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Euclid: Field-level inference of primordial non-Gaussianity and cosmic initial conditions
A. Andrews, J. Jasche, G. Lavaux, F. Leclercq, F. Finelli, Y. Akrami, M. Ballardini, D. Karagiannis, J. Valiviita, N. Bartolo, G. Cañas-Herrera, S. Casas, B. R. Granett, F. Pace, D. Paoletti, N. Porqueres, Z. Sakr, D. Sapone, N. Aghanim, A. Amara, S. Andreon, C. Baccigalupi, M. Baldi, S. Bardelli, D. Bonino, et al. Astronomy & Astrophysics 711 A94 (2026) https://doi.org/10.1051/0004-6361/202553802
MadEvolve: evolutionary optimization of cosmological algorithms with large language models
Hybrid summary statistics: neural weak lensing inference beyond the power spectrum
T. Lucas Makinen, Alan Heavens, Natalia Porqueres, Tom Charnock, Axel Lapel and Benjamin D. Wandelt Journal of Cosmology and Astroparticle Physics 2025(01) 095 (2025) https://doi.org/10.1088/1475-7516/2025/01/095
Straightening the ruler: field-level inference of the BAO scale with LEFTfield
A Parameter-masked Mock Data Challenge for Beyond-two-point Galaxy Clustering Statistics*
Elisabeth Krause, Yosuke Kobayashi, Andrés N. Salcedo, Mikhail M. Ivanov, Tom Abel, Kazuyuki Akitsu, Raul E. Angulo, Giovanni Cabass, Sofia Contarini, Carolina Cuesta-Lazaro, ChangHoon Hahn, Nico Hamaus, Donghui Jeong, Chirag Modi, Nhat-Minh Nguyen, Takahiro Nishimichi, Enrique Paillas, Marcos Pellejero Ibañez, Oliver H. E. Philcox, Alice Pisani, Fabian Schmidt, Satoshi Tanaka, Giovanni Verza, Sihan Yuan and Matteo Zennaro The Astrophysical Journal 990(2) 99 (2025) https://doi.org/10.3847/1538-4357/ad781d
Learning the Universe: learning to optimize cosmic initial conditions with non-differentiable structure formation models
Bayesian inference of initial conditions from non-linear cosmic structures using field-level emulators
Ludvig Doeser, Drew Jamieson, Stephen Stopyra, Guilhem Lavaux, Florent Leclercq and Jens Jasche Monthly Notices of the Royal Astronomical Society 535(2) 1258 (2024) https://doi.org/10.1093/mnras/stae2429
Map-based cosmology inference with weak lensing – information content and its dependence on the parameter space
Bayesian field-level inference of primordial non-Gaussianity using next-generation galaxy surveys
Adam Andrews, Jens Jasche, Guilhem Lavaux and Fabian Schmidt Monthly Notices of the Royal Astronomical Society 520(4) 5746 (2023) https://doi.org/10.1093/mnras/stad432
Cosmological constraints from the density gradient weighted correlation function
Xiaoyuan Xiao, Yizhao Yang, Xiaolin Luo, et al. Monthly Notices of the Royal Astronomical Society 513(1) 595 (2022) https://doi.org/10.1093/mnras/stac879
Field-level inference of galaxy intrinsic alignment from the SDSS-III BOSS survey
Eleni Tsaprazi, Nhat-Minh Nguyen, Jens Jasche, Fabian Schmidt and Guilhem Lavaux Journal of Cosmology and Astroparticle Physics 2022(08) 003 (2022) https://doi.org/10.1088/1475-7516/2022/08/003
Optimal machine-driven acquisition of future cosmological data
Tomographic Alcock–Paczynski method with redshift errors
Liang Xiao, Zhiqi Huang, Yi Zheng, Xin Wang and Xiao-Dong Li Monthly Notices of the Royal Astronomical Society 518(4) 6253 (2022) https://doi.org/10.1093/mnras/stac2996
Fisher matrix for the angular power spectrum of multi-tracer galaxy surveys
L. Raul Abramo, João Vitor Dinarte Ferri, Ian Lucas Tashiro and Arthur Loureiro Journal of Cosmology and Astroparticle Physics 2022(08) 073 (2022) https://doi.org/10.1088/1475-7516/2022/08/073
ADDGALS: Simulated Sky Catalogs for Wide Field Galaxy Surveys
Risa H. Wechsler, Joseph DeRose, Michael T. Busha, Matthew R. Becker, Eli Rykoff and August Evrard The Astrophysical Journal 931(2) 145 (2022) https://doi.org/10.3847/1538-4357/ac5b0a
An n-th order Lagrangian forward model for large-scale structure
Lifting weak lensing degeneracies with a field-based likelihood
Natalia Porqueres, Alan Heavens, Daniel Mortlock and Guilhem Lavaux Monthly Notices of the Royal Astronomical Society 509(3) 3194 (2021) https://doi.org/10.1093/mnras/stab3234
Cosmic Velocity Field Reconstruction Using AI
Ziyong Wu, Zhenyu Zhang, Shuyang Pan, Haitao Miao, Xiaolin Luo, Xin Wang, Cristiano G. Sabiu, Jaime Forero-Romero, Yang Wang and Xiao-Dong Li The Astrophysical Journal 913(1) 2 (2021) https://doi.org/10.3847/1538-4357/abf3bb
Impacts of the physical data model on the forward inference of initial conditions from biased tracers
Information content of higher order galaxy correlation functions
Lado Samushia, Zachary Slepian and Francisco Villaescusa-Navarro Monthly Notices of the Royal Astronomical Society 505(1) 628 (2021) https://doi.org/10.1093/mnras/stab1199
Bayesian forward modelling of cosmic shear data
Natalia Porqueres, Alan Heavens, Daniel Mortlock and Guilhem Lavaux Monthly Notices of the Royal Astronomical Society 502(2) 3035 (2021) https://doi.org/10.1093/mnras/stab204
Cosmology inference from a biased density field using the EFT-based likelihood
Franz Elsner, Fabian Schmidt, Jens Jasche, Guilhem Lavaux and Nhat-Minh Nguyen Journal of Cosmology and Astroparticle Physics 2020(01) 029 (2020) https://doi.org/10.1088/1475-7516/2020/01/029
Neural physical engines for inferring the halo mass distribution function
Michael J Hudson, Jens Jasche, Supranta Sarma Boruah, et al. Monthly Notices of the Royal Astronomical Society 494(1) 50 (2020) https://doi.org/10.1093/mnras/staa682
Cosmological information content in redshift-space power spectrum of SDSS-like galaxies in the quasinonlinear regime up to
k=0.3 h Mpc−1
Super-resolution emulator of cosmological simulations using deep physical models
Benjamin D Wandelt, Francisco Villaescusa-Navarro, Tom Charnock and Doogesh Kodi Ramanah Monthly Notices of the Royal Astronomical Society 495(4) 4227 (2020) https://doi.org/10.1093/mnras/staa1428
A hierarchical field-level inference approach to reconstruction from sparse Lyman-α forest data
Cosmological Constraints from the Redshift Dependence of the Alcock–Paczynski Effect: Possibility of Estimating the Nonlinear Systematics Using Fast Simulations
Qinglin Ma, Yiqing Guo, Xiao-Dong Li, Xin Wang, Haitao Miao, Zhigang Li, Cristiano G. Sabiu and Hyunbae Park The Astrophysical Journal 890(2) 92 (2020) https://doi.org/10.3847/1538-4357/ab6aa3
Sampling-based inference of the primordial CMB and gravitational lensing
Nonparametric Dark Energy Reconstruction Using the Tomographic Alcock–Paczynski Test
Zhenyu Zhang, Gan Gu, Xiaoma Wang, Yun-He Li, Cristiano G. Sabiu, Hyunbae Park, Haitao Miao, Xiaolin Luo, Feng Fang and Xiao-Dong Li The Astrophysical Journal 878(2) 137 (2019) https://doi.org/10.3847/1538-4357/ab1ea4
Painting halos from cosmic density fields of dark matter with physically motivated neural networks
Cosmological Constraints from the Redshift Dependence of the Alcock–Paczynski Effect: Fourier Space Analysis
Xiaolin Luo, Ziyong Wu, Miao Li, Zhigang Li, Cristiano G. Sabiu and Xiao-Dong Li The Astrophysical Journal 887(2) 125 (2019) https://doi.org/10.3847/1538-4357/ab50b5
Alcock–Paczynski Test with the Evolution of Redshift-space Galaxy Clustering Anisotropy
Hyunbae 현배 Park 박, Changbom Park, Cristiano G. Sabiu, Xiao-dong Li, Sungwook E. 성욱 Hong 홍, Juhan 주한 Kim 김, Motonari Tonegawa and Yi Zheng The Astrophysical Journal 881(2) 146 (2019) https://doi.org/10.3847/1538-4357/ab2da1