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dc.contributor.advisorTheodorsen, Audun
dc.contributor.authorKočiščák, Samuel
dc.date.accessioned2024-09-17T12:51:38Z
dc.date.available2024-09-17T12:51:38Z
dc.date.issued2024-10-04
dc.description.abstractIt is apparent from remote observations that the density of interplanetary dust is the highest in the near-solar region. Yet, there were little in-situ data available from measurements from within 1 AU before Parker Solar Probe (PSP) and Solar Orbiter (SolO) missions. They both venture close to the Sun on elliptical orbits, and they are equipped with electrical antennas capable of dust detection, but the detection is ambiguous, since the dust impacts leave intermittent and difficult to identify signals in the time-series. In this thesis, a novel convolutional neural network method for dust identification is presented and applied to SolO data, yielding the highest quality SolO dust data product to date. The data is analyzed statistically, in the Bayesian framework, using Integrated Nested Laplace Approximation, which allows for usage of more sophisticated models. Important characteristics of the dust cloud are revealed this way, such as the deceleration of the hyperbolic dust component. Dust detection with electrical antennas is complicated, and steps towards the full understanding of the process are taken as a part of this thesis. By closely inspecting the signals recorded by SolO, the importance of photoelectron sheath for the antenna measurements is investigated, and it is likely that the photoelectron sheath makes the antennas a lot more sensitive to the presence of free ions. Meanwhile, the data of PSP is compared to a phase-space distribution flux model and to the measurements of SolO. This way, an indication of a dust depletion zone is found in the PSP data. A lot remains to be done before the interplanetary dust environment is fully understood, but SolO and PSP are going to continue providing more unprecedented data, analysis of which is made easier and more rigorous by the tools developed in this thesis.en_US
dc.description.abstractDet er tydelig fra fjernobservasjoner at tettheten av interplanetarisk støv er den høyeste i nær-solområdet. Likevel var det lite in-situ data tilgjengelig fra målinger fra innenfor 1 AU før Parker Solar Probe (PSP) og Solar Orbiter (SolO) oppdrag. De våger seg begge nær Solen på elliptiske baner, og de er utstyrt med elektriske antenner som er i stand til støvdeteksjon, men deteksjonen er tvetydig, siden støvpåvirkningene etterlater intermitterende og vanskelig å identifisere signaler i tidsserien. I denne oppgaven blir en ny konvolusjonell nevrale nettverksmetode for støvidentifikasjon presentert og brukt på SolO-data, noe som gir SolO-støvdataproduktet av høyeste kvalitet til dags dato. Dataene analyseres statistisk, i det Bayesianske rammeverket, ved hjelp av Integrated Nested Laplace Approximation, som tillater bruk av mer sofistikerte modeller. Viktige egenskaper ved støvskyen avsløres på denne måten, for eksempel retardasjonen av den hyperbolske støvkomponenten. Støvdeteksjon med elektriske antenner er komplisert, og skritt mot full forståelse av prosessen tas som en del av denne oppgaven. Ved å nøye inspisere signalene registrert av SolO, undersøkes betydningen av fotoelektronkappe for antennemålingene, og det er sannsynlig at fotoelektronkappen gjør antennene mye mer følsomme for tilstedeværelsen av frie ioner. I mellomtiden blir dataene til PSP sammenlignet med en fase-romfordelingsfluksmodell og med målingene til SolO. På denne måten finner du en indikasjon på en støvutarmingssone i PSP-dataene. Det gjenstår mye å gjøre før det interplanetariske støvmiljøet er fullt ut forstått, men SolO og PSP kommer til å fortsette å tilby flere enestående data, analyse av disse er gjort enklere og mer streng med verktøyene utviklet i denne oppgaven.en_US
dc.description.abstractZo vzdialených pozorovaní meziplanetárneho prahu je zrejmé, že jeho hustota je najvyššia v blízkosti Slnka. Pred misiami Parker Solar Probe (PSP) a Solar Orbiter (SolO) však bolo dostupných len málo in-situ meraní zo vzdialenosti od Slnka menšej, než 1 astronomická jednotka. Obe misie sa približujú k Slnku na eliptickej orbite a sú vybavené elektrickými anténami schopnými detegovať nárazy prachu. Detekcia je ale nejednoznačná, pretože nárazy prachu zanechávajú prerušované a ťažko identifikovateľné signály v časových radách. V tejto práci je predstavujeme novú metóda konvolučnej neurónovej siete na identifikáciu prachových signálov a aplikujeme ju na dáta zo SolO, čím vytvárame doposiaľ najkvalitnejšiu dátovú radu o prachu na SolO. Údaje analyzujeme Bayesovskou štatistikou pomocou integrovanej vnorenej Laplaceovej aproximácie (INLA), ktorá umožňuje použitie sofistikovanejších modelov, než tomu bolo doposiaľ. Dakto odhalíme dôležité vlastnosti prachového oblaku, ako napríklad spomaľovanie zŕn na hyperbolických dráhach. Detekcia prachu pomocou elektrických antén je komplikovaná a súčasťou tejto práce sú kroky k plnému pochopeniu procesu. Dôkladnou kontrolou signálov zaznamenaných SolO skúmame vplyv fotoelektrónového obalu na merania antén a zisťujeme, že je pravdepodobné, že fotoelektrónový obal antén spôsobuje, že antény sú veľmi citlivé na prítomnosť voľných iónov. Údaje z PSP porovnávame s modelom toku na bázi fázovo-priestorovej distribúcie a s meraniami SolO. Týmto spôsobom nachádzame v údajoch PSP náznaky o zóne úbytku prachu v blízkosti Slnka. O medziplanetárnom prachu v blízkosti Slnka toho stále veľa nevieme, ale SolO a PSP budú naďalej poskytovať bezprecedentné údaje, ktorých analýza bude jednoduchšia a presnejšia vďaka nástrojom vyvinutým vrámci tejto práce.en_US
dc.description.doctoraltypeph.d.en_US
dc.description.popularabstractCosmic dust is an integral part of the solar system, observable by naked eye in the form of zodiacal light. It is apparent from remote observations that the density of interplanetary dust is the highest close to the Sun. That is the region we investigated using the data of Parker Solar Probe (PSP) and Solar Orbiter (SolO) spacecraft. Dust impacts show signals in electrical measurements which are difficult to identify and are studied as a part of this thesis. Identification is successfully approached by machine learning, which provides a consistent and reliable data set of dust detections. The physical properties of the dust cloud are obtained by analysis of the dust counts by the methods of Bayesian inference and by comparing the dust data to models of phase-space dust density. Not only is the near-solar dust cloud characterized in this thesis, but the presented tools remain useful for future analysis, since SolO and PSP are going to keep providing unprecedented data.en_US
dc.description.sponsorshipTromsø Forskningsstiftelse, grant 19_SG_ATen_US
dc.identifier.isbn978-82-8236-590-1 - trykk
dc.identifier.issn978-82-8236-591-8 - pdf
dc.identifier.urihttps://hdl.handle.net/10037/34747
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.relation.haspart<p>Paper I: Kvammen, A., Wickstrøm, K., Kočiščák, S., Vaverka, J., Nouzák, L., Zaslavsky, A., … Mann, I. (2023). Machine learning detection of dust impact signals observed by the Solar Orbiter. <i>Annales Geophysicae, 41</i>(1), 69-86. Also available in Munin at <a href=https://hdl.handle.net/10037/26412>https://hdl.handle.net/10037/26412</a>. <p>Paper II: Kočiščák, S., Kvammen, A., Mann, I., Holbek Sørbye, S., Theodorsen, A. & Zaslavsky, A. (2023). Modeling Solar Orbiter dust detection rates in the inner heliosphere as a Poisson process. <i>Astronomy and Astrophysics, 670</i>, A140. Also available in Munin at <a href=https://hdl.handle.net/10037/28867>https://hdl.handle.net/10037/28867</a>. <p>Paper III: Kočiščák, S., Kvammen, A., Mann, I., Meyer-Vernet, N., Píša, D., Souček, J., … Zaslavsky, A. (2024). Impact ionization double peaks analyzed in high temporal resolution on Solar Orbiter. <i>Annales Geophysicae, 42</i>(1), 191-212. Also available in Munin at <a href=https://hdl.handle.net/10037/33647>https://hdl.handle.net/10037/33647</a>. <p>Paper IV: Kočiščák, S, Theodorsen, A. & Mann, I. On the distribution of the near-solar bound dust grains detected with Parker Solar Probe. (Submitted manuscript). Also available on arXiv at <a href=https://doi.org/10.48550/arXiv.2408.05031>https://doi.org/10.48550/arXiv.2408.05031</a>.en_US
dc.relation.isbasedon<p>Data and code for Paper I: Kvammen, A. (2022). ML_dust_detection (Version 1.0.0) [Computer software]. GitHub <a href=https://doi.org/10.5281/zenodo.7404457> https://doi.org/10.5281/zenodo.7404457</a>. <p>Data and code for Paper II available on GitHub at <a href= https://github.com/SamuelKo1607/solo_dust_2022>https://github.com/SamuelKo1607/solo_dust_2022</a>. <p>Data and code for Paper III available on GitHub at <a href=https://github.com/SamuelKo1607/solo_dust_2023>https://github.com/SamuelKo1607/solo_dust_2023</a>. <p>Code for Paper IV available on GitHub at <a href=https://github.com/SamuelKo1607/psp_dust_2024>https://github.com/SamuelKo1607/psp_dust_2024</a>. <p>Solar Orbiter / Radio and Plasma Waves data available at <a href=https://rpw.lesia.obspm.fr/roc/data/pub/solo/rpw/data/> https://rpw.lesia.obspm.fr/roc/data/pub/solo/rpw/data/</a>. <p>Parker Solar Probe / FIELDS data available at <a href=https://research.ssl.berkeley.edu/data/psp/data/sci/fields/> https://research.ssl.berkeley.edu/data/psp/data/sci/fields/</a>.en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)
dc.subject.courseIDDOKTOR-004
dc.subjectcosmic dusten_US
dc.titleUnderstanding Inner Solar System Dust Environment Through In-Situ Measurementsen_US
dc.typeDoctoral thesisen_US
dc.typeDoktorgradsavhandlingen_US


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