A simulation-based inference pipeline for cosmic shear with the Kilo-Degree Survey

The standard approach to inference from cosmic large-scale structure data employs summary statistics that are compared to analytic models in a Gaussian likelihood with pre-computed covariance. To overcome the idealising assumptions about the form of the likelihood and the complexity of the data inherent to the standard approach, we investigate simulation-based inference (SBI), which learns the likelihood as a probability density parameterised by a neural network. We construct suites of simulated, exactly Gaussian-distributed data vectors for the most recent Kilo-Degree Survey (KiDS) weak gravitational lensing analysis and demonstrate that SBI recovers the full 12-dimensional KiDS posterior distribution with just under $10^4$ simulations. We optimise the simulation strategy by initially covering the parameter space by a hypercube, followed by batches of actively learnt additional points. The data compression in our SBI implementation is robust to suboptimal choices of fiducial parameter values and of data covariance. Together with a fast simulator, SBI is therefore a competitive and more versatile alternative to standard inference.Comment: 14 pages, 8 figures; submitted to MNRA

GLASS: Generator for Large Scale Structure

We present GLASS, the Generator for Large Scale Structure, a new code for the simulation of galaxy surveys for cosmology, which iteratively builds a light cone with matter, galaxies, and weak gravitational lensing signals as a sequence of nested shells. This allows us to create deep and realistic simulations of galaxy surveys at high angular resolution on standard computer hardware and with low resource consumption. GLASS also introduces a new technique to generate transformations of Gaussian random fields (including lognormal) to essentially arbitrary precision, an iterative line-of-sight integration over matter shells to obtain weak lensing fields, and flexible modelling of the galaxies sector. We demonstrate that GLASS readily produces simulated data sets with per cent-level accurate two-point statistics of galaxy clustering and weak lensing, thus enabling simulation-based validation and inference that is limited only by our current knowledge of the input matter and galaxy properties.Comment: 23 pages, 15 figures; v2 accepted by OJAp with minor changes; code available at https://github.com/glass-dev/glas

Magnification bias in galaxy surveys with complex sample selection functions

Gravitational lensing magnification modifies the observed spatial distribution of galaxies and can severely bias cosmological probes of large-scale structure if not accurately modelled. Standard approaches to modelling this magnification bias may not be applicable in practice as many galaxy samples have complex, often implicit, selection functions. We propose and test a procedure to quantify the magnification bias induced in clustering and galaxy-galaxy lensing (GGL) signals in galaxy samples subject to a selection function beyond a simple flux limit. The method employs realistic mock data to calibrate an effective luminosity function slope, $\alpha_{\rm{obs}}$, from observed galaxy counts, which can then be used with the standard formalism. We demonstrate this method for two galaxy samples derived from the Baryon Oscillation Spectroscopic Survey (BOSS) in the redshift ranges $0.2 < z \leq 0.5$ and $0.5 < z \leq 0.75$, complemented by mock data built from the MICE2 simulation. We obtain $\alpha_{\rm{obs}} = 1.93 \pm 0.05$ and $\alpha_{\rm{obs}} = 2.62 \pm 0.28$ for the two BOSS samples. For BOSS-like lenses, we forecast a contribution of the magnification bias to the GGL signal between the angular scales of $100$ and $4600$ with a cumulative signal-to-noise ratio between $0.1$ and $1.1$ for sources from the Kilo-Degree Survey (KiDS), between $0.4$ and $2.0$ for sources from the Hyper Suprime-Cam survey (HSC), and between $0.3$ and $2.8$ for ESA Euclid-like source samples. These contributions are significant enough to require explicit modelling in future analyses of these and similar surveys.Comment: 15 pages, 13 figure

Inpatient or day clinic treatment? Results of a multi-site-study

Objective: This naturalistic study aimed to identify criteria which are of relevance for making a decision as to whether inpatient or day hospital treatment is indicated

Typical disease courses of patients with unipolar depressive disorder after in-patient treatments–results of a cluster analysis of the INDDEP project

IntroductionPreviously established categories for the classification of disease courses of unipolar depressive disorder (relapse, remission, recovery, recurrence) are helpful, but insufficient in describing the naturalistic disease courses over time. The intention of the present study was to identify frequent disease courses of depression by means of a cluster analysis.MethodsFor the longitudinal cluster analysis, 555 datasets of patients who participated in the INDDEP (INpatient and Day clinic treatment of DEPression) study, were used. The present study uses data of patients with at least moderate depressive symptoms (major depression) over a follow-up period of 1 year after their in-patient or day-care treatments using the LIFE (Longitudinal Interval Follow-Up Evaluation)-interview. Eight German psychosomatic hospitals participated in this naturalistic observational study.ResultsConsidering only the Calinski–Harabatz index, a 2-cluster solution gives the best statistical results. In combination with other indices and clinical interpretations, the 5-cluster solution seems to be the most interesting. The cluster sizes are large enough and numerically balanced. The KML-cluster analyses revealed five well interpretable disease course clusters over the follow-up period: “sustained treatment response” (N = 202, 36.4% of the patients), “recurrence” (N = 80, 14.4%), “persisting relapse” (N = 115, 20.7%), “temporary relapse” (N = 95, 17.1%), and remission (N = 63, 11.4%).ConclusionThe disease courses of many patients diagnosed with a unipolar depression do not match with the historically developed categories such as relapse, remission, and recovery. Given this context, the introduction of disease course trajectories seems helpful. These findings may promote the implementation of new therapy options, adapted to the disease courses

KiDS-1000: Constraints on the intrinsic alignment of luminous red galaxies

We constrain the luminosity and redshift dependence of the intrinsic alignment (IA) of a nearly volume-limited sample of luminous red galaxies selected from the fourth public data release of the Kilo-Degree Survey (KiDS-1000). To measure the shapes of the galaxies, we used two complementary algorithms, finding consistent IA measurements for the overlapping galaxy sample. The global significance of IA detection across our two independent luminous red galaxy samples, with our favoured method of shape estimation, is ∼10.7σ. We find no significant dependence with redshift of the IA signal in the range 0.2 < z < 0.8, nor a dependence with luminosity below Lr ≲ 2.9 × 1010 h−2Lr, ⊙. Above this luminosity, however, we find that the IA signal increases as a power law, although our results are also compatible with linear growth within the current uncertainties. This behaviour motivates the use of a broken power law model when accounting for the luminosity dependence of IA contamination in cosmic shear studies

High-utilizing Crohn's disease patients under psychosomatic therapy*

<p>Abstract</p> <p>Objective</p> <p>Few studies have been published on health care utilization in Crohn's disease and the influence of psychological treatment on high utilizers.</p> <p>Methods</p> <p>The present sub study of a prospective multi center investigation conducted in 87 of 488 consecutive Crohn's disease (CD) patients was designed to investigate the influence of the course of Crohn's disease on health care utilization (hospital days (HD) and sick leave days (SLD) collected by German insurance companies) and to examine the conditions of high-utilizing patients. Predictors of health care utilization should be selected. Based on a standardized somatic treatment, high health care utilizing patients of the psychotherapy and control groups should be compared before and after a one-year treatment.</p> <p>Results</p> <p>Multivariate regression analysis identified disease activity at randomization as an important predictor of the clinical course (r²= 0.28, p < 0.01). Health care utilization correlated with duration of disease (p < 0.04), but the model was not significant (r²= 0.15, p = 0.09). The patients' level of anxiety, depression and lack of control at randomization predicted their health-related quality of life at the end of the study (r²= 0.51, p < 0.00001). Interestingly, steroid intake and depression (t1) predicted the combined outcome measure (clinical course, HRQL, health care utilization) of Crohn's disease at the end of the study (r²= 0.22, p < 0.001).</p> <p>Among high utilizers, a significantly greater drop in HD (p < 0.03) and in mean in SLD were found in the treatment compared to the control group.</p> <p>Conclusion</p> <p>The course of Crohn's disease is influenced by psychological as well as somatic factors; especially depression seems important here. A significant drop of health care utilization demonstrates the benefit of psychological treatment in the subgroup of high-utilizing CD patients. Further studies are needed to replicate the findings of the clinical outcome in this CD subgroup.</p

KiDS-1000: Constraints on the intrinsic alignment of luminous red galaxies

Large scale structure and cosmolog