Dynamical clusters masses with Machine Leaning

by Mr Matthew Ho (Carnegie Mellon University)

Friday 14 Jun 2019, 10:00 → 11:00 Europe/Berlin

seminar room first floow (University Observatory)

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 begin with a brief introduction to deep learning models and their applications in astronomy and cosmology. Then, 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.