"The most incomprehensible thing about the world is that it is comprehensible." - A. Einstein

The Computational Modeling of Galaxies

How do galaxies and many of their properties evolve over cosmic history? Which processes determine their sizes, regulate their star formation rates, or shape their morphology? Nobody really knows for sure. What makes this research so challenging is the large range of scales (from the cosmic web to individual molecular clouds) and the complexity of the physical processes operating in the interstellar and circumgalactic medium. However, compared with just 10 years ago, we are now much closer to answering those questions. The continuous increase in computing power enables astrophysicists to develop and evaluate increasingly sophisticated computational models of galaxy evolution, mitigating the dynamic range problem. These model building efforts were greatly helped by the advent of large, digital galaxy surveys that resulted in the quantitative analysis of millions of galaxies -- a development that is still revolutionizing our understanding of galaxy evolution.

About me

I am a computation- and data-science-oriented astrophysicist with research interests in the formation and evolution of galaxies, in structure formation, and in the physical processes that take place in the interstellar medium. A significant part of my research relies on state-of-the-art high-performance computing and data analysis.


You can contact me at feldmann[at]physik.uzh.ch. The Institute for Computational Science is located at the Irchel campus, building 11, floor F.

Selected Recent Publications

From EMBER to FIRE: predicting high resolution baryon fields from dark matter simulations with Deep Learning
Bernardini, Mauro; Feldmann, Robert; Angles-Alcazar, Daniel; Boylan-Kolchin, Mike; Bullock, James; Stadel, Joachim 2021, MNRAS

The link between star formation and gas in nearby galaxies
Feldmann, Robert 2020, Communications Physics, 3, 226

Predicting dark matter halo formation in N-body simulations with deep regression networks
Bernardini, Mauro; Mayer, Lucio; Reed, Darren; Feldmann, Robert 2020, MNRAS, 496, 5116

LEO-Py: Estimating likelihoods for correlated, censored, and uncertain data with given marginal distributions
Feldmann, Robert 2019, Astronomy & Computing 29, 100331

The Galaxy–Halo Connection in Low-mass Halos
Feldmann, Robert; Faucher-Giguère, Claude-André; Kereš, Dušan 2019, APJ Letters, 871, L21

Are star formation rates of galaxies bimodal?
Feldmann, Robert 2017, MNRAS 470, L59

A stellar feedback origin for neutral hydrogen in high-redshift quasar-mass haloes
Faucher-Giguère, Claude-André; Feldmann, Robert; Quataert, Eliot; Kereš, Dušan; Hopkins, Philip F.; Murray, Norman 2016, MNRAS 461, L32

The formation of massive, quiescent galaxies at cosmic noon
Feldmann, Robert; Hopkins, Philip F.; Quataert, Eliot; Faucher-Giguère, Claude-André; Kereš, Dušan 2016, MNRAS 458, L14

The formation of submillimetre-bright galaxies from gas infall over a billion years
Narayanan, Desika; Turk, Matthew; Feldmann, Robert; Robitaille, Thomas; Hopkins, Philip; Thompson, Robert; Hayward, Christopher; Ball, David; Faucher-Giguère, Claude-André; Kereš, Dušan 2015, Nature 525, 496

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