5 citations · 7 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI
Kai Lehman, Sven Krippendorf, Jochen Weller +1
How much cosmological information can we reliably extract from existing and upcoming large-scale structure observations? Many summary statistics fall short in describing the non-Ga…