Michele Cappellari: Publications

Searching for the Supermassive Black Hole in NGC 1265 (3C 83.1B)

CO-EVOLUTION OF CENTRAL BLACK HOLES AND GALAXIES (2010) 195-195

Authors:

Alessandra Beifiori, Marc Sarzi, Roger L Davies, Michele Cappellari, Ryan CW Houghton

The Kinematics of Core and Cusp Galaxies: Comparing HST Imaging and Integral-Field Observations

IMPACT OF HST ON EUROPEAN ASTRONOMY (2010) 127-+

Authors:

J Falcon-Barroso, R Bacon, M Cappellari, RL Davies, PT de Zeeuw, E Emsellem, D Krajnovic, H Kuntschner, RM McDermid, RF Peletier, M Sarzi, G van de Yen

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Weighing black holes using open-loop focus corrections for LGS-AO observations of galaxy nuclei at Gemini Observatory

ADAPTIVE OPTICS SYSTEMS II 7736 (2010) ARTN 77365W

Authors:

Richard M McDermid, Davor Krajnovic, Michele Cappellari, Chadwick Trujillo, Julian Christou, Roger L Davies

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Dark matter and the Tully-Fisher relations of spiral and S0 galaxies

(2009)

Authors:

Michael J Williams, Martin Bureau, Michele Cappellari

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Voronoi binning: Optimal adaptive tessellations of multi-dimensional data

(2009)

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Michele Cappellari

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Searching for the Supermassive Black Hole in NGC 1265 (3C 83.1B)

Authors:

The Kinematics of Core and Cusp Galaxies: Comparing HST Imaging and Integral-Field Observations

Authors:

Weighing black holes using open-loop focus corrections for LGS-AO observations of galaxy nuclei at Gemini Observatory

Authors:

Dark matter and the Tully-Fisher relations of spiral and S0 galaxies

Authors:

Voronoi binning: Optimal adaptive tessellations of multi-dimensional data

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Michele Cappellari

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Searching for the Supermassive Black Hole in NGC 1265 (3C 83.1B)

Authors:

The Kinematics of Core and Cusp Galaxies: Comparing HST Imaging and Integral-Field Observations

Authors:

Weighing black holes using open-loop focus corrections for LGS-AO observations of galaxy nuclei at Gemini Observatory

Authors:

Dark matter and the Tully-Fisher relations of spiral and S0 galaxies

Authors:

Voronoi binning: Optimal adaptive tessellations of multi-dimensional data