JuanCastillo29/space-mission-intelligence-data huggingface dataset:
We present results from a multi-decade investigation of solar activity-driven variability in Jupiter’s emissions, using solar X-ray flux and sunspot numbers as activity indicators and ultraviolet (UV) and X-ray observations from the International Ultraviolet Explorer (IUE 1978-1996) and the Chandra X-ray Observatory (2011-2021). Analysis of 51 high-SNR UV spectra spanning two solar cycles shows that Jupiter’s Lyα emission contains narrow and broad components, likely associated with the disk and auroral regions, respectively. The Lyα line and the 1330-1400 ˚Acontinuum flux closely follow variations in solar X-ray flux and sunspot numbers throughout all phases of two consecutive solar cycles, indicating a direct connection with solar irradiation processes, including resonant scattering of solar Lyα photons and photoelectron-driven atmospheric excitation. In contrast, ionised UV lines such as Fe ii (1608 ˚Aand 1575 ˚A) show no correlation with solar activity over a solar cycle, suggesting an internal or magnetospheric origin, potentially linked to Io-derived charged particles or UV/X-ray radiation. To determine whether Jupiter’s X-ray response resembles its UV response to solar activity, we analysed 29 Chandra/HRC observations obtained during 2014-2021 and two Chandra/ACIS observations from 2011. Significant X-ray flares are detected in both ACIS and HRC lightcurves 7-15 days after major reported coronal mass ejections (CMEs). Post-CME ACIS auroral spectra reveal a significant (⩾3σ) Ne8+ emission feature (∼0.94-0.98 keV) near 70-80◦latitude at Jupiter’s north pole. Owing to the high ionisation energy required (∼1.19 keV for the Ne viii to Ne ix bound-bound transition), this feature is unlikely to arise from local interactions, supporting CME-driven auroral excitation on Jupiter.
Key words: planets and satellites: aurorae – X-rays: individual: Jupiter – ultraviolet: planets – Sun: coronal mass ejections (CMEs) – techniques: spectroscopic
followed by the IUE provided the first extensive spectroscopic detections of Jupiter, identifying strong H-Lyα and H2 band emission together with a suite of atomic and ionic lines that trace ionospheric and magnetospheric processes (Broadfoot et al. 1979; Clarke et al. 1980). These UV features arise predominantly from electron-impact excitation of atmospheric H and H2 (Yelle et al. 1986) and from resonant and collisional excitation of minor species originating in the Io plasma torus (Bagenal, Dowling, & McKinnon 2004). As such, UV spectroscopy directly constrains precipitating-electron energies, auroral power, and the coupling between Io-supplied plasma and the magnetosphere (Clarke et al. 1996). Frequent monitoring in EUV/UV by subsequent missions like the Hubble Space Telescope (HST ) and the Japanese Hisaki mission have extended these insights, linking short-term auroral morphology changes and long-term torus variability to both internal magnetospheric
Introduction

Jupiter emits the most intense auroral emissions in the Solar System, powered by its rapid rotation and strong magnetic field interaction with trapped charged particles (either from Solar origin or internal plasma activities). These auroral emissions have been observed across multi-wavelength regimes including radio, UV, and X-ray bands (Clarke et al. 2002; Elsner et al. 2004; Branduardi-Raymont et al. 2004, 2006; Bhardwaj et al. 2006; Branduardi-Raymont et al. 2007; Kharchenko et al. 2008; Hui et al. 2010; Badman et al. 2015; Dunn et al. 2020a,b; Mori et al. 2022). Among multi-wavelength emissions, UV and X-ray regimes are particularly significant because they directly constrain the energy input, composition, and dynamics of the magnetosphere (Clarke et al. 2002; Weigt et al. 2023).
The Voyager UV spectrometers (Sandel et al. 1979),
aki have each provided valuable insights into energetic activities of the Jovian surface and poles.

For example, Dunn et al. (2022) analysed 14 simultaneous Chandra and HST observations of Jupiter’s northern aurora (2016-2019) and found that the dark polar region (DPR), already known to be UV-faint, also produces almost no intrinsic X-ray emission.

X-ray observations with Chandra and XMM-Newton have revealed rich auroral dynamics, pulsations (Dunn et al. 2016), and ion line features, but analyses have typically focused on individual observing windows or coordinated campaigns.

Although few individual spectral lines in the soft X-ray band have been reported (Branduardi-Raymont et al. 2004, 2006; Elsner et al. 2005; Hui et al. 2010), their variability across longer timescales and broader significance remain comparatively less explored.

Much of the work done in the field has been centred on shortterm campaigns, isolated events, or spectra from particular epochs.

To our knowledge, no comprehensive and systematic study has yet been undertaken to model the long-term evolution of Jupiter’s auroral line and continuum flux across multiple solar cycles within a unified framework that links these emissions to both internal magnetospheric processes and external solar activity.

Therefore, driven by curiosity, we aim to investigate the variability of Jovian UV and auroral X-ray emissions across multiple solar cycles using archival observations.

We analysed archival ultraviolet spectra from the IUE (1978-1996), providing a baseline of two subsequent solar cycles for Jupiter’s UV line and continuum flux.

To determine the temporal variability of Jupiter’s X-ray auroral emission, we analysed the entire Chandra/HRC catalogue (2014–2021) in a statistical study, focusing on typical and flaring behaviours of the auroral zones through light curves to derive average count rates from Jupiter’s X-ray emission and quantified their statistical reliability.

We also analysed the Chandra/ACIS observations from 2011 and identified an additional emission feature near ∼0.94 keV which has not been reported in previous studies of Jupiter’s X-ray aurora.

To place Jupiter’s UV/X-ray activity in the context of solar variability, we systematically compared all UV and Xray datasets with contemporaneous solar X-ray flux measurements from the Geostationary Operational Environmental Satellite (GOES ) in the 1.55-12.4 keV range, as well as with sunspot number indices spanning multiple solar cycles.

This approach allowed us to probe the correspondence between solar activities and Jovian auroral response on long timescales.

Overall, this study aims to place the multisolar-cycle behaviour of Jovian auroral emissions within the broader framework of solar activity, magnetospheric dynamics, and transient extreme solar events.

We outline the observations and data reductions in Section 2, while the analysis methods and results are explained in Section 3.

The correlation of Jupiter’s UV and X-ray emissions with solar activity is presented in Section 4, and the discussion and conclusions are given in Section 5.
Solar Activity Proxies and Observations

Solar activity was characterised using contemporaneous solar X-ray flux measurements together with the sunspot number, both of which serve as widely used diagnostics of solar variability and solar-cycle evolution. Solar X-ray flux data obtained from the GOES satellites⋆, operated by the National Oceanic and Atmospheric Administration (NOAA), were used as a proxy for coronal activity. The GOES satellites are equipped with an X-ray Sensor (xrs) that continuously monitors the solar X-ray flux in two wavelength bands: a short channel (0.05–0.4 nm) and a long channel (0.1–0.8 nm). In this work, we used the 1-minute averaged xrs data obtained from the NOAA National Centres for Environmental Information (NCEI ) archive. Monthly csv files were downloaded and processed, and the b avg parameter corresponding to the long channel (0.1–0.8 nm), covering photon energies of approximately 1.55–12.4 keV, was used for analysis. This energy range overlaps with the bandpass observable by the Chandra, making it directly relevant for comparison with the Jovian X-ray measurements. Depending on the requirements of the analysis, the flux values were further averaged or regrouped over hourly or daily intervals. Multiple solar-cycle variations were additionally examined using the sunspot number obtained from the World Data Centre silso (Sunspot Index and Long-term Solar Observations)† database, maintained by the Royal Observatory of Belgium. The silso dataset provides a standardised and widely used measure of solar-cycle variability. Figure 1 places the observational epochs of IUE and Chandra used in this study in the context of long-term solar-cycle evolution. Our choice of data selection is driven by the unavailability of IUE observations beyond 1996 and a scattered, irregular, and limited number of X-ray observations during Solar Cycle 23; hence, an observational gap may be noted in Figure 1. Hence, the UV and X-ray datasets in this work are non-contemporaneous.
The IUE obtained a substantial number of low-resolution short-wavelength spectra of Jupiter between September 13, 1978, and September 25, 1996, covering the 1150–1978 ˚A wavelength range. For data reduction and analysis, we follow the methodology of Wanders et al. (1997). The raw spectral images are processed using both the tomsips (Ayres 1993) and newsips (Nichols & Linsky 1996) pipelines. In line with the recommendation of Wanders et al. (1997), we adopt the newsips reductions, since the tomsips spectra suffer from wavelength calibration nonlinearities associated with uncorrected long-term drifts. By contrast, the newsips products are in agreement with HST observations (Wanders et al. 1997). However, a 10% uncertainty has been incorporated in the line and continuum flux measurements from spectral modelling as pointed out in earlier analysis with newsips reductions (Massa & Fitzpatrick 2000). Each spectrum is examined in the 1160–1460 ˚A window to exclude
⋆https://ssd.jpl.nasa.gov/horizons/app.html#/ † https://www.sidc.be/SILSO/datafiles
cases with high noise levels, saturation, or inadequate exposure. After applying these quality checks and taking into account the availability of concurrent solar data, 51 spectra are selected for subsequent analysis, which are listed in Table 1. Example UV spectra during solar maxima and solar minima are shown in the top panel of Figure 2. The observation dates chosen in Figure 2 are mere representations of all 51 UV spectra to show how UV emission from Jupiter behaves in terms of spectral shape and line properties during Solar minima and maxima. For clarity of short-term continuum variability and better visibility of line features, both spectra are fitted with a single powerlaw and the spectral counts to powerlaw fitted model counts ratio is shown as a function of wavelength. While details of spectral analysis have been provided later, a substantial line and continuum variation can be observed in the UV regime between solar maxima and minima.
Chandra

Jupiter has been observed extensively with Chandra using the Advanced CCD Imaging Spectrometer (ACIS) and the High Resolution Camera (HRC). The Chandra/ACIS observations were carried out in ACIS-S mode using the backilluminated ACIS-S3 chip having 1024 × 1024 pixels with a pixel size of approximately 0.492 arcseconds, allowing spatially resolved spectroscopy of Jupiter’s auroral regions over the photon energy range of 0.2–10 keV (Garmire et al. 2003). In this work, we have focused on the 2011 observations of Jupiter through Chandra/ACIS, which are summarised in Table 2. The Chandra/HRC observations were carried out in HRC-I mode, between 2014 and 2021, which captured auroral emission, morphology, and variability. Details of observations are provided in Table 3, forming a rich dataset for studying Jupiter’s X-ray auroral activity.
ACIS data reduction

Data reduction was performed with the Chandra Interactive Analysis of Observations (ciao) software version 4.17.0 using Calibration Database (caldb) version 4.12.0 (Fruscione et al. 2006). The chandra repro pipeline was executed for each ObsID with the sso freeze option enabled to correct for any apparent motion of Jupiter during the observation. The X-ray image of Jupiter in the bottom panel of Figure 2 clearly indicates that the northern auroral region shows the brightest X-ray emission. For both the northern and southern poles, elliptical source region files were created in ds9 with [bin=ocx,ocy], while background regions of a similar shape and size were selected from areas above the poles (to exclude contributions from Io’s plasma torus), which are away by three times the diameter of Jupiter’s disc. For consistency throughout the Chandra analyses, elliptical source regions of similar sizes (semi-major axis of 30′′ and semi-minor axis of 8′′) were adopted for both the northern and southern auroral emissions. The coordinates and dimensions of these elliptical regions were selected such that they consistently encompass the auroral zones extending from the poles down to approximately 75◦latitude in both hemispheres. This choice 4 Tomer et al.

Monthly Mean Sunspot Number

[Table: Table 1. IUE observations of Jupiter. The top segment lists the observation IDs used for Lyα line analysis. The middle segment contains the observation IDs used for Feii analysis, while the bottom segment lists the observation in which the Siii line is detected.]

| IUE (1978 1996) SC 21 SC 22 | I C (201 SC 23 SC 2 |  | othed ns rvations SC 25 |
| --- | --- | --- | --- |
|  |  | Monthly Mean 13-Month Smo UE Observatio Chandra Obse | othed ns rvations |
|  |  | handra 1 2021) 4 |  |

Year

[Figure: Figure 1. Evolution of the monthly averaged sunspot number from 1978 to 2021 derived from the daily total sunspot number dataset obtained from silso. The blue thin line represents the monthly mean sunspot number, while the orange thick curve shows the 13-month smoothed variation, highlighting the evolution of the solar cycle. Shaded regions indicate the observational periods corresponding to IUE (1978–1996) and Chandra (2011–2021). Blue circles denote the IUE observation dates, while red stars represent the Chandra observation epochs. The labels SC 21–SC 25 indicate Solar Cycles 21 to 25.]
[Table: Table 2.

Chandra/ACIS Observations of Jupiter, where NP and SP are the north pole and south pole, respectively.

The revised count rates are obtained after distance (dEJ) normalisation
No Obs ID Obs Date Start time Exposure time dEJ Revised NP Revised SP (ksec) (AU) count rate(x10−3) count rate(x10−3)
obs1 12315 2011-10-02 21:54:26 39.25 4.06 10.17±0.66 2.23±0.64 obs2 12316 2011-10-04 14:33:08 39.61 4.05 5.99±0.54 1.78±0.49
Table 3.
isation
No Obs ID Obs Date Start time Exposure time dEJ Revised NP Revised SP (ksec) (AU) count rate(x10−3) count rate(x10−3)
obs1 12315 2011-10-02 21:54:26 39.25 4.06 10.17±0.66 2.23±0.64 obs2 12316 2011-10-04 14:33:08 39.61 4.05 5.99±0.54 1.78±0.49
Table 3.

Chandra/HRC Observations of Jupiter with distance-normalised count rates scaled to the first observation’s Jupiter distance
No ObsID Obs Date Start Time Exposure Average Count Rate dEJ Revised Count Rate (ksec) (10−3 s−1) (AU) (10−3 s−1)
obs1 15671 2014-04-08 08:18:10 40.73 25 ± 2 5.24 25.00 ± 2.00 obs2 16299 2014-04-10 01:09:23 37.53 15 ± 1 5.27 14.85 ± 0.99 obs3 15672 2014-04-12 22:09:30 39.51 15 ± 2 5.32 14.59 ± 1.95 obs4 15669 2014-04-15 20:43:04 37.85 11 ± 1 5.36 10.54 ± 0.96 obs5 16300 2014-04-17 12:19:31 39.46 11 ± 2 5.39 10.45 ± 1.90 obs6 15670 2014-04-20 02:19:30 23.12 15 ± 3 5.43 13.98 ± 2.80 obs7 18608 2016-05-24 10:21:59 39.74 11 ± 2 5.17 11.30 ± 2.05 obs8 18609 2016-06-01 11:31:01 39.72 9 ± 2 5.30 8.82 ± 1.96 obs9 18301 2017-02-02 09:56:57 32.73 9 ± 2 5.03 9.77 ± 2.17 obs10 18302 2017-05-19 00:27:33 37.32 14 ± 2 4.69 18.27 ± 2.61 obs11 20000 2017-02-28 12:38:55 70.60 7 ± 2 4.68 8.78 ± 2.51 obs12 18676 2017-03-27 08:30:56 9.30 9 ± 3 4.48 12.31 ± 4.10 obs13 20001 2017-06-18 18:37:56 36.67 8 ± 2 5.09 8.48 ± 2.12 obs14 18677 2017-07-10 21:11:18 39.56 9 ± 1 5.43 8.34 ± 0.93 obs15 20002 2017-08-06 01:55:48 34.98 12 ± 3 5.81 9.73 ± 2.43 obs16 18303 2018-07-15 23:43:48 40.03 12 ± 1 4.96 13.42 ± 1.12 obs17 18678 2018-03-31 23:10:42 40.05 9 ± 2 4.63 11.55 ± 2.57 obs18 20733 2018-04-01 10:38:00 38.79 15 ± 3 4.62 19.29 ± 3.86 obs19 18679 2018-05-23 23:59:44 39.54 7 ± 2 4.43 9.80 ± 2.80 obs20 22159 2019-05-29 03:26:26 35.23 9 ± 2 4.31 13.28 ± 2.95 obs21 22146 2019-07-13 01:26:23 24.46 16 ± 3 4.42 22.53 ± 4.22 obs22 22147 2019-07-13 21:08:30 22.66 14 ± 3 4.43 19.59 ± 4.20 obs23 22148 2019-07-15 12:58:56 24.73 6 ± 4 4.44 8.37 ± 5.58 obs24 22149 2019-07-16 08:44:51 24.80 18 ± 3 4.45 25.02 ± 4.17 obs
.53 ± 4.22 obs22 22147 2019-07-13 21:08:30 22.66 14 ± 3 4.43 19.59 ± 4.20 obs23 22148 2019-07-15 12:58:56 24.73 6 ± 4 4.44 8.37 ± 5.58 obs24 22149 2019-07-16 08:44:51 24.80 18 ± 3 4.45 25.02 ± 4.17 obs

25 22150 2019-07-18 20:17:53 24.75 18 ± 3 4.47 24.69 ± 4.11 obs26 22151 2019-09-08 22:58:16 24.33 11 ± 2 5.18 11.29 ± 2.05 obs27 23370 2021-07-16 14:02:24 35.69 20 ± 4 4.18 31.40 ± 6.28 obs28 23366 2021-09-08 11:17:15 35.87 55 ± 5 4.07 91.46 ± 8.31 obs29 23369 2021-09-15 12:32:16 140.19 67 ± 5 4.11 108.67 ± 8.11
Horizons System‡ corresponding to each observation.

Using parameters like the apparent right ascension and declination of the centre of Jupiter’s disc together with the subobserver and sub-solar longitudes and latitudes, as well as parameters such as the north-pole position angle in degrees, angular diameter of disc in arcsec, and sky motion, we determine the apparent orientation and viewing geometry of Jupiter during each observation, and identify both poles in Chandra images.

Spectral extraction was performed with the specextract task, which generated the associated auxiliary response files (ARFs), redistribution matrix files (RMFs), along with background-subtracted pulse invariant (PI) files.

For each spectrum, spectral channels are binned such that the minimum signal-to-noise ratio of each spectral bin is 3.

With this criterion, each spectrum contains a minimum of 20 spectral bins for further modelling.

Backscale corrections were applied to both source and background spectra based on the areas of the defined regions.

All the net count rates derived for the northern and
‡ https://ssd.jpl.nasa.gov/horizons/app.html#/]

southern auroral regions from these spectral extractions are summarised in Table 2. For the 2011 Chandra/ACIS observations, in addition to spectral extraction, a backgroundsubtracted light curve was also generated for the northern auroral region using the ciao tool dmextract. The source light curve was extracted from the northern auroral region with a time bin size of 1024 seconds. With the 1024 sec bin size, we obtain a minimum signal-to-noise ratio of 2 for each count rate bin. The extracted light curve was then corrected for background and used for further analysis.
HRC data reduction

Similar to the Chandra/ACIS data, the initial steps of reduction for Chandra/HRC observations were carried out with the ciao software using caldb, and for each observation, the chandra repro pipeline was executed to produce Level 2 event files. These event files were subsequently examined in ds9 to define the auroral source and background extraction regions. Throughout the Chandra/HRC analyses, elliptical source regions of similar sizes (semi-major axis of 9′′ and Tomer et al.
|  | ratio 101 model powerlaw to 100 Data 1200 1400 | 4 May 1991 (Solar Maxima) 2 August 1987 (Solar Minima) 1600 1800 |
| --- | --- | --- |
| Wavelength (Å) R.A. (deg) Background region North Auroral Region Declination (deg) South Auroral Region Counts per pixel | Wavele |  |

|  | 10−11 L y -α Å−1 s−1 cm−2 10−12 ergs 10−13 (data−model)/error 10 0 −10 1200 1250 1300 1350 1400 1450 Wavelength (Å) |
| --- | --- |
| 10−11 L y -α Å−1 s−1 10−12 cm−2 ergs 10−13 (data−model)/error 2 0 −2 1200 1250 1300 1350 1400 1450 Wavelength (Å) |  |

[Figure: Figure 3. A representative UV spectrum from Jupiter fitted with continuum and line models along with residuals Top panel: An example of a poor fit (χ2/d.o.f. = 777.38/162) with a single Lyα line (SWP23780), illustrating the complex excess in the residuals near 1220 ˚A. Bottom panel: The best-fit (χ2/d.o.f. = 164.67/155) UV spectrum (1160–1460 ˚A) is shown for the same observation. The best-fit model (red) includes continuum and emission-line features, and the fitting residuals are shown in the bottom panel. The model-fitting procedure, similar to that shown in the bottom panel, was applied to all individual UV spectra.]
relation to solar activity (Figure 13) for the Chandra/HRC
observations. The top panel of Figure 13 shows the compar-
ison of average Chandra/HRC-derived count rates with the
monthly-averaged sunspot number, thereby placing the au-
roral variability within the broader framework of solar cycle
evolution. No apparent long-term correlation is observed be-
tween Jovian X-ray intensity and solar X-ray flux, consistent
with earlier work. The middle and bottom panels of Figure
13 show the zoomed segments for comparison during solar
maxima (averaged over a 6-hour period during April 2014)
and solar minima (May-September 2019). These datasets
highlight the auroral response to varying levels of solar flux
across different epochs. In either epoch, no significant corre-
lation is observed, despite the limited number of data points.
Data analysis and Results

To illustrate the energy-dependent morphology of Jupiter’s X-ray emission, we present a Chandra/HRC image of Jupiter (top left panel of Figure 7), in which the northern and southern auroral zones have been explicitly marked to indicate the regions selected for subsequent analysis. This image was visualised in ds9 using object-centred coordinates, ensuring that the emission is displayed relative to the Jupiter disc.
semi-minor axis of 3.5′′) were adopted for both the northern and southern auroral emissions, while the circular background region of radius 8.3′′ is chosen at a separation of 55′′ as shown in the top left panel of Figure 7. The southern auroral region consistently showed poor photon statistics across all 29 Chandra/HRC observations; no source is detected above the background count rate of 1.43×10−3 counts/sec (determined from a source-free, circular region which is sufficiently away from Jupiter’s disc) with at least 2σ significance. Therefore, only the northern auroral emission was considered for further analysis. Light curves corresponding to the source and background regions were extracted for each dataset using the dmextract tool, binned at 1024-second intervals. The background-subtracted light curves were produced using lcmath, where the background counts were scaled and subtracted from the source counts to isolate Jupiter’s auroral emission. Light curves were then analysed using lcstats, from which the average count rates were calculated for each observation and shown in Table 3.
IUE spectral analysis

In Jupiter’s IUE/SWP data, we see clear variability in both the UV continuum and the emission lines. We may note that due to the non-imaging capabilities of IUE, both disk and auroral regions will contribute to the observed flux in the UV spectra. The top panel of Figure 2 shows the UV datato-model ratio spectra over 1150–1925 ˚A for observations acquired during solar minimum (1987) and solar maximum Fitted Jovian spectra excluding 0.94 keV feature (02 October 2011)