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20142024
most citedMachine Learning for Neuroimaging with Scikit-Learn

172 citations · 195 across the 12 of their papers we have counts for

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Showing astro-ph.COShow all

5 papers · 1 filter

astro-ph.CO20244 cited

{\sc SimBIG}: Cosmological Constraints using Simulation-Based Inference of Galaxy Clustering with Marked Power Spectra

Elena Massara, ChangHoon Hahn, Michael Eickenberg +7

We present the first CDM cosmological analysis performed on a galaxy survey using marked power spectra. The marked power spectrum is the two-point function of a marked field, wh…

astro-ph.CO20241 cited

: Cosmological Constraints from the Redshift-Space Galaxy Skew Spectra

Jiamin Hou, Azadeh Moradinezhad Dizgah, ChangHoon Hahn +7

Extracting the non-Gaussian information of the cosmic large-scale structure (LSS) is vital in unlocking the full potential of the rich datasets from the upcoming stage-IV galaxy su…

astro-ph.CO20231 cited

SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering

Pablo Lemos, Liam Parker, ChangHoon Hahn +8

We present the first simulation-based inference (SBI) of cosmological parameters from field-level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzi…

astro-ph.CO20236 cited

: The First Cosmological Constraints from Non-Gaussian and Non-Linear Galaxy Clustering

ChangHoon Hahn, Pablo Lemos, Liam Parker +8

The 3D distribution of galaxies encodes detailed cosmological information on the expansion and growth history of the Universe. We present the first cosmological constraints that ex…

astro-ph.CO20231 cited

: The First Cosmological Constraints from the Non-Linear Galaxy Bispectrum

ChangHoon Hahn, Michael Eickenberg, Shirley Ho +7

We present the first cosmological constraints from analyzing higher-order galaxy clustering on non-linear scales. We use , a forward modeling framework…