Study of the solar wind at 1 AU and its outliers

El objetivo principal de la tesis es el análisis de la parte central de la función de distribución del viento solar así como sus valores atípicos. Proponemos la función de distribución bi-Gaussiana para caracterizar la parte central de la distribución del viento solar a 1 UA. Para ello, analizamos la función de distribución de varias magnitudes físicas: velocidad de protones, temperatura de protones, densidad de protones, campo magnético interplanetario o composición de algunos elementos químicos. Nuestros resultados concluyen que el viento solar tiene una distribución bimodal que caracteriza ambos tipos de viento: lento y rápido. Nuestra investigación también proporciona resultados sobre la correlación de estas magnitudes con el ciclo solar. Para completar el estudio de la función de distribución, analizamos los valores atípicos de la distribución del viento solar mediante la teoría de los valores extremos. Esta teoría nos permite estimar los valores de retorno esperados en 40 y 80 años. Las investigaciones anteriores nos permiten desarrollar una herramienta para monitorizar el estado del viento solar en tiempo real. Utilizando el conjunto de datos históricos de los últimos 20 años, definimos una escala de riesgo para clasificar las condiciones del viento solar.The main objective of the thesis is the analysis of the bulk solar wind distribution and its outliers. We propose the bi-Gaussian distribution function to characterize the distribution of the bulk solar wind at 1 AU. In order to do that, we analyze the distribution function of several physical magnitudes: proton speed, proton temperature, proton density, interplanetary magnetic field or composition. Our results conclude that the solar wind has a bimodal distribution and we characterize both solar wind: fast and slow. Our research also provides results about the correlation of these magnitudes with the solar cycle. To complete the study of the distribution function, we analyze the outliers of the solar wind distribution through the extreme value theory. This theory allows us to estimate the return values expected in 40 and 80 years. The previous research allow us to develop a tool to monitor the solar wind status in real time. Using the historical data set of the last 20 years, we define a risk scale to classify the solar wind conditions

Estudio de las propiedades de las ondas en simulaciones de magneto-convección

The study of the sun was based on the analysis of images obtained using ground based telescopes and, later, using space based telescopes. Through these images we could analyze the observed structures (for example, sunspots), or also the polarization of the sun light using spectropolarimeters. Over the years, the telescopes and the tools for the analysis have been improving looking for a better spatial resolution and magnetic field sensitivity, allowing to solve the smallest structures observed to better determine their characteristics. The theoretical framework was developed, leading to the equations which define the movement, and its properties, of a plasma with a magnetic field, to result in the magnetohydrodynamics equations or MHD equations. This theoretical framework showed the existence of unknown features, like Alfv´en waves or the transformation between waves when passing through the equipartition layer with β=1. It was this frame, together with the computer development, which allowed the growing of the numerical simulations, which are able to solve the MHD equations to simulate the behavior of the sun and reproduce the observed structures.(Sec:2) In this work we have analyzed the result of a simulation made with the MANCHA 3D code (Khomenko & Collados, 2006), who solved the MHD equations with a realistic equation of state and radiative transfer in a grey atmosphere. The domain of the simulation covers 5, 8 × 5, 8 × 1, 6 Mm3 , located in such a way that the z axis covers from a depth z=0,95 Mm below the surface to z=0,62 Mm above it. In the article that describes the simulation, Khomenko et al. (2017) analyze a model based on the action of the Biermann battery effect. This effect generates a magnetic field due to the local imbalance of the electronic pressure in the partially ionized solar plasma. It is shown that the battery effect by itself is able, to create a seed magnetic field with a strength around µG, and together with the amplification of the dynamo mechanism, they allow the generation of a magnetic field with an average strength similar to that obtained from the observations. The generated magnetic field represents, the distribution of the quiet sun with a average value of = 100 G. During the simulation, one 3D snapshot has been saved each 0,4 s, with a total time of 79 s. The grid has 20 km horizontally and 14 km vertically, with dimensions of 5, 8 × 5, 8 × 1, 6 Mm3 , creating 199 data cubes with a size of 180 GB. The importance of this type of simulations is the high frequency of saving the snapshots, in this case each 0,4 s, which allow to study the behavior of the waves in the interval between 100-200 mHz, while observations currently only reach up to 10-20 mHz. This type of waves have the property that they are not affected by the acoustic cut-off frequency, so they can spread to the upper layers of the atmosphere. Because they have a shorter wavelength, the theoretical models that are generally based on homogeneous or slightly stratified situations serve better to explain their behavior. The analysis of the data has been based on the study of the compressible and incompressible waves at a given height of z=0,31 Mm above the surface to avoid the contamination by the convection, through the Fourier temporal transform of ∇ × ~ ~v and ∇ · ~ ~v as representative magnitudes of the compressible and incompressible waves respectively. We have also analyzed the relation between these magnitudes and the physical magnitudes such as the magnetic field strength (B~ ), temperature (T) and azimuth (φ) in order to establish possible phase differences, useful to localized these kind of waves.(Sec:3) The simulation represents a region below and above the surface. With the data, we have calculated the value of β for the height of study and also in the surface, and in both layers, using a histogram we can see that the value of β is much greater than 1.(Sec:2.4) All the analysis has been carried out in two different intervals of frequency, low and high, (0,013-0,1 Hz y 0,11-0,2 Hz), chosen in such a way that there is a complementarity in temporal power, so that the distribution of power in the low frequency range is anticorrelated with the power in the high frequency range.(Sec:4.1) In the study of power maps, we have shown that the temporal evolution of the incompressible waves is defined by the fluctuation of the magnetic tension, but for the compressible waves the representative magnitude is the fluctuation of the total pressure.(Sec:4.2) In the last part, we have studied the differences between the phases of the Fourier transform of ∇ × ~ ~v and ∇ · ~ ~v, and those of B~ , T and φ. With this analysis, we want to determine which phase difference may be most useful to determine the existence of one or other kind of waves. So, for example if we want to study the differences between the compressible waves and the temperature, we begin by analyzing the difference in all the points of the maps of power distribution (from now on we call it “Total”case). Then we select those points where the power is larger than a threshold in both maps to obtain the most representative points. We call this the “Mask”case. For the low frequencies, the magnitude that can serve as an indicator of waves is the temperature. For the phase shift ΦI − ΦT , the histograms in both cases, “Mask”and “Total”, appear to be most different. For the high frequencies, this method seems more effective and we obtained results which are different from zero, not only with T, but also with B~ and φ. In the “Mask”case, for the phase shift ΦI − ΦB, the histogram has two peaks around -30o and 40o , and in the one of ΦC − ΦB, 3 peaks appear at -80o ,0o and 70o . When comparing with φ, is representative the histogram of the phase shift ΦC − Φφ in the “Total”case, where a peak appears in the positive part (around 20o ) and a tail in the negative part, and when we study the “Mask”case, both characteristics are reinforced. And for the comparison with T, we obtained significant differences in the “Total”case for both phase shift histograms, ΦI −ΦT and ΦC −ΦT . In the “Mask”case, the peak for the histogram of ΦI − ΦT is reinforced around 80o , while the phase shift ΦC − ΦT unfolds into two peaks, one near 0o and the other around 110o (Sec:4.3)

Estimation of the solar wind extreme events

This research provides an analysis of extreme events in the solar wind and in the magnetosphere due to disturbances of the solar wind. Extreme value theory has been applied to a 20-year data set from the Advanced Composition Explorer spacecraft for the period 1998–2017. The solar proton speed, solar proton temperature, solar proton density, and magnetic field have been analyzed to characterize extreme events in the solar wind. The solar wind electric field, vBz has been analyzed to characterize the impact from extreme disturbances in the solar wind to the magnetosphere. These extreme values were estimated for 1-in-40- and 1-in-80-year events, which represent two and four times the range of the original data set. The estimated values were verified in comparison with measured values of extreme events recorded in previous years. Finally, our research also suggests the presence of an upper boundary in the magnitudes under study.Ministerio de Economía y CompetitividadAgencia Estatal de Investigació

Bimodal distribution of the solar wind at 1 AU

Aims. Here we aim to separate the two main contributions of slow and fast solar wind that appear at 1 AU. Methods. The Bi-Gaussian function is proposed as the probability distribution function of the two main components of the solar wind. The positions of the peaks of every simple Gaussian curve are associated with the typical values of every contribution to solar wind. We used the entire data set from the Advanced Composition Explorer (ACE) mission in an analysis of the data set as a whole and as yearly series. Solar cycle dependence is considered to provide more accurate results for the typical values of the different parameters. Results. The distribution of the solar wind at 1 AU is clearly bimodal, not only for velocity, but also for proton density, temperature and magnetic field. New typical values for the main parameters of the slow and fast components of the solar wind at 1 AU are proposed.Ministerio de Economía y Competitivida

The distribution function of the average iron charge state at 1 AU: from a bimodal wind to ICME identification

We aim to investigate the distribution function of the iron charge state, at 1 AU to check if it corresponds to a bimodal wind. We use data from the Solar Wind Ion Composition Spectrometer (SWICS) instrument on board the Advanced Composition Explorer (ACE) along 20 years. We propose the bi-Gaussian function as the probability distribution function that fits the average iron charge state, ⟨QFe⟩, distribution. We study the evolution of the parameters of the bimodal distribution with the solar cycle. We compare the outliers of the sample with the existing catalogs of interplanetary coronal mass ejections (ICMEs) and identify new ICMEs. The ⟨QFe⟩ at 1 AU shows a bimodal distribution related to the solar cycle. Our results confirm that ⟨QFe⟩>12 is a trustworthy proxy for ICME identification and a reliable signature in the ICME boundary definition.Ministerio de Economía y Competitivida