SunPy Environment Application

This presentation at Python in Astronomy workshop talks about the development of SEA - SunPy Environment Application. SEA is a software to gather space weather data from different instruments.

Transcript

1. High Performance Intelligent Decision Systems Space Weather Team Sunpy Environment Application – SEA Prof. André Leon S. Gradvohl, Dr. gradvohl@ft.unicamp.br
2. Space Weather team Agenda • Quick introduction to Space Weather • Motivation for developing SEA • SEA main features • BTW, who we are? 2
3. Space Weather team Quick introduction to Space Weather • The Sun is an enormous sphere of extremely hot, largely ionized gas (plasma), with internal convective motion that generates a magnetic field via a dynamo process. • The structure of the Sun: 3
4. Space Weather team Quick introduction to Space Weather • Three solar phenomena account for most of the space weather we experience: • coronal holes • solar flares • CMEs 4
5. Space Weather team Quick introduction to Space Weather • It is the Sun’s changing magnetic field that drives solar activity. • One of the results of the Solar Activity are the Sunspots. • Sunspots are temporary phenomena on the photosphere of the Sun that appear as dark spots compared to surrounding regions. They are areas of reduced surface temperature caused by concentrations of magnetic field flux that inhibit convection. 5 Credit: SOHO/NASA/ESA
6. Space Weather team Quick introduction to Space Weather • Coronal holes are result of solar activity too. • Coronal holes are areas where the Sun's corona is colder, hence darker, and has lower-density plasma than average because there is lower energy and gas levels. • Coronal holes allow the solar wind to flow freely away from the Sun, unhindered by solar magnetic fields. 6 Credit: SOHO/NASA/ESA
7. Space Weather team Quick introduction to Space Weather • When magnetic fields, pointing in opposite directions, release energy by interacting with and destroying each other, then solar flares occur. • Flares release a tremendous amount of electromagnetic radiation. • A typical flare lasts for 5 to 10 minutes and releases a total amount of energy equivalent to that of perhaps a million hydrogen bombs. • In some cases, immense quantities of coronal material – mainly protons and electrons – may also be ejected at high speeds (500–1000 kilometers per second) into interplanetary space. We call this phenomena a coronal mass ejection (CME). 7 Credit: DOI: 10.1098/rsta.2008.0161
8. Space Weather team Quick introduction to Space Weather 8
9. Space Weather team Quick introduction to Space Weather • Coronal mass ejections (CMEs) are huge explosions of magnetic field and plasma from the Sun's corona. • When CMEs impact the Earth’s magnetosphere, they are responsible for geomagnetic storms and enhanced aurora. • CMEs originate from highly twisted magnetic field structures, or “flux ropes”. • When these flux ropes erupt from active regions on the Sun (regions associated with sunspots and very strong magnetic fields), they are often accompanied by large solar flares; eruptions from quiet regions of the Sun, such as the “polar crown” filament eruptions, sometimes do not have accompanying flares. 9 Credit: NASA/ESA
10. Space Weather team Quick introduction to Space Weather • Space weather effects on earth 10 Credits: NASA
11. Space Weather team Motivation for developing SEA • Predicting the occurrence of solar events in advance can mitigate the effects of space weather on terrestrial activities. • However, for this task it is necessary to collect a large volume of data – in several formats, in different periods of time – to use in data mining algorithms. • Collecting this data can become a very labor-intensive task, since it requires variety and volume to test and validate the algorithms for Space Weather forecasting. 11
12. Space Weather team Motivation for developing SEA • Therefore, we needed software that made it easier to fetch data from multiple instruments. • This software should allow the specification of dates and times, as well as allow the download of images, light curves and other important data for our research. • Thus, we implemented the SEA 12
13. Space Weather team SEA main features 13 Create Maps from different Observatories, with specific instruments and measurements. Create light curves from different instruments.
14. Space Weather team SEA main features - Maps 14 We specify the Start and End dates; Start and End times; the Observatory; the instrument; and measurement. The result is a list of images in FITS format. We can render these images later.
15. Space Weather team SEA main features - Maps 15 SEA rendered these images from downloaded files in FITS format.
16. Space Weather team SEA main features - LightCurves 16 We can specify similar information for light curves generation. We can generate raw data in “comma separated values” format.
17. Space Weather team SEA main features - LightCurves 17 SEA rendered the graphs from a specific instrument.
18. Space Weather team Use of data gathered by the SEA 18 We may use the raw data gathered by the SEA to perform tests, validations and forecasts in data mining algorithms.
19. Space Weather team Use of data gathered by the SEA 19 We may use the images gathered by the SEA to perform image processing for filament detections.
20. Space Weather team BTW, who we are? • HighPIDS - High Performance Intelligent Decision Systems is a research group dedicated to the design and implementation of decision support systems based on intelligent algorithms to work on high-performance computer architectures. • http://highpids.ft.unicamp.br • The HighPIDS conducts research on two fronts: • Data streaming algorithms and systems • Space weather forecasting using data mining algorithms • HighPIDS is linked to the São Paulo Astronomy Network – SPAnet • http://www.iag.usp.br/spanet/?q=en 20
21. Space Weather team BTW, who we are? • In Space Weather field, some of the works we are working or worked before: • Contributions to the prediction of solar flares C, M and X using neural networks, statistical and image analysis. • Analysis of the Multi-layer Perceptrons applied to solar flares forecasting • Solar explosion forecasting through time-series of X-ray fluxes via MLP neural nets • Predicting Solar Activity via Artificial Neural Networks Applied to Flow Data of X-Rays • Feature selection for Solar Flare forecast based on Evolutionary Algorithms • Study of solar flares using data clustering 21
22. High Performance Intelligent Decision Systems Space Weather Team Thank you! For more information, please send email to: gradvohl@ft.unicamp.br 22