improves the KS to −2.58, inclusion of the second Gaussian
near 0.96–0.98 keV results in a total KS value of −3.91, in-
dicating a substantially improved fit. A similar trend is seen
for the 2011 Chandra/ACIS obsID 12316: fitting with only
the bremsstrahlung model gives a KS value of −1.64. Adding
the first Gaussian at 0.7 keV improves the KS to −2.52; in-
clusion of the second Gaussian near 0.96–0.98 keV results in
a total KS value of −4.52. The resultant fitting demonstrates
that both line components are required to accurately repro-
duce the auroral spectra. The observed excess near 0.94–
0.98 keV with broad line widths (σ ≈ 0.07–0.14 keV) in-
dicates the presence of a highly ionised element in Jupiter.
We may note that a feature in a similar energy has been
identified by Dunn et al. (2016) during the modelling of the
hotspot quadrant spectra from Chandra. The line centre en-
ergy is consistent with the Ne VIII to Ne IX bound-bound
tra previously reported in the literature (Elsner et al. 2005;
Branduardi-Raymont et al. 2008; Dunn et al. 2020b) sug-
gests that the line is likely transient, possibly associated with
enhanced solar radiation or magnetospheric conditions. The
bremsstrahlung continuum temperatures are found to lie in
the range kT ≈ 0.6-1.3 keV. Best-fit parameters for both the
spectra are provided in Table 4, and the fitted spectra along
with the model components (shown by thin dotted lines) are
shown in Figure 4. Fluxes in the 0.5–10 keV band for dif-
ferent Chandra/ACIS observations for the north and south
auroral region are corrected for the Jupiter-Earth distance
and reported in Table 4.
The top panels of Figure 4 show the fitted spectra with
the 0.94 keV Gaussian feature excluded, revealing significant
residuals around 0.9–1.0 keV. After including Gaussian line
models near 0.94 keV, the residuals disappear. Fitted spec-
tra after including additional Gaussian lines are shown in
the bottom panels of Figure 4 along with residuals. For the
Observation ID 12316, we assessed the significance of this
newly identified line through Markov Chain Monte Carlo
simulations of the spectral parameters from the joint spec-
tral fits. When spectral modelling involves many free pa-
sation alone is not always reliable for parameter estimation
(Reynolds et al. 2012). Since X-ray spectral counts typically
follow a Poisson distribution, we replace the conventional
chi-squared statistic with pgstat in XSpec, which assumes
Poisson-distributed source counts and Gaussian-distributed
background counts. The profile likelihood of pgstat is de-
rived in a manner similar to that of the C statistic. Using
carding the first 50,000 as burn-in, and initialise the chains
with random perturbations around the best-fit values. The
proposal distribution at each Monte Carlo step is Gaussian,
rescaled by a factor of 0.001. For rigorous testing, we adopt
the Goodman-Weare algorithm with 60 walkers. Through
this MCMC exercise, we find that the 0.94 keV line is sta-
tistically significant at least up to 3σ. Posterior contours
corresponding to 1, 2 and 3σ are shown in the top panel
of Figure 5. We show the Chandra/ACIS image extracted
in the 0.8–1.2 keV energy range from the observations con-
ducted on 02 October, 2011 (bottom panel of Fig. 5), high-
lighting the specific region associated with this emission.

Contour levels =  1σ (red), 2σ (grey) and 3σ (blue) Integrated probability contours from MCMC run
Line Normalisation
5×10−6 10−5
5×10−6 10−5
+
0.2 0.4 0.6 0.8 1
0.7 0.8 0.9 1
0.7 0.8 0.9 1 Line energy (keV)