arXiv subjects

Julien Hiegel

Publications and source records attributed to Julien Hiegel.

4 recordsLinked to original sources

arXiv2026

Inferring Cosmology and Astrophysics from the High-redshift 21cm Signal with SKA-Low

The Square Kilometre Array's low frequency telescope (SKA-Low) will enable inference of astrophysical and cosmological parameters from the redshifted 21 cm signal, probing the Cosmic Dawn and Epoch of Reionisation. While the power spectrum is the primary target for initial detection, the inherently non-Gaussian nature of the 21 cm signal, driven by the patchy evolution of ionised regions and spin temperature fluctuations, encodes rich information accessible through higher-order statistics and morphological measurements. Extracting these constraints requires diverse inference tools, encompassing both sophisticated modelling frameworks (analytical, semi-numerical, numerical, and emulators) used to predict the 21 cm signal, and advanced inference techniques (Bayesian, simulation-based, field-level) to connect statistics to the underlying physics. This chapter reviews these tools and explores the constraining power of different statistical probes accessible with SKA-Low, including the power spectrum, statistics beyond order two, moments of the signal distribution, and morphological measures. Combining these complementary statistics is crucial for breaking parameter degeneracies and unveiling the properties of the early Universe. We specifically assess the potential of the initial SKA-Low configuration (AA*) to measure galaxy and IGM properties, demonstrating its capability for early science results. This chapter forms part of a comprehensive set detailing the Epoch of Reionisation and Cosmic Dawn science case for the SKA-Low telescope.

astro-ph.CO↗

arXiv2026

Reionisation time field reconstruction from 21-cm Maps: Investigating predictor coherence in WDM cosmology

The reionisation time field treion(r) captures the entire history of cosmic reionisation by mapping the moment where each region of the Universe became ionised. Previous work has shown that treion(r) can be inferred from 21-cm observations, using convolutional neural networks (CNNs). However, these CNN predictors are trained on specific reionisation models, raising critical concerns about their reliability when applied to observational data potentially differing from their training assumptions. This paper aims to propose and test a method to evaluate the coherence of our CNN predictors with respect to their input model, thereby enabling the validation or exclusion of underlying reionisation models based on their reconstruction behaviour. By setting the CDM model as reference input, we evaluate the coherence of treion(r) reconstructions by comparing them across different redshifts for several prediction models as the statistics of treion (r) reconstructions should be the same for every redshift of the input maps. Our study particularly investigates CNNs trained on cold and warm dark matter (WDM) models, with WDM particle masses of 2, 3, 5, and 7 keV. We find that the predictors trained on 5 and 7 keV WDM models exhibit high-level self-consistency similar to the CDM predictor, while the 2 keV predictor, and to a lesser extent the 3 keV predictor, display significant deviations across several metrics. These findings seem to demonstrate that CNN predictors retain sensitivity to differences in the underlying reionisation model and can be used to assess model compatibility with observations. Our results highlight the necessity of validating machine-learning predictors against their input models before applying them to real data. The method proposed here offers a pathway to more trustworthy applications of CNNs in the study of reionisation.

astro-ph.CO↗

arXiv2023

Reionisation time fields reconstruction from 21 cm signal maps

During the Epoch of reionisation, the intergalactic medium is reionised by the UV radiation from the first generation of stars and galaxies. One tracer of the process is the 21 cm line of hydrogen that will be observed by the Square Kilometre Array (SKA) at low frequencies, thus imaging the distribution of ionised and neutral regions and their evolution. To prepare for these upcoming observations, we investigate a deep learning method to predict from 21 cm maps the reionisation time field (treion(r)), i.e. the time at which each location has been reionised. treion(r) encodes the propagation of ionisation fronts in a single field, gives access to times of local reionisation or to the extent of the radiative reach of early sources. Moreover it gives access to the time evolution of ionisation on the plane of sky, when such evolution is usually probed along the line-of-sight direction. We trained a convolutional neural network (CNN) using simulated 21 cm maps and reionisation times fields produced by the simulation code 21cmFAST . We also investigate the performance of the CNN when adding instrumental effects. Globally, we find that without instrumental effects the 21 cm maps can be used to reconstruct the associated reionisation times field in a satisfying manner: the quality of the reconstruction is dependent on the redshift at which the 21 cm observation is being made and in general it is found that small scale (<10cMpc/h) features are smoothed in the reconstructed field, while larger scale features are well recovered. When instrumental effects are included, the scale dependance of reconstruction is even further pronounced, with significant smoothing on small and intermediate scales.

astro-ph.CO↗

arXiv2023

Topology of Reionisation times: concepts, measurements and comparisons to gaussian random field predictions

In the next decade, radio telescopes like the Square Kilometer Array (SKA) will explore the Universe at high redshift, and particularly during the Epoch of Reionisation (EoR). The first structures emerged during this epoch, and their radiations have reionised the previously cold and neutral gas of the Universe creating ionised bubbles that percolate at the end of the EoR (at a redshift of approximately 6). SKA will produce 2D images of the distribution of the neutral gas at many redshifts, pushing us to develop tools and simulations to understand its properties. This paper aims at measuring topological statistics of the EoR in the "reionisation times" fields from both cosmological and semi-analytical simulations. This field informs us about the time of reionisation of the gas at each position, is used to probe the inhomogeneities of reionisation histories and can possibly be extracted from 21 cm maps. We also compare these measurements with analytical predictions from the gaussian random field (GRF) theory. The GRF theory allows us to compute many statistics of a field: PDFs of the field or its gradient, isocontour length, critical point distributions, and skeleton length. We compare these theoretical predictions to measurements made on reionisation time fields extracted from an EMMA and a 21cmFAST simulations at 1 a cMpc/h resolution. We also compared our results to GRFs generated from the fitted power spectra of the simulation maps. Both EMMA and 21cmFAST reionisation time fields (treion(r)) are close to be gaussian fields, in contrast with the 21 cm, density or ionisation fraction that are all proven to be non-gaussian. Only accelerating ionisation fronts at the end of the EoR seem to be a cause of small non-gaussianities in treion(r). Overall our results indicate that an analytical description of the reionisation percolation can be reasonably made within the framework of GRF theory.

astro-ph.CO↗