cycle variability in disk-integrated line profiles can be explained by changes seen within specific magnetic structures. Modelling of X-ray activity cycles in solar analogues (e.g., Hempelmann et al., 2006; Robrade et al., 2012; Orlando et al., 2017; Coffaro et al., 2020) has consistently considered varying active region filling factors as the mechanism linking chromospheric and coronal cycles, with per-region plasma treated as more fixed. That has rested largely on the assumption that coronal loop physics is determined locally. Our results suggest, albeit with limited data points, that the rise phase of the solar cycle and the related magnetic complexity may complicate this relationship.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
Author contributions
JM: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Methodology, Software, Visualization, Writing – original draft. IU-U: Conceptualization, Formal Analysis, Methodology, Resources, Writing – review and editing. PY: Formal Analysis, Methodology, Resources, Writing – review and editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. JM was supported by STFC PhD Studentship number ST/X508858/1. IU-U was supported by the NASA Hinode program.
Acknowledgments
We thank the reviewers for their thoughtful and thorough comments, suggestions, and insight, which greatly improved this manuscript. We thank Prof. Manuel Güdel for his insightful comments on the solar-stellar connection. PY acknowledges funding from the GSFC Internal Scientist Funding Model competitive work package program, and the Hinode project.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was used in the creation of this manuscript. GitHub Copilot was used to assist with code review.
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Author disclaimer
Data analysis was performed using Austrian Scientific Computing infrastructure (https://asc.ac.at/). Hinode is a Japanese mission developed and launched by ISAS/JAXA, with NAOJ as domestic partner and NASA and STFC (United Kingdom) as international partners. It is operated by these agencies in co-operation with ESA and NSC (Norway).
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fspas.2026.1813447/full#supplementary-material
Footnotes
1.^Available under the Hinode Operations Plan (HOP) 0130: https://www.isas.jaxa.jp/home/solar/hinode_op/hop.php?hop=0130
3.^Here we use version 0.1.2: https://doi.org/10.5281/zenodo.17641183. The source code and ongoing development are available at: https://github.com/jamesmckevitt/eismaps
References
1
BabcockH. W. (1961). The topology of the Sun’s magnetic field and the 22-Year cycle. Astrophysical J.133, 572. 10.1086/147060
2
BobraM. G.SunX.HoeksemaJ. T.TurmonM.LiuY.HayashiK.et al (2014). The helioseismic and magnetic imager (HMI) vector magnetic field pipeline: SHARPs - space-weather HMI active region patches. Sol. Phys.289, 3549–3578. 10.1007/s11207-014-0529-3
3
BrooksD. H.Ugarte-UrraI.WarrenH. P. (2015). Full-sun observations for identifying the source of the slow solar wind. Nat. Commun.6, 5947. 10.1038/ncomms6947
4
CletteF.LefèvreL. (2015). SILSO Sunspot Number V2.0. World Data Center. 10.24414/QNZA-AC80
5
CoffaroM.StelzerB.OrlandoS.HallJ.MetcalfeT. S.WolterU.et al (2020). An X-ray activity cycle on the young solar-like star ∊ Eridani. Astronomy and Astrophysics636, A49. 10.1051/0004-6361/201936479
6
CostesJ. C.WatsonC. A.de MooijE.SaarS. H.DumusqueX.CameronC.et al (2021). Long-term stellar activity variations and their effect on radial-velocity measurements. Mon. Notices R. Astronomical Soc.505, 830–850. 10.1093/mnras/stab1183
7
CulhaneJ. L.HarraL. K.JamesA. M.Al-JanabiK.BradleyL. J.ChaudryR. A.et al (2007). The EUV imaging spectrometer for hinode. Sol. Phys.243, 19–61. 10.1007/s01007-007-0293-1
8
Del ZannaG.WebergM. J.WarrenH. P. (2025). Hinode EIS: updated in-flight radiometric calibration. Astrophysical J. Suppl. Ser.276, 42. 10.3847/1538-4365/ad981f
9
DoschekG. A. (2012). The dynamics and heating of active region loops. Astrophysical J.754, 153. 10.1088/0004-637X/754/2/153
10
DoschekG. A.WarrenH. P.MariskaJ. T.MuglachK.CulhaneJ. L.HaraH.et al (2008). Flows and nonthermal velocities in solar active regions observed with the EUV imaging spectrometer on hinode: a tracer of active region sources of heliospheric magnetic fields?Astrophysical J.686, 1362–1371. 10.1086/591724
11
HasslerD. M.DammaschI. E.LemaireP.BrekkeP.CurdtW.MasonH. E.et al (1999). Solar wind outflow and the chromospheric magnetic network. Science283, 810–813. 10.1126/science.283.5403.810
12
HempelmannA.RobradeJ.SchmittJ. H. M. M.FavataF.BaliunasS. L.HallJ. C. (2006). Coronal activity cycles in 61 cygni. Astronomy and Astrophysics460, 261–267. 10.1051/0004-6361:20065459
13
JarolimR.VeronigA. M.HofmeisterS.HeinemannS. G.TemmerM.PodladchikovaT.et al (2021). Multi-channel coronal hole detection with convolutional neural networks. Astronomy and Astrophysics652, A13. 10.1051/0004-6361/202140640
14
KosugiT.MatsuzakiK.SakaoT.ShimizuT.SoneY.TachikawaS.et al (2007). The hinode (Solar-B) mission: an overview. Sol. Phys.243, 3–17. 10.1007/s11207-007-9014-6
15
KowalskiA. F. (2024). Stellar flares. Living Rev. Sol. Phys.21, 1. 10.1007/s41116-024-00039-4
16
LemenJ. R.TitleA. M.AkinD. J.BoernerP. F.ChouC.DrakeJ. F.et al (2012). The atmospheric imaging assembly (AIA) on the solar dynamics observatory (SDO). Sol. Phys.275, 17–40. 10.1007/s11207-011-9776-8
17
MackayD.YeatesA. (2012). The Sun’s global photospheric and coronal magnetic fields: observations and models. Living Rev. Sol. Phys.9. 10.12942/lrsp-2012-6
18
MarkwardtC. B. (2009). Non-linear least squares fitting in IDL with MPFIT. ArXiv:0902.2850. 10.48550/arXiv.0902.2850
19
McKevittJ.MatthewsS.BakerD.ReidH.BrooksD.Ugarte-UrraI.et al (2026a). Coronal non-thermal and doppler plasma flows driven by photospheric flux in 28 active regions. Publ. Astronomical Soc. Jpn.78 (3), 867–877. 10.1093/pasj/psag024
20
McKevittJ.MatthewsS.BrooksD.ShimizuT.TeiA.Ugarte-UrraI.et al (2026b). Pre-flare and active region plasma motion seen by the short wavelength camera on SOLAR-C/EUVST. Publ. Astronomical Soc. Jpn.78, psag078. 10.1093/pasj/psag078
21
MorganH.TaroyanY. (2017). Global conditions in the solar corona from 2010 to 2017. Sci. Adv.3, e1602056. 10.1126/sciadv.1602056
22
OgawaraY.TakanoT.KatoT.KosugiT.TsunetaS.WatanabeT.et al (1991). The SOLAR-A mission: an overview. Sol. Phys.136, 1–16. 10.1007/BF00151692
23
OrlandoS.PeresG.RealeF. (2000). The Sun as an X-Ray star. I. Deriving the emission measure distribution versus temperature of the whole solar Corona from theYohkoh/Soft X-Ray telescope data. Astrophysical J.528, 524–536. 10.1086/308137
24
OrlandoS.PeresG.RealeF. (2001). The Sun as an X-Ray star. IV. The contribution of different regions of the Corona to its X-Ray spectrum. Astrophysical J.560, 499–513. 10.1086/322333
25
OrlandoS.FavataF.MicelaG.SciortinoS.MaggioA.SchmittJ. H. M. M.et al (2017). Fifteen years in the high-energy life of the solar-type star HD 81809: XMM-newton observations of a stellar activity cycle. Astronomy and Astrophysics605, A19. 10.1051/0004-6361/201731301
26
PesnellW. D.ThompsonB. J.ChamberlinP. C. (2012). The solar dynamics observatory (SDO). Sol. Phys.275, 3–15. 10.1007/s11207-011-9841-3
27
PevtsovA. A.FisherG. H.ActonL. W.LongcopeD. W.Johns-KrullC. M.KankelborgC. C.et al (2003). The relationship between X-Ray radiance and magnetic flux. Astrophysical J.598, 1387–1391. 10.1086/378944
28
RobradeJ.SchmittJ. H. M. M.FavataF. (2012). Coronal activity cycles in nearby G and K stars: Xmm-newton monitoring of 61 Cygni and α Centauri. Astronomy and Astrophysics543, A84. 10.1051/0004-6361/201219046
29
ScherrerP. H.SchouJ.BushR. I.KosovichevA. G.BogartR. S.HoeksemaJ. T.et al (2012). The helioseismic and magnetic imager (HMI) investigation for the solar dynamics observatory (SDO). Sol. Phys.275, 207–227. 10.1007/s11207-011-9834-2
30
SchrijverC. J.HigginsP. A. (2015). A statistical study of distant consequences of large solar energetic events. Sol. Phys.290, 2943–2950. 10.1007/s11207-015-0785-x
31
SchrijverC. J.TitleA. M.YeatesA. R.DeRosaM. L. (2013). Pathways of large-scale magnetic couplings between solar coronal events. Astrophysical J.773, 93. 10.1088/0004-637X/773/2/93
32
TianH.HarraL.BakerD.BrooksD. H.XiaL. (2021). Upflows in the upper solar atmosphere: invited review. Sol. Phys.296, 47. 10.1007/s11207-021-01792-7
33
ToriumiS. (2026). Bridging solar and stellar physics: Role of SDO in understanding stellar active regions and atmospheric heating. Sol. Phys.301, 48. 10.1007/s11207-026-02634-0
34
ToriumiS.AirapetianV. S. (2022). Universal scaling laws for solar and stellar atmospheric heating. Astrophysical J.927, 179. 10.3847/1538-4357/ac5179
35
ToriumiS.AirapetianV. S.HudsonH. S.SchrijverC. J.CheungM. C. M.DeRosaM. L. (2020). Sun-as-a-star spectral irradiance observations of transiting active regions. Astrophysical J.902, 36. 10.3847/1538-4357/abadf9
36
TsunetaS.ActonL.BrunerM.LemenJ.BrownW.CaravalhoR.et al (1991). The soft X-ray telescope for the SOLAR-A mission. Sol. Phys.136, 37–67. 10.1007/BF00151694
37
Ugarte-UrraI.YoungP. R.BrooksD. H.WarrenH. P.De PontieuB.BryansP.et al (2023). The case for solar full-disk spectral diagnostics: chromosphere to corona. Front. Astronomy Space Sci.9, 1064192. 10.3389/fspas.2022.1064192
38
WarrenH. P.Ugarte-UrraI.LandiE. (2014). The absolute calibration of the EUV imaging spectrometer on hinode. Astrophysical J. Suppl. Ser.213, 11. 10.1088/0067-0049/213/1/11
39
WebergM. J.WarrenH. P.CrumpN.BarnesW. (2023). EISPAC - the EIS python analysis code. J. Open Source Softw.8, 4914. 10.21105/joss.04914
40
WoodsT. N.EparvierF. G.HockR.JonesA. R.WoodraskaD.JudgeD.et al (2012). Extreme ultraviolet variability experiment (EVE) on the solar dynamics observatory (SDO): overview of science objectives, instrument design, data products, and model developments. Sol. Phys.275, 115–143. 10.1007/s11207-009-9487-6
41
YoungP. R.ViallN. M. (2022). Scattered light in the Hinode/EIS and SDO/AIA instruments measured from the 2012 Venus transit. Astrophysical J.938, 27. 10.3847/1538-4357/ac8472
Summary
Keywords
active regions, EUV spectroscopy, full-disk spectroscopy, solar corona, solar cycle
Citation
McKevitt J, Ugarte-Urra I and Young PR (2026) Full-disk spectroscopy of the solar corona across a solar cycle with Hinode/EIS. Front. Astron. Space Sci. 13:1813447. doi: 10.3389/fspas.2026.1813447
Received
18 February 2026
Revised
22 April 2026
Accepted
30 May 2026
Published
14 July 2026
Volume
13 - 2026
Edited by
Gottfried Mann, Leibniz-Institut für Astrophysik Potsdam (LG), Germany
Reviewed by
Thomas Schad, National Solar Observatory, United States
Amoré Nel, South African National Space Agency, South Africa
Updates
Copyright
© 2026 McKevitt, Ugarte-Urra and Young.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: James McKevitt, james.mckevitt.21@ucl.ac.uk
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