Machine Learning for Scientific Discovery Article Swipe
Shraddha Surana
,
Yogesh Wadadekar
,
Divya Oberoi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.12712
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.12712
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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Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- http://arxiv.org/abs/2102.12712
- https://arxiv.org/pdf/2102.12712
- OA Status
- green
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3173132254
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3173132254Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2102.12712Digital Object Identifier
- Title
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Machine Learning for Scientific DiscoveryWork title
- Type
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book-chapterOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-02-25Full publication date if available
- Authors
-
Shraddha Surana, Yogesh Wadadekar, Divya OberoiList of authors in order
- Landing page
-
https://arxiv.org/abs/2102.12712Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2102.12712Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2102.12712Direct OA link when available
- Concepts
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Interpretability, Machine learning, Artificial intelligence, Computer science, Online machine learning, Class (philosophy), Unsupervised learning, Instance-based learning, Computational learning theoryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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