home

=**Welcome to the Astronomy Pattern Recognition Wiki!**= Managed by: William P. Armentrout, Graduating Senior: Westminster College

Have you ever wondered how the Universe began? If you flew out into space, would you ever reach an end? What are Black Holes? Is there life on other worlds?

Throughout history, we have gazed out into the night sky and wondered what was beyond our world. Scientists have spent centuries compiling theories for how the Universe works, and now is no different! Below is a video giving a snapshot of some hot topics in Astronomy now. If you don't feel inquisitive and awe-struck after watching it, nothing will get you excited on the topic!

media type="youtube" key="D8zZZZzppIM" height="315" width="560"

The problem now is that with the multitude of satellites, telescopes, etc. in operation, we have more data than can be interpreted by scientists [1]. The obvious idea would be to write a computer program to go through large data sets in a fraction of the time it takes people. However, standard computing systems are not nearly as good at pattern recognition as humans. Serial computing (the string of instructions used in the computer you're reading this on!) cannot hold a candle to your brains neural network architecture! Your brain's neurons are set up in parallel, and can easily determine pattern in the world around you.

Some scientists have found creative ways around this lack of professionals, opening up the data to everyday Astronomy enthusiasts. These projects capitalize on our ability to recognize patterns in data sets after only minimal instruction. Specifically, the "Zooniverse" projects have been highly successful (discussed further in Citizen Science).

How can this be replicated? One possibility is through something called "Artificial Neural Networks," or ANNs for short. ANNs make use of parallel computing systems in order to mimic the human brain's pattern recognition abilities (discussed further in Neural Networks).

Linked articles and abstracts related to this topic are available in Further Reading. media type="custom" key="16913472"