I am interested in the evolution of the interstellar medium, galactic outflows, and how massive stars shape their environment and regulate star formation with their feedback.
Within the SILCC project,

I systematically study the impact of supernovae, stellar winds, ionising UV radiation, cosmic rays and runaway stars on star formation and the capabilities to drive and sustain galactic outflows.
Using state-of-the-art simulation techniques, particularly focusing on the role of cosmic rays, I delve into understanding the mechanisms driving galactic outflows and regulating star formation processes.
I mainly use the MPI parallel, magneto-hydrodynamics (MHD), 3D adaptive mesh refinement (AMR) code FLASH to carry out my research with high-resolution ISM simulations on high-performance computing (HPC) clusters like SuperMUC-NG.

Star formation rate surface density vs gas surface density of ISM simulations with increasing feedback complexity.

Stratified disk simulation with an initial gas surface density of Σ = 50 M_☉ / pc². The simulation includes stellar feedback in the form of type II supernovae, stellar winds, ionising UV radiation, and cosmic rays, as well as a type Ia supernova background, and runaway stars. Shown are the edge-on (top row) and face-on (bottom row) views of the total gas, ionised, atomic, and molecular hydrogen column densities. Individual HII regions (3rd panel) from active star clusters are visible. Also shown is the density-weighted column of the magnetic field strength (6th panel) and slices through the centre of the simulation box with temperature (2nd panel) and CR energy density (7th panel). The star-forming galactic ISM is concentrated around the midplane. White circles in the 1st and 3rd panels indicate star clusters with different masses. The smallest white circles are individual runaway OB stars. Translucent symbols indicate old star clusters with no active massive stars in them. Stellar feedback generates a highly structured and turbulent multiphase ISM with all its major thermal and non-thermal components.

In addition to simulation-based research, I am actively involved in predicting and analysing the observable properties of the ISM through post-processing and synthetic observation techniques. Utilising sophisticated tools such as the photo-ionisation code Cloudy, I focus on studying the optical emission line properties of simulated ISM environments. By combining simulation data with observational constraints, I study the underlying physical processes governing the ISM its emission lines. Through detailed analysis of optical line emission and line ratio diagnostics, I strive to bridge the gap between theoretical simulations and observational data, advancing our understanding of the ISM and its role in galactic evolution.

Emission map of the simulated ISM within the SILCC framework.

In addition to my research endeavors in ISM simulation and emission line studies, I am actively engaged in projects that intersect with machine learning (ML) and research data management (RDM), particularly within the Collaborative Research Center "Habitats of Massive Stars across Cosmic Time" (CRC1601) and the "Big Bang to Big Data" (B3D) consortium.
Within these collaborative frameworks, I contribute to initiatives aimed at integrating machine learning techniques into astrophysical research. I am exploring new ways of ultilising machine learning techniques for data reduction andi post-processing of the large datasets which are produced by simulations.
Furthermore, I play a role in research data management efforts within both the CRC and the B3D project. Recognising the importance of adhering to FAIR principles (Findable, Accessible, Interoperable, Reusable) , I am involved in developing metadata standards for astrophysical simulation data, ensuring that research data remains accessible and comprehensible for future generations of scientists.