Europa, there is a limited possibility to obtain such measurements from in-
situ data. Remote sensing observations with the other JUICE instruments or by
the Europa Clipper mission could provide more measurements.

### Outlook

In this work I used a Monte Carlo particle tracing code and applied it to the
different scenario’s. This method was selected because it allows fast
computation and limits the required computer resources. Furthermore, it allows
to easily discriminate the contribution of different physical effects. I have
reached the limit of what can be done solely using this simulation method, in
particular for the simulations of energetic particles and their interaction
with the atmosphere and plumes. The logical next step is to include the result
of self consistent models of the fields around Europa. This will allow to
identify the effect of the complicated magnetic field topology during the E12
flyby and others. Here I have investigated only the E12 flyby in detail, other
flybys show potential signs of interaction with Europa’s ionosphere and
atmosphere too, in particular E4, E6 and E26. My results show that the
depletion in the wake is also affected by the atmosphere, therefore the
depletions in the wake flybys E11 and E15 should also be simulated. My
simulation method can also be expanded to the flybys of other moons such as
Callisto and Ganymede. The tenuous atmospheres of these objects has not been
previously analysed using the energetic particle data. The obtained
constraints on the properties of these moons will be relevant for the
preparation of the measurement campaigns of future missions such as JUICE and
Europa Clipper. Furthermore the method I used here to determine atmospheric
properties, could be applied to data collected by the high energy particle
detectors of JUICE’s Particle Environment Package.

Though the most direct measurements of Europa’s tenuous atmosphere and plumes
will come from optical imaging or the in-stu detection of neutral particles,
this works shows that the atmosphere and plumes can influence energetic
particle and plasma properties near the moon. The synergy between the optical
measurement and the in-situ measurements of neutral and charged particles will
allow a detailed characterization of the atmosphere-plume system. Such a
capability is available both in the upcoming JUICE and Europa clipper
missions, and can be extended to the atmospheres of other moons of Jupiter,
such as Ganymede and Callisto.

##  Appendix A Appendix: Galileo data analysis

Technical details of the data analysis are discussed in this appendix. At the
end an overview of the available data is presented. To analyse the EPD data
the "Galileo EPD Data Processing Software" is used, which was originally
developed by Dr. Andreas Lagg, see for example Lagg et al. (2003). Products of
this software are shown in Figure 3.12, 3.18 and 4.12. The overview plots in
Appendix A.3 have been made with the CCATI software, originally developed by
Dr. Markus Fränz. For enquiries, contact Dr. Norbert Krupp<EMAIL_ADDRESS>about the EPD software and Dr. Markus Fränz<EMAIL_ADDRESS>about CCATI
111CCATI website http://www2.mps.mpg.de/projects/mars-express/aspera/ccati/.

First, the details of the PLS data processing are discussed in Section A.2.
Next, I present an overview of the EPD, PLS and MAG data in Section A.3.

###  A.1 Processing of EPD data

The Energetic Particle Detector EPD is equipped with the CMS time of flight
(TOF) mass spectrometer (see Section 2.1.2). The data from the CMS TOF
telescope is separated in several channels that separate species and energies.
Of particular importance for this work are the energetic proton channels TP1,
TP2 and TP3 and the energetic oxygen ion channels TO2 and TO3. Here I explain
how these channels are defined.

Every measurement of the CMS TOF consists of a start signal, a stop signal and
the total energy of the particle. The total energy is measured by detector KT
(part of CMS). From the stop and start signal the time of flight can be
determined. In Figure A.1 it can be seen how the EPD channels are defined as a
function of the TOF and the deposited energy. The channels are demarcated by
solid lines. The dashed lines indicate the predicted tracks of several
elements. The dots indicate calibration data. Note that the TO1 channel was
predicted to contain oxygen only, but has later been shown to be contaminated
by sulphur too. Therefore only TO2 and TO3 are considered as oxygen channels
in this thesis.

Figure A.1: Time Of Flight versus energy measured in detector KT. The solid
lines demarcate the different channels (TP1, TO2…), the dashed lines indicate
the predicted TOF vs E curves, and the black dots indicate the data from
calibrations. [NASA PDS]

###  A.2 Processing of the PLS data

In this section it is described how the PLS ion data is processed. A basic
description of the instrument and its operating principles are given in
Section 2.1. As described previously a major challenge for processing and
analysing the PLS data is the poor spatial and time coverage, resulting from
the malfunction of the main antenna. An additional challenge is the noise
introduced in the data by penetrating radiation. This penetrating radiation
consist of highly energetic particles (such as electrons) that penetrate the
shielding of the instrument and directly hit the detector, without passing
through the electrostatic analyser.

For the processing of the data a modified version of the CCATI software,
originally developed by Dr. Markus Fränz, was utilized. Prior to this thesis
already several fundamental steps of PLS data processing were implemented in
this software. Development of the software continued as part of this thesis
project.

The raw PLS data is obtained from NASA’s Planetary Data System (PDS). In this
thesis I am only using the HIGH_RES product. The raw data is available in the
unit of counts per second. Besides the measurement, for every data point a
time tag, the number of the respective detector, the energy bin number and the
sector number are available. The sector number represents the spin angle at
which the measurement was made.

####  A.2.1 Energy spectrum

Figure A.2: Visual overview of the PLS data processing

The PLS data is processed in several steps to compensate for the poor coverage
in time and energy, and remove the effect of the background noise. Here it is
described how the raw data in count per second is converted into an
omnidirectional energy spectrum, and converted into differential flux with
unit of cm-2 s-1 sr-1 eV-1 (sometimes referred to as intensity). A visual
overview of the different steps is provided in Figure A.2.

Per detector, for the same timetag, multiple data points with different sector
numbers can be available in the raw data. To deal with this, the data is
averaged over the sectors for each detector separately. This works as follows.
Every single data value from a certain energy bin is averaged with the data
from the previous and next time step, corresponding to the same energy bin,
but having different sector numbers.

The next issue to deal with is the sparse coverage of energy bins. After the
sector averaging, a cubic spline interpolation is applied to fill in the empty
energy bins.

Subsequently the noise due to penetrating radiation is removed. First the
contribution of the noise is estimated based on the count rate in the four
highest energy bins. The average and standard deviation are calculated from
the count rate data in these four bins. Empty bins are neglected for this
procedure. The sum of the average and the standard deviation multiplied by 0.2
is then considered as the noise, and the obtained noise is subtracted from the
count rate data of the other energy channels. This procedure of noise removal
is equivalent to previous PLS studies, such as Paterson et al. (1999) and
Frank and Paterson (1999), when comparing to the published results.

Next, averaging in time is performed together with averaging over the seven
detectors. The ’box car’ averaging method is used for this. This is
implemented as follows. First the time frame is divided in equally sized bins
and then all the values in each energy-time bin (taken from all the detectors)
are summed up. Finally this sum is divided by the number of values in the
energy-time bin. Here I average over 60 seconds. Figure A.3 shows the data
before and after processing in units of count/s. This data product can be seen
for the other flybys in the figures in Section A.3.

Figure A.3: Top panel: unprocessed data, bottom panel: processed data. This
example is for the E12 flyby.

The the data (prior to box car averaging) can be converted from counts/s to
differential flux (cm-2 s-1 sr-1 eV-1). This is done by using the calibration
data provided in the PDS. Calibration data are provided as a factor that has
to be multiplied with the data. This factor is referred to as ’chi-factor’.
The chi-factor was obtained by the PLS team from calibration experiments in
laboratory. Box car averaging is performed in the same way as described
previously. The resulting data product in units of cm-2 s-1 sr-1 eV-1 can be
seen in the figures in Section A.3. Two higher level data products can be
derived from the differential flux: the plasma velocity magnitude and the
density. These are discussed in the next section.

The PLS data collected during the close Europa flybys has generally been
obtained with two different scanning modes: one low-energy mode that only
scans below 15 eV and one that scans above 15 eV. Because of uncertainties in
the processing of the low energy scans, I only consider data above 15 eV to be
reliable.

####  A.2.2 Other data products

In Section A.3 the following additional data products are also shown:

  * •

Density: plasma density is calculated from the differential flux, by
integrating the differential flux in every energy bin and taking the sum. This
is done using Equation A.1, which is based on Fränz et al. (2007).

$\rho=4\pi\sum J_{i}\Delta
E_{i}\frac{\sqrt{m_{factor}}}{\sqrt{E_{i}}}1.686e^{-8}$ (A.1)

In this Equation $J_{i}$ is the differential flux of the bin in cm-2 s-1 sr-1
eV-1, $\Delta E_{i}$ the energy width of the bin in eV, ${E_{i}}$ the energy
of the bin in eV and $m_{factor}$ expresses the mass of the particles in
electron mass, density $\rho$ is then in $cm^{-3}$. I use a mass factor of
18.5, based on Table 1.4.

  * •

Velocity magnitude: The velocity magnitude of the corotational plasma is
determined by first multiplying every energy bin with the corresponding energy
bin width and converting the energy of the bin with the highest value into a
velocity. The multiplication of differential flux and energy bin width scales
with the count rate. This method thus looks for the bin with the highest
probability of particle detections. I consider that this a reliable way to
estimate the velocity of the corotational plasma. Since the corotational
plasma is the dominant source of plasma, the bin with the most detected
particles should correspond to the corotational plasma.

####  A.2.3 PLS instrument pointing

Determining the pointing of the PLS instrument and linking observed features
to a direction in physical space has been traditionally difficult. This is due
to an absence of SPICE kernels for the PLS instrument and a lack of clear and
consistent documentation. Bagenal et al. (2016) and supplementary material
therein have documented several key points needed to understand instrument
pointing and provided corrections for the documentation provided in Frank et
al. (1992). Following the suggestions of Bagenal et al. (2016) I implemented
my own code to understand the looking direction of PLS. I do this by providing
figures of PLS data in a non-spinning spacecraft frame, which has a constant
orientation with respect to physical directions such as the corotational
plasma. This frame shares its z-axis with the spin axis of the spacecraft. I
plot the data as a function of the polar angle by separating it in polar bins
and azimuth bins, both in the non-spinning spacecraft frame. I determine the
polar and azimuth angles as follows, using information from Bagenal et al.
(2016). For the polar angle:

  * •

I define the polar angle of the data as the polar angle with respect to the
z-axis (spin-axis) in the spacecraft frame. The polar angles of the PLS
detectors in the spinning spacecraft frame are known. The number of polar bins
is seven. There are seven detectors. Each detector has a different polar
angle.

  * •

A direction of interest (such as that of the corotational plasma) can be
transformed to the spacecraft frame (available in SPICE as SC_BUS = -77000),
in which the polar angle of the direction can be calculated. It is then clear
which detectors are best covering this polar angle.

For the azimuth angle:

  * •

I define the azimuth angle as the azimuth angle in a non-rotating spacecraft
frame.

  * •

Since Galileo is spinning, the spin angle of each detector is continuously
changing. To account for the spin, every data point has a sector number. This
sector number expresses the spin angle corresponding to the measurement. The
key difficulty is finding which direction sector 0 corresponds to.

  * •

Sector number 0 occurs when PLS detector 1 and 7 are in the plane containing
the north pointing vector ([0,0,1]) in the ECL50 reference frame. ECL50 is an
abbreviation of the Earth Mean Ecliptic equinox 1950 frame.

  * •

No SPICE kernel is available for the ECL50 frame, but ECL50 north is
(approximately) the same as +Z in the ECLIPB1950 reference frame, which is
available in SPICE.

  * •

By knowing the orientation of directions of interest (such as that of the
corotational plasma) in the ECLIPB1950 frame it can be determined which sector
numbers cover which directions. I divide the azimuth range in 8 parts and
separate the data for each part, so that a plot can be made which shows the
data in each part of the full azimuth range.

I have converted the following physically interesting directions in the non-
spinning spacecraft frame:

  * •

The direction of Jupiter. I use the IAU Europa x-axis

  * •

The direction of the corotating plasma. I use the y-axis of the IAU Europa
frame

  * •

The rotation axis of Europa, which is the z-axis of the IAU Europa frame

  * •

The ram direction of the spacecraft, which is the velocity of the spacecraft
in the IAU frame, namely the velocity relative to Europa.

###  A.3 Overview of the data

In this Appendix an overview of the EPD, PLS and MAG data collected during the
Europa flybys of Galileo is provided. Data products that are presented are the
count rates in the EPD E, A, TS, TO and TP channels (panel 1-5 from the top).
For PLS both the differential flux, the count rate, the plasma density and
magnitude of the velocity are presented (panel 6-9 from the top). For MAG the
magnitude of the magnetic field is shown (panel 10). The flybys data are shown
in sequential order in Figures A.4 to A.11. In every figure the solid vertical
line indicates the time of the closest approach.

Figure A.4: E4 flyby data. From the top: EPD data (panel 1-5), PLS data (panel
6-9), magnetic field data (panel 10) and altitude (panel 11). The dashed
vertical lines indicate the geometrical wake. The dotted lines indicate the
region in which Paterson et al. (1999) reports deflection of the corotational
plasma based on PLS data. Figure A.5: E6 flyby data in the same format as
Figure A.4. The dotted lines indicate the region in which Paterson et al.
(1999) reports deflection of the corotational plasma based on PLS data. Note
that no magnetic field data is available for this flyby. Figure A.6: E11 flyby
data in the same format as Figure A.4. The dashed vertical lines indicate the
geometrical wake. Figure A.7: E12 flyby data in the same format as Figure A.4.
The dotted lines indicate the region during which the direction of the
corotational plasma changes according to the analysis of the PLS data
presented in this thesis, see Chapter 3. Jia et al. (2018) reports a change in
magnetic field direction between the first dotted line and the closest
approach (solid line), possibly related to a plume. Figure A.8: E14 flyby data
in the same format as Figure A.4. Figure A.9: E15 flyby data in the same
format as Figure A.4. The dashed vertical lines indicate the geometrical wake.
Figure A.10: E19 flyby data in the same format as Figure A.4. Figure A.11: E26
flyby data in the same format as Figure A.4.

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## Acknowledgements

I worked on this project at three institutes, the Swedish Institute of Space
Physics in Kiruna, the Max Planck Institute for Solar System Research in
Göttingen and the technical University of Braunschweig, there are people at
each of these locations that I would like to thank for helping me during the
past few years.

First I would like to thank Prof. Dr. Stas Barabash, Dr. Norbert Krupp, Dr.
Sonja Schuh, Prof. Dr. Karl-Heinz Glassmeier, the International Max Planck
Research School for Solar System Research at the Max Planck Institute for
Solar System Research, the Swedish Institute of Space Physics and the
Braunschweig University of Technology who have made it possible for me to
conduct this PhD project on Europa. I couldn’t have imagined a more
interesting project.

Secondly I would like to thank my supervisors. Starting with Dr. Yoshifumi
Futaana, who was my daily supervisor back in Kiruna. Futaana, thank you for
getting me started with particle simulations and the many, many teachings by
the drawing board in your office. Next, Dr. Elias Roussos and Dr. Norbert
Krupp my two supervisors in Göttingen. Elias, I remember the first talk by you
that I listened to, it inspired me by showing how interesting magnetospheric
physics really can be. Thank you for always being ready to help and sharing
your endless knowledge and ideas. Norbert, I remember our first meeting at the
PEP team meeting in Kiruna, thanks for taking me as a PhD student and for your
advice, support and ideas. Thanks also go to Prof. Dr. Karl-Heinz Glassmeier
for supporting the project and giving advice. Finally thanks go to Prof. Dr.
Stas Barabash. Stas, I remember our first meeting at the airport in Schiphol,
thank you for getting me started and giving me a chance to do the research I’m
most interested in.

Beyond my direct supervisors I would like to acknowledge a few more people.
Thanks go to Dr. Markus Fränz for the countless times you have patiently
helped me understand CCATI and deal with the PLS data. Also, thanks go to Dr.
Kostas Dialynas for sharing your vast database of charge exchange cross
sections, to Boris Semenov at NAIF for answering my questions about the
Galileo SPICE files, to Dr. Andreas Lagg for helping me with the EPD software
and to Dr. Jesper Lindkvist for helping me implement the induced dipole.

Thanks go to all my friends and colleagues at IRF and MPS for advice and
support, I started out with listing you all, but I decided not to in the end,
thank you all! Finally, I would also like to thank my family and friends back
home, for having to miss me during my stay in the North and East.

Europa is the new Mars.
I’m a believer, I believe in the plume. - Pontus C. Brandt
Hans is looking for monsters on Europa. - Stas Barbash
You got the Americans pretty scared. - Magnus Emanuelsson

## Curriculum Vitae

Personal information

Name: Hans Leo Frans Huybrighs

Date of birth: 8 June 1990

Place of birth: Etterbeek (Belgium) Education

Braunschweig University of Technology (Braunschweig, Germany)
PhD thesis at the Max Planck Institute for Solar System Research,
International Max Planck Research School, IMPRS (Göttingen, Germany).

Delft University of Technology (Delft, the Netherlands)
Master of Science: Aerospace Engineering: Space Flight
Thesis: The feasibility of in-situ observations of Europa’s water vapour
plumes. Thesis work and internship conducted at the Swedish Institute of Space
Physics, IRF (Kiruna, Sweden).

Delft University of Technology (Delft, the Netherlands)
Bachelor: Aerospace Engineering