Modeling quasar variability through self-organizing map-based process
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916310437330944 |
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| author | Cvorovic-Hajdinjak, Iva |
| author_facet | Cvorovic-Hajdinjak, Iva |
| contents | Conditional Neural Process (QNPy) has shown to be a good tool for modeling quasar light curves. However, given the complex nature of the source and hence the data represented by light curves, processing could be time-consuming. In some cases, accuracy is not good enough for further analysis. In an attempt to upgrade QNPy, we examine the effect of the prepossessing quasar light curves via the Self-Organizing Map (SOM) algorithm on modeling a large number of quasar light curves. After applying SOM on SWIFT/BAT data and modeling curves from several clusters, results show the Conditional Neural Process performs better after SOM classification. We conclude that SOM classification of quasar light curves could be a beneficial prepossessing method for QNPy. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_02843 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling quasar variability through self-organizing map-based process Cvorovic-Hajdinjak, Iva Instrumentation and Methods for Astrophysics Conditional Neural Process (QNPy) has shown to be a good tool for modeling quasar light curves. However, given the complex nature of the source and hence the data represented by light curves, processing could be time-consuming. In some cases, accuracy is not good enough for further analysis. In an attempt to upgrade QNPy, we examine the effect of the prepossessing quasar light curves via the Self-Organizing Map (SOM) algorithm on modeling a large number of quasar light curves. After applying SOM on SWIFT/BAT data and modeling curves from several clusters, results show the Conditional Neural Process performs better after SOM classification. We conclude that SOM classification of quasar light curves could be a beneficial prepossessing method for QNPy. |
| title | Modeling quasar variability through self-organizing map-based process |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2407.02843 |