In this diploma thesis, we present a thorough investigation of the Vavilov-Cerenkov (VC) effect and its application to the detection of ultrahigh energyneutrinos. Neutrinos have no net charge and cannot emit VC radiation(VCR) but when they interact with a stationary medium such radiation isproduced by the Askar'yan effect. It has been proposed to use the lunarregolith as a detector material for such neutrino interactions, but in orderto do so, the VCR emitted from neutrino interactions has to be filtered outfrom that of other energetic particles impinging upon and penetrating intothe lunar surface. Since the principal difference between these emissions isdependent on the depth at which they occur, any boundary effects dependenton the distance from the point of emission to the surface is of interest.Therefore we examine the behavior of VCR near dielectric boundaries withextra care, both analytically and numerically. In order to keep the physicalpicture as clear as possible the analytical derivations are conducted in thetemporal domain. The boundary problem is studied both with respect tothe Maxwell boundary conditions and the method of images. In order toverify the analytic results and to make further investigations, a numericalMaxwell solver for the general VCR problem was constructed. The analyticand numeric results are then compared and proven to be equivalent. TheMaxwell solver is used to study the boundary problem and it is shown thatthe VCR emitted in a medium near the surface does not depend on the distanceto the surface in the same way as the opposite problem with a chargeabove the medium. In the case of a charge distribution traveling partiallyon both sides of the boundary some frequency dependent effects are shown.

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GZK neutrino are interesting messenger particles since, if detected, they can transmit<p>us exclusive information about ultra-high energy processes in the Universe. These particles,<p>which hold energies above 10^16 eV, interact very rarely. Therefore, detectors of<p>several gigatons of matter are needed to discover them. The ARA detector is planned<p>and currently being constructed at the South Pole. It is designed to use the Askaryan<p>effect, the emission of radio waves from neutrino induced cascades in the South Pole ice,<p>to detect neutrino interactions. With antennas distributed in 37 stations in the ice, such<p>interactions can be observed in a volume of several hundred cubic kilometers. Currently<p>2 ARA stations have been deployed in the ice and are taking data since the beginning<p>of the year 2013.<p>The first part of this thesis summarizes the current theories concerning the GZK mechanism<p>and the Askaryan effect to explain the interest in GZK neutrinos and in the used<p>detection method.<p>In the second part the ARA detector is described and calibrations of different detector<p>parts are presented. In this work, the digitization chips have been calibrated concerning<p>their timing precision and signal amplitude. In this way a timing precision of 100 ps<p>between antennas could be achieved. Furthermore, the geometry of the antenna clusters<p>is determined by cuts based on external signals to allow for a proper radio vertex reconstruction.<p>In the third part of the thesis the development of methods to distinguish radio signals<p>from thermal noise are presented. Moreover, a reconstruction method, developed to determine<p>the position of radio sources, is described. With only two stations operational<p>a discovery of GZK neutrinos is not expected and in fact no signal candidate has been<p>found in the analysis of the data. A neutrino <p>ux limit is calculated. This limit is not<p>competitive yet with the current best limits, but very promising for the full ARA detector.<p>The work shows that after completion this detector is expected to be capable of a<p>neutrino discovery. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

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Neutrinos are the perfect cosmic messengers when it comes to investigating the most violent and mysterious astronomical and cosmological events in the Universe. The interaction probability of neutrinos is small, and the flux of high-energy neutrinos decreases quickly with increasing energy. In order to find high-energy neutrinos, large bodies of matter needs to be instrumented. A proposed detector station design called ARIANNA is designed to detect neutrino interactions in the Antarctic ice by measuring radio waves that are created due to the Askaryan effect. In this paper, we present a method based on state-of-the-art machine learning techniques to reconstruct the direction of the incoming neutrino, based on the radio emission that it produces. We trained a neural network with simulated data, created with the NuRadioMC framework, and optimized it to make the best possible predictions. The number of training events used was on the order of 106. Using two different emission models, we found that the network was able to learn and generalize on the neutrino events with good precision, resulting in a resolution of 4-5°. The model could also make good predictions on a dataset even if it was trained with another emission model. The results produced are promising, especially due to the fact that classical techniques have not been able to reproduce the same results without having prior knowledge of where the neutrino interaction took place. The developed neural network can also be used to assess the performance of other proposed detector designs, to quickly and reliably give an indication of which design might yield the most amount of value to the scientific community. / Neutriner är de perfekta kosmiska budbärarna när det kommer till att undersöka de mest våldsamma och mystiska astronomiska och kosmologiska händelserna i vårt universum. Sannolikheten för en neutrinointeraktion är dock liten, och flödet av högenergetiska neutriner minskar kraftigt med energin. För att hitta dessa högenergetiska neutriner måste stora volymer av materia instrumenteras. Ett förslag på en design för en detektorstation kallas ARIANNA, och är framtagen för att detektera neutrinointeraktioner i den antarktiska isen genom att mäta radiopulser som bildas på grund av Askaryan-effekten. I denna rapport presenterar vi en metod baserad på toppmoderna maskininlärningstekniker för att rekonstruera riktningen på en inkommande neutrino, utifrån den radiostrålning som produceras. Vi tränade ett neuralt nätverk med simulerade data, som skapades med hjälp av ramverket NuRadioMC, och optimerade nätverket för att göra så bra förutsägelser som möjligt. Antalet interaktionshändelser som användes för att träna nätverket var i storleksordningen 106. Genom att undersöka två olika emissionsmodeller fann vi att nätverket kunde generalisera med god precision. Detta resulterade i en upplösning på 4-5°. Modellen kunde även göra goda förutsägelser på en datamängd trots att nätverket var tränat med en annan emissionsmodell. De resultat som metoden framtog är lovande, särskilt med avseende på att tidigare klassiska metoder inte har lyckats reproducera samma resultat utan att metoden redan innan vet var i isen som neutrinointeraktionen skedde. Nätverket kan också komma att användas för att utvärdera prestandan hos andra designförslag på detektorstationer för att snabbt och säkert ge en indikation på vilken design som kan tillhandahålla mest vetenskapligt värde.

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Simulations have demonstrated that high-energy neutrinos (E &gt; 1017 eV) are detected cost-efficiently via the Askaryan effect in ice, where a particle cascade induced by the neutrino interaction produces coherent radio emission that can be picked up by antennas installed below the surface. A good knowledge of the near surface ice (aka firn) properties is required to reconstruct the neutrino properties. In particular, a continuous monitoring of the snow accumulation (which changes the depth of the antennas) and the index-of-refraction profile are crucial for an accurate determination of the neutrino's direction and energy. 14 months of data of the ARIANNA detector on the Ross Ice Shelf, Antarctica, are presented where a prototype calibration system was successfully used to monitor the snow accumulation with unprecedented precision of 1 mm. Several algorithms to extract the time differences of direct and reflected (off the surface) signals (D'n'R time difference) from noisy data (including deep learning) are explored. This constitutes an in-situ test of the neutrino vertex distance reconstruction using the D'n'R technique which is needed to determine the neutrino energy. Additionally, an in-situ calibration system is proposed that extends the radio detector station with a radio emitter to continuously monitor the firn properties by measuring D'n'R time difference. In a simulation study the station layout is optimized and the achievable precision is quantified.

High-energy neutrino physics offers a unique way to investigate the most violent phenomena in our universe. The detection of energies above E &gt; 1017 eV is most efficient using the Askaryan effect, where a neutrino-induced particle shower produces coherent radio emission that is detectable with radio antennas. By using radio techniques large volumes can be covered with few stations at moderate cost exploiting the large attenuation length of radio in cold ice. Key to the reconstruction of the neutrino properties visa precise and continuous monitoring of the firn properties. In particular the snow accumulation (changing the absolute depth of the antennas thus the propagation path of the signal) and the index-of-refraction profile are crucial for the neutrino energy and direction reconstruction. This work presents an in-situ calibration design that acts as an detector extension by adding additional emitter antennas to the station design to continuously monitor the firn properties by measuring the direct and reflected signals (D’n’R). In a simulation study the optimal station layout is determined and the achievable precision is quantified. Furthermore 14 months of data from an ARIANNA station at the Ross Ice Shelf, Antarctica, are presented where a prototype of this calibration system has been successfully installed to monitor the snow accumulation with unprecedented precision of 1 mm. Several algorithms, including deep learning algorithms, to compute the D’n’R time difference from radio traces are considered.

The Standard Model of particle physics successfully describes present collider data. Nevertheless, theoretical and cosmological results call for its extension. A softly broken supersymmetric completion around the TeV scale solves several of the outstanding issues. Supersymmetry requires two Higgs doublets, leading to five physical Higgs states. These include a pair of charged Higgs bosons H±, which are a generic feature of theories with multiple Higgs doublets. Using results from high-energy colliders and flavour physics, constraints are derived on the charged Higgs boson mass and couplings; both for constrained scenarios in the minimal supersymmetric standard model (MSSM) with grand unification, and for general two-Higgs-doublet models. The MSSM results are compared to the projected reach for charged Higgs searches at the Large Hadron Collider (LHC). At the LHC, a light charged Higgs is accessible through top quark decay. Beyond a discovery, it is demonstrated how angular distributions sensitive to top quark spin correlations can be used to determine the structure of the H±tb coupling. The public code 2HDMC, which performs calculations in a general, CP-conserving, two-Higgs-doublet model, is introduced. In parallel to the developments at colliders, the most energetic particles ever recorded are the ultra-high-energy (UHE) cosmic rays. To gain more insight into their origin, new experiments are searching for UHE neutrinos. These searches require detectors of vast volume, which can be achieved by searching for coherent radio pulses arising from the Askaryan effect. The prospects of using a satellite orbiting the Moon to search for neutrino interactions are investigated, and a similar study for an Earth-based radio telescope is presented. In both cases, the method is found competitive for detection of the very highest energy neutrinos considered.