Education

• 2025 - Present

  Ph.D. Physics (Cosmology)

  Topic: Combining analytical and simulation-based methods to extract information from wide-area galaxy surveys. Supervisor: Prof. Dr. Cora Uhlemann.

  Bielefeld University

  Bielefeld, Germany

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• 2022 - 2024

  M.Sc. Astrophysics

  Final grade: 1.15. Thesis: "Simulation Based Inference with the Integrated 3-Point Correlation Function of Cosmic Shear". Supervisor: Dr. Stella Seitz.

  LMU Munich

  Munich, Germany

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• 2018 - 2022

  B.Sc. Physics

  Final grade: 2.12. Thesis: "Dark Energy: Constraining cosmological models with extra spatial dimensions with Type Ia Supernovae". Supervisor: Prof. Dr. Jochen Weller.

  LMU Munich

  Munich, Germany

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Experiences

• 2025 - Present

  Supervision of Bachelor's Thesis

  Topic: Simulation-Based Inference with the Weak Lensing PDF.

  Bielefeld University

  Bielefeld, Germany

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• 2025 - Present

  PhD Student

  Combining analytical and simulation-based methods to extract information from wide-area galaxy surveys. Supervisor: Prof. Dr. Cora Uhlemann.

  Bielefeld University

  Bielefeld, Germany

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• 2024 - 2025

  Research Assistant

  Worked on validation and publication of the SBi3PCF project.

  Max Planck Institute for Extraterrestrial Physics (MPE)

  Garching, Germany

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• 2021 - 2022

  Lab Course Supervisor

  Supervised the physics laboratory course for medical students and graded experimental reports.

  LMU Munich

  Munich, Germany

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Last 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

  arXiv:2510.13805 • 2025

  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.

• DES Year 3: Cosmology with the Integrated 3-point Correlation Function of cosmic shear

  Authors: A. Halder, Z. Gong, D. Gebauer, S. Seitz, DES Collaboration

  in prep. •

• Application of SBi3PCF to DES Y3 Catalog

  Authors: D. Gebauer, A. Halder, C. Uhlemann, Z. Gong

  in prep. •

Skills