In my opinion, the best– and simplest– way to gain some exposure to real-life science research is to participate in citizen science programs. Why? If you don’t have access to official research programs or mentors, citizen science programs can give you a good grounding in the field you want to study further. Though some may argue classifying images isn’t ‘real’ work, it forms the basis of much of the work scientists do. By participating in citizen science, you are contributing to ongoing research!
Moreover, work you do can lead to an independent research project. Not only can you work with the data you’ve analyzed, because it hasn’t been analyzed by anyone else, but you can also use your citizen science foundation to explore specialized questions within the field you are contributing to. You do not need a mentor or money to do ‘real’ science.
With that explained, let me get to the crux of this post. Today I’ll be discussing one of the largest citizen science data repositories online: the Zooniverse, and specifically their project ‘Gravity Spy’.
About 100 years ago, Albert Einstein predicted the existence of ripples in spacetime that he dubbed ‘gravitational waves’, as a consequence of his theory of general relativity. An experiment called LIGO (Laser Interferometer Gravitational-wave Observatory) was designed to detect these. I won’t be explaining the engineering and progress of LIGO in this post, but this video should cover the basics.
What is relevant to us is that LIGO is extremely sensitive. It can be triggered by the vibrations caused by a truck passing nearby, a phone ringing, and even natural seismic vibrations. Some of these signals can mimic those from real astrophysical objects, decreasing the sensitivity of the detector towards real gravitational waves. This is why it’s so important to identify and sort out these ‘glitches’. Many of these are anomalous and must be identified by the human eye, as opposed to computer algorithms. This is where Gravity Spy comes in. By classifying images into different glitch morphologies (types), citizen scientists are training computers to understand and classify these glitches with greater accuracy.
So if improving methods of detecting the gravitational signatures from neutron stars, pulsars, and black holes appeals to you, get started with Gravity Spy.