19-23 October 2020
Europe/Prague timezone
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Training a Machine Learning Classifier to Predict Quasar Photometric Redshifts

20 Oct 2020, 13:45


Enda Farrell (Nicolaus Copernicus Astronomical Center (CAMK))


In the era of massive all-sky surveys, calculating spectroscopic redshifts (Spec-Z) is time-consuming and costly. Instead, broadband photometry may be used as a proxy for spectrographic measurements. We train an off-the-shelf classifier to estimate photometric redshifts (Photo-Z) from SDSS Quasar imaging data. We outline the technique of cross-validation to reduce bias and variance in the model and improve calibration. Reasons for so-called catastrophic prediction failures are discussed and popular packages such as scikit-learn and AstroML examined. The modern Deep Learning package 'fastai' is introduced along with a survey of Neural Net (NN) approaches. Despite Machine Learning being something of a dark art, surprisingly useful results may be extracted using modern software packages.

Primary author

Enda Farrell (Nicolaus Copernicus Astronomical Center (CAMK))

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