A3D3 Outreach Activities
A3D3 Institutions perform outreach within their communities:
- University of Minnesota
- The University of Minnesota has an ongoing partnership with Galtier
Elementary, a local elementary school in St. Paul, Minnesota. Our group
works with Peter Ratzloff, the science teacher at Galtier, and his classes
which span kindergarten to 5th grade. In the pictures above, we show a day
where we put together telescopes with a 3rd grade classroom, and we will be
using them to observe the sky at future visits.
- The University of Minnesota has also hosted the ZTF Summer School the past
two summers. The School and its staff are dedicated to the education of
undergraduates, graduate students and postdocs in the fields of physics,
astronomy, and data science. Open to the entire astronomical community, and
free to attend, it is an intensive one week workshop with training by
experts within the collaboration. During the summer of 2021, the online
event focused on variable stars; during the summer of 2022, the hybrid event
focused on multi-messenger astronomy; during the summer of 2023, the school
will focus on transient, time-domain astronomy. The school provides a
collaborative learning experience, provided by both theoretical and
experimental leaders in the field. It seeks to help students understand both
the technology behind the surveys and the scientific avenues they explore,
all the while giving a hands-on introduction to the latest techniques in
scientific programming and data analysis.
- MIT
- MIT hosted the LIGO-Virgo-KAGRA low latency face to face meeting the week of October 10th. As an offshoot from this meeting, LVK members applying machine learning to gravitational-wave data met to discuss ongoing development. This included discussions on noise subtraction, event detection, and parameter estimation using machine learning. Tools developed to simplify their deployment in the context of A3D3 (ML4GW, HERMES, etc.) were presented to participants. The development and demonstration of a mock data challenge where the performance of these machine learning toolkits will be validated were also discussed.
