About me

I am a theoretical and computational astrophysicist. I am mostly interested in the rich phenomena of star formation. I utilize both analytical and numerical tools to answer questions like:
  • What regulates star formation?
  • What sets the characteristic mass of stars?
  • Why are stars clustered?
  • What determines the properties of stellar binaries?
  • How is star formation different in other galaxies?
These questions have far reaching implications beyond the field of star formation. The interpretation of observed starlight from any extragalactic sources rely on our understanding and assumptions about star formation. Similarily answering these questions would greatly enhance our knowledge about the circumstances of planet formation.

I am originally from Hungary where I got my bachelor's and master's degrees in physics. I have recently received my PhD in phyiscs from the California Institute of Technology and I am currently a Harlan J. Smith postdoctoral fellow at the University of Texas at Austin.


About star formation

Stars are the fundamental pieces of cosmic evolution and their formation is a key process that influences the evolution of galaxies, planets, and the development of life. Despite its importance star formation is not-yet understood. This is because of the wide range of complicated physical processes involved (e.g., gravity, turbulence, magnetic fields, radiation).
We know that stars form in dense clouds of molecular Hydrogen that collapses under its own gravity and form stars of various masses. My goal is to understand what sets the masses and distribution of these newly-formed stars and what roles do turbulence, magnetic fields, feedback and the local environment play.

Orion A
Infrared emission map of the Orion A molecular cloud. The cloud exhibits a filamentary structure in which dense cores form that will later collapse to form stars (Source: ESA/Herschel).

Simulating star formation

Star formation is known to happen in giant molecular clouds but so far no radiation-hydro simulation has managed to follow the evolution of such a cloud throughout its lifetime while also resolving the formation of individual low-mass stars. My current project is a suite of such simulations with the aim identifying the role individual physical processes play in star formation. The different have progressively more complicated physics: starting from pure isothermal collapse, then adding magnetic fields, cooling physics, dust, protostellar heating and outflows and main sequence stellar feedback.
For these simulations I am using GIZMO, a fully-adaptive, meshless, MHD code. Unlike previous studies that were confined to a small region of a cloud, these simulations will provide a self-consistent picture of a cloud disrupted by long-range feedback (e.g., winds and radiation from massive stars, supernovae), which is observed to be the primary mode of star formation.

Density (left) and temperature (right) map of a simulated star-forming cloud.

Semi-analytic modeling

Analytical models of star formation are useful for understanding the principal mechanisms behind different observed phenomena but are often unable to make quantitative predictions. To study in detail how the different physical processes affect the masses and distribution of stars I developed the MISFIT (MInimalist Star Formation Including Turbulence) semi-analytical framework. Due to its semi-analytical nature MISFIT can simulate a much wider dynamic range than conventional hydro codes: it can follow the evolution of clouds from the scale of giant molecular clouds (10 pc) down to the scales of protostars (0.1 AU). This framework provides a way to explore different star formation models and parameters at a modest computational cost.

Stellar initial mass function Stellar correlation function Binary companion mass distribution
Predicted mass distribution (left), correlation function (middle) and companion mass distribution (right) of stars using MISFIT with various physical models. We see that isothermal models (which are the basis of many modern star formation theories) fail to reproduce the mass distribution (IMF) of stars (Guszejnov+2016). Meanwhile almost all theories reproduce the large-scale correlation of stars (Guszejnov+2018) and observational biases ensure that all models predic similar companion mass distributions (Guszejnov+2017)