Prospects of constraining reionization model parameters using Minkowski tensors and Betti numbers

Kapahtia, Akanksha; Chingangbam, Pravabati; Ghara, Raghunath; Appleby, Stephen; Choudhury, Tirthankar Roy

doi:10.1088/1475-7516/2021/05/026

Cited by 34 publications

(17 citation statements)

References 67 publications

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“…See however, for instance,Kapahtia et al (2021) for the attempts to constrain these astrophysical parameters, given similar observations to those we consider here. 4 Implemented using the PYTHON package pyDOE https:// pythonhosted.org/pyDOE/…”

supporting

confidence: 57%

Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks

Sabiu,

Kadota,

Asorey

et al. 2021

Preprint

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We present forecasts on the detectability of Ultra-light axion-like particles (ULAP) from future 21cm radio observations around the epoch of reionization (EoR). We show that the axion as the dominant dark matter component has a significant impact on the reionization history due to the suppression of small scale density perturbations in the early universe. This behavior depends strongly on the mass of the axion particle.Using numerical simulations of the brightness temperature field of neutral hydrogen over a large redshift range, we construct a suite of training data. This data is used to train a convolutional neural network that can build a connection between the spatial structures of the brightness temperature field and the input axion mass directly. We construct mock observations of the future Square Kilometer Array survey, SKA1-Low, and find that even in the presence of realistic noise and resolution constraints, the network is still able to predict the input axion mass. We find that the axion mass can be recovered over a wide mass range with a precision of approximately 20%, and as the whole DM contribution, the axion can be detected using SKA1-Low at 68% if the axion mass is M X < 1.86 × 10 −20 eV although this can decrease to M X < 5.25 × 10 −21 eV if we relax our assumptions on the astrophysical modeling by treating those astrophysical parameters as nuisance parameters.

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supporting

confidence: 57%

Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks

Sabiu,

Kadota,

Asorey

et al. 2021

Preprint

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“…These include the one-point statistics of the brightness temperature (Watkinson & Pritchard 2014;Shimabukuro et al 2015;Kubota et al 2016;Banet et al 2021;Gorce et al 2021), the morphological and/or topological features of the 21-cm signal (e.g. Yoshiura et al 2017;Bag et al 2019;Chen et al 2019;Elbers & van de Weygaert 2019;Kapahtia et al 2019;Gazagnes et al 2021;Kapahtia et al 2021) and the distribution of the sizes of ionised regions (Kakiichi et al 2017;Giri et al 2018aGiri et al ,b, 2019bBianco et al 2021). Alternatively, convolutional neural networks (CNNs) have been considered which are trained to be able to extract 2D or 3D features from images of the cosmic 21-cm signal and used to extract astrophysical information from mock observations (e.g.…”

Section: Introductionmentioning

confidence: 99%

Exploring the cosmic 21-cm signal from the Epoch of Reionisation using the Wavelet Scattering Transform

Greig,

Ting,

Kaurov

2022

Preprint

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Detecting the cosmic 21-cm signal during the Epoch of Reionisation and Cosmic Dawn will reveal insights into the properties of the first galaxies and advance cosmological parameter estimation. Until recently, the primary focus for astrophysical parameter inference from the 21-cm signal centred on the power spectrum (PS). However, the cosmic 21-cm signal is highly non-Gaussian rendering the PS sub-optimal for characterising the cosmic signal. In this work, we introduce a new technique to analyse the non-Gaussian information in images of the 21-cm signal called the Wavelet Scattering Transform (WST). This approach closely mirrors that of convolutional neural networks with the added advantage of not requiring tuning or training of a neural network. Instead, it compresses the 2D spatial information into a set of coefficients making it easier to interpret while also providing a robust statistical description of the non-Gaussian information contained in the cosmic 21-cm signal. First, we explore the application of the WST to mock 21-cm images to gain valuable physical insights by comparing to the known behaviour from the 21-cm PS. Then we quantitatively explore the WST applied to the 21-cm signal by extracting astrophysical parameter constraints using Fisher Matrices from a realistic 1000 hr mock observation with the Square Kilometre Array. We find that: (i) the WST applied only to 2D images can outperform the 3D spherically averaged 21-cm PS, (ii) the excision of foreground contaminated modes can degrade the constraining power by a factor of ∼ 1.5 − 2 with the WST and (iii) higher cadences between the 21-cm images can further improve the constraining power.

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“…These are also methods based on percolation theory (Iliev et al 2006;Iliev et al 2014;Furlanetto & Oh 2016;Bag et al 2018Bag et al , 2019, granulometry (Kakiichi et al 2017) and persistence theory (Elbers & van de Weygaert 2019) which have been used for the theoretical study of the topological phases of H regions during EoR. In addition to these, a method based on the Betti numbers (Giri & Mellema 2020;Kapahtia et al 2021) provide the number of connected regions, tunnels and cavities to describe the state of the IGM. A recent study of Gorce et al (2021) used a method of local variance which probes the reionization history of the observed patches of the sky as well as trace the ionization morphology.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing reionization models using the largest cluster statistics of the 21-cm maps

Aadarsh¹,

Bag²,

Majumdar³

et al. 2022

Preprint

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The evolution of topology and morphology of ionized or neutral hydrogen during different stages of the Epoch of Reionization (EoR) have the potential to provide us a great amount of information about the properties of the ionizing sources during this era. We compare a variety of reionization source models in terms of the geometrical properties of the ionized regions. We show that the percolation transition in the ionized hydrogen, as studied by tracing the evolution of the Largest Cluster Statistics (LCS), is a robust statistic that can distinguish the fundamentally different scenarios -inside-out and outside-in reionization. Particularly, the global neutral fraction at the onset of percolation is significantly higher for the inside-out scenario as compared to that for the outside-in reionization. In complementary to percolation analysis, we explore the shape and morphology of the ionized regions as they evolve in different reionization models in terms of the Shapefinders (SFs) that are ratios of the Minkowski functionals (MFs). The shape distribution can readily discern the reionization scenario with extreme non-uniform recombination in the IGM, such as the clumping model. In the rest of the reionization models, the largest ionized region abruptly grows only in terms of its third SF -'length' -during percolation while the first two SFs -'thickness' and 'breadth' -remain stable. Thus the ionized hydrogen in these scenarios becomes highly filamentary near percolation and exhibit a 'characteristic cross-section' that varies among the source models. Therefore, the geometrical studies based on SFs, together with the percolation analysis can shed light on the reionization sources.

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Prospects of constraining reionization model parameters using Minkowski tensors and Betti numbers

Cited by 34 publications

References 67 publications

Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks

Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks

Exploring the cosmic 21-cm signal from the Epoch of Reionisation using the Wavelet Scattering Transform

Distinguishing reionization models using the largest cluster statistics of the 21-cm maps

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