
Neural Network Approach for Halo Properties Analysis
Explore how neural networks are used to analyze halo properties in galactic environments. The study focuses on finding observable environmental measures of halo properties and achieving high accuracy predictions using ML techniques.
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Presentation Transcript
Finding Observable Environmental Measures of Halo Properties Using Neural Networks Haley Bowden Collaborators: Peter Behroozi, Andrew Hearin ML-IAP/CCA-2023 Robotham et al. 2012
????= ?(?,???????????) Train a neural network to serve as a function from our input vector to our desired output Consider what information about the environment is available Input Vector ? ??? Condense that information into an input vector
High accuracy from stellar mass alone High accuracy from environmental information Comparing ML predictions to true values from the simulation *Check out: Bowden, Behroozi & Hearin 2023 (arxiv:2307.07549)
Halo Concentration (PRELIMINARY) But what happens when we expand to secondary halo properties? Lower accuracy (but there is information there!)
Conclusions: Conclusions: A neural network allows us to capture information about the galactic environment as it pertains to halo mass and concentration. A halo catalog for the GAMA Survey is in the works! Density of galaxies in a redshift slice for one survey region ??