Bringing Manifold Learning and Dimensionality Reduction to SED Fitters
Published in Astrophysical Journal (ApJ), 2019
Recommended citation: Hemmati et al. 2019 https://ui.adsabs.harvard.edu/abs/2019ApJ...881L..14H/exportcitation
Published in Astrophysical Journal (ApJ), 2019
Recommended citation: Hemmati et al. 2019 https://ui.adsabs.harvard.edu/abs/2019ApJ...881L..14H/exportcitation
Published in Astrophysical Journal (ApJ), 2020
Recommended citation: Chartab et al. 2020 https://ui.adsabs.harvard.edu/abs/2020ApJ...890....7C/exportcitation
Published in Astrophysical Journal (ApJ), 2020
Recommended citation: Darvish et al. 2020 https://ui.adsabs.harvard.edu/abs/2020ApJ...892....8D/exportcitation
Published in Astrophysical Journal (ApJ), 2020
Recommended citation: Hemmati et al. 2020 https://ui.adsabs.harvard.edu/abs/2020ApJ...896L..17H/exportcitation
Published in Astrophysical Journal (ApJ), 2020
Recommended citation: Shahidi et al. 2020 https://ui.adsabs.harvard.edu/abs/2020ApJ...897...44S/exportcitation
Published:
In this talk I presented my work on the estimation of the Neutral hydrogen and dark matter bias and the effect of the extended Limber approximation, instead of the usual one. Basically, the main idea is to check whether a common estimator for quantifying deviation of modified gravity from general relativity, holds even under the scale dependent bias. ($bias(k)$)
Published:
In this talk I presented my work on the how to find the massive and evolved galaxies in the multiwavelength catalogs using several methods found in the literature.
Published:
In this talk I presented my work on the how to find the massive and evolved galaxies in the multiwavelength catalogs using several methods found in the literature.
Published:
In this talk I presented my work on the how to find the massive and evolved galaxies in the multiwavelength catalogs using several methods found in the literature.
Online/in-Person Graduate course, University of California, Riverside, Department of Physics and Astronomy, 2019
This is the webpage for “The foundation of applied machine learning” for Spring 2019 by Prof. Bahram Mobasher. You can find the related materials (homeworks, codes, jupyter notebooks, …) under the link. You can get/clone the repository of this course from my github page as well.
Online/in-Person Graduate course, University of California, Riverside, Department of Physics and Astronomy, 2019
This is the webpage for “The foundation of applied machine learning” for Summer 2019 by Prof. Bahram Mobasher. You can find the related materials (homeworks, codes, jupyter notebooks, …) under the link. You can get/clone the repository of this course from my github page as well.