Tools21cm: A python package to analyse the large-scale 21-cm signal from the Epoch of Reionization and Cosmic Dawn

Giri, Sambit K.; Mellema, Garrelt; Jensen, Hannes

doi:10.21105/joss.02363

Cited by 35 publications

(16 citation statements)

References 16 publications

(19 reference statements)

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“…The modules to implement RSD to them are provided in a publicly available package called TOOLS21CM (Giri et al 2020). Recently, Chapman & Santos (2019) presented a new method to implement RSD in 21-cm light cones.…”

Section: Redshift-space Distortionsmentioning

confidence: 99%

Redshift-space distortions in simulations of the 21-cm signal from the cosmic dawn

Ross

Giri

Mellema

et al. 2021

Monthly Notices of the Royal Astronomical Society

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The 21-cm signal from the Cosmic Dawn (CD) is likely to contain large fluctuations, with the most extreme astrophysical models on the verge of being ruled out by observations from radio interferometers. It is therefore vital that we understand not only the astrophysical processes governing this signal, but also other inherent processes impacting the signal itself, and in particular line-of-sight effects. Using our suite of fully numerical radiative transfer simulations, we investigate the impact on the redshifted 21-cm from the CD from one of these processes, namely the redshift-space distortions (RSDs). When RSDs are added, the resulting boost to the power spectra makes the signal more or equally detectable for our models for all redshifts, further strengthening hopes that a power spectra measurement of the CD will be possible. RSDs lead to anisotropy in the signal at the beginning and end of the CD, but not while X-ray heating is underway. The inclusion of RSDs, however, decreases detectability of the non-Gaussianity of fluctuations from inhomogeneous X-ray heating as measured by the skewness and kurtosis. On the other hand, mock observations created from all our simulations that include telescope noise corresponding to 1000 hours of observation with the Square Kilometre Array telescope show that we may be able image the CD for all heating models considered and suggest RSDs dramatically boost fluctuations coming from the inhomogeneous Ly-α background.

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Section: Redshift-space Distortionsmentioning

confidence: 99%

Redshift-space distortions in simulations of the 21-cm signal from the cosmic dawn

Ross

Giri

Mellema

et al. 2021

Monthly Notices of the Royal Astronomical Society

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“…For our archetypal 21 cm interferometer we use SKA1-Low, whose design is optimized for high signal-to-noise (S/N) images (Dewdney et al 2013). The uv coverage and instrumental noise are calculated using tools21cm 4 (Giri et al 2020). We assume a tracked scan of t daily = 6 h/day, tint = 10 s integration time, and a total t obs = 1000 h measurement.…”

Section: Instrumental Noisementioning

confidence: 99%

Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination

Prelogović,

Mesinger,

Murray

et al. 2021

Preprint

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Imaging the cosmic 21 cm signal will map out the first billion years of our Universe. The resulting 3D lightcone (LC) will encode the properties of the unseen first galaxies and physical cosmology. Unfortunately, there is no obvious summary statistic to use when interpreting this non-Gaussian image, and the commonly-used power spectrum may waste valuable information. Here we build on previous work using Convolutional Neural Networks (CNNs) to infer astrophysical parameters directly from 21 cm LC images. Guided by the properties of LCs, we combine recurrent layers characterizing evolution along the redshift axis with 2D convolutional layers characterizing local correlations in the sky-plane. Such Recursive Neural Networks (RNNs) are known for efficiently learning temporal correlations in sequential data. Using a large database of simulated cosmic 21 cm LCs, we confirm that RNNs outperform previously-used CNNs in recovering UV and X-ray galaxy properties, reducing the mean squared parameter estimation error by factors of ∼ 2 − 8. We also corrupt the cosmic signal by adding noise expected from a 1000 h integration with the Square Kilometre Array, as well as excising a foreground-contaminated "horizon wedge". Parameter prediction errors increase when the NNs are trained on these contaminated LC images, though recovery is still good even in the most pessimistic case (with R 2 ∼ > 0.5−0.95). However, we find no notable differences in performance between network architectures on the contaminated images. We argue this is due to the size of our dataset, highlighting the need for larger datasets and/or better data augmentation in order to maximize the potential of NNs in 21 cm parameter estimation.

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“…The 21cm signal is computed on the fly by the EMMA simulation code for the two Ly-𝛼 regimes (saturated and average background). The power spectrum (PS) of the simulated temperature brightness fields are computed using 21 (Giri et al 2020) in post-processing. The spherically average dimensionless power spectrum (Δ 2 (𝑘)) is computed using:…”

Section: Simulation Of the Signalmentioning

confidence: 99%

21cm signal predictions at Cosmic Dawn and Reionization with coupled radiative-hydrodynamics

Gillet,

Aubert,

Mertens

et al. 2021

Preprint

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The process of heating and reionization of the Universe at high redshift links small scale structures/galaxy formation and large scale inter-galactic medium properties. Even if the first one is difficult to observe, an observation window is opening on the second one, with the promising development of current and future radio telescopes. They will permit to observe the 21cm brightness temperature global signal and fluctuations. The need of large scale simulations is therefore strong to understand the properties of the IGM that will be observed. But at the same time the urge to resolve the structures responsible of those process is important. We introduce in this study, a coupled hydro-radiative transfer simulations of the Cosmic Dawn and Reionization with a simple sub-grid star formation process developed and calibrated on the state of the art simulation CoDaII. This scheme permits to follow consistently dark matter, hydrodynamics and radiative transfer evolution's on large scales, while the sub-grid models bridges to the galaxy formation scale. We process the simulation to produce 21cm signal as close as possible to the observations.

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Tools21cm: A python package to analyse the large-scale 21-cm signal from the Epoch of Reionization and Cosmic Dawn

Cited by 35 publications

References 16 publications

Redshift-space distortions in simulations of the 21-cm signal from the cosmic dawn

Redshift-space distortions in simulations of the 21-cm signal from the cosmic dawn

Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination

21cm signal predictions at Cosmic Dawn and Reionization with coupled radiative-hydrodynamics

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