Professor Michele Cappellari Homepage

	Astrophysics University of Oxford Denys Wilkinson Building Keble Road OXFORD OX1 3RH United Kingdom		Voronoi binned myself
	michele.cappellaribest@physics.ox.ac.uk
	Office 755 (top floor)
	https://www-astro.physics.ox.ac.uk/~cappellari/
Synthetic CV: I obtained my PhD in astrophysics 🧑🏻‍🔬🪐🔭⭐☄️ at the University of Padova. I was awarded a VENI Fellowship by NWO at the University of Leiden and both an Advanced Fellowship by STFC and a University Research Fellowship by the Royal Society, at the University of Oxford, where I am now a Professor of Astrophysics.

See my review on galaxy structure and evolution

Current Interests

My work focuses on understanding how galaxies 🌌 were assembled using two complementary approaches. I observe nearby objects, which can be analyzed in detail due to their proximity and contain the fossil record 🐚🦴 of billions of years of evolution. Additionally, I observe distant galaxies whose light 💥reaches us after billions of years of travel 🚀 through space, allowing us to see them as they were when the Universe was young 🐣 galaxies were still forming ✨🌠.

I was awarded the 🏆 Tartufari International Prize for Astronomy by the Lincei Academy “for the profound effect on our knowledge of the structure of galaxies” achieved by my research using integral-field spectroscopy. I have been included in the list of 🏆 Highly Cited Researchers “recognizing the true pioneers in their fields over the last decade, demonstrated by the production of multiple highly-cited papers that rank in the top 1% by citations for field and year.”

Research Areas

• Tracing the Time Evolution of Galaxies: Using observations from the largest telescopes 🔭 in the world, such as the ESO Very Large Telescope, the Japanese Subaru Telescope, and the American Keck Telescope, along with the Hubble Space Telescope and Webb Space Telescope, I trace the assembly of galaxy masses over time.
• The Fossil Record of Galaxy Formation: As Principal Investigator of the influential ATLAS^3D project, I studied in detail the assembly of a sample of nearby elliptical and lenticular galaxies 🌌 by measuring rotation, mass, 🧪⚛️ chemical composition, age, and gas content (ATLAS^3D team papers).
• Integral-Field Spectroscopy: As a member of the SAURON team, we received the 🏆 Group Award by the Royal Astronomical Society. We mapped kinematics and stellar populations in galaxies using integral-field spectroscopy, providing new observational constraints to galaxy formation models (SAURON team papers). I was part of the MaNGA collaboration and now a member of the Harmoni/ELT science team.
• Supermassive Black Holes and Dark Matter: I study the link between the evolution of black holes 🕳️and their host galaxies (Nature article). Using adaptive-optics assisted observations (SINFONI, NACO, OASIS, GMOS), I construct dynamical models for stellar or gaseous components. I am part of the Harmoni/ELT science team, planning the next revolution in black hole studies.
• Cosmology: As a member of the TDCOSMO team, we combine strong gravitational 🍎👓 lensing and galaxy stellar dynamical modelling ⭐🔄 to measure the scale of the Universe and resolve tensions between different cosmological probes. We use integral-field spectroscopy with observations from the VLT and Keck telescopes and the JWST 🛰️ from Space.

Publications

• Publications 📖 from ADS
• Refereed Publications from ADS
• Publications from ORCID 0000-0002-1283-84200
• Preprints 📰 in the past year on arXiv

Citation Metrics

Software

• MgeFit: Multi-Gaussian Expansion (MGE) photometric models of galaxies
• JamPy: Jeans Anisotropic Models (JAM) of the dynamics of axisymmetric or spherical galaxies
• VorBin: Adaptive Voronoi Spatial 2D-Binning of integral-field spectroscopy, X-ray, or imaging data
• pPXF: Stellar kinematics and stellar population of galaxies using Penalized Pixel-Fitting
• CapFit: Constrained nonlinear least-squares
• PaFit: Global kinematic position-angle of galaxies
• LtsFit: Extremely robust lines, planes, or hyperplane fits
• LOESS: Smoothing in one or two dimensions
• AdaMet: Adaptive Metropolis for efficient Bayesian analysis

Teaching

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