Circling the Gap: Analyzing the Clustering of Lyman Alpha Emitting Galaxies and Improving Photometric Redshifts for LSST Cosmology
Invited Talk: Eric Gawiser
I will describe two recent advances that bridge the gap between galaxy properties and large scale structure, one in each direction. The One hundred deg^2 DECam Imaging in Narrowbands (ODIN) has obtained the widest deep narrow-band imaging ever obtained and is using it to select over 100,000 Lyman Alpha Emitting (LAE) galaxies at z=2.4, 3.1, and 4.5. Firestone et al. (2024) describe the LAE samples in the 9 square degree COSMOS field, and Herrera et al. (2024) analyze their angular clustering to infer linear bias factors, dark matter halo masses, and halo occupation fractions. Bridging back to LSS, in a recent paper from the LSST Dark Energy Science Collaboration, Moskowitz et al. (2024) implemented realistic spectroscopic incompleteness for photometric redshift training sets and showed that augmenting these with simulated galaxies that are dimmer, bluer, and higher in redshift yields significantly better photometric redshifts. Moskowitz et al. (2023) described an optimal binning strategy and introduced the use of Neural Network Classifiers to remove the least trustworthy galaxies from the sample, resulting in significant improvements to the cosmological parameter figure-of-merit expected for LSST.
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Friday
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