Events Calendar

Astro Lunch Seminar: Brett Andrews

This is a past event.

Leveraging Statistics and Machine Learning for Probing Galaxy Evolution and Measuring Galaxy Distances

I will talk about using statistics and machine learning methods, such as Gaussian processes and deep learning, to extract new insights from archival data.  First, I will discuss our work on using galaxies similar to the Milky Way to probe properties of the Milky Way that are otherwise not accessible: namely, its UV-to-IR spectral energy distribution and how frequently its supermassive black hole is actively accreting material.  Second, I will present state-of-the-art results using a novel neural network architecture, called a deep capsule network, to estimate photometric redshifts of galaxies directly from their images.  I will discuss a use case for finding faint satellite galaxies to help identify a probe for dark matter halo assembly history.  Finally, I will highlight some recent work on recalibrating photometric redshift probability distribution functions that could prove critical for optimizing cosmological results from upcoming surveys.

Dial-In Information

Department members, see email for access.
 Non-department members, contact paugrad@pitt.edu for access or to be added to the weekly newsletter.

Friday, May 20 at 12:00 p.m. to 1:00 p.m.

Allen Hall, 321
3941 O'Hara Street, Pittsburgh, PA 15213

Astro Lunch Seminar: Brett Andrews

Leveraging Statistics and Machine Learning for Probing Galaxy Evolution and Measuring Galaxy Distances

I will talk about using statistics and machine learning methods, such as Gaussian processes and deep learning, to extract new insights from archival data.  First, I will discuss our work on using galaxies similar to the Milky Way to probe properties of the Milky Way that are otherwise not accessible: namely, its UV-to-IR spectral energy distribution and how frequently its supermassive black hole is actively accreting material.  Second, I will present state-of-the-art results using a novel neural network architecture, called a deep capsule network, to estimate photometric redshifts of galaxies directly from their images.  I will discuss a use case for finding faint satellite galaxies to help identify a probe for dark matter halo assembly history.  Finally, I will highlight some recent work on recalibrating photometric redshift probability distribution functions that could prove critical for optimizing cosmological results from upcoming surveys.

Dial-In Information

Department members, see email for access.
 Non-department members, contact paugrad@pitt.edu for access or to be added to the weekly newsletter.

Friday, May 20 at 12:00 p.m. to 1:00 p.m.

Allen Hall, 321
3941 O'Hara Street, Pittsburgh, PA 15213

Topic

Research

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