Education
Experience
Publications
C3NN-SBI: Learning Hierarchies of N-Point Statistics from Cosmological Fields with Physics-Informed Neural Networks
Authors: K. Lehman, Z. Gong, D. Gebauer, S. Seitz, J. Weller
arXiv:2602.16768 • 2026
A simulation-based inference framework using a constrained C3NN architecture to learn summary statistics that correspond to N-point correlation functions of specified orders from cosmological fields.
SBi3PCF: Simulation-based inference with the integrated 3PCF
Authors: D. Gebauer, A. Halder, S. Seitz, D. Anbajagane
JCAP 06(2026)036 • 2026
A framework for higher-order weak lensing analysis using the integrated 3-point correlation function, employing masked autoregressive flows for neural likelihood estimation. Including the i3PCF yields a 63.8% improvement in the figure of merit for cosmological parameters.
Cosmology with second and third-order shear statistics for the Dark Energy Survey: Methods and simulated analysis
Authors: 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
A pipeline for robust inference of cosmological parameters using second- and third-order shear statistics, demonstrating an 83% improvement in the figure of merit when combining third-order with second-order statistics for DES Year 3 data.
C3NN: Cosmological Correlator Convolutional Neural Network -- an interpretable machine learning tool for cosmological analyses
Authors: Z. Gong, A. Halder, A. Bohrdt, S. Seitz, D. Gebauer
ApJ 971 156 • 2024
A convolutional neural network architecture whose outputs can be expressed in terms of analytically tractable N-point correlation functions, enabling interpretable and physically meaningful feature extraction from cosmological fields.
In Preparation
DES Year 3: Cosmology with the Integrated 3-point Correlation Function of cosmic shear
A. Halder, Z. Gong, D. Gebauer, S. Seitz, DES Collaboration
SBi3PCF II: Cosmology from DES Y3 cosmic shear
D. Gebauer, A. Halder, C. Uhlemann, Z. Gong
Where the matter PDF keeps its cosmological information: an interpretable simulation-based inference study
D. Gebauer, C. Uhlemann
Supervision
- Summer Semester 2026
Jannis Schrepel — Bachelor's Thesis
Comparing Constraining Power of One-Point Weak Lensing Statistics with Simulation-Based Inference
Grants & Awards
- 2026
Wilhelm and Else Heraeus Communication Programme — Wilhelm and Else Heraeus Foundation
Travel grant to attend and present at the DPG Spring Meeting in March 2026
- 2024
Dark Energy Survey (DES) Spring 2024 Award — Dark Energy Survey Collaboration
Travel grant to attend and present at the DES International Collaboration Meeting in Spain
Collaborations
Skills
Programming
Python, C, CUDA; Linux, Git, SLURM/HPC environments
ML Frameworks
JAX, PyTorch, TensorFlow; sbi, zuko, Optuna
Machine Learning
Neural density estimation (masked autoregressive and neural spline flows), convolutional neural networks, moment networks, neural emulators, interpretability (SHAP, LIME)
Statistical Inference
Simulation-based inference, GPU-accelerated MCMC, MOPED data compression, posterior calibration and coverage testing, blinded analysis design
Cosmological Analysis
Weak lensing, galaxy clustering, higher-order and one-point statistics, forward modelling with N-body suites (CosmoGridV1, Quijote), HEALPix map analysis, covariance estimation