A novel estimator for the equation of state of the IGM by Ly α forest tomography

Müller, Hendrik; Behrens, Christoph; Marsh, David J. E.

doi:10.1093/mnras/stab906

Cited by 6 publications

(4 citation statements)

References 83 publications

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“…In the real Universe, photo-heating will change the gas pressure, and self consistent modeling would include hydrodynamics (e.g., as carried out by La Plante et al 2017). Including information on adjacent sightlines could in principle allow the NN to disentangle the effects of pressure smoothing which occur in three dimensions, and thermal broadening along the line-of-sight (see e.g., Peeples et al 2010;Müller et al 2021).…”

Section: Discussionmentioning

confidence: 99%

“…If we take more than one spectrum as the input, for example capturing information about nearby pixels in three-dimensions, this would feed more information into the NN at once. Recently, Müller et al (2021) have shown how the use of such tomographic data can lead to improvements in temperature measurement from the forest. Because the source positions are unstructured (sightlines do not lie on a regular grid, but are essentially randomly distributed), this will require development of analysis techniques that that take this into account (e.g., Metcalf et al 2020).…”

Section: Potential Improvements To Nn Architecturementioning

confidence: 99%

“…More recent ideas include the reconstruction techniques of Müller et al (2021) that use 3D tomographic data. In the current work we will use a Neural Network (NN, see below) trained on simulated Ly𝛼 spectra to infer the temperature from the smoothness of absorption features.…”

Section: Introductionmentioning

confidence: 99%

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Deep Forest: Neural Network reconstruction of intergalactic medium temperature

Wang,

Croft,

Shaw

2021

Preprint

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We explore the use of Deep Learning to infer the temperature of the intergalactic medium from the transmitted flux in the high redshift Ly𝛼 forest. We train Neural Networks on sets of simulated spectra from redshift 𝑧 = 2 − 3 outputs of cosmological hydrodynamic simulations, including high temperature regions added in post-processing to approximate bubbles heated by Helium-II reionization. We evaluate how well the trained networks are able to reconstruct the temperature from the effect of Doppler broadening in the simulated input Ly𝛼 forest absorption spectra. We find that for spectra with high resolution (10 km s −1 pixel) and moderate signal to noise (20-50), the neural network is able to reconstruct the IGM temperature smoothed on scales of ∼ 6ℎ −1 Mpc quite well. Concentrating on discontinuities we find that high temperature regions of width 25ℎ −1 Mpc and temperature 20, 000K can be fairly easily detected and characterized. We show an example where multiple sightlines are combined to yield tomographic images of hot bubbles. Deep Learning techniques may be useful in this way to help us understand the complex temperature structure of the intergalactic medium around the time of Helium reionization.

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Section: Discussionmentioning

confidence: 99%

Section: Potential Improvements To Nn Architecturementioning

confidence: 99%

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Deep Forest: Neural Network reconstruction of intergalactic medium temperature

Wang,

Croft,

Shaw

2021

Preprint

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“…Ill-posed inverse problems arise in a variety of settings, for example, in medium scattering experiments (e.g., Colton & Kress 2013), medical imaging (e.g., see the review in Spencer & Bi 2020), or microscopy. In an astrophysical context, ill-posed inverse problems include, among others with respect to Lyα forest tomography (Lee et al 2018;Müller et al 2020bMüller et al , 2021, lensing (e.g., see the review Mandelbaum 2018), or (helio)seismology (Gizon et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Identifying synergies between VLBI and STIX imaging

Müller,

Massa,

Mus

et al. 2024

A&A

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Reconstructing an image from noisy, sparsely sampled Fourier data is an ill-posed inverse problem that occurs in a variety of subjects within science, including data analysis for Very Long Baseline Interferometry (VLBI) and the Spectrometer/Telescope for Imaging X-rays (STIX) with respect to solar observations. The need for high-resolution, high-fidelity imaging fosters the active development of a range of novel imaging algorithms in a variety of different algorithmic settings. However, despite these ongoing, parallel developments, such synergies remain unexplored. We study, for the first time, the synergies between the data analysis for the STIX instrument and VLBI. In particular, we compare the methodologies that have been developed in both fields and evaluate their potential. In this way, we identify key trends in the performance of several algorithmic ideas and draw recommendations for the future spending of resources in the study and implementation of novel imaging algorithms. To this end, we organized a semi-blind imaging challenge with data sets and source structures that are typical for sparse VLBI, specifically in the context of the Event Horizon Telescope (EHT) as well as STIX observations. We used 17 different algorithms from both communities, from six different imaging frameworks, in the challenge, making this work the largest scale code comparison for STIX and VLBI to date. We identified strong synergies between the two communities, as proven by the success of the imaging methods proposed for STIX in imaging VLBI data sets and vice versa. Novel imaging methods outperform the standard CLEAN algorithm significantly in every test case. Improvements over the performance of CLEAN offer deeper updates to the inverse modeling pipeline necessary or, consequently, the possibility to replace inverse modeling with forward modeling. Entropy-based methods and Bayesian methods perform best on STIX data. The more complex imaging algorithms utilizing multiple regularization terms (recently proposed for VLBI) add little to no additional improvements for STIX. However, they do outperform the other methods on EHT data, which correspond to a larger number of angular scales. This work demonstrates the great synergy between the STIX and VLBI imaging efforts and the great potential for common developments. The comparison identifies key trends on the efficacy of specific algorithmic ideas for the VLBI and the STIX setting that may evolve into a roadmap for future developments.

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Deep forest: neural network reconstruction of intergalactic medium temperature

Wang

Croft

Shaw

2022

Monthly Notices of the Royal Astronomical Society

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We explore the use of deep learning to infer the temperature of the intergalactic medium from the transmitted flux in the high-redshift Ly α forest. We train neural networks on sets of simulated spectra from redshift z = 2–3 outputs of cosmological hydrodynamic simulations, including high-temperature regions added in post-processing to approximate bubbles heated by He ii reionization. We evaluate how well the trained networks are able to reconstruct the temperature from the effect of Doppler broadening in the simulated input Ly α forest absorption spectra. We find that for spectra with high resolution (10 $\, {\rm km}\, {\rm s}^{-1}$ pixel) and moderate signal-to-noise ratio (20–50), the neural network is able to reconstruct the intergalactic medium temperature smoothed on scales of $\sim 6 \, h^{-1}\, {\rm Mpc}$ quite well. Concentrating on discontinuities, we find that high-temperature regions of width $25 \, h^{-1}\, {\rm Mpc}$ and temperature $20\, 000$ K can be fairly easily detected and characterized. We show an example where multiple sightlines are combined to yield tomographic images of hot bubbles. Deep learning techniques may be useful in this way to help us understand the complex temperature structure of the intergalactic medium around the time of helium reionization.

show abstract

A novel estimator for the equation of state of the IGM by Ly α forest tomography

Cited by 6 publications

References 83 publications

Deep Forest: Neural Network reconstruction of intergalactic medium temperature

Deep Forest: Neural Network reconstruction of intergalactic medium temperature

Identifying synergies between VLBI and STIX imaging

Deep forest: neural network reconstruction of intergalactic medium temperature

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