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A experiência AMS

A experiência AMS (ver link da nasa por exemplo, http://ams.nasa.gov/) encontra-se instalada na estação espacial internacional desde Maio de 2011 a recolhar dados, isto é, raios cósmicos - maioritariamente protões - que atravessam o detector e nele deixam sinais. AMS possui todas as características de um detector de partículas e permite por isso, a identificação dos diferentes tipos de partículas que o atravessam: protões, electrões, positrões, antiprotões, hélios, ...
A instalação do detector fora da atmosfera terrestre transforma-o num observatório único do Universo e onde se esperam contribuições para o problema "de que é feita a matéria escura?" Para além disso, AMS permitirá medir com uma resolução nunca antes alcançada, os espectros das diferentes partículas constituintes dos raios cósmicos até energias de alguns TeV. AMS detecta e regista cerca de 500 acontecimentos por segundo. Isto corresponde a cerca de 15 mil milhões de acontecimentos acumulados por ano, dos quais cerca de 90% são protões, 1% electrões, 0.1% positrões, 0.01% antiprotões, etc.

Bibliografia:

Stochastic resolution of solar modulation equations

The Sun emits a continuous stream of highly conductive plasma that permeates the entire Solar system, transporting Solar magnetic field lines with it. The Solar magnetic field changes the direction and energy of cosmic-rays inside the Solar system, creating an effect known as Solar modulation. The cosmic ray flux is especially sensitive to this effect on the low energy range, up to 30 GV.
The goal of this work is to explore this phenomenon in 1 and 2 dimensions (radial and polar) under a stochastic resolution approach.

The students are proposed to work on the stochastic resolution of diffusion-like equations in order to get acquainted with the resolution technique and to gain knowledge in the area. After this process we want to be able to develop a 2D stochastic solution in order to master the technique and compare to the 1D finite difference method previously developed by other students.

Bibliografia:

• Geral
• Cosmic ray modulation equations (Moraal, 2011) SSR, Space Sci Rev, DOI 10.1007/s11214-011-9819-3
• Limitations of the force field equation to describe cosmic ray modulation (R. A. Caballero-Lopez, H. Moraal) JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, A01101, doi:10.1029/2003JA010098, 2004
• Stochastic aspects of magnetic lines of force with applications to cosmic-ray propagation (Jokipii, Parker) Astroph J V155 (1969)
• Effects of particle drift on the transport of cosmic rays. III—Numerical models of galactic cosmic-ray modulation (Jokipii, Kopriva) Astrophys. J. 234, 384–392 (1979)
• A Markov stochastic process theory of cosmic-ray modulation (Zhang) Astrophys. J. 513, 409–420 (1999)
• Stochastic Approach

Detailed working plan:

• numerical resolution of the heat equation using the stochastic differential equation approach
• study of diffusion-like equations as function of their boundary conditions
• solving solar modulation equations using stochastic algorithms

Selection of Helium nuclei using multivariate data analysis in AMS data

About 10% of all cosmic particles are helium (Z=2), they are composed mainly by He3 and He4 isotopes and the ratio He3/He4 varies from 10 to 20%, as a function of magnetic rigidity. By studying AMS data, understanding the different measurements involved and their significance in the selection of cosmic ray events, one can develop a multivariate analysis framework in order to identify helium nuclei and accurately separate its isotopes.
Due to the sheer amount of data and variables involved, state-of-the-art data analysis techniques are growing in popularity due to their speed and discrimination capabilities. They usually require the user to choose appropriately significant variables and to be trained using either monte-carlo events or highly pure data samples.

The students are proposed to develop reduced datasets from AMS trees (known as miniDST's) and to study the data in order to identify the key observables in cosmic ray event selection (namely helium). These key variables would be used to train a neural network selection framework (based on the ROOT TMVA). This selection platform developed by the students would then serve to identify helium and its isotopes from AMS data. As an optional end-goal, students would try to estimate the time-variable helium flux from their selected events.

Bibliografia:

Detailed working plan:

• develop a miniDST from AMS data
• study AMS data in order to identify key selection variables
• develop a selection framework based on the ROOT TMVA project
• using "pure" helium or monte-carlo helium events to train the neural network