A Comprehensive Analysis of Uncertainties Affecting the Stellar Mass - Halo Mass Relation for 0<z<4

We conduct a comprehensive analysis of the relationship between central galaxies and their host dark matter halos, as characterized by the stellar mass-halo mass (SM-HM) relation, with rigorous consideration of uncertainties. Our analysis focuses on results from the abundance matching technique, which assumes that every dark matter halo or subhalo above a specific mass threshold hosts one galaxy. We discuss the quantitative effects of uncertainties in observed galaxy stellar mass functions (GSMFs) (including stellar mass estimates and counting uncertainties), halo mass functions (including cosmology and uncertainties from substructure), and the abundance matching technique used to link galaxies to halos (including scatter in this connection). Our analysis results in a robust estimate of the SM-HM relation and its evolution from z=0 to z=4. The shape and evolution are well constrained for z < 1. The largest uncertainties at these redshifts are due to stellar mass estimates; however, failure to account for scatter in stellar masses at fixed halo mass can lead to errors of similar magnitude in the SM-HM relation for central galaxies in massive halos. We also investigate the SM-HM relation to z=4, although the shape of the relation at higher redshifts remains fairly unconstrained when uncertainties are taken into account. These results will provide a powerful tool to inform galaxy evolution models. [Abridged]Comment: 27 pages, 12 figures, updated to match ApJ accepted version

The Rockstar Phase-Space Temporal Halo Finder and the Velocity Offsets of Cluster Cores

We present a new algorithm for identifying dark matter halos, substructure, and tidal features. The approach is based on adaptive hierarchical refinement of friends-of-friends groups in six phase-space dimensions and one time dimension, which allows for robust (grid-independent, shape-independent, and noise-resilient) tracking of substructure; as such, it is named Rockstar (Robust Overdensity Calculation using K-Space Topologically Adaptive Refinement). Our method is massively parallel (up to 10^5 CPUs) and runs on the largest current simulations (>10^10 particles) with high efficiency (10 CPU hours and 60 gigabytes of memory required per billion particles analyzed). A previous paper (Knebe et al 2011) has shown Rockstar to have class-leading recovery of halo properties; we expand on these comparisons with more tests and higher-resolution simulations. We show a significant improvement in substructure recovery as compared to several other halo finders and discuss the theoretical and practical limits of simulations in this regard. Finally, we present results which demonstrate conclusively that dark matter halo cores are not at rest relative to the halo bulk or satellite average velocities and have coherent velocity offsets across a wide range of halo masses and redshifts. For massive clusters, these offsets can be up to 350 km/s at z=0 and even higher at high redshifts. Our implementation is publicly available at http://code.google.com/p/rockstar .Comment: 20 pages, 14 figures. Minor revisions to match accepted versio

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'The risks of playing it safe': a prospective longitudinal study of response to reward in the adolescent offspring of depressed parents

BACKGROUND Alterations in reward processing may represent an early vulnerability factor for the development of depressive disorder. Depression in adults is associated with reward hyposensitivity and diminished reward seeking may also be a feature of depression in children and adolescents. We examined the role of reward responding in predicting depressive symptoms, functional impairment and new-onset depressive disorder over time in the adolescent offspring of depressed parents. In addition, we examined group differences in reward responding between currently depressed adolescents, psychiatric and healthy controls, and also cross-sectional associations between reward responding and measures of positive social/environmental functioning. Method We conducted a 1-year longitudinal study of adolescents at familial risk for depression (n = 197; age range 10-18 years). Reward responding and self-reported social/environmental functioning were assessed at baseline. Clinical interviews determined diagnostic status at baseline and at follow-up. Reports of depressive symptoms and functional impairment were also obtained. RESULTS Low reward seeking predicted depressive symptoms and new-onset depressive disorder at the 1-year follow-up in individuals free from depressive disorder at baseline, independently of baseline depressive symptoms. Reduced reward seeking also predicted functional impairment. Adolescents with current depressive disorder were less reward seeking (i.e. bet less at favourable odds) than adolescents free from psychopathology and those with externalizing disorders. Reward seeking showed positive associations with social and environmental functioning (extra-curricular activities, humour, friendships) and was negatively associated with anhedonia. There were no group differences in impulsivity, decision making or psychomotor slowing. CONCLUSIONS Reward seeking predicts depression severity and onset in adolescents at elevated risk of depression. Adaptive reward responses may be amenable to change through modification of existing preventive psychological interventions

Galaxy Cluster Mass Estimation from Stacked Spectroscopic Analysis

We use simulated galaxy surveys to study: i) how galaxy membership in redMaPPer clusters maps to the underlying halo population, and ii) the accuracy of a mean dynamical cluster mass, $M_\sigma(\lambda)$, derived from stacked pairwise spectroscopy of clusters with richness $\lambda$. Using $\sim\! 130,000$ galaxy pairs patterned after the SDSS redMaPPer cluster sample study of Rozo et al. (2015 RMIV), we show that the pairwise velocity PDF of central--satellite pairs with $m_i < 19$ in the simulation matches the form seen in RMIV. Through joint membership matching, we deconstruct the main Gaussian velocity component into its halo contributions, finding that the top-ranked halo contributes $\sim 60\%$ of the stacked signal. The halo mass scale inferred by applying the virial scaling of Evrard et al. (2008) to the velocity normalization matches, to within a few percent, the log-mean halo mass derived through galaxy membership matching. We apply this approach, along with mis-centering and galaxy velocity bias corrections, to estimate the log-mean matched halo mass at $z=0.2$ of SDSS redMaPPer clusters. Employing the velocity bias constraints of Guo et al. (2015), we find $\langle \ln(M_{200c})|\lambda \rangle = \ln(M_{30}) + \alpha_m \ln(\lambda/30)$ with $M_{30} = 1.56 \pm 0.35 \times 10^{14} M_\odot$ and $\alpha_m = 1.31 \pm 0.06_{stat} \pm 0.13_{sys}$. Systematic uncertainty in the velocity bias of satellite galaxies overwhelmingly dominates the error budget.Comment: 14 pages, 7 figure