36 citations · 57 across the 11 of their papers we have counts for
11 papers
Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters
Christopher C. Lovell, Max E. Lee, William J. Roper +4
We present a new method for emulating the halo mass function (HMF) and other distribution functions in large effective volumes, down to low halo masses, whilst simultaneously modif…
Galactification: painting galaxies onto dark matter only simulations using a transformer-based model
Shivam Pandey, Christopher C. Lovell, Chirag Modi +1
Connecting the formation and evolution of galaxies to the large-scale structure is crucial for interpreting cosmological observations. While hydrodynamical simulations accurately m…
Symbolic Emulators for Cosmology: Accelerating Cosmological Analyses Without Sacrificing Precision
Deaglan J. Bartlett, Shivam Pandey
In cosmology, emulators play a crucial role by providing fast and accurate predictions of complex physical models, enabling efficient exploration of high-dimensional parameter spac…
Fisher Score Matching for Simulation-Based Forecasting and Inference
Ce Sui, Shivam Pandey, Benjamin D. Wandelt
We propose a method for estimating the Fisher score--the gradient of the log-likelihood with respect to model parameters--using score matching. By introducing a latent parameter mo…
The effect of intrinsic alignments on weak lensing statistics in hydrodynamical simulations
Max E. Lee, Zoltán Haiman, Shivam Pandey +1
The next generation of weak gravitational lensing surveys has the potential to place stringent constraints on cosmological parameters. However, their analysis is limited by systema…
syren-new: Precise formulae for the linear and nonlinear matter power spectra with massive neutrinos and dynamical dark energy
Ce Sui, Deaglan J. Bartlett, Shivam Pandey +3
Current and future large scale structure surveys aim to constrain the neutrino mass and the equation of state of dark energy. We aim to construct accurate and interpretable symboli…
Teaching dark matter simulations to speak the halo language
Shivam Pandey, Francois Lanusse, Chirag Modi +1
We develop a transformer-based conditional generative model for discrete point objects and their properties. We use it to build a model for populating cosmological simulations with…
CHARM: Creating Halos with Auto-Regressive Multi-stage networks
Shivam Pandey, Chirag Modi, Benjamin D. Wandelt +7
To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations…
Zooming by in the CARPoolGP lane: new CAMELS-TNG simulations of zoomed-in massive halos
Max E. Lee, Shy Genel, Benjamin D. Wandelt +10
Galaxy formation models within cosmological hydrodynamical simulations contain numerous parameters with non-trivial influences over the resulting properties of simulated cosmic str…
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier +12
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inferenc…
Predicting the impact of feedback on matter clustering with machine learning in CAMELS
Ana Maria Delgado, Daniel Angles-Alcazar, Leander Thiele +7
Extracting information from the total matter power spectrum with the precision needed for upcoming cosmological surveys requires unraveling the complex effects of galaxy formation…