165 research outputs found
Metacognitieve therapie voor de obsessieve-compulsieve stoornis
De obsessieve-compulsieve stoornis (OCS) is een veelvoorkomende en invaliderende stoornis. Cognitieve gedragstherapie (CGT) in de vorm van exposure met responspreventie (ERP) is de psychologische behandeling van eerste voorkeur. Ondanks de aangetoonde werkzaamheid van ERP is verbetering van de effectivitei
Metacognitive therapy versus exposure and response prevention for obsessive-compulsive disorder
Background: The recommended psychological treatment of choice for obsessive-compulsive disorder (OCD) is
exposure with response prevention (ERP). However, recovery rates are relatively modest, so better treatments are
needed. This superiority study aims to explore the relative efficacy of metacognitive therapy (MCT), a new form
of cognitive therapy based on the metacognitive model of OCD.
Design and method: In a randomized controlled trial, we will compare MCT with ERP. One hundred patients
diagnosed with OCD will be recruited in an outpatient mental health center in Rotterdam (the Netherlands). The
primary outcome measure is OCD severity, measured by the Yale-Brown Obsessive Compulsive Scale (Y-BOCS).
Data are assessed at baseline, after treatment, and at 6 and 30 months follow-up.
Discussion: By comparing MCT with ERP we hope to provide an indication whether MCT is efficacious in the
treatment of OCD and, if so, whether it has the potential to be more efficacious than the current “gold standard”
psychological treatment for OCD, ERP
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H0LiCOW X: Spectroscopic/imaging survey and galaxy-group identification around the strong gravitational lens system WFI2033-4723
Galaxies and galaxy groups located along the line of sight towards
gravitationally lensed quasars produce high-order perturbations of the
gravitational potential at the lens position. When these perturbation are too
large, they can induce a systematic error on of a few-percent if the lens
system is used for cosmological inference and the perturbers are not explicitly
accounted for in the lens model. In this work, we present a detailed
characterization of the environment of the lens system WFI2033-4723 (, = 0.6575), one of the core targets of the H0LICOW
project for which we present cosmological inferences in a companion paper (Rusu
et al. 2019). We use the Gemini and ESO-Very Large telescopes to measure the
spectroscopic redshifts of the brightest galaxies towards the lens, and use the
ESO-MUSE integral field spectrograph to measure the velocity-dispersion of the
lens ( km/s) and of several nearby
galaxies. In addition, we measure photometric redshifts and stellar masses of
all galaxies down to mag, mainly based on Dark Energy Survey imaging
(DR1). Our new catalog, complemented with literature data, more than doubles
the number of known galaxy spectroscopic redshifts in the direct vicinity of
the lens, expanding to 116 (64) the number of spectroscopic redshifts for
galaxies separated by less than 3 arcmin (2 arcmin) from the lens. Using the
flexion-shift as a measure of the amplitude of the gravitational perturbation,
we identify 2 galaxy groups and 3 galaxies that require specific attention in
the lens models. The ESO MUSE data enable us to measure the
velocity-dispersions of three of these galaxies. These results are essential
for the cosmological inference analysis presented in Rusu et al. (2019).Comment: Matches the version accepted for publication by MNRAS. Note that this
paper previously appeared as H0LICOW X
LSST Observing Strategy White Paper: LSST Observations of WFIRST Deep Fields
The Wide-Field Infrared Survey Telescope (WFIRST) is expected to launch in
the mid-2020s. With its wide-field near-infrared (NIR) camera, it will survey
the sky to unprecedented detail. As part of normal operations and as the result
of multiple expected dedicated surveys, WFIRST will produce several relatively
wide-field (tens of square degrees) deep (limiting magnitude of 28 or fainter)
fields. In particular, a planned supernova survey is expected to image 3 deep
fields in the LSST footprint roughly every 5 days over 2 years. Stacking all
data, this survey will produce, over all WFIRST supernova fields in the LSST
footprint, ~12-25 deg^2 and ~5-15 deg^2 regions to depths of ~28 mag and ~29
mag, respectively. We suggest LSST undertake mini-surveys that will match the
WFIRST cadence and simultaneously observe the supernova survey fields during
the 2-year WFIRST supernova survey, achieving a stacked depth similar to that
of the WFIRST data. We also suggest additional observations of these same
regions throughout the LSST survey to get deep images earlier, have long-term
monitoring in the fields, and produce deeper images overall. These fields will
provide a legacy for cosmology, extragalactic, and transient/variable science.Comment: White Paper in response to LSST Call for Observing Strategy Inpu
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An Ultra Deep Field survey with WFIRST
Studying the formation and evolution of galaxies at the earliest cosmic
times, and their role in reionization, requires the deepest imaging possible.
Ultra-deep surveys like the HUDF and HFF have pushed to mag \mAB30,
revealing galaxies at the faint end of the LF to 911 and
constraining their role in reionization. However, a key limitation of these
fields is their size, only a few arcminutes (less than a Mpc at these
redshifts), too small to probe large-scale environments or clustering
properties of these galaxies, crucial for advancing our understanding of
reionization. Achieving HUDF-quality depth over areas 100 times larger
becomes possible with a mission like the Wide Field Infrared Survey Telescope
(WFIRST), a 2.4-m telescope with similar optical properties to HST, with a
field of view of 1000 arcmin, 100 the area of the
HST/ACS HUDF.
This whitepaper motivates an Ultra-Deep Field survey with WFIRST, covering
100300 the area of the HUDF, or up to 1 deg, to
\mAB30, potentially revealing thousands of galaxies and AGN at the
faint end of the LF, at or beyond \,\,910 in the epoch of
reionization, and tracing their LSS environments, dramatically increasing the
discovery potential at these redshifts.
(Note: This paper is a somewhat expanded version of one that was submitted as
input to the Astro2020 Decadal Survey, with this version including an Appendix
(which exceeded the Astro2020 page limits), describing how the science drivers
for a WFIRST Ultra Deep Field might map into a notional observing program,
including the filters used and exposure times needed to achieve these depths.
Dark Energy Survey Year 3 results: Calibration of lens sample redshift distributions using clustering redshifts with BOSS/eBOSS
We present clustering redshift measurements for Dark Energy Survey (DES) lens sample galaxies used in weak gravitational lensing and galaxy clustering studies. To perform these measurements, we cross-correlate with spectroscopic galaxies from the Baryon Acoustic Oscillation Survey (BOSS) and its extension, eBOSS. We validate our methodology in simulations, including a new technique to calibrate systematic errors that result from the galaxy clustering bias, and we find that our method is generally unbiased in calibrating the mean redshift. We apply our method to the data, and estimate the redshift distribution for 11 different photometrically selected bins. We find general agreement between clustering redshift and photometric redshift estimates, with differences on the inferred mean redshift found to be below |Δz| = 0.01 in most of the bins. We also test a method to calibrate a width parameter for redshift distributions, which we found necessary to use for some of our samples. Our typical uncertainties on the mean redshift ranged from 0.003 to 0.008, while our uncertainties on the width ranged from 4 to 9 per cent. We discuss how these results calibrate the photometric redshift distributions used in companion papers for DES Year 3 results
redMaGiC: selecting luminous red galaxies from the DES Science Verification data
We introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z ∈ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10-3 (h-1 Mpc)-3, and a median photo-z bias (zspec - zphoto) and scatter (sigmaz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5sigma outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level
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