4 papers
Simulation-Based Inference for Cluster Cosmology with Set-Based Neural Network Architectures
S. Zelmer, E. Bulbul, K. Lehman +18
The unprecedented statistical power of galaxy cluster catalogs from the SRG (Spectrum Roentgen Gamma)/eROSITA All-Sky Survey provides a unique opportunity to place stringent constr…
Cosmological gravity on all scales V: MCMC forecasts combining large scale structure and CMB lensing for binned phenomenological modified gravity
Sankarshana Srinivasan, Shreya Prabhu, Kai Lehman +2
As cosmology rapidly approaches the data-dominated phase of stage IV large scale structure surveys, the modelling of nonlinear scales has become a serious challenge that faces the…
C3NN-SBI: Learning Hierarchies of -Point Statistics from Cosmological Fields with Physics-Informed Neural Networks
Kai Lehman, Zhengyangguang Gong, David Gebauer +2
Cosmological analyses are moving past the well understood 2-point statistics to extract more information from cosmological fields. A natural step in extending inference pipelines t…
Cosmological Inference with Cosmic Voids and Neural Network Emulators
Kai Lehman, Nico Schuster, Luisa Lucie-Smith +3
Cosmic Voids are a promising probe of cosmology for spectroscopic galaxy surveys due to their unique response to cosmological parameters. Their combination with other probes promis…