Probing the Intergalactic Medium with Lyα\mathrm{\alpha} and 21 cm Fluctuations

We study 21cm and Ly$\mathrm{\alpha}$ fluctuations, as well as H$\mathrm{\alpha}$, while distinguishing between Ly$\mathrm{\alpha}$ emission of galactic, diffuse, and scattered intergalactic medium (IGM) origin. Cross-correlation information about the state of the IGM is obtained, testing neutral versus ionized medium cases with different tracers in a seminumerical simulation setup. In order to pave the way toward constraints on reionization history and modeling beyond power spectrum information, we explore parameter dependencies of the cross-power signal between 21$\,$cm and Ly$\mathrm{\alpha}$, which displays a characteristic morphology and a turnover from negative to positive correlation at scales of a couple Mpc$^{-1}$. In a proof of concept for the extraction of further information on the state of the IGM using different tracers, we demonstrate the use of the 21$\,$cm and H$\mathrm{\alpha}$ cross-correlation signal to determine the relative strength of galactic and IGM emission in Ly$\mathrm{\alpha}$. We conclude by showing the detectability of the 21$\,$cm and Ly$\mathrm{\alpha}$ cross-correlation signal over more than one decade in scale at high signal-to-noise ratio for upcoming probes like SKA and the proposed all-sky intensity mapping satellites SPHEREx and CDIM, while also including the Ly$\mathrm{\alpha}$ damping tail and 21cm foreground avoidance in the modeling.Comment: 26 pages, 18 figures, 3 tables. Accepted for publication in Ap

Extensive search for bias in SNIa data

The use of advanced statistical analysis tools is crucial in order to improve cosmological parameter estimates via removal of systematic errors and identification of previously unaccounted for cosmological signals. Here we demonstrate the application of a new fully-Bayesian method, the internal robustness formalism, to scan for systematics and new signals in the recent supernova Ia Union compilations. Our analysis is tailored to maximize chances of detecting the anomalous subsets by means of a variety of sorting algorithms. We analyse supernova Ia distance moduli for effects depending on angular separation, redshift, surveys and hemispherical directions. The data have proven to be robust within 2 sigma, giving an independent confirmation of successful removal of systematics-contaminated supernovae. Hints of new cosmology, as for example the anisotropies reported by Planck, do not seem to be reflected in the supernova Ia data.Comment: 11 pages, 7 figures, matches version accepted for publication in MNRA

Deep Learning 21cm Lightcones in 3D

Interferometric measurements of the 21cm signal are a prime example of the data-driven era in astrophysics we are entering with current and upcoming experiments. We showcase the use of deep networks that are tailored for the structure of 3D tomographic 21cm light-cones to firstly detect and characterise HI sources and to secondly directly infer global astrophysical and cosmological model parameters. We compare different architectures and highlight how 3D CNN architectures that mirror the data structure are the best-performing model.Comment: 5 pages, 2 figures. This is a preprint of the following chapter: Heneka, C., Deep Learning 21 cm Lightcones in 3D, published in Machine Learning for Astrophysics, ML4Astro 2022, edited by Bufano, F., Riggi, S., Sciacca, E., Schilliro, F., 2023, Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-031-34167-0_3

On the general nature of 21cm-Lyman-α\alpha emitters cross-correlations during reionisation

We explore how the characteristics of the cross-correlation functions between the 21cm emission from the spin-flip transition of neutral hydrogen (HI) and early Lyman-$\alpha$ (Ly$\alpha$) radiation emitting galaxies (Ly$\alpha$ emitters, LAEs) depend on the reionisation history and topology and the simulated volume. For this purpose, we develop an analytic expression for the 21cm-LAE cross-correlation function and compare it to results derived from different Astraeus and 21cmFAST reionisation simulations covering a physically plausible range of scenarios where either low-mass ($<10^{9.5}M_\odot$) or massive ($>10^{9.5}M_\odot$) galaxies drive reionisation. Our key findings are: (i) the negative small-scale ($<2$ cMpc) cross-correlation amplitude scales with the intergalactic medium's (IGM) average HI fraction ($\langle\chi_\mathrm{HI}\rangle$) and spin-temperature weighted overdensity in neutral regions ($\langle1+\delta\rangle_\mathrm{HI}$); (ii) the inversion point of the cross-correlation function traces the peak of the size distribution of ionised regions around LAEs; (iii) the cross-correlation amplitude at small scales is sensitive to the reionisation topology, with its anti-correlation or correlation decreasing the stronger the ionising emissivity of the underlying galaxy population is correlated to the cosmic web gas distribution (i.e. the more low-mass galaxies drive reionisation); (iv) the required simulation volume to not underpredict the 21cm-LAE anti-correlation amplitude when the cross-correlation is derived via the cross-power spectrum rises as the size of ionised regions and their variance increases. Our analytic expression can serve two purposes: to test whether simulation volumes are sufficiently large, and to act as a fitting function when cross-correlating future 21cm signal Square Kilometre Array and LAE galaxy observations.Comment: 13 pages, 5 figures; accepted for publication in MNRA

Stress testing the dark energy equation of state imprint on supernova data

International audienceThis work determines the degree to which a traditional analysis of the standard model of cosmology (ΛCDM) based on type Ia supernovae can identify deviations from a cosmological constant in the form of a redshift-dependent dark energy equation of state w(z). We introduce and apply a novel random curve generator to simulate instances of w(z) from constraint families with increasing distinction from a cosmological constant. After producing a series of mock catalogs of binned type Ia supernovae corresponding to each w(z) curve, we perform a standard ΛCDM analysis to estimate the corresponding posterior densities of the absolute magnitude of type Ia supernovae, the present-day matter density, and the equation of state parameter. Using the Kullback-Leibler divergence between posterior densities as a difference measure, we demonstrate that a standard type Ia supernova cosmology analysis has limited sensitivity to extensive redshift dependencies of the dark energy equation of state. In addition, we report that larger redshift-dependent departures from a cosmological constant do not necessarily manifest easier-detectable incompatibilities with the ΛCDM model. Our results suggest that physics beyond the standard model may simply be hidden in plain sight

Rare mutations in SQSTM1 modify susceptibility to frontotemporal lobar degeneration

Mutations in the gene coding for Sequestosome 1 (SQSTM1) have been genetically associated with amyotrophic lateral sclerosis (ALS) and Paget disease of bone. In the present study, we analyzed the SQSTM1 coding sequence for mutations in an extended cohort of 1,808 patients with frontotemporal lobar degeneration (FTLD), ascertained within the European Early-Onset Dementia consortium. As control dataset, we sequenced 1,625 European control individuals and analyzed whole-exome sequence data of 2,274 German individuals (total n = 3,899). Association of rare SQSTM1 mutations was calculated in a meta-analysis of 4,332 FTLD and 10,240 control alleles. We identified 25 coding variants in FTLD patients of which 10 have not been described. Fifteen mutations were absent in the control individuals (carrier frequency < 0.00026) whilst the others were rare in both patients and control individuals. When pooling all variants with a minor allele frequency < 0.01, an overall frequency of 3.2 % was calculated in patients. Rare variant association analysis between patients and controls showed no difference over the whole protein, but suggested that rare mutations clustering in the UBA domain of SQSTM1 may influence disease susceptibility by doubling the risk for FTLD (RR = 2.18 [95 % CI 1.24-3.85]; corrected p value = 0.042). Detailed histopathology demonstrated that mutations in SQSTM1 associate with widespread neuronal and glial phospho-TDP-43 pathology. With this study, we provide further evidence for a putative role of rare mutations in SQSTM1 in the genetic etiology of FTLD and showed that, comparable to other FTLD/ALS genes, SQSTM1 mutations are associated with TDP-43 pathology