KICP Colloquia
KICP Wednesday Colloquia - Usually Wednesdays, 3:30 PM, ERC 161, unless otherwise specified. Reception starts at 4:30 PM in ERC 161. For more information visit the KICP website.

Past KICP Colloquia
DateTalk TitleSpeaker
March 1, 2017 cancelledDigging into the Large Scale Structure of the UniverseShirley Ho, Berkeley Lab/ BCCP / Carnegie Mellon
February 8, 2017 cancelledDigging into the Large Scale Structure of the UniverseShirley Ho, Berkeley Lab/ BCCP / Carnegie Mellon
January 18, 2017The Milky Way's Dark CompanionsAlex Drlica-Wagner, Fermilab

The Milky Way's Dark Companions
January 18, 2017 | ERC 161 | 3:30 PM
Alex Drlica-Wagner, Fermilab

PDF | Video
Our Milky Way galaxy is surrounded by a host of small, dark-matter-dominated satellite galaxies. Over the past two years, the Dark Energy Camera (DECam) has nearly doubled the number of known Milky Way satellite galaxies compared to the previous 80 years combined. While these discoveries continue to help resolve the "missing satellites problem", they have also raised new questions about the influence of the Magellanic Clouds on the Milky Way's satellite population. In the near future, the rapidly growing population of dwarf galaxies will be sensitive to deviations from ΛCDM at small scales, while definitively testing whether the annihilation of dark matter particles could be responsible for excess gamma-ray emission from the Galactic center. I will summarize recent results, outstanding questions, and upcoming advancements in the study of the Milky Way's dark companions.

Digging into the Large Scale Structure of the Universe
February 8, 2017 cancelled | ERC 161 | 3:00 PM
Shirley Ho, Berkeley Lab/ BCCP / Carnegie Mellon

CANCELLED

Galaxy spectroscopic surveys provide the means to map out this cosmic large-scale structure in three dimensions, furnishing a cornerstone of observational cosmology. The information is given in the form of galaxy locations, and is typically condensed into a single function of scale, such as the galaxy correlation function or power-spectrum. However, galaxy correlation functions are not the only information those surveys provide. One of the most striking features of N-body simulations is the network of filaments into which dark matter particles arrange themselves. We however traditionally only use the information contained in the positions of the galaxies, and in some occasions, we look at other cosmic structures of the Universe such as voids.
In this colloquium, I explore the information beyond the galaxy positions in large sky surveys combining novel ideas with recent techniques in statistical methods and machine learning algorithms. In particular, we will investigate the following two topics: the ''cosmic web'' that are mostly ignored in any large scale structure analyses in the Universe and how it affects the surrounding galaxies; and explores the additional information beyond the typical 2 point statistics by using novel statistical and machine learning techniques.

Digging into the Large Scale Structure of the Universe
March 1, 2017 cancelled | ERC 161 | 3:00 PM
Shirley Ho, Berkeley Lab/ BCCP / Carnegie Mellon

CANCELLED

Galaxy spectroscopic surveys provide the means to map out this cosmic large-scale structure in three dimensions, furnishing a cornerstone of observational cosmology. The information is given in the form of galaxy locations, and is typically condensed into a single function of scale, such as the galaxy correlation function or power-spectrum. However, galaxy correlation functions are not the only information those surveys provide. One of the most striking features of N-body simulations is the network of filaments into which dark matter particles arrange themselves. We however traditionally only use the information contained in the positions of the galaxies, and in some occasions, we look at other cosmic structures of the Universe such as voids.
In this colloquium, I explore the information beyond the galaxy positions in large sky surveys combining novel ideas with recent techniques in statistical methods and machine learning algorithms. In particular, we will investigate the following two topics: the ''cosmic web'' that are mostly ignored in any large scale structure analyses in the Universe and how it affects the surrounding galaxies; and explores the additional information beyond the typical 2 point statistics by using novel statistical and machine learning techniques.