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24/06/2019-28/06/2019

SHAO Astrophysics Colloquium

Title: New insights on the X-ray Source Populations in the Galactic Center and Nearby Galaxy Clusters

Speaker: Zhiyuan LiNanjing University

Time: 3 pm, June 27 (Thursday)
Location: Lecture Hall, 3rd floor

Abstract:

X-ray-emitting, close binary systems involving a black hole (BH), a neutron star (NS) or a white dwarf (WD), are among the first objects discovered in the X-ray sky and now understood to be ubiquitous in the Universe. As such, X-ray binaries can serve as a useful probe of their parent stellar populations, on scales from star clusters to galaxy clusters. In this talk, I will present our recent studies of the X-ray source populations in (i) the Nuclear Star Cluster (NSC) at the Galactic center, and (ii) the two nearest galaxy clusters (Virgo and Fornax), based primarily on Chandra observations. In the NSC, the high stellar density together with an ultradeep Chandra exposure results in the detection of a record number of over 3500 X-ray sources. The majority of these sources are believed to be cataclysmic variables (i.e., accreting WDs), with a dozen of strongly variable sources being BH- or NS-binaries. The source spatial distribution, luminosity function and spectral properties provide interesting implications on the dynamics and building history of the NSC. In Virgo and Fornax, a careful analysis of the source spatial distribution reveals strong evidence for the existence of intracluster X-ray sources (ICXs), i.e., sources that are not associated with the member galaxies. These ICXs may have a mixed origin, including in particular low-mass X-ray binaries associated with the diffuse intracluster light. This opens a new avenue for studying the structural growth of galaxy clusters in the era of eROSITA.

 

 

Special Colloquium

Title: Deep Neural Network in Big Astronomical Data Era

Speaker: Jian Ge (葛健教授,University of Florida)

Time: 3 pm, June 25 (Tuesday)

Location: Lecture Hall, 3rd floor

Abstract: Astronomical large-scale surveys, such as SDSS, Kepler, Gaia, LAMOST, WISE, PAN-STARRS, and ZTF, have accumulated a massive volume of data which provide an enormous challenge in data handling, processing, and analysis using traditional methods and tools. New efficient, accurate, and fast methods and tools are urgently needed for analyzing big data for new discoveries. Deep neural network with layers of mathematical functions and algorithms, which mimic the behavior of neurons in the human brain in object recognition, has been rapidly developed in astronomy and successfully applied in detection of transit planets, identification of near-Earth objects, and absorption lines in quasar spectra and is becoming a drive force in data mining and analysis in the big data era. I will provide a brief review of the state of this emerging field, present early science results from our deep neural network applications in quasar absorption line study and transit planet detection, and also offer future perspectives in this exploding field.

 

 

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