arXiv subjects

Hendrik Müller

Publications and source records attributed to Hendrik Müller.

23 records · Page 2Linked to original sources

arXiv2023

Multi-scale and Multi-directional VLBI Imaging with CLEAN

Very long baseline interferometry (VLBI) is a radio-astronomical technique in which the correlated signal from various baselines is combined into an image of highest angular resolution. Due to sparsity of the measurements, this imaging procedure constitutes an ill-posed inverse problem. For decades the CLEAN algorithm was the standard choice in VLBI studies, although having some serious disadvantages and pathologies that are challenged by the requirements of modern frontline VLBI applications. We develop a novel multi-scale CLEAN deconvolution method (DoB-CLEAN) based on continuous wavelet transforms that address several pathologies in CLEAN imaging. We benchmark this novel algorithm against CLEAN reconstructions on synthetic data and reanalyze BL Lac observations of RadioAstron with DoB-CLEAN. DoB-CLEAN approaches the image by multi-scalar and multi-directional wavelet dictionaries. Two different dictionaries are used. Firstly, a difference of elliptical spherical Bessel functions dictionary fitted to the uv-coverage of the observation that is used to sparsely represent the features in the dirty image. Secondly, a difference of elliptical Gaussian wavelet dictionary that is well suited to represent relevant image features cleanly. The deconvolution is performed by switching between the dictionaries. DoB-CLEAN achieves super-resolution compared to CLEAN and remedies the spurious regularization properties of CLEAN. In contrast to CLEAN, the representation by basis functions has a physical meaning. Hence, the computed deconvolved image still fits the observed visibilities, opposed to CLEAN. State-of-the-art multi-scalar imaging approaches seem to outperform single-scalar standard approaches in VLBI and are well suited to maximize the extraction of information in ongoing frontline VLBI applications.

astro-ph.IM↗

arXiv2022

Searching for dilaton fields in the Ly$α$ forest

Dilatons (and moduli) couple to the masses and coupling constants of ordinary matter, and these quantities are fixed by the local value of the dilaton field. If, in addition, the dilaton with mass $m_ϕ$ contributes to the cosmic dark matter density, then such quantities oscillate in time at the dilaton Compton frequency. We show how these oscillations lead to broadening and shifting of the Voigt profile of the Ly$α$ forest, in a manner that is correlated with the local dark matter density. We further show how tomographic methods allow the effect to be reconstructed by observing the Ly$α$ forest spectrum of distant quasars. We then simulate a large number of quasar lines of sight using the lognormal density field, and forecast the ability of future astronomical surveys to measure this effect. We find that in the ultra low mass range $10^{-32}\text{ eV}\leq m_ϕ\leq 10^{-28}\text{ eV}$ upcoming observations can improve over existing limits to the dilaton electron mass and fine structure constant couplings set by fifth force searches by up to five orders of magnitude. Our projected limits apply assuming that the ultralight dilaton makes up a few percent of the dark matter density, consistent with upper limits set by the cosmic microwave background anisotropies.

astro-ph.CO↗

arXiv2022

DoG-HiT: A novel VLBI Multiscale Imaging Approach

Reconstructing images from very long baseline interferometry (VLBI) data with sparse sampling of the Fourier domain (uv-coverage) constitutes an ill-posed deconvolution problem. It requires application of robust algorithms maximizing the information extraction from all of the sampled spatial scales and minimizing the influence of the unsampled scales on image quality. We develop a new multiscale wavelet deconvolution algorithm DoG-HiT for imaging sparsely sampled interferometric data which combines the difference of Gaussian (DoG) wavelets and hard image thresholding (HiT). Based on DoG-HiT, we propose a multi-step imaging pipeline for analysis of interferometric data. DoG-HiT applies the compressed sensing approach to imaging by employing a flexible DoG wavelet dictionary which is designed to adapt smoothly to the uv-coverage. It uses closure properties as data fidelity terms only initially and perform non-convex, non-smooth optimization by an amplitude conserving and total flux conserving hard thresholding splitting. DoG-HiT calculates a multiresolution support as a side product. The final reconstruction is refined through self-calibration loops and imaging with amplitude and phase information applied for the multiresolution support only. We demonstrate the stability of DoG-HiT and benchmark its performance against image reconstructions made with CLEAN and Regularized Maximum-Likelihood (RML) methods using synthetic data. The comparison shows that DoG-HiT matches the superresolution achieved by the RML reconstructions and surpasses the sensitivity to extended emission reached by CLEAN. Application of regularized maximum likelihood methods outfitted with flexible multiscale wavelet dictionaries to imaging of interferometric data matches the performance of state-of-the art convex optimization imaging algorithms and requires fewer prior and user defined constraints.

astro-ph.IM↗

arXiv2021

A Novel Estimator for the Equation of State of the IGM by Ly$α$ Forest Tomography

We present a novel procedure to estimate the Equation of State of the intergalactic medium in the quasi-linear regime of structure formation based on Ly$α$ forest tomography and apply it to 21 high quality quasar spectra from the UVES\_SQUAD survey at redshift $z=2.5$. Our estimation is based on a full tomographic inversion of the line of sight. We invert the data with two different inversion algorithms, the iterative Gauss-Newton method and the regularized probability conservation approach, which depend on different priors and compare the inversion results in flux space and in density space. In this way our method combines fitting of absorption profiles in flux space with an analysis of the recovered density distributions featuring prior knowledge of the matter distribution. Our estimates are more precise than existing estimates, in particular on small redshift bins. In particular, we model the temperature-density relation with a power law and observe for the temperature at mean density $T_0 = 13400^{+1700}_{-1300}\,\mathrm{K}$ and for the slope of the power-law (polytropic index) $γ= 1.42 \pm 0.11$ for the power-law parameters describing the temperature-density relation. Moreover, we measure an photoionization rate $Γ_{-12} = 1.1^{+0.16}_{-0.17}$. An implementation of the inversion techniques used will be made publicly available.

astro-ph.CO↗

arXiv2020

An Optimized Ly$α$ Forest Inversion Tool Based on a Quantitative Comparison of Existing Reconstruction Methods

We present a same-level comparison of the most prominent inversion methods for the reconstruction of the matter density field in the quasi-linear regime from the Ly$α$ forest flux. Moreover, we present a pathway for refining the reconstruction in the framework of numerical optimization. We apply this approach to construct a novel hybrid method. The methods which are used so far for matter reconstructions are the Richardson-Lucy algorithm, an iterative Gauss-Newton method and a statistical approach assuming a one-to-one correspondence between matter and flux. We study these methods for high spectral resolutions such that thermal broadening becomes relevant. The inversion methods are compared on synthetic data (generated with the lognormal approach) with respect to their performance, accuracy, their stability against noise, and their robustness against systematic uncertainties. We conclude that the iterative Gauss-Newton method offers the most accurate reconstruction, in particular at small S/N, but has also the largest numerical complexity and requires the strongest assumptions. The other two algorithms are faster, comparably precise at small noise-levels, and, in the case of the statistical approach, more robust against inaccurate assumptions on the thermal history of the intergalactic medium (IGM). We use these results to refine the statistical approach using regularization. Our new approach has low numerical complexity and makes few assumptions about the history of the IGM, and is shown to be the most accurate reconstruction at small S/N, even if the thermal history of the IGM is not known. Our code will be made publicly available under https://github.com/hmuellergoe/reglyman.

astro-ph.CO↗