A bias to CMB lensing measurements from the bispectrum of large-scale structure

The rapidly improving precision of measurements of gravitational lensing of the Cosmic Microwave Background (CMB) also requires a corresponding increase in the precision of theoretical modeling. A commonly made approximation is to model the CMB deflection angle or lensing potential as a Gaussian random field. In this paper, however, we analytically quantify the influence of the non-Gaussianity of large-scale structure lenses, arising from nonlinear structure formation, on CMB lensing measurements. In particular, evaluating the impact of the non-zero bispectrum of large-scale structure on the relevant CMB four-point correlation functions, we find that there is a bias to estimates of the CMB lensing power spectrum. For temperature-based lensing reconstruction with CMB Stage-III and Stage-IV experiments, we find that this lensing power spectrum bias is negative and is of order one percent of the signal. This corresponds to a shift of multiple standard deviations for these upcoming experiments. We caution, however, that our numerical calculation only evaluates two of the largest bias terms and thus only provides an approximate estimate of the full bias. We conclude that further investigation into lensing biases from nonlinear structure formation is required and that these biases should be accounted for in future lensing analyses.Comment: 15+19 pages, 9 figures. Comments welcom

Bayesian weak lensing tomography: Reconstructing the 3D large-scale distribution of matter with a lognormal prior

We present a Bayesian reconstruction algorithm that infers the three-dimensional large-scale matter distribution from the weak gravitational lensing effects measured in the image shapes of galaxies. The algorithm is designed to also work with non-Gaussian posterior distributions which arise, for example, from a non-Gaussian prior distribution. In this work, we use a lognormal prior and compare the reconstruction results to a Gaussian prior in a suite of increasingly realistic tests on mock data. We find that in cases of high noise levels (i.e. for low source galaxy densities and/or high shape measurement uncertainties), both normal and lognormal priors lead to reconstructions of comparable quality, but with the lognormal reconstruction being prone to mass-sheet degeneracy. In the low-noise regime and on small scales, the lognormal model produces better reconstructions than the normal model: The lognormal model 1) enforces non-negative densities, while negative densities are present when a normal prior is employed, 2) better traces the extremal values and the skewness of the true underlying distribution, and 3) yields a higher pixel-wise correlation between the reconstruction and the true density.Comment: 23 pages, 12 figures; updated to match version accepted for publication in PR

Probabilistic Auto-Encoder

We introduce the Probabilistic Auto-Encoder (PAE), a generative model with a lower dimensional latent space that is based on an Auto-Encoder which is interpreted probabilistically after training using a Normalizing Flow. The PAE combines the advantages of an Auto-Encoder, i.e. it is fast and easy to train and achieves small reconstruction error, with the desired properties of a generative model, such as high sample quality and good performance in downstream tasks. Compared to a VAE and its common variants, the PAE trains faster, reaches lower reconstruction error and achieves state of the art samples without parameter fine-tuning or annealing schemes. We demonstrate that the PAE is further a powerful model for performing the downstream tasks of outlier detection and probabilistic image reconstruction: 1) Starting from the Laplace approximation to the marginal likelihood, we identify a PAE-based outlier detection metric which achieves state of the art results in Out-of-Distribution detection outperforming other likelihood based estimators. 2) Using posterior analysis in the PAE latent space we perform high dimensional data inpainting and denoising with uncertainty quantification.Comment: 11 pages, 6 figures. Code available at https://github.com/VMBoehm/PAE. Updated version with additional references and appendi

Lensing corrections on galaxy-lensing cross correlations and galaxy-galaxy auto correlations

We study the impact of lensing corrections on modeling cross correlations between CMB lensing and galaxies, cosmic shear and galaxies, and galaxies in different redshift bins. Estimating the importance of these corrections becomes necessary in the light of anticipated high-accuracy measurements of these observables. While higher order lensing corrections (sometimes also referred to as post Born corrections) have been shown to be negligibly small for lensing auto correlations, they have not been studied for cross correlations. We evaluate the contributing four-point functions without making use of the Limber approximation and compute line-of-sight integrals with the numerically stable and fast FFTlog formalism. We find that the relative size of lensing corrections depends on the respective redshift distributions of the lensing sources and galaxies, but that they are generally small for high signal-to-noise correlations. We point out that a full assessment and judgement of the importance of these corrections requires the inclusion of lensing Jacobian terms on the galaxy side. We identify these additional correction terms, but do not evaluate them due to their large number. We argue that they could be potentially important and suggest that their size should be measured in the future with ray-traced simulations. We make our code publicly available.Comment: 26 pages, 6 figures. Code available at https://github.com/VMBoehm/lensing-corrections. Minor updates in tex

Fast and efficient identification of anomalous galaxy spectra with neural density estimation

Current large-scale astrophysical experiments produce unprecedented amounts of rich and diverse data. This creates a growing need for fast and flexible automated data inspection methods. Deep learning algorithms can capture and pick up subtle variations in rich data sets and are fast to apply once trained. Here, we study the applicability of an unsupervised and probabilistic deep learning framework, the Probabilistic Autoencoder (PAE), to the detection of peculiar objects in galaxy spectra from the SDSS survey. Different to supervised algorithms, this algorithm is not trained to detect a specific feature or type of anomaly, instead it learns the complex and diverse distribution of galaxy spectra from training data and identifies outliers with respect to the learned distribution. We find that the algorithm assigns consistently lower probabilities (higher anomaly score) to spectra that exhibit unusual features. For example, the majority of outliers among quiescent galaxies are E+A galaxies, whose spectra combine features from old and young stellar population. Other identified outliers include LINERs, supernovae and overlapping objects. Conditional modeling further allows us to incorporate additional information. Namely, we evaluate the probability of an object being anomalous given a certain spectral class, but other information such as metrics of data quality or estimated redshift could be incorporated as well. We make our code publicly available at https://github.com/VMBoehm/Spectra_PAEComment: 16 pages, 14 figures, MNRAS revised manuscript after addressing the report from the referee. Our first paper is available at arXiv:2211.11783 . Our code is publicly available at https://github.com/VMBoehm/Spectra_PA

Abnormal Excitation-Contraction Coupling and Calcium Homeostasis in Myopathies and Cardiomyopathies

International audienceMuscle contraction requires specialized membrane structures with precise geometry and relies on the concerted interplay of electrical stimulation and Ca 2+ release, known as excitation-contraction coupling (ECC). The membrane structure hosting ECC is called triad in skeletal muscle and dyad in cardiac muscle, and structural or functional defects of triads and dyads have been observed in a variety of myopathies and cardiomyopathies. Based on their function, the proteins localized at the triad/dyad can be classified into three molecular pathways: the Ca 2+ release complex (CRC), store-operated Ca 2+ entry (SOCE), and membrane remodeling. All three are mechanistically linked, and consequently, aberrations in any of these pathways cause similar disease entities. This review provides an overview of the clinical and genetic spectrum of triad and dyad defects with a main focus of attention on the underlying pathomechanisms