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The document introduces CondXD, a new algorithm that generalizes extreme deconvolution (XD) to handle non-Gaussian features in density estimation for astronomical data. By utilizing neural networks and Gaussian mixture models, CondXD effectively deconvolves conditional distributions, outperforming traditional binning methods in both accuracy and speed in quasar classification tasks. The method has the potential to enhance the analysis of noisy astronomical observations and facilitate new discoveries.

Author

deyoc31487

Language

EN