SMAUG: a new technique for the deprojection of galaxy clusters

This paper presents a new technique for reconstructing the spatial distributions of hydrogen, temperature and metal abundance of a galaxy cluster. These quantities are worked out from the X-ray spectrum, modeled starting from few analytical functions describing their spatial distributions. These functions depend upon some parameters, determined by fitting the model to the observed spectrum. We have implemented this technique as a new model in the XSPEC software analysis package. We describe the details of the method, and apply it to work out the structure of the cluster A1795. We combine the observation of three satellites, exploiting the high spatial resolution of Chandra for the cluster core, the wide collecting area of XMM-Newton for the intermediate regions and the large field of view of Beppo-SAX for the outer regions. We also test the validity and precision of our method by i) comparing its results with those from a geometrical deprojection, ii) examining the spectral residuals at different radii of the cluster and iii) reprojecting the unfolded profiles and comparing them directly to the measured quantities. Our analytical method yields the parameters defining the spatial functions directly from the spectra. Their explicit knowledge allows a straightforward derivation of other indirect physical quantities like the gravitating mass, as well as a fast and easy estimate of the profiles uncertainties.Comment: 24 pages, 11 figures, 3 tables; emulateapj; accepted for publication in the Astrophysical Journa

Mass accretion rates of clusters of galaxies: CIRS and HeCS

We use a new spherical accretion recipe tested on N-body simulations to measure the observed mass accretion rate (MAR) of 129 clusters in the Cluster Infall Regions in the Sloan Digital Sky Survey (CIRS) and in the Hectospec Cluster Survey (HeCS). The observed clusters cover the redshift range of $0.01<z<0.30$ and the mass range of $\sim 10^{14}-10^{15} {h^{-1}~\rm{M_\odot}}$. Based on three-dimensional mass profiles of simulated clusters reaching beyond the virial radius, our recipe returns MARs that agree with MARs based on merger trees. We adopt this recipe to estimate the MAR of real clusters based on measurements of the mass profile out to $\sim 3R_{200}$. We use the caustic method to measure the mass profiles to these large radii. We demonstrate the validity of our estimates by applying the same approach to a set of mock redshift surveys of a sample of 2000 simulated clusters with a median mass of $M_{200}= 10^{14} {h^{-1}~\rm{M_{\odot}}}$ as well as a sample of 50 simulated clusters with a median mass of $M_{200}= 10^{15} {h^{-1}~\rm{M_{\odot}}}$: the median MARs based on the caustic mass profiles of the simulated clusters are unbiased and agree within $19\%$ with the median MARs based on the real mass profile of the clusters. The MAR of the CIRS and HeCS clusters increases with the mass and the redshift of the accreting cluster, which is in excellent agreement with the growth of clusters in the $\Lambda$CDM model.Comment: 25 pages, 19 figures, 7 table

Personality types revisited–a literature-informed and data-driven approach to an integration of prototypical and dimensional constructs of personality description

A new algorithmic approach to personality prototyping based on Big Five traits was applied to a large representative and longitudinal German dataset (N = 22,820) including behavior, personality and health correlates. We applied three different clustering techniques, latent profile analysis, the k-means method and spectral clustering algorithms. The resulting cluster centers, i.e. the personality prototypes, were evaluated using a large number of internal and external validity criteria including health, locus of control, self-esteem, impulsivity, risk-taking and wellbeing. The best-fitting prototypical personality profiles were labeled according to their Euclidean distances to averaged personality type profiles identified in a review of previous studies on personality types. This procedure yielded a five-cluster solution: resilient, overcontroller, undercontroller, reserved and vulnerable-resilient. Reliability and construct validity could be confirmed. We discuss wether personality types could comprise a bridge between personality and clinical psychology as well as between developmental psychology and resilience research

Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients

The heterogeneity of cognitive profiles among psychiatric patients has been reported to carry significant clinical information. However, how to best characterize such cognitive heterogeneity is still a matter of debate. Despite being well suited for clinical data, cluster analysis techniques, like the Two-Step and the Latent Class, received little to no attention in the literature. The present study aimed to test the validity of the cluster solutions obtained with Two-Step and Latent Class cluster analysis on the cognitive profile of a cross-diagnostic sample of 387 psychiatric inpatients. Two-Step and Latent Class cluster analysis produced similar and reliable solutions. The overall results reported that it is possible to group all psychiatric inpatients into Low and High Cognitive Profiles, with a higher degree of cognitive heterogeneity in schizophrenia and bipolar disorder patients than in depressive disorders and personality disorder patients

Simulation tests of galaxy cluster constraints on chameleon gravity

We use two new hydrodynamical simulations of Lambda cold dark matter (LambdaCDM) and f(R) gravity to test the methodology used by Wilcox et al. (W15) in constraining the effects of a fifth force on the profiles of clusters of galaxies. We construct realistic simulated stacked weak lensing and X-ray surface brightness cluster profiles from these cosmological simulations, and then use these data projected along various lines of sight to test the spherical symmetry of our stacking procedure. We also test the applicability of the NFW profile to model weak lensing profiles of clusters in f(R) gravity. Finally, we test the validity of the analytical model developed in W15 against the simulated profiles. Overall, we find our methodology is robust and broadly agrees with these simulated data. We also apply our full Markov Chain Monte Carlo analysis from W15 to our simulated X-ray and lensing profiles, providing consistent constraints on the modified gravity parameters as obtained from the real cluster data, e.g. for our LambdaCDM simulation we obtain |fR0| < 8.3 × 10-5 (95 per cent CL), which is in good agreement with the W15 measurement of |fR0| < 6 × 10-5. Overall, these tests confirm the power of our methodology which can now be applied to larger cluster samples available with the next generation surveys

Personality traits and beliefs about peers\u2019 on-road behaviors as predictors of adolescents\u2019 moped-riding profiles.

Several efforts aimed at discriminating between different degrees of on-road risky attitudes have been devoted to the identification of personality profiles among young drivers. However, the results are often inconsistent because of the limits of selfreport measures. To overcome these limits, we tried to identify different profiles based on our study participants\u2019 driving performances in a virtual environment and to look for psychological predictors of inclusion in one of three profiles. One-hundred and fourteen inexperienced adolescents were involved in this study, which included two experimental sessions. During the first, before riding along five virtual courses on a moped simulator, participants\u2019 sensation seeking, locus of control, aggressiveness and beliefs about their peers\u2019 on-road behaviors were measured by means of self-report tools. During the second session, the participants drove the simulator along six courses that were different from those faced in the first session. A cluster analysis was run on a wide number of indexes extracted from the participants\u2019 performances to detect different riding profiles. Three profiles emerged (Imprudent, Prudent and Insecure), with specific riding patterns. The profiles also differed in terms of riding safety, assessed by means of the scores automatically given by the simulator to the participants\u2019 performances. Reporting an external locus of control, underestimating peers\u2019 on-road risky behaviors and showing less concern for fate among the possible causes of crashes are predictors that increase the risk of being included in the Imprudent profile. Low levels of dangerous thrill seeking predict inclusion in the Prudent profile, whereas high rates of self-reported anger play a role in discriminating the Insecure riders from the other profiles. The study indicates that it is possible to identify riding profiles with different degrees of on-road safety among inexperienced adolescents by means of simulated road environments. Moreover, inclusion in these profiles is predicted by different patterns of personality variables and beliefs. Further research is needed to verify the validity of these conclusions in real road conditions

Time dependent coupled cluster approach to Resonance Raman excitation profiles from general anharmonic surfaces

A time dependent coupled cluster approach to the calculation of Resonance Raman excitation profiles on general anharmonic surfaces is presented. The vibrational wave functions on the ground electronic surface are obtained by the coupled cluster method (CCM). It is shown that the propagation of the vibrational ground state on the upper surface is equivalent to propagation of the vacuum state by an effective hamiltonian generated by the similarity transformation of the vibrational hamiltonian of that surface by the CCM wave operator of the lower surface up to a normalization constant. This time propagation is carried out by the time-dependent coupled cluster method in a time dependent frame. Numerical studies are presented to asses the validity of the approach

Motivation profiles in sport: A self-determination theory perspective

The present study examined the link between motivation profiles among adult sports participants and the outcomes of enjoyment, effort, positive and negative affect, attitude toward sport participation, intention to continue sport participation, satisfaction, and persistence in sport. Two samples of participants (n = 590 and n = 555) completed the Sport Motivation Scale and a range of self-report measures to assess the outcome variables. Exploratory cluster analyses applied to Sample 1 and confirmatory cluster analysis applied to Sample 2 identified two clusters of sport participants. The first comprised participants with high scores on both non self-determined and self-determined motives. The second comprised participants with high scores on self-determined motives but low scores on non self- determined motives. Participants in the first cluster scored higher on all outcome variables. The results are discussed with reference to a more in-depth understanding of the motivation dynamics of sport participation based on Self-Determination Theory

Motivational profiles and their relationships with basic psychological needs, academic performance, study strategies, self-esteem, and vitality in dental students in Chile

Purpose To determine dental students’ motivational profiles through a person-centred approach and to analyse the associations with the satisfaction of their basic psychological needs, study strategies, academic performance, self-esteem, and vitality. Methods A total of 924 students from the University of San Sebastian (Chile) participated in this cross-sectional cor¬relational study in spring 2016. Data were collected through 5 self-reported instruments, in addition to students’ academic performance. The Cronbach alpha, descriptive statistics, and correla¬tion scores were computed. A k-means cluster analysis with intrinsic and controlled motivation was conducted to identify different mo-tivational profiles. Subsequently, multivariate analysis of covariance controlling for the effects of gender and year of study was carried out to assess differences among the retained motivational profiles and learning variables. Results All instruments showed acceptable Cronbach alpha scores. A 4-cluster solution was retained for the motivational profile over a 3- or 5-cluster solution. Students’ motiva-tional profiles were characterized by different degrees of intrinsic and controlled motivation. The high intrinsic motivation groups showed higher perceptions of their basic psychological, a greater propensity for a deep rather than surface study strategy, better academic performance, and higher scores for self-esteem and vitality than the low intrinsic motivation groups, regardless of the degree of controlled motivation. Conclusion Students with a high intrinsic motivation profile, regardless of their controlled motivation scores, reported better learning characteristics. Therefore, special attention should be paid to students’ motivational profiles, as the quality of motivation might serve as a basis for interventions to support their academic success and well-being