Tiziana Di Matteo

About

Tiziana Di Matteo is a Professor in the Department of Physics at Carnegie Mellon University. Her research focuses on several key areas in astrophysics and cosmology, including black holes, high energy astrophysics, and cosmology. She is involved in projects such as cosmological simulations of black hole formation, studies of galaxy mergers involving black holes, 21cm tomography and foregrounds, X-ray background and accretion models, as well as neutrino transport and gamma-ray bursts. Professor Di Matteo has contributed to the scientific community through her research on the complex interactions between black holes and their environments, as well as the large-scale structure of the universe. She has also been active in teaching astrophysics courses, including Astrophysics of Stars and the Galaxy and Extragalactic Astrophysics and Cosmology. Her work has received attention in various media outlets, highlighting the significance of her simulations and studies on black hole and galaxy collisions.

Research topics

• Artificial Intelligence
• Computer Science
• Statistical physics
• Quantum mechanics
• Astrophysics
• Algorithm
• Physics

Selected publications

• AI-assisted superresolution cosmological simulations

  Proceedings of the National Academy of Sciences · 2021 · 98 citations

  Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we ar…

  DOI

• MAGICS. II. Seed Black Holes Stripped of Their Surrounding Stars Do Not Sink

  The Astrophysical Journal · 2025-02-05 · 13 citations

  articleOpen access

  Abstract Massive black hole (MBH) seed mergers are expected to be among the loudest sources of gravitational waves detected by the Laser Interferometer Space Antenna, providing a unique window into the birth and early growth of MBHs. We present the MAGICS-II simulation suite, which consists of six galaxy mergers that result in MBH seed mergers identified in the cosmological simulation ASTRID. With the enhanced resolution (mass resolution: 500 M ⊙ ; softening length: 5 pc), improved subgrid model…

  PublisherDOI

• MAGICS. III. Seeds Sink Swiftly: Nuclear Star Clusters Dramatically Accelerate Seed Black Hole Mergers

  The Astrophysical Journal · 2025-03-10 · 13 citations

  articleOpen accessSenior author

  Abstract Merger rate predictions of massive black hole (MBH) seeds from large-scale cosmological simulations differ widely, with recent studies highlighting the challenge of low-mass MBH seeds failing to reach the galactic center, a phenomenon known as the seed sinking problem. In this work, we tackle this issue by integrating cosmological simulations and galaxy merger simulations from the MAGICS-I and MAGICS-II resimulation suites with high-resolution N -body simulations. Building on the findin…

  PublisherDOI

• Tracking Supermassive Black Hole Mergers from kpc to sub-pc Scales with AXIS

  Universe · 2024-05-28 · 11 citations

  articleOpen access

  We present an analysis showcasing how the Advanced X-ray Imaging Satellite (AXIS), a proposed NASA Probe-class mission, will significantly increase our understanding of supermassive black holes undergoing mergers—from kpc to sub-pc scales. In particular, the AXIS point spread function, field of view, and effective area are expected to result in (1) the detection of hundreds to thousands of new dual AGNs across the redshift range 0<z<5 and (2) blind searches for binary AGNs that are exhibit…

  PublisherDOI

• AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations

  The Open Journal of Astrophysics · 2025-02-10 · 4 citations

  articleOpen access

  Super-resolution (SR) models in cosmological simulations use deep learning (DL) to rapidly enhance low-resolution (LR) runs with statistically correct fine details. These models preserves large-scale structures by conditioning on an LR version of the simulation. On smaller scales, the generative process is inherently stochastic, producing multiple possible SR realizations with distinct small-scale structures. Validation of reconstructed SR runs from LR simulations requires ensuring that specific…

  PublisherDOI

Recent grants

• Quasars and Large Scale Structure: Gigaparsec-scale simulations confront Large Survey Data

  NSF · $535k · 2016–2022

• Toward Petascale Cosmology with GADGET

  NSF · $836k · 2007–2014

• Miniquasars in the Dark Ages: Cosmological simulations of black holes with radiative transfer

  NSF · $435k · 2010–2016

Frequent coauthors

• Tomaso Aste

  330 shared

• Rupert A. C. Croft

  151 shared

• Yueying Ni

  119 shared

• Volker Springel

  73 shared

• Nianyi Chen

  Carnegie Mellon University

  64 shared

• Simeon Bird

  63 shared

• Yu Feng

  Université de Technologie de Troyes

  63 shared

• Noemi Nava

  University College London

  52 shared

Education

• Ph.D., Astrophysics

  University of Cambridge (U.K.)

  1998

• B.S., Astrophysics

  University College London (U.K)

  1995

Awards & honors

• Carnegie Science Award of Excellence (2008)
• Berkham Faculty Grant (2006)
• Michael Penston Prize of the Royal Astronomical Society (199…

Similar researchers at Carnegie Mellon University

• Scott Dodelsonh-99
• Rachel Mandelbaumh-80
• Antonella Palmeseh-65