arXiv subjects

Benoit Semelin

Publications and source records attributed to Benoit Semelin.

At least 19 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.

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arXiv2026

Overview of 21cm Experiments at high redshift with SKAO

We provide an overview of the eight SKAO Science Book chapters that motivate the Epoch of Reionisation and Cosmic Dawn experiments with SKA-Low. We describe the individual SKA-Low experiments and expected sensitivity - power spectrum, tomography, 21-cm forest, cross-correlations, building on the broad observational plan laid out in the 2015 SKA Science Book. Finally, we outline features of the telescope that will be critical for the success of EoR/CD science, e.g., beam apodization, substations, and multi-beaming.

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arXiv2025

Generative Models of 21cm EoR Lightcones with 3D Scattering Transforms

The 21cm signal from the Epoch of Reionization (EoR) is observed as a three-dimensional data set known as a lightcone, consisting of a redshift (frequency) axis and two spatial sky plane axes. When observed by radio interferometers, this EoR signal is strongly obscured by foregrounds that are several orders of magnitude stronger. Due to its inherently non-Gaussian nature, the EoR signal requires robust statistical tools to accurately separate it from these foreground contaminants, but current foreground separation techniques focus primarily on recovering the EoR power spectrum, often neglecting valuable non-Gaussian information. Recent developments in astrophysics, particularly in the context of the Galactic interstellar medium, have demonstrated the efficacy of scattering transforms - novel summary statistics for highly non-Gaussian processes - for component separation tasks. Motivated by these advances, we extend the scattering transform formalism from two-dimensional data sets to three-dimensional EoR lightcones. To this end, we introduce a 3D wavelet set from the tensor product of 2D isotropic wavelets in the sky plane domain and 1D wavelets in the redshift domain. As generative models form the basis of component separation, our focus here is on building and validating generative models that can be used for component separation in future projects. To achieve this, we construct maximum entropy generative models to synthesise EoR lightcones, and statistically validate the generative model by quantitatively comparing the synthesised EoR lightcones with the single target lightcone used to construct them, using independent statistics such as the power spectrum and Minkowski Functionals. The synthesised lightcones agree well with the target lightcone both statistically and visually, opening up the possibility of developing for component separation methods using 3D scattering transforms.

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arXiv2024

Combining summary statistics with simulation-based inference for the 21 cm signal from the Epoch of Reionization

The 21 cm signal from the Epoch of Reionization will be observed with the up-coming Square Kilometer Array (SKA). SKA should yield a full tomography of the signal which opens the possibility to explore its non-Gaussian properties. How can we extract the maximum information from the tomography and derive the tightest constraint on the signal? In this work, instead of looking for the most informative summary statistics, we investigate how to combine the information from two sets of summary statistics using simulation-based inference. To this purpose, we train Neural Density Estimators (NDE) to fit the implicit likelihood of our model, the LICORICE code, using the Loreli II database. We train three different NDEs: one to perform Bayesian inference on the power spectrum, one to do it on the linear moments of the Pixel Distribution Function (PDF) and one to work with the combination of the two. We perform $\sim 900$ inferences at different points in our parameter space and use them to assess both the validity of our posteriors with Simulation-based Calibration (SBC) and the typical gain obtained by combining summary statistics. We find that our posteriors are biased by no more than $\sim 20 \%$ of their standard deviation and under-confident by no more than $\sim 15 \%$. Then, we establish that combining summary statistics produces a contraction of the 4-D volume of the posterior (derived from the generalized variance) in 91.5 % of our cases, and in 70 to 80 % of the cases for the marginalized 1-D posteriors. The median volume variation is a contraction of a factor of a few for the 4D posteriors and a contraction of 20 to 30 % in the case of the marginalized 1D posteriors. This shows that our approach is a possible alternative to looking for sufficient statistics in the theoretical sense.

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arXiv2024

Comparison of Bayesian inference methods using the Loreli II database of hydro-radiative simulations of the 21-cm signal

While the observation of the 21 cm signal from the Cosmic Dawn and Epoch of Reionization is an instrumental challenge, the interpretation of a prospective detection is still open to questions regarding the modelling of the signal and the Bayesian inference techniques that bridge the gap between theory and observations. To address some of these questions, we present Loreli II, a database of nearly 10 000 simulations of the 21 cm signal run with the Licorice 3D radiative transfer code. With Loreli II, we explore a 5-dimensional astrophysical parameter space where star formation, X-ray emissions, and UV emissions are varied. We then use this database to train neural networks and perform Bayesian inference on 21 cm power spectra affected by thermal noise at the level of 100 hours of observation with the Square Kilometer Array. We study and compare three inference techniques : an emulator of the power spectrum, a Neural Density Estimator that fits the implicit likelihood of the model, and a Bayesian Neural Network that directly fits the posterior distribution. We measure the performances of each method by comparing them on a statistically representative set of inferences, notably using the principles of Simulation-Based Calibration. We report errors on the 1-D marginalized posteriors (biases and over/under confidence) below $15 \%$ of the standard deviation for the emulator and below $25 \%$ for the other methods. We conclude that at our noise level and our sampling density of the parameter space, an explicit Gaussian likelihood is sufficient. This may not be the case at lower noise level or if a denser sampling is used to reach higher accuracy. We then apply the emulator method to recent HERA upper limits and report weak constraints on the X-ray emissivity parameter of our model.

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arXiv2023

Wavelet Based Statistics for Enhanced 21cm EoR Parameter Constraints

We propose a new approach to improve the precision of astrophysical parameter constraints for the 21cm signal from the epoch of reionisation (EoR). Our method introduces new sets of summary statistics, hereafter `evolution compressed' statistics, which quantify the spectral evolution of the 2D spatial statistics computed at fixed redshift. We defined such compressed statistics for power spectrum (PS), wavelet scattering transforms (WST), and wavelet moments (WM), which also characterise non-Gaussian features. To compare these different statistics, along with the 3D power spectrum, we estimated their Fisher information on three cosmological parameters from an ensemble of simulations of 21cm EoR data, both in noiseless and noisy scenarios using Square Kilometre Array (SKA) noise levels equivalent to 100 and 1000 hours of observations. We also compare wavelet statistics, in particular WST, built from standard directional Morlet wavelets, as well as from a set of isotropic wavelets derived from the binning window function of the 2D power spectrum. For the noiseless case, the compressed wavelet statistics give constraints that are up to five times more precise than those obtained from the 3D isotropic power spectrum. At the same time, for 100h SKA noise, from which it is difficult to extract non-Gaussian features, compressed wavelet statistics still give over 30\% tighter constraints. We find that the wavelet statistics with wavelets derived from the power-spectrum binning window function provide the tightest constraints of all the statistics, with the WSTs seemingly performing better than the WMs, in particular when working with noisy data. The findings of this study demonstrate that evolution-compressed statistics extract more information than usual 3D isotropic power-spectra approaches and that our wavelet-based statistics can consistently outmatch power-spectrum-based statistics.

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arXiv2023

The LoReLi database: 21 cm signal inference with 3D radiative hydrodynamics simulations

The Square Kilometer array is expected to measure the 21cm signal from the Epoch of Reionization (EoR) in the coming decade, and its pathfinders may provide a statistical detection even earlier. The currently reported upper limits provide tentative constraints on the astrophysical parameters of the models of the EoR. In order to interpret such data with 3D radiative hydrodynamics simulations using Bayesian inference, we present the latest developments of the \textsc{Licorice} code. Relying on an implementation of the halo conditional mass function to account for unresolved star formation, this code now allows accurate simulations of the EoR at $256^3$ resolution. We use this version of \textsc{Licorice} to produce the first iteration of \textsc{LoReLi}, a public dataset now containing hundreds of 21cm signals computed from radiative hydrodynamics simulations. We train a neural network on \textsc{LoReLi} to provide a fast emulator of the \textsc{Licorice} power spectra, \textsc{LorEMU}, which has $\sim 5\%$ rms error relative to the simulated signals. \textsc{LorEMU} is used in a Markov Chain Monte Carlo framework to perform Bayesian inference, first on a mock observation composed of a simulated signal and thermal noise corresponding to 100h observations with the SKA. We then apply our inference pipeline to the latest measurements from the HERA interferometer. We report constraints on the X-ray emissivity, and confirm that cold reionization scenarios are unlikely to accurately represent our Universe.

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arXiv2023

Accurate modelling of the Lyman-$\alpha$ coupling for the 21-cm signal, observability with NenuFAR and SKA

The measurement of the $21$ cm signal from the Cosmic Dawn is a major goal for several existing and upcoming radio interferometers such as NenuFAR and the SKA. During this era before the beginning of the Epoch of Reionization, the signal is more difficult to observe due to brighter foregrounds but reveals additional information on the underlying astrophysical processes encoded in the spatial fluctuations of the spin temperature of hydrogen. To interpret future measurements, controlling the level of accuracy of the Lyman-$\alpha$ flux modelling is mandatory. In this work, we evaluate the impact of various approximations that exist in the main fast modelling approach compared to the results of a costly full radiative transfer simulation. The fast SPINTER code, presented in this work, computes the Lyman-$\alpha$ flux including the effect of wing scatterings for an inhomogeneous emissivity field, but assuming an otherwise homogeneous expanding universe. The LICORICE code computes the full radiative transfer in the Lyman-$\alpha$ line without any substantial approximation. We find that the difference between homogeneous and inhomogeneous gas density and temperature is very small for the computed flux. On the contrary, neglecting the effect of gas velocities produces a significant change in the computed flux. We identify the causes (mainly Doppler shifts due to velocity gradients) and quantify the magnitude of the effect in both an idealised setup and a realistic cosmological situation. We find that the amplitude of the effect, up to a factor of $\sim 2$ on the $21$ cm signal power spectrum on some scales (depending on both other model parameters and the redshift), can be easily discriminated with an SKA-like survey and already be approached, particularly for exotic signals, by the ongoing NenuFAR Cosmic Dawn Key Science Program.

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arXiv2022

A bubble size distribution model for the Epoch of Reionization

The bubble size distribution is a summary statistics that can be computed from the observed 21-cm signal from the Epoch of Reionization. As it depends only on the ionization field and is not limited to gaussian information, it is an interesting probe, complementary to the power spectrum of the full 21-cm signal. Devising a flexible and reliable theoretical model for the bubble size distribution paves the way for using it for astrophysical parameters inference. The proposed model is built from the excursion set theory and a functional relation between the bubble volume and the collapsed mass in the bubble. Unlike previous models it accommodates any functional relation or distributions. Using parameterized relations allows us to test the predictive power of the model by performing a minimization best-fit to the bubble size distribution obtained from a high resolution, fully coupled radiative hydrodynamics simulations, HIRRAH-21. Our model is able to provide a better fit to the numerical bubble size distribution at ionization fraction of $x_{\text{H}_{\text{II}}} \sim 1\%$ and $3\%$ than other existing models. Moreover, the bubble volume to collapsed mass relation corresponding to the best-fit parameters, which is not an observable, is compared to numerical simulation data. A good match is obtained, confirming the possibility to infer this relation from an observed bubble size distribution using our model. Finally we present a simple algorithm that empirically implements the process of percolation. We show that it extends the usability of our bubble size distribution model up to $x_{\text{H}_{\text{II}}} \sim 30\%$.

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arXiv2019

Peering into the Dark (Ages) with Low-Frequency Space Interferometers

Neutral hydrogen pervades the infant Universe, and its redshifted 21-cm signal allows one to chart the Universe. This signal allows one to probe astrophysical processes such as the formation of the first stars, galaxies, (super)massive black holes and enrichment of the pristine gas from z~6 to z~30, as well as fundamental physics related to gravity, dark matter, dark energy and particle physics at redshifts beyond that. As one enters the Dark Ages (z>30), the Universe becomes pristine. Ground-based low-frequency radio telescopes aim to detect the spatial fluctuations of the 21-cm signal. Complementary, global 21-cm experiments aim to measure the sky-averaged 21-cm signal. Escaping RFI and the ionosphere has motivated space-based missions, such as the Dutch-Chinese NCLE instrument (currently in lunar L2), the proposed US-driven lunar or space-based instruments DAPPER and FARSIDE, the lunar-orbit interferometer DSL (China), and PRATUSH (India). To push beyond the current z~25 frontier, though, and measure both the global and spatial fluctuations (power-spectra/tomography) of the 21-cm signal, low-frequency (1-100MHz; BW~50MHz; z>13) space-based interferometers with vast scalable collecting areas (1-10-100 km2), large filling factors (~1) and large fields-of-view (4pi sr.) are needed over a mission lifetime of >5 years. In this ESA White Paper, we argue for the development of new technologies enabling interferometers to be deployed, in space (e.g. Earth-Sun L2) or in the lunar vicinity (e.g. surface, orbit or Earth-Moon L2), to target this 21-cm signal. This places them in a stable environment beyond the reach of most RFI from Earth and its ionospheric corruptions, enabling them to probe the Dark Ages as well as the Cosmic Dawn, and allowing one to investigate new (astro)physics that is inaccessible in any other way in the coming decades. [Abridged]

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arXiv2019

Improved supervised learning methods for EoR parameters reconstruction

Within the next few years, the Square Kilometer Array (SKA) or one of its pathfinders will hopefully provide a detection of the 21-cm signal fluctuations from the Epoch of Reionization (EoR). Then, the main goal will be to accurately constrain the underlying astrophysical parameters. Currently, this is mainly done with Bayesian inference using Markov Chain Monte Carlo sampling. Recently, studies using neural networks trained to performed inverse modelling have shown interesting results. We build on these by improving the accuracy of the predictions using neural network and exploring other supervised learning methods: the kernel and ridge regressions. Based on a large training set of 21-cm power spectra, we compare the performances of these supervised learning methods. When using an un-noised signal as input, we improve on previous neural network accuracy by one order of magnitude and, using local ridge kernel regression, we gain another factor of a few. We then reach a rms prediction error of a few percents of the 1-sigma confidence level due to SKA thermal noise (as estimated with Bayesian inference). This last performance level requires optimizing the hyper-parameters of the method: how to do that perfectly in the case of an unknown signal remains an open question. For an input signal altered by a SKA-type thermal noise, our neural network recovers the astrophysical parameter values with an error within half of the 1$\sigma$ confidence level due to the SKA thermal noise. This accuracy improves to 10$\%$ of the 1$\sigma$ level when using the local ridge kernel regression (with optimized hyper-parameters). We are thus reaching a performance level where supervised learning methods are a viable alternative to determine the best-fit parameters values.

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arXiv2018

Imprints of quasar duty cycle on the 21cm signal from the Epoch of Reionization

Quasars contribute to the 21-cm signal from the Epoch of Reionization (EoR) primarily through their ionizing UV and X-ray emission. However, their radio continuum and Lyman-band emission also regulates the 21-cm signal in their direct environment, potentially leaving the imprint of their duty cycle. We develop a model for the radio and UV luminosity functions of quasars from the EoR, and constrain it using recent observations. Our model is consistent with the z~7.5 quasar from Banados et al 2017, and also predicts only a few quasars suitable for 21-cm forest observations (10mJy) in the sky. We exhibit a new effect on the 21-cm signal observed against the CMB: a radio-loud quasar can leave the imprint of its duty cycle on the 21-cm tomography. We apply this effect in a cosmological simulation and conclude that the effect of typical radio-loud quasars is most likely negligible in an SKA field of view. For a 1-10mJy quasar the effect is stronger though hardly observable at SKA resolution. Then we study the contribution of the lyman band Ly-alpha to Ly-beta) emission of quasars to the Wouthuisen-Field coupling. The collective effect of quasars on the 21-cm power spectrum is larger than the thermal noise at low k, though featureless. However, a distinctive pattern around the brightest quasars in an SKA field of view may be observable in the tomography, encoding the duration of their duty cycle. This pattern has a high signal-to-noise ratio for the brightest quasar in a typical SKA shallow survey.

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arXiv2018

SEURAT: SPH scheme extended with ultraviolet line radiative transfer

We present a novel Lyman alpha (Ly$\alpha$) radiative transfer code, SEURAT, where line scatterings are solved adaptively with the resolution of the smoothed particle hydrodynamics (SPH). The radiative transfer method implemented in SEURAT is based on a Monte Carlo algorithm in which the scattering and absorption by dust are also incorporated. We perform standard test calculations to verify the validity of the code; (i) emergent spectra from a static uniform sphere, (ii) emergent spectra from an expanding uniform sphere, and (iii) escape fraction from a dusty slab. Thereby we demonstrate that our code solves the Ly$\alpha$ radiative transfer with sufficient accuracy. We emphasise that SEURAT can treat the transfer of Ly$\alpha$ photons even in highly complex systems that have significantly inhomogeneous density fields. The high adaptivity of SEURAT is desirable to solve the propagation of Ly$\alpha$ photons in the interstellar medium of young star-forming galaxies like Ly$\alpha$ emitters (LAEs). Thus, SEURAT provides a powerful tool to model the emergent spectra of Ly$\alpha$ emission, which can be compared to the observations of LAEs.

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arXiv2017

21SSD: a public database of simulated 21-cm signals from the epoch of reionization

The 21-cm signal from the Epoch of Reionization (EoR) is expected to be detected in the next few years, either with existing instruments or by the upcoming SKA and HERA projects. In this context there is a pressing need for publicly available high-quality templates covering a wide range of possible signals. These are needed both for end-to-end simulations of the up-coming instruments, as well as to develop signal analysis methods. In this work we present such a set of templates, publicly available, for download at https://21ssd.obspm.fr/. The database contains 21-cm brightness temperature lightcones at high and low resolution, and several derived statistical quantities for 45 models spanning our choice of 3D parameter space. These data are the result of fully coupled radiative hydrodynamic high resolution ($1024^3$) simulations performed with the LICORICE code. Both X-ray and Lyman line transfer is performed to account for heating and Wouthuysen-Field coupling fluctuations. We also present a first exploitation of the data using the power spectrum and the Pixel Distribution Function (PDF) as functions of redshifts, computed from lightcone data. We analyse how these two quantities behave when varying the model parameters while taking into account the thermal noise expected of a typical SKA survey. Finally, we show that the power spectrum and the PDF have different -- and to some extent complementary -- abilities to distinguish between different models. This opens the door to formulating an optimal sampling of the parameter space, dependant on the chosen diagnostics.

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arXiv2017

Analysing the 21cm signal from the Epoch of Reionization with artificial neural networks

The 21 cm signal from the Epoch of Reionization should be observed within the next decade. While a simple statistical detection is expected with SKA pathfinders, the SKA will hopefully produce a full 3D mapping of the signal. To extract from the observed data constraints on the parameters describing the underlying astrophysical processes, inversion methods must be developed. For example, the Markov Chain Monte Carlo method has been successfully applied. Here we test another possible inversion method: artificial neural networks (ANN). We produce a training set which consists of 70 individual sample. Each sample is made of the 21 cm power spectrum at different redshifts produced with the 21cmFast code plus the value of three parameters used in the semi-numerical simulations that describe astrophysical processes. Using this set we train the network to minimize the error between the parameter values it produces as an output and the true values. We explore the impact of the architecture of the network on the quality of the training. Then we test the trained network on the new set of 54 test samples with different values of the parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameters at a given redshift, that including thermal noise and sample variance decreases the quality of the reconstruction and that using the power spectrum at several redshifts as an input to the ANN improves the quality of the reconstruction. We conclude that ANNs are a viable inversion method whose main strength is that they require a sparse exploration of the parameter space and thus should be usable with full numerical simulations.

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arXiv2015

Detailed modelling of the 21-cm Forest

The 21-cm forest is a promising probe of the Epoch of Reionization. The local state of the intergalactic medium (IGM) is encoded in the spectrum of a background source (radio-loud quasars or gamma ray burst afterglow) by absorption at the local 21-cm wavelength, resulting in a continuous and fluctuating absorption level. Small-scale structures (filaments and minihaloes) in the IGM are responsible for the strongest absorption features. The absorption can also be modulated on large scales by inhomogeneous heating and Wouthuysen-Field coupling. We present the results from a simulation that attempts to preserve the cosmological environment while resolving some of the small-scale structures (a few kpc resolution in a 50 Mpc/h box). The simulation couples the dynamics and the ionizing radiative transfer and includes X-ray and Lyman lines radiative transfer for a detailed physical modelling. As a result we find that soft X-ray self-shielding, Lyman-alpha self-shielding and shock heating all have an impact on the predicted values of the 21-cm optical depth of moderately overdense structures like filaments. An correct treatment of the peculiar velocities is also critical. Modelling these processes seems necessary for accurate predictions and can be done only at high enough resolution. As a result, based on our fiducial model, we estimate that LOFAR should be able to detect a few (strong) absorptions features in a frequency range of a few tens of MHz for a 20 mJy source located at z=10, while the SKA would extract a large fraction of the absorption information for the same source.

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arXiv2015

The physics of Reionization: processes relevant for SKA observations

The local intensity of the 21 cm signal emitted during the Epoch of Reionization that will be mapped by the SKA is modulated by the amount of neutral hydrogen. Consequently, understanding the process of reionization of the intergalactic medium (IGM) is crucial for predicting and interpreting the upcoming observations. After presenting the basic physics and most meaningful quantities pertaining to the process of reionization, we will review recent progress in our understanding of the production and escape of ionizing photons in primordial galaxies and of their absorption in the IGM especially in so-called minihalos and Lyman Limit Systems.

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arXiv2014

Light cone anisotropy in 21 cm signal from the epoch of reionization

Using a suite of detailed numerical simulations we estimate the level of anisotropy generated by the time evolution along the light cone of the 21cm signal from the epoch of reionization. Our simulations include the physics necessary to model the signal during both the late emission regime and the early absorption regime, namely X-ray and Lyman-band 3D radiative transfer in addition to the usual dynamics and ionizing UV transfer. The signal is analysed using correlation functions perpendicular and parallel to the line of sight (LOS). We reproduce general findings from previous theoretical studies: the overall amplitude of the correlations and the fact that the light cone anisotropy is visible only on large scales (100 cMpc). However, the detailed behaviour is different. At 3 different epochs, the amplitude of the correlations along and perpendicular to the LOS differ from each other, indicating anisotropy. These 3 epochs are associated with 3 events of the global reionization history: the overlap of ionized bubbles, the onset of mild heating by X-rays in regions around the sources, and the onset of efficient Lyman-alpha coupling in regions around the sources. A 20x20 deg^2 survey area may be necessary to mitigate sample variance when we use the directional correlation functions. On a 100 cMpc scale the light cone anisotropy dominates over the anisotropy generated by peculiar velocity gradients computed in the linear regime. By modelling instrumental noise and limited resolution, we find that the anisotropy should be easily detectable by the SKA, assuming perfect foreground removal, the limiting factor being a large enough survey size. In the case of the LOFAR, it is likely that only first anisotropy episode will fall in the observing frequency range and will be detectable only if sample variance is much reduced (i.e. a larger than 20x20 deg^2 survey, which is not presently planned).

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