How is Big Data applied to astronomical surveys?
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Big Data is revolutionizing astronomical surveys by handling the massive volumes of diverse data generated. Astronomical studies, like photometric reverberation mapping, utilize Big Data to measure time delays between light curves in different bands to determine the accretion disc size in Active Galactic Nuclei . Machine learning algorithms, such as Artificial Neural Networks and Random Forest, are applied using Apache Spark to process large astronomical datasets for tasks like photometric redshift estimation . The rise of Big Data in astronomy is evident from the exponential growth in data size, with archives containing over 100 terabytes of data and billions of detected sources . Polystore systems are proposed to integrate heterogeneous data in astronomy, addressing the challenge of managing unstructured data from various sources .
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| Papers (5) | Insight |
|---|---|
| Big Data is applied in astronomical surveys through large digital sky archives with terabytes of data, billions of detected sources, and exponential growth in information due to advancing technology. | |
18 Dec 2022 | Big Data is utilized in astronomical surveys like LSST to model photometric reverberation mapping data, enabling accurate measurement of quasar accretion disk sizes and black hole masses. |
| Big Data is utilized in astronomy through large-scale data archives, managing diverse data types from various sources, enabling efficient data integration and access for astronomical surveys. | |
| Big Data is utilized in astronomical surveys like LSST to model photometric reverberation mapping data, enabling accurate measurement of quasar accretion disk sizes and potential standard candle use. | |
01 Jan 2022 5 Citations | Big Data is utilized in astronomical surveys through machine learning algorithms like ANN, Random Forest, Linear Regression, and Decision Tree on Apache Spark for efficient analysis of massive observational data. |
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