Clustering-based Redshift Estimation: Comparison to Spectroscopic Redshifts

We investigate the potential and accuracy of clustering-based redshift estimation using the method proposed by M\'enard et al. (2013). This technique enables the inference of redshift distributions from measurements of the spatial clustering of arbitrary sources, using a set of reference objects for which redshifts are known. We apply it to a sample of spectroscopic galaxies from the Sloan Digital Sky Survey and show that, after carefully controlling the sampling efficiency over the sky, we can estimate redshift distributions with high accuracy. Probing the full colour space of the SDSS galaxies, we show that we can recover the corresponding mean redshifts with an accuracy ranging from $\delta$z=0.001 to 0.01. We indicate that this mapping can be used to infer the redshift probability distribution of a single galaxy. We show how the lack of information on the galaxy bias limits the accuracy of the inference and show comparisons between clustering redshifts and photometric redshifts for this dataset. This analysis demonstrates, using real data, that clustering-based redshift inference provides a powerful data-driven technique to explore the redshift distribution of arbitrary datasets, without any prior knowledge on the spectral energy distribution of the sources.Comment: 13 pages. Submitted to MNRAS. Comments welcom

Evidence against memorial facilitation and context-dependent memory effects through the chewing of gum

The experiment examined the prediction that chewing gum at learning and/or recall facilitated subsequent word recall. Chewing gum at learning significantly impaired recall, indicating that the chewing of gum has a detrimental impact upon initial word encoding. In addition, a context-dependent memory effect was reported for those participants who both learned and recalled in the absence of gum, however a context dependent effect was not found with chewing gum. The findings contradict previous research

Cluster Magnification & the Mass-Richness Relation in CFHTLenS

Gravitational lensing magnification is measured with a significance of 9.7 sigma on a large sample of galaxy clusters in the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). This survey covers ~154 deg^2 and contains over 18,000 cluster candidates at redshifts 0.2 <= z <= 0.9, detected using the 3D-Matched Filter cluster-finder of Milkeraitis et al. (2010). We fit composite-NFW models to the ensemble, accounting for cluster miscentering, source-lens redshift overlap, as well as nearby structure (the 2-halo term), and recover mass estimates of the cluster dark matter halos in range of ~10^13 M_sun to 2*10^14 M_sun. Cluster richness is measured for the entire sample, and we bin the clusters according to both richness and redshift. A mass-richness relation M_200 = M_0 (N_200 / 20)^beta is fit to the measurements. For two different cluster miscentering models we find consistent results for the normalization and slope, M_0 = (2.3 +/- 0.2)*10^13 M_sun, beta = 1.4 +/- 0.1 and M_0 = (2.2 +/- 0.2)*10^13 M_sun, beta = 1.5 +/- 0.1. We find that accounting for the full redshift distribution of lenses and sources is important, since any overlap can have an impact on mass estimates inferred from flux magnification.Comment: 11 pages, 8 figures, Accepted to MNRA

Patient preference as a predictor of outcomes in a pilot trial of person-centred counselling versus low-intensity cognitive behavioural therapy for persistent sub-threshold and mild depression

The aim of this analysis was to explore whether pre-treatment intervention preferences were related to outcomes for patients with persistent sub-threshold and mild depression who received one of two treatment types. Thirty-six patients took part in a two-arm, parallel group, pilot randomized controlled trial that compared short term (3 month and 6 month) outcomes of person-centred counselling (PCC) compared with low-intensity, CBT-based guided self-help (LICBT). Patient preferences for the two interventions were assessed at baseline assessment, and analysed as two independent linear variables (pro-PCC, pro-LICBT). Eight out of 30 interactions between baseline treatment preferences and treatment type were found to be significant at the p &lt; .05 level. All were in the predicted direction, with patients who showed a stronger preference for a treatment achieving better outcomes in that treatment compared with the alternative. However, pro-LICBT was a stronger predictor of outcomes than pro-PCC. The findings provide preliminary support that treatment preferences should be taken into account when providing interventions for patients with persistent sub-threshold and mild depression. It is recommended that further research analyses preferences for different treatment types as independent variables, and examines preferences for format of treatment (e.g. guided self-help vs. face-to-face)

Guided self-help cognitive behavioural therapy for depression in primary care : a randomised controlled trial

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