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Abstract:
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Utilizing galaxy cluster abundance in precision cosmology requires large, well-defined cluster samples and robust mass measurement methods. In addition, modern cluster measurement techniques are expected to place a strong emphasis on efficiency and automation, as the wealth of detailed cluster data is expected to greatly increase with current and upcoming surveys such as DES, LSST, WFIRST, Euclid, and eROSITA. In this talk, I will discuss how we can leverage the use of deep learning models to infer dynamical cluster masses from spectroscopic samples with high precision and computational efficiency. I will demonstrate the ability of Convolutional Neural Networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters, using projected galaxies, with remarkably low bias and scatter. I will then discuss the performance of these methods relative to other leading analytic and machine learning dynamical mass estimators. Lastly, I will discuss our ongoing work in quantifying uncertainties in CNN mass predictions and our applications on spectroscopic datasets from the SDSS and GAMA surveys.
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