Expansion of magnetic clouds in the outer heliosphere

A large amount of magnetized plasma is frequently ejected from the Sun as coronal mass ejections (CMEs). Some of these ejections are detected in the solar wind as magnetic clouds (MCs) that have flux rope signatures. Magnetic clouds are structures that typically expand in the inner heliosphere. We derive the expansion properties of MCs in the outer heliosphere from one to five astronomical units to compare them with those in the inner heliosphere. We analyze MCs observed by the Ulysses spacecraft using insitu magnetic field and plasma measurements. The MC boundaries are defined in the MC frame after defining the MC axis with a minimum variance method applied only to the flux rope structure. As in the inner heliosphere, a large fraction of the velocity profile within MCs is close to a linear function of time. This is indicative of} a self-similar expansion and a MC size that locally follows a power-law of the solar distance with an exponent called zeta. We derive the value of zeta from the insitu velocity data. We analyze separately the non-perturbed MCs (cases showing a linear velocity profile almost for the full event), and perturbed MCs (cases showing a strongly distorted velocity profile). We find that non-perturbed MCs expand with a similar non-dimensional expansion rate (zeta=1.05+-0.34), i.e. slightly faster than at the solar distance and in the inner heliosphere (zeta=0.91+-0.23). The subset of perturbed MCs expands, as in the inner heliosphere, at a significantly lower rate and with a larger dispersion (zeta=0.28+-0.52) as expected from the temporal evolution found in numerical simulations. This local measure of the expansion also agrees with the distribution with distance of MC size,mean magnetic field, and plasma parameters. The MCs interacting with a strong field region, e.g. another MC, have the most variable expansion rate (ranging from compression to over-expansion)

Corrección de errores de nivelación de datos aerogeofísicos

En el presente trabajo se realiza una mejora a la técnica de correlación línea a línea (line-to-line correlation) para la remoción de errores de nivelación de datos geofísicos obtenidos mediante prospección aérea. Esta técnica, bajo la hipótesis de continuidad y fuerte correlación de los registros de perfiles de líneas de vuelo adyacentes, logra la nivelación mediante la construcción de un filtro funcional mediante una ventana móvil en una dimensión. Se aplica este filtro línea a línea en forma recursiva logrando disminuir los errores de nivelación y ajustarlos en el sentido de los cuadrados mínimos. Nuestra mejora consiste en la implementación de la técnica mediante la aplicación de filtros n-dimensionales, sobre bases de sucesiones polinomiales ortogonales. El procedimiento utilizado consistió en la generación de datos sintéticos a los cuales se les introdujo errores de nivelación ad-hoc de diferentes características típicas para simular los encontrados en una aeroprospección real. Luego aplicamos la técnica con este método mejorado como si se tratase de datos reales y comparamos con los datos sintéticos sin error de nivelación. Hemos hallado los rangos en que las bases ortogonales de Legendre y Chebyshev mejoran notablemente la estabilidad y resultados de la técnica, respecto de la implementación usando la base canónica. Luego se ha aplicado la técnica mejorada a un registro aeromagnético real adquirido sobre el área volcánica del Archipiélago James Ross, en el Mar de Weddell, en el extremo nororiental de la Península Antártica, obteniendo un muy buen resultado en la remoción de los errores de nivelación de la prospección. La remoción efectiva de los errores de nivelación resulta de fundamental importancia para lograr obtener, a partir de la inversión de los datos, modelos consistentes y representativos de la realidad geofísica que intenta describirse.Eje: Geofísica Aplicada y Ambiental.Facultad de Ciencias Astronómicas y Geofísica

Calibration of the operative cosmic ray detector at Marambio Base in the Antarctic Peninsula

During 2019 an Antarctic Space Weather Laboratory was deployed at Marambio base in the Antarctic Peninsula. The main instrument installed was a cosmic ray detector based on water Cherenkov radiation (WCD). This detector is the first permanent Antarctic node of the LAGO (Latin American Giant Observatory) Collaboration. Long-term calibrated observations of the WCD will be presented here. Finally, the global galactic cosmic rays variability observed with the WCD will be compared with observations of a neutron monitor with similar rigidity cut off than the Marambio site

A big data platform for large scale event processing

To date, big data applications have focused on the store-and-process paradigm. In this paper we describe an initiative to deal with big data applications for continuous streams of events. In many emerging applications, the volume of data being streamed is so large that the traditional ‘store-then-process’ paradigm is either not suitable or too inefficient. Moreover, soft-real time requirements might severely limit the engineering solutions. Many scenarios fit this description. In network security for cloud data centres, for instance, very high volumes of IP packets and events from sensors at firewalls, network switches and routers and servers need to be analyzed and should detect attacks in minimal time, in order to limit the effect of the malicious activity over the IT infrastructure. Similarly, in the fraud department of a credit card company, payment requests should be processed online and need to be processed as quickly as possible in order to provide meaningful results in real-time. An ideal system would detect fraud during the authorization process that lasts hundreds of milliseconds and deny the payment authorization, minimizing the damage to the user and the credit card company

StreamCloud: An elastic and scalable data streaming system

Many applications in several domains such as telecommunications, network security, large scale sensor networks, require online processing of continuous data lows. They produce very high loads that requires aggregating the processing capacity of many nodes. Current Stream Processing Engines do not scale with the input load due to single-node bottlenecks. Additionally, they are based on static con?gurations that lead to either under or over-provisioning. In this paper, we present StreamCloud, a scalable and elastic stream processing engine for processing large data stream volumes. StreamCloud uses a novel parallelization technique that splits queries into subqueries that are allocated to independent sets of nodes in a way that minimizes the distribution overhead. Its elastic protocols exhibit low intrusiveness, enabling effective adjustment of resources to the incoming load. Elasticity is combined with dynamic load balancing to minimize the computational resources used. The paper presents the system design, implementation and a thorough evaluation of the scalability and elasticity of the fully implemented system