Building fast and accurate ways to model the distribution of neutral hydrogen during the Epoch of Reionization (EoR) is essential for interpreting upcoming 21 cm observations. A key component of semi-numerical models of reionization is the collapse fraction field $f_{\text{coll}}(\mathbf{x})$, which represents the fraction of mass within dark matter halos at each location. Using high-dynamic range N-body simulations to obtain this is computationally prohibitive and semi-analytical approaches, while being fast, end up compromising on accuracy. In this work, we bridge the gap by developing a machine learning model that can generate $f_{\text{coll}}$ maps by sampling from the full distribution of $f_{\text{coll}}$ conditioned on the dark matter density contrast $δ$. The conditional distribution functions and the input density field to the model are taken from low-dynamic range N-body simulations that are more efficient to run. We evaluate the performance of our ML model by comparing its predictions to a high-dynamic range N-body simulation. Using these $f_{\text{coll}}$ maps, we compute the HI and HII maps through a semi-numerical code for reionization. We are able to recover the large-scale HI density field power spectra $(k \lesssim 1\ h\,{\rm Mpc}^{-1})$ at the $\lesssim10\%$ level, while the HII density field is reproduced with errors well below 10% across all scales. Compared to existing semi-analytical prescriptions, our approach offers significantly improved accuracy in generating the collapse fraction field, providing a robust and efficient alternative for modeling reionization.
An emulator for the ionizing photon mean free path in ultra-high resolution simulations: the implications of mean free path measurements for the reionization history
University of Washington（华盛顿大学）；Arizona State University（亚利桑那州立大学）；University of California Riverside（加州大学河滨分校）
Using Neural Emulators and Hamiltonian Monte Carlo to constrain the Epoch of Reionization's History with the Ly$α$ Forest Power Spectrum
University of California, Santa Barbara（加州大学圣塔芭芭拉分校）；Max-Planck-Institut für Astrophysik（马克斯·普朗克天体物理学研究所）；The Ohio State University（俄亥俄州立大学）；Leiden University（莱顿大学）；University of Washington（华盛顿大学）；University of California, Berkeley（加州大学伯克利分校）
From ANN to BNN: Inferring Reionization Parameters using Uncertainty-aware Emulators of 21-cm Summaries
Indian Institute of Technology Indore（印度印多尔理工学院）；The Open University of Israel（以色列开放大学）；University of Cambridge（剑桥大学）；University of Manchester（曼彻斯特大学）；Curtin University（科廷大学）
Current and upcoming radio interferometric experiments are aiming to make a statistical characterization of the high-redshift 21cm fluctuation signal spanning the hydrogen reionization and X-ray heating epochs of the universe. However, connecting 21cm statistics to underlying physical parameters is complicated by the theoretical challenge of modeling the relevant physics at computational speeds quick enough to enable exploration of the high dimensional and weakly constrained parameter space. In this work, we use machine learning algorithms to build a fast emulator that mimics expensive simulat…
Upcoming experiments such as the SKA will provide huge quantities of data. Fast modelling of the high-redshift 21cm signal will be crucial for efficiently comparing these data sets with theory. The most detailed theoretical predictions currently come from numerical simulations and from faster but less accurate semi-numerical simulations. Recently, machine learning techniques have been proposed to emulate the behaviour of these semi-numerical simulations with drastically reduced time and computing cost. We compare the viability of five such machine learning techniques for emulating the 21cm pow…
What can we learn about Reionization astrophysical parameters using Gaussian Process Regression?
Centre for Theoretical Physics, Jamia Millia Islamia（理论物理中心，Jamia Millia Islamia大学）；Physics and Applied Mathematics Unit, Indian Statistical Institute（印度统计研究所物理与应用数学单元）；Institute of Physics, Bhubaneswar（物理研究所，Bhubaneswar）
We present a deep learning model trained to emulate the radiative transfer during the epoch of cosmological reionization. CRADLE (Cosmological Reionization And Deep LEarning) is an autoencoder convolutional neural network that uses two-dimensional maps of the star number density and the gas density field at z=6 as inputs and that predicts 3D maps of the times of reionization $\mathrm{t_{reion}}$ as outputs. These predicted single fields are sufficient to describe the global reionization history of the intergalactic medium in a given simulation. We trained the model on a given simulation and te…
Reionization, UV Luminosity and 21$\,$cm Sensitivity to Primordial Magnetic Fields: Impact of Energy Losses
Université Libre de Bruxelles（布鲁塞尔自由大学）；Institute for Nuclear Research of the Russian Academy of Sciences（俄罗斯科学院核物理研究所）；Vrije Universiteit Brussel（布鲁塞尔自由大学）
Radio Frequency Interference (RFI) is one of the systematic challenges preventing 21cm interferometric instruments from detecting the Epoch of Reionization. To mitigate the effects of RFI on data analysis pipelines, numerous inpaint techniques have been developed to restore RFI corrupted data. We examine the qualitative and quantitative errors introduced into the visibilities and power spectrum due to inpainting. We perform our analysis on simulated data as well as real data from the Hydrogen Epoch of Reionization Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural netw…
Simulations have become an indispensable tool for accurate modelling of observables measured in galaxy surveys, but can be expensive if very large dynamic range in scale is required. We describe how to combine Lagrangian perturbation theory models with N-body simulations to reduce the effects of finite computational volume in the prediction of ensemble average properties in the simulations within the context of control variates. In particular we use the fact that Zel'dovich displacements, computed during initial condition generation for any simulation, correlate strongly with the final density…
