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| Paper: |
Machine Learning for Scientific Discovery |
| Volume: |
527, Astronomical Data Analysis Software and Systems XXIX |
| Page: |
205 |
| Authors: |
Surana, S.; Wadadekar, Y.; Oberoi, D. |
| Abstract: |
Machine Learning algorithms are good tools for both classification and
prediction purposes. These algorithms can further be used for
scientific discoveries from the enormous data being collected in our
era. We present ways of discovering and understanding astronomical
phenomena by applying machine learning algorithms to data collected
with radio telescopes. We discuss the use of supervised machine
learning algorithms to predict the free parameters of star formation
histories and also better understand the relations between the
different input and output parameters. We made use of Deep Learning
to capture the non-linearity in the parameters. Our models are able to
predict with low error rates and give the advantage of predicting in
real time once the model has been trained. The other class of machine
learning algorithms viz. unsupervised learning can prove to be very
useful in finding patterns in the data. We explore how we use such
unsupervised techniques on solar radio data to identify patterns and
variations, and also link such findings to theories, which help to
better understand the nature of the system being studied. We highlight
the challenges faced in terms of data size, availability, features,
processing ability and importantly, the interpretability of
results. As our ability to capture and store data increases, increased
use of machine learning to understand the underlying physics in the
information captured seems inevitable. |
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