JuanCastillo29/space-mission-intelligence-data huggingface dataset:
joint-usafnoaa-report-of-solar-and-geophysical-activity-24-september-
¶ https://cdaw.gsfc.nasa.gov/CME_list/

1032 ergs. From the same location, the second CME speed is measured around 972-788 km/s with the kinetic energy of 1.4 × 1031 ergs (Aschwanden et al. 2015). Considering the above speed estimations, we assume 972 km/s and 2089 km/s as the lower and upper bounds for CME speed at which they leave the Sun. Using Keplerian propagation from J2000 orbital elements∥, the Sun-Jupiter average distance is estimated to be 741942506.90 km. (averaging between 24 September and 3rd October), Then the CME will reach roughly between 4.5 and 9 days, considering the upper and lower bounds of speed. Therefore, the CME encounter on the Jovian atmosphere is expected to occur between 29 September and 03 October, 2011. However, following Drag Based Model (DBM) for the propagation of CME beyond 1 AU, proposed by Vrˇsnak et al. (2014), we expect a moderate drag by solar wind such that CME accelaration, a = γ(VCME −Vwind)|VCME −Vwind|, where VCME and Vwind are CME speed and ambient solar wind speed respectively while γ is drag parameter. With the typical γ value of 1-5 × 10−8 km−1 for a fast CME (Vrˇsnak 2008; Vrˇsnak et al. 2010; ˇZic, Vrˇsnak, & Temmer 2015), we may expect a further delay on arrival of CMEs by a couple of days. Therefore, CME-induced activities are expected to be detected 7-11 days (between 01 and 05 October, 2011) from the origin of CMEs. This is supported by our results: the significant detection of extremely ionised Ne8+ line signatures in X-ray spectra, along with enhanced Bremsstrahlung emission (Figure 4 and top panel of Figure 5), and strong flarings in X-ray lightcurves observed with Chandra on 2nd October and 4th October, 2011 (Figure 6). Following the above arguments, we may note that transient solar events (M-class flares, ICME/CME-driven shocks) can compress Jupiter’s magnetosphere, trigger dayside reconnection or injections, and increase particle acceleration, producing episodic X-ray brightening as observed in the top right and bottom panels of Figure 7. As observed from Figure 7, Chandra/HRC count rate increases by 4-8 times on two occasions: 6-8 days and 12-15 days after the CME, consistent with solar wind/CME propagation time scales of the order of a week and interaction and subsequent magnetospheric response as discussed earlier.
Ne8+ is a high charge state ion, formed at coronal temperatures of a few million degrees Kelvin. Such ions are abundant in the solar wind, especially during CMEs or high-speed streams. The solar wind consistently carries a fraction of highly charged neon ions, detectable in situ by spacecraft like ACE, Ulysses, and Solar Orbiter. Two plausible mechanisms may explain the origin of the Ne8+ line in the auroral region: (a) Solar-wind/CME material injection and charge exchange (SWCX) (Asakura et al. 2021): when a CME carrying high-charge-state heavy ions reaches Jupiter, those ions undergo charge exchange with exospheric neutrals, producing characteristic line emission. (b) As proposed in Dunn et al. (2016), magnetospheric acceleration of solar-derived
∥https://ssd.jpl.nasa.gov/horizons/app.html#/
heavy ions: CME-induced compression/reconnection accelerates heavy ions (from solar wind or magnetospheric reservoirs) onto polar field lines where they produce ion-line emission by charge exchange or direct collisional excitation. The observed time delay matches expected transit times for fast CMEs with moderate values of drag parameter (γ ≃1-5 × 10−8 km−1) to Jupiter and the results of dedicated Chandra campaigns that linked strong X-ray enhancements to solar storms. Together, these lines of evidence point to direct CME-induced auroral excitation on Jupiter.
We are grateful to the referee for constructive comments, and we gratefully acknowledge the detailed discussions and constructive suggestions provided by Dr William Dunn and Bryn Parry, which significantly improved the quality of the manuscript. M.T. is grateful to the University Grants Commission of India (UGC-Grant No. 231620114372) and the Ministry of Science and Technology, Government of India, for financial support of the research work carried out at the Indian Institute of Technology, Hyderabad. AB acknowledges DST, Government of India, for the award of INSPIRE fellowship (IF230384) and the research facility of the Department of Physics, IIT Hyderabad.
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Morgan A. Mitchell ,1,2★James McCormac ,1,2,3 Don Pollacco ,1,2,3 Emmanuel Grolleau ,4 Nicholas Jannsen ,5 Daniel R. Reese ,4 Réza Samadi ,4 Yoshi Nike Emilia Eschen ,1,2,6 Ioannis Apergis ,1,2 James A. Blake ,1,3 David J. A. Brown ,1,2 Lauren Doyle ,1,2 1,2 and Lixian Shen 1,2
1Department of Physics, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK 2Centre for Exoplanets and Habitability, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK 3Centre for Space Domain Awareness, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK 4LIRA, Observatoire de Paris, Université PSL, Sorbonne Université, Université Paris Cité, CY Cergy Paris Université, CNRS, 92190 Meudon, France 5Institute for Astronomy, KU Leuven, Celestijnenlaan 200D bus 2401, 3001 Leuven, Belgium 6Isaac Newton Group of Telescopes, Apartado de Correos 321, E-38700, Santa Cruz de la Palma, Spain
The European Space Agency is set to launch PLATO, the third medium-class mission of its Cosmic Vision programme, in early 2027. Using the transit method, PLATO is expected to detect thousands of exoplanets orbiting bright, nearby stars of spectral types F5–K7. Although the mission is primarily designed to enable mass measurements via radial velocities, its precise photometry and long observational baselines may also permit the detection of transit timing variations (TTVs), which can provide complementary dynamical constraints in multi-planet systems. One possible PLATO observing scenario involves a two-yearlong observation of a Northern field that may partially or fully overlap with the original Kepler field, creating an opportunity to revisit known multi-planet systems with a photometric baseline exceeding 20 years. We simulate PLATO observations of 152 Kepler host stars containing at least one planet with previously detected TTVs, yielding a sample of 361 transiting planets. Our CCD-level simulations incorporate realistic stellar variability and employ both aperture and point spread function (PSF)-fitting photometry, accounting for each target’s real photometric contaminants. While the extended temporal baseline offers the potential for improved dynamical constraints in favourable cases, our simulations show that this potential is strongest for carefully selected systems, as PLATO’s smaller collecting area and larger pixel scale limit the achievable per-transit precision relative to Kepler. We identify a subset of systems most likely to benefit from complementary dynamical constraints through PLATO observations.
Key words: space vehicles – planets and satellites: detection
2025), as of February 2026, 379 of the 477 planets with detected TTVs were discovered by Kepler in its nominal mission. This follows from several aspects of the Kepler design, including its longduration stare of a single field, high photometric precision, and shortcadence observations of many known planet-hosting stars. Many transits are typically required to detect measurable deviations from a linear ephemeris (Kane et al. 2019), and more still to sample a substantial portion of a TTV super-period – the long-timescale ephemeris modulation defined by Lithwick et al. (2012), whose duration depends on the proximity of a planetary pair to resonance. Sampling the super-period is important for constraining the properties of perturbing companions, and Agol & Fabrycky (2025) note that two well-sampled super-periods are preferable for a robust analysis. Although not all periodic TTV signals arise from near-resonant super-periods, Kane et al. (2019) showed that strongly periodic TTV signals from confirmed Kepler planets commonly span hundreds to more than 1000 days.
systems likely to benefit from PLATO follow-up.

Planet Radius (R⊕)
Sample Planets Other Kepler Planets 100 101 102 103 Orbital Period (days) Figure 1.

Period-radius diagram of the 361 sample planets and the remaining 2,331 transiting exoplanets discovered by the Kepler satellite.

The data were taken from the NASA Exoplanet Archive.
the sample to single-star hosts, reducing the number of targets to 165.

Finally, we used the host stars’ temperatures and radii to exclude likely sub-giant hosts, as our stellar variability simulator cannot handle these stars.

For this, we used the cutoff described in Nascimbeni et al. (2022), which is based on the work of Pecaut & Mamajek (2013).

This cut reduced the number of hosts to a final sample of 152 stars, which, between them, host 361 known transiting exoplanets.

We include all of these 361 planets in our sample regardless of whether they show transit timing variations.

We show the sample planets’ periods and radii in the context of all Kepler discoveries in Fig. 1.

The host brightnesses span the interval 9.77 ≤𝑉≤16.69 mag, with a median brightness of 𝑉= 14.67 mag.

This is marginally brighter than the median brightness of all Kepler hosts at 𝑉= 14.83 mag.

We note that PLATO’s primary P1 and P5 samples include only stars brighter than 𝑉= 11 and 𝑉= 13 mag, respectively.

Within our sample, only four stars are brighter than 𝑉= 11 mag, and twenty are in the range 11 < 𝑉≤13 mag.

Additionally, Rauer et al. (2025) lists the photometric range of PLATO to be 4 ≤𝑉≤16 mag, while 11 of our targets are dimmer than 𝑉= 16 mag.

Hence, we naturally expect the majority of hosts in our sample to be challenging targets for PLATO.

With the sample chosen, we used a modified version of the picsim module in PlatoSim to generate target and contaminant catalogues for the targets.

This method queries Simbad (Wenger et al. 2000) to identify the named target star and find its coordinates, and then Gaia DR3 (Gaia Collaboration et al. 2023) to find the target’s DR3 photometric properties as well as those of contaminating sources in its vicinity.

The Gaia DR3 G, GBP, and GRP magnitudes are used to estimate the PLATO passband P magnitudes using Eq. 9 of Marchiori et al. (2019).

For each target, we opted to include all contaminants brighter than 1 per cent of the target brightness (Δmag < 5) within 45 arcsec (3 pixels) of the target.

This choice struck a balance between including the effect of contamination while avoiding an excessive increase in photometry extraction time, which is strongly dependent on the number of contaminants.

The prime Kepler field is encompassed in its entirety by the provisional northern PLATO field, LOPN1 (Nascimbeni et al. 2022, shown with Kepler field and the sample stars in Fig. 2), and spans the full range of the number of co-observing, co-pointing camera groups, from one to four.

Assuming LOPN1 pointing, the number of host stars in our sample observed by each possible number of normal cameras (N-CAMs) is: 6 cams – 11, 12 cams – 85, 18 cams – 23,
We first used the NASA Exoplanet Archive to identify all of the systems hosting at least one planet exhibiting transit timing variations and at least one planet discovered by the prime Kepler mission. This identified 178 potential hosts. For simplicity in simulation, we limited 45° served by Kepler.

Additionally, rather than attempting to generate some modelled intrinsic variability for all the contaminating stars, we modelled them as constant sources. We expect PSF-fitting photometry to mitigate much of the impact of varying contaminants in real data, although targets with aperture photometry will likely be more severely affected. This method is a practical approach for simulating large numbers of stars, which, in addition to enabling statistical inferences, also enables meaningful predictions for individual systems.
30°
Host Brightness (V )
Galactic Latitude
15°
The final ingredient needed to generate CCD imagettes is the payload module, which manages the telescope systematics. This module injects alignment errors between the cameras and the optical bench, pointing repeatability errors for each observing quarter, thermoelastic distortion of the optics, a realistic distribution of observational downtime, and gain-thermal transients that increase the CCD temperature after quarterly rolls. We used all of the default parameters of this module (see Jannsen et al. 2024) to generate eight quarters of payload systematics.