Studying Black Holes With Python

Black holes are among the most fascinating objects in our universe, with hundreds of hours of astronomical observations available in public archives around the world. In this talk I will give a brief overview of why and how we study black holes. I will show how python helps us make sense of our data, leverage modern data analysis methods efficiently and help us understand how black holes form and the role they play in our universe.

Transcript

1. Black Holes Studying with Python Daniela Huppenkothen NYU Center for Data Science ! Tiana_Athriel " dhuppenkothen
2. What?
3. Why? Was Einstein right?
4. How?
5. How?
6. star black hole
8. The sky looks different at different wavelengths
10. 12 Spectra Wavelength-1 Brightness
11. (and other objects) Black holes vary with time!
12. credit: NASA/DOE/Fermi LAT Collaboration
13. credit: NASA/DOE/Fermi LAT Collaboration
14. Time-Domain Astronomy The era of is here!
15. 10 million alerts per night!
16. 10 million alerts per night! 3200 Mpx camera!
17. credit: SKA Organisation
18. Time [years] Brightness credit: Grinberg et al, 2013
19. Astronomy Software observatory pipeline
20. ✦ Individual/small-group efforts ✦ Mostly private/not open-source ✦ Untested + undocumented ✦ Fear of being scooped Astronomy Software
21. Does it matter?
22. ✦ Reproducibility Does it matter?
23. ✦ Reproducibility ✦ Transparency Does it matter?
24. ✦ Reproducibility ✦ Transparency ✦ Reusability Does it matter?
25. ✦ Reproducibility ✦ Transparency ✦ Reusability ✦ Transfers to other fields? Does it matter?
26. Why Python? credit: Jake Vanderplas
27. Why Python? credit: Jake Vanderplas
28. Why Python? credit: Jake Vanderplas
29. There is no reliable open- source software for time series analysis in (X-ray) astronomy
30. Wouldn’t it be cool to have an open-source time series software for (X-ray) astronomy? #AstroHackWeek
31. Stingray!* *github.com/StingraySoftware/stingray
32. Do you want this?
33. Do you want this? Sure!*
34. Do you want this? Sure!* *but we have grants and papers and stuff to write, too
35. Stingray
36. ✦ 5 core developers Stingray
37. ✦ 5 core developers ✦ 3 GSoC students Stingray
38. ✦ 5 core developers ✦ 3 GSoC students ✦ community buy-in Stingray
39. Stingray
40. ✦ library + API Stingray
41. ✦ library + API ✦ convenience functions + tutorials Stingray
42. ✦ library + API ✦ convenience functions + tutorials ✦ GUI Stingray
43. What have we learned so far?
44. Scientists and programmers have a lot to learn from each other.
45. Writing better software has made us more confident in our scientific results.
46. Small, focused workshops are essential for open-source efforts in science.
47. Start small, scope carefully.
48. Decide coding/testing standards before starting to code!
49. Questions?