Welcome! I'm David Gebauer
Cosmology PhD Student at Universität Bielefeld (he/him)
I am a PhD student in the Faculty of Physics at Bielefeld University. My research is in weak gravitational lensing cosmology, where I work on higher-order statistics and simulation-based inference. I am particularly interested in making machine learning methods for cosmological analyses interpretable.
Member of the Dark Energy Survey (DES), the Euclid Consortium, and the LSST Dark Energy Science Collaboration (DESC).
Research Areas
Weak Lensing & Higher-Order Statistics
Constraining cosmology with the distortion of galaxy shapes by large-scale structure, beyond standard two-point statistics.
Simulation-Based Inference
Likelihood-free inference pipelines that constrain cosmology with forward-modelled simulations instead of analytical likelihoods.
Interpretable Machine Learning
Neural network architectures whose learned features correspond to known physical quantities, keeping machine learning analyses transparent.
Recent Publications
C3NN-SBI: Learning Hierarchies of N-Point Statistics from Cosmological Fields with Physics-Informed Neural Networks
K. Lehman, Z. Gong, D. Gebauer, S. Seitz, J. Weller
arXiv:2602.16768 • 2026
SBi3PCF: Simulation-based inference with the integrated 3PCF
D. Gebauer, A. Halder, S. Seitz, D. Anbajagane
JCAP 06(2026)036 • 2026
Cosmology with second and third-order shear statistics for the Dark Energy Survey: Methods and simulated analysis
R. C. H. Gomes, S. Sugiyama, B. Jain, M. Jarvis, D. Anbajagane, M. Gatti, D. Gebauer, Z. Gong, A. Halder, G. A. Marques, S. Pandey, J. L. Marshall, DES Collaboration
Phys. Rev. D 112, 123514 • 2025