Research

Quasars in a Neutral Universe

Date:2022-05-21

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Title:Quasars in a Neutral Universe

Time: Thursday, May 26, 2022, 04:00pm

Speaker: Prof. Joe Hennawi (Leiden)

Address:Online via Zoom

主讲人  Prof. Joe Hennawi (Leiden) 时间  Thursday, May 26, 2022, 04:00pm
地点 Online via Zoom 报告语言
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During the Epoch of Reionization (EoR) primeval galaxies and accreting black holes reionized and reheated the hydrogen in the intergalactic medium (IGM) ending the preceding cosmic `dark ages'. Understanding how reionization occurred and the nature of the early sources that drove it are among the most important open questions in cosmology. The latest cosmic microwave background constraints suggest reionization occurred at z ~ 7-8, within the realm of the highest redshift quasars known. I will argue that the discovery of bright quasar beacons in a neutral IGM enables a set of qualitatively new absorption spectroscopy experiments which can teach us about reionization, supermassive black hole growth, and early cosmic enrichment. Current constraints based on ground based spectra of the handful of known z > 7 quasars will be presented. But this is only scratching the surface -- the successful launch of JWST in 2021 and imminent launch of the ESA/Euclid mission will deliver exquisite data for over a hundred quasars deep into the EoR transforming our knowledge of the high redshift universe.


BIO

Prof. Joseph Hennawi got his PhD from Princeton University in 2004. He was a Hubble Fellow in UC Berkeley from 2004-2007, worked as staff scientist in MPI from 2009 and became research group leader during 2010-2016, he was an associate professor in UC Santa Barbara since 2016 and professor since 2020. Now he is a professor in Leiden University since 2021.

Prof. Hennawi's research interests are: observational and theoretical cosmology, dark matter, gravitational lensing, cosmic reionization, quasar absorption lines, the inter- and circumgalactic media, high-redshift galaxies, supermassive black holes and active galactic nuclei, data mining, machine learning and statistical methods.


Host: Zheng Cai


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