I will present recent results of the kinematics of planetary nebulae (PNe) and globular clusters (GCs) out to about 200 kpc from the center of the Fornax cluster. We have collected more than 2000 new radial velocities using VIMOS and FORS2 spectrographs at the VLT which, combined with previous literature, make one of the largest data sample covering a volume of a galaxy cluster up to about 1/4 of its viral radius.
I will show the signature of an intracluster stellar population both on the velocity field of PNe and GCs and report the discovery of 13 candidate stellar streams in the Fornax core. We show evidence for a variety of cold substructures predicted in simulations. Most of the streams are kinematically connected to UCDs, supporting the scenario that they can be remnants of disrupted dwarf systems. However, we also show the presence of long coherent substructures connecting cluster members and isolated clumps of tracers possibly left behind by their parent systems before these merged into the central galaxy.

BIO
Prof. Nicola R. Napolitano got his PhD in 2001 at the University of Naples. He was a Post-doc at INAF – Osservatorio Astronomico di Capodimonte, Italy during 2002-2003, a Marie Curie Fellow at Kapteyn Institute, University of Groningen, Netherlands during 2003-2004, a Permanent Research astronomer, INAF – Observatory of Capodimonte, Italy from 2005 to 2018.Since 2018 to now, he became professor at the Sun-Yat Sen University. His research interests are: Extragalactic Astronomy: Dynamics of early-type galaxies, dark matter, galaxy formation and evolution; N-body simulations; large sky surveys; machine learning techniques applied to astronomy. His collaborations: Chinese Space Station Telescope (Galaxies & AGN, Strong Lensing and Selection effect WGs), Kilo Degree Surveys (KiDS), The Fornax VLT Spectroscopic Survey (PI), Fornax Deep Survey, Planetary Nebula Spectrograph consortium. Member of the Board of the European Horizon-2020 Innovative Training Network “SUNDIAL: SUrvey Network for Deep Imaging Analysis and Learning”.