{"arxiv_id":"2105.01016","incoming":{"papers":9,"sentences":10,"groups":[{"kind":"background","label":"Cites it as background","papers":4,"sentences":4,"quotes":[{"arxiv_id":"2408.07699","title":"Field-level Emulation of Cosmic Structure Formation with Cosmology and Redshift Dependence","url":"/paper/2408.07699","section":"Introduction","sentence":"These include error corrections for fast particle-mesh-based simulations [ref] , interpolation between two simulation snapshots [ref] , generative methods for super-resolution simulations [ref] along with their redshift dependence [ref] , normalizing flows [ref] , and mapping between standard $Λ$CDM and modified gravity cosmologies [ref] .","intent_p":0.95,"contests_p":null},{"arxiv_id":"2205.10881","title":"Improving cosmological covariance matrices with machine learning","url":"/paper/2205.10881","section":"Introduction","sentence":"There are efforts trying to speed-up the process of producing high-resolution N-body simulations, by starting from lower-resolution ones, and letting the ML fill in the detailed structures on small scales [ref] .","intent_p":0.92,"contests_p":null},{"arxiv_id":"2201.01300","title":"The CAMELS project: public data release","url":"/paper/2201.01300","section":"Introduction","sentence":"Applications include parameter inference [ref] , superresolution [ref] , generation of mock data [ref] , painting hydrodynamic properties on N-body simulations [ref] , improving the halo-galaxy connection [ref] , removing/cleaning astrophysical effects [ref] , emulating non-linear evolution and speeding up numerical simulations [ref] , learning functions to interpolate among simulation properties [ref] , estimating masses of dark matter halos [ref] and galaxy clusters [ref] , finding universal relations in subhalo properties [ref] , generating realistic galaxy images [ref] , model selection and classification [ref] , and improving SED fitting techniques [ref] , among many others (see [ref] 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Networks","url":"/paper/1902.05965","section":"Introduction","sentence":"Models have also been trained to predict various baryonic properties from dark matter only simulation, such as galaxy distribution [ref] , thermal Sunyaev-Zeldovich (tSZ) effect [ref] , 21 cm emission from neutral hydrogen [ref] , stellar maps and various gas properties [ref] , etc.","intent_p":0.97,"contests_p":null},{"arxiv_id":"2007.10340","title":"HInet: Generating neutral hydrogen from dark matter with neural networks","url":"/paper/2007.10340","section":"Introduction","sentence":"Models have also been trained to predict various baryonic properties from dark matter only simulation, such as galaxy distribution [ref] , thermal Sunyaev-Zeldovich (tSZ) effect [ref] , 21 cm emission from neutral hydrogen [ref] , stellar maps and various gas properties [ref] , 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