Heliophysics and Space Geophysics Seminar – D. Hathaway

Seminar:

Title: “Sunspot Cycle”

David H. Hathaway
NASA Ames Research Center, U.S.A.
http://solarscience.msfc.nasa.gov

Date: 29.04.2020 (WED), 15h (Brazilian official time)

Venue: This seminar will be online, via Zoom platform (instructions below).

Abstract:
The solar cycle is reviewed. The 11-year cycle of solar activity is characterized by the rise and fall in the numbers and surface area of sunspots. A number of other solar activity indicators also vary in association with the sunspots including; the 10.7 cm radio flux, the total solar irradiance, the magnetic field, flares and coronal mass ejections, geomagnetic activity, galactic cosmic ray fluxes, and radioisotopes in tree rings and ice cores. Individual solar cycles are characterized by their maxima and minima, cycle periods and amplitudes, cycle shape, the equatorward drift of the active latitudes, hemispheric asymmetries, and active longitudes. Cycle-to-cycle variability includes the Maunder Minimum, the Gleissberg Cycle, and the Gnevyshev-Ohl (even-odd) Rule. Short-term variability includes the 154-day periodicity, quasi-biennial variations, and double-peaked maxima. We conclude with an examination of prediction techniques for the solar cycle and a closer look at cycles 23 and 24.

(From https://link.springer.com/article/10.1007/lrsp-2015-4)

Galileo Space Solar Telescope Workshop

The deadline for subscribing to the GSST workshop is 15 feb. 2020. There is no registration fee (it’s fee now) and there will be foreign participants in fields of solar physics, magnetospheric physics and instrumentation for satellites (NASA, APL among other institutions).

People can participate presenting poster or just attending the event (without presenting anything).
It is a good opportunity for scientists, students and technicians from fields of space physics and space engineering.

Please see the news at the link below

SPATIAL GEOPHYSICAL SEMINARS – E. Tirado Bueno – 05.02.2020, 15h (english)

“Cosmic Ray Picture in Solar Phenomena”

Msc. Eduardo Tirado Bueno

Instituto Nacional de Astrofsica Optica y Electronica, Mexico

05.02.2020, 15h
Auditório CEA-II

Solar phenomena such as solar ares and solar energetic particles (SEP’s) events are suitable candidates to aect the galactic cosmic ray intensity which arrive continuously and isotropically to Earth, although their intensity varies depending on the activity of the Sun (11-year cycle). Studies of these eects can be carried out using the superposed epoch analysis (SEA) which provides a better visualization of small eects in a time series. In this talk I give an overview over some results on the analysis of the cosmic rays variations in the course of solar ares and SEP’s, through the solar cycle 24, in order to avoid solar inuences or geomagnetic disturbances, I only select events in which the Kp index is less than 4.0 for three consecutive days, that is, one day before and two days after the event. In the same way, events in which a Forbush decrease occurred during that three-day period are ignored.

“First Galileo Solar Space Telescope Workshop” – INPE, 16-18 de march 2020

Dear Postgraduate students,

INPE is organizing the First Galileo Solar Space Telescope Workshop, LIT, 16-18 de março de 2020. The Galileo Solar Space Telescope, GSST is a space mission which aims at the providing magnetic field measurements at the solar atmosphere, within the effort to better understand the Heliosphere. The mission also intends to provide measurements of the earth’s magnetosphere, geomagnetic field and solar irradiance. The main objective is to prepare to a phase 0/A study. However, the event is an excellent opportunity for students to present their work for the national and international participants that are expected to attend the meeting. It is also an excellent opportunity for students to learn how a scientific space mission is planned.

Submission of abstracts can be made at: http://www.inpe.br/gsst/registration.php. The deadline is January 24th, 2020.

More information at: http://www.inpe.br/gsst/

Visit of Prof. Randal Burns from Johns Hopkins University to INPE (schedule update)

Lecture: External Memory Systems for Data Science and Machine Learning
 
In modern computer architectures, the movement of data from storage or memory to the processor limits the performance and scale of scientific data analysis and machine learning. This bottleneck has grown much more acute as we use multicore processors  and GPUs.  This talk covers a decade of research in my lab that redesigned computer systems to move data through the memory hierarchy and applied these systems to make data science (graph analytics and sparse linear algebra) and machine learning (k-means and random forests) more efficient.
 
The lecture is self-contained and designed for the computational scientist that is familiar with using data science and machine learning programming tools.  It will lightly review computer science concepts, including the memory hierarchy, external memory algorithms, and non-uniform memory architectures.

Dia: 21 de Janeiro, 2 da tarde, no Auditório do CEA II.

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Lecture: Reproducible Data Science with Gigantum

Best practices in software engineering have defined programming environments for reproducibility and code sharing based on a combination of versioning (git), containers (docker), and tools for documentation, continuous integration and code review. Data science development environments (Jupyter labs and R Studio) have become literate, mixing code and markdown, but they do not provide meaningul support for versioning and sharing.

This talk presents the Gigantum open-source data science work environment that automates the best practices and skill-intensive tasks that are crucial to good data science. The data scientist works in familiar tools, such as RStudio and Jupyter and Gigantum makes sure that all aspects of a data science project–code, data, and environment–portable, shareable, and continuously versioned. Gigantum runs on locally (on laptops) as well as the cloud so that the data scientist can work without incurring cloud computing costs.  Users can collaborate in groups or on public projects, exploring by launching on the cloud, contributing in their own branch, or customizing with new code or private data in their own fork.

Dia: 23 de Janeiro, 2 da tarde, no Auditório do CEA II.

A mesma palestra será apresentada no CPTEC no dia 22 de Janeiro, 2 da tarde.

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Bio: Randal Burns is a Professor of Computer Science at Johns Hopkins University and has served as Department Chair since 2018. Randal’s research has pushed the scalability limits of data science based on emergent storage technologies.  This has ranged from engineering file systems for storage area networks in the 1990s, building scientific Web services on scale-out cloud storage in the 2000s, and developing graph and sparse-matrix engines for machine learning in the 2010s.  His work has been inspired by high-throughput science, including numerical simulations for turbulence, neuroscience microscopy, and observational astronomy.

Randal earned his PhD in Computer Science from the University of California Santa Cruz in 2000 and a BS in Geophysics from Stanford in 1993. Prior to joining the faculty at Johns Hopkins in 2002, he was a Research Staff Member at IBM’s Almaden Research Center where he won and Outstanding Innovation Award. Randal is a recipient of the NSF Career Award and was a DOE Early Career Principal Investigator. He is a Kavli Fellow and served as a member of the Defense Science Study Group class of 2012-2013.

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Course: Parallel Programming for Data Science

Course: Parallel Programming for Data Science

This course will introduce the data scientist familiar with Python to the parallel programming tools to tackle the analysis of massive data sets. It will cover fundamental concepts in parallelism, including Amdahl’s law, weak and strong scaling, and shared and distributed memory models. The student will then apply these concept in practical programming assignments in the cloud programming frameworks dask and python and for GPU accelerated data analysis in pytorch.

Datas: 13 a 16 de Janeiro, 2 – 4 da tarde, no Auditório do LabGEO.

Este curso será hands-on, por favor traga seu laptop. Temos vagas limitadas. Peço aos interessados que se inscrevam em https://forms.gle/S4MM63yEAs9k6GEA9 — a confirmação será enviada por e-mail até sábado, dia 11.

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Lecture: External Memory Systems for Data Science and Machine Learning

In modern computer architectures, the movement of data from storage or memory to the processor limits the performance and scale of scientific data analysis and machine learning. This bottleneck has grown much more acute as we use multicore processors and GPUs. This talk covers a decade of research in my lab that redesigned computer systems to move data through the memory hierarchy and applied these systems to make data science (graph analytics and sparse linear algebra) and machine learning (k-means and random forests) more efficient.

The lecture is self-contained and designed for the computational scientist that is familiar with using data science and machine learning programming tools. It will lightly review computer science concepts, including the memory hierarchy, external memory algorithms, and non-uniform memory architectures.
Dia: 21 de Janeiro, 2 da tarde, no Auditório do CEA II.

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Lecture: Reproducible Data Science with Gigantum

Best practices in software engineering have defined programming environments for reproducibility and code sharing based on a combination of versioning (git), containers (docker), and tools for documentation, continuous integration and code review. Data science development environments (Jupyter labs and R Studio) have become literate, mixing code and markdown, but they do not provide meaningul support for versioning and sharing.

This talk presents the Gigantum open-source data science work environment that automates the best practices and skill-intensive tasks that are crucial to good data science. The data scientist works in familiar tools, such as RStudio and Jupyter and Gigantum makes sure that all aspects of a data science project–code, data, and environment–portable, shareable, and continuously versioned. Gigantum runs on locally (on laptops) as well as the cloud so that the data scientist can work without incurring cloud computing costs. Users can collaborate in groups or on public projects, exploring by launching on the cloud, contributing in their own branch, or customizing with new code or private data in their own fork.
Dia: 23 de Janeiro, 2 da tarde, no Auditório do CEA II.

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Bio: Randal Burns is a Professor of Computer Science at Johns Hopkins University and has served as Department Chair since 2018. Randal’s research has pushed the scalability limits of data science based on emergent storage technologies. This has ranged from engineering file systems for storage area networks in the 1990s, building scientific Web services on scale-out cloud storage in the 2000s, and developing graph and sparse-matrix engines for machine learning in the 2010s. His work has been inspired by high-throughput science, including numerical simulations for turbulence, neuroscience microscopy, and observational astronomy.

Randal earned his PhD in Computer Science from the University of California Santa Cruz in 2000 and a BS in Geophysics from Stanford in 1993. Prior to joining the faculty at Johns Hopkins in 2002, he was a Research Staff Member at IBM’s Almaden Research Center where he won and Outstanding Innovation Award. Randal is a recipient of the NSF Career Award and was a DOE Early Career Principal Investigator. He is a Kavli Fellow and served as a member of the Defense Science Study Group class of 2012-2013.

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AAS Selections from 2019: A Giant Planet Around an Evolved Binary

A paper led by a previous PhD student of the Astrophysics Course of the National Institute of Space Research (INPE) and co-authored by INPE faculty members was selected as one of the 2019 highlights of the American Astronomical Society publications, which include the prestigious journals The Astrophysical Journal and The Astronomical Journal.

Selections from 2019: A Giant Planet Around an Evolved Binary

Heliophysics Seminar: “Status and Progress in European / German Space Weather Service”

Dr. Frank Jansen, German Aerospace Center DLR, Institute of Space Systems Bremen, Germany 

05.07.2019 (Friday), 3pm, Sérgio Sobral Auditorium (former IAI building)

Abstract
This talk will summarize the last decade activities of European and German space weather service. It will also describe the progress related to a space weather satellite made in Europe. Moreover the space weather service at DLR in Bremen related to the GMDN (Global Muon Detector Network) cosmic ray muon data including usage of Brazilian Santa Maria Scintillator Telescope (SMST) and space weather traffic signal promotion activities will be described. The cosmic ray detector MEDIPIX / TIMEPIX will be sketched for future space weather measurements in deep space explorations.      

Heliophysics Seminar: “Progress and prospect of Space Science Satellite program in China”

Prof. Chi Wang
Director General – National Space Science Center, CAS, China

19.06.2019 – Wed.
3pm, Fernando de Mendonça Auditorium @ LIT (Entrance A)

“Wang Chi, Research Professor, Director General of National Space Science Center, Chinese Academy of Sciences, the Chief Scientist of the Strategic Pioneer Program on Space Science (II), Deputy head of the aerospace and deep space working group of the science and technology committee, Chinese National Space Administration, the Winner of the National Outstanding Youth Fund from CNSF. He has been engaged in space physics research for a long time and has published more than 170 papers in peer-reviewed academic journals. “