Von Braun Astronomical Society will celebrate Astronomy Day on September 23rd in person at Monte Sano State Park! Beginning at 1:00 p.m., events will include: solar viewing, mini-planetarium shows, rockets, vacuum-chamber demonstrations of what it’s like in space, and several other fun STEAM activities.
Astronomy Day is FREE and open to the public!
Participating Organizations
Daytime Planetarium Show Schedule
Free-admission planetarium shows will be hosted through the day. Show times will be 1:30pm, 2:30pm, 3:30pm, and 4:30pn.
7:30PM – Keynote Speaker:
MSFC Solar Sounding Rocket History, Present and
Future!
Sounding rockets have been an important mechanism for testing new methods to observe the Sun for several decades and have played a significant role in advancing our understanding of our backyard Star. Some of the recent research efforts performed by our team at Marshall Space Flight Center include missions like the Hi-resolution Coronal Imager (Hi-C), the Chromospheric LAyer SpectroPolarimeter (CLASP) and the Marshall Grazing Incidence X-ray Spectrometer (MaGIXS). These instruments and the data collected from several successful ~5-minute suborbital flights have and continue to help us answer questions about solar atmospheric dynamics, magnetic field, heating and more. In this talk we will cover some important solar sounding rocket history highlights, results from some recent missions and some exciting missions on the horizon.
Genevieve Vigil earned a B.S. in Electrical Engineering from the University of Washington, Seattle, and a Ph.D. in EE from the University of Notre Dame in 2017. She turned toward Solar Physics as a NASA Post-Doctoral Fellow in the Solar Sounding Rocket group here at Marshal Space Flight Center from 2017-2020 and is currently a Civil Servant Researcher in the same group. Research interests include novel instruments, optics and detectors for high resolution imaging and spectroscopy, EUV and X-ray solar applications including solar atmosphere dynamics, magnetic field studies, and applications of machine learning.