Sample selection: Dwarf galaxies - 8 < logM⋆9, z < 0.02, no AGN Massive galaxies - 10 < logM⋆ < 11, z < 0.07, no AGN Comparison sample - Same except out to z < 2 × zmax, sample. Incomplete, but we only use these objects to derive fiber corrections. Dust correction - derive E(B-V) from Balmer decrement Fiber corrections (requires involved discussion): -Define “overlap sample” between science sample and corresponding comparison sample: -“Sceince overlap sample” - objects in the science sample that do not fit into fiber at sample redshifts, but would at comparison redshifts -“Comparison overlap sample” - objects in the science sample that do fit into fiber at comparison redshifts, but would not at sample redshifts -At fixed size, how do we scale the science overlap sample so as to bring it into agreement with the comparison overlap sample? -Assume correction factor goes as Ψ−α, where Ψ is the fraction of the galaxy’s area contained within the fiber -Fit for alpha such that the median parameters (e.g. Hα EW) of the overlap sample are equal

East Africa has two rainy seasons: the long rains (March–May, MAM) and the short rains (October–December, OND). Most CMIP3/5 coupled models overestimate the short rains while underestimating the long rains. In this study, the East African rainfall bias is investigated by comparing the coupled historical simulations from CMIP5 to the corresponding SST-forced AMIP simulations. Much of the investigation is focused on the MRI-CGCM3 model, which successfully reproduces the observed rainfall annual cycle in East Africa in the AMIP experiment but its coupled historical simulation has a similar but stronger bias as the coupled multimodel mean. The historical−AMIP monthly climatology rainfall bias in East Africa can be explained by the bias in the convective instability (CI), which is dominated by the near surface moisture static energy (MSE) and ultimately by the MSE’s moisture component. The near surface MSE bias is modulated by the sea surface temperature (SST) over the western Indian Ocean. The warm SST bias in OND can be explained by both insufficient ocean dynamical cooling and latent flux, while the insufficient short wave radiation and excess latent heat flux mainly contribute to the cool SST bias in MAM.

INTRODUCTION The standard picture of the evolution of substructure in the Universe involves the collapse of dark matter into halos, which may host luminous galaxy. Such halos may exist within the bounds of larger halos; in these cases the galaxies they may host are typically called satellite galaxies, and their evolution differs substantially from galaxies that are not satellites in ways not fully understood. Analysis of the spatial and kinematic distributions of such galaxies can inform our ideas of how satellites and the systems in which they are found evolve. Substantial evidence exists that satellite galaxies are not isotropically distributed around their hosts. . This is also seen in simulations; subhaloes of hosts typically are typically distributed anisotropiclly in both position and velocity space . Local group satellites are highly anisotropically distributed both around the Milky Way and M31. The disk-like arrangement of MW satellites was first pointed out by . Later studies argued further for the existence of a disk-like structure of Milky Way satellites , and argued that the MW satellite disk was rotationally supported . further argues that the distribution of satellite galaxies around the MW is not predicted by LCDM. Around M31, dramatic evidence has been found for a disk of satellites, many of which exhibit coherent rotation along the line of sight . The M31 structure seems particularly difficult to square with our picture of galaxy evolution; argues that that alignments similar to the one found around M31 are essentially non-existant in numerical simulations. Much recent work has gone into investigating the possibility of similar satellite distributions around galaxies outside of the local group. Recently, work by (hereafter I14) pointed to the possibility of corotation seen in diametrically opposed satellite pairs in Sloan Digital Sky Survey (SDSS), finding 20 out of 22 oppositely aligned satellite pairs corotating along the line of sight. This result was contested by , arguing that the results of are strongly dependant on selection criteria and are not robust. The original authors then claimed that less-massive satellites than originally considered exhibit a spacial over-density consistent with the claimed existence of co-rotating sturctures frequently seen in SDSS . Still, the consensus on the prevelence of co-rotating satellite disks in the non-local Universe is unclear. In this paper we examine kinematic evidence for the existence of rotating planes. We compare the kinematic results we obtain from selection criteria modelled after that of I14 to simple numerical models of satellite behavior. The structure of the paper is as follows: In Section [sec:data] we discuss the selection of the observational sample and the presense of the co-rotation signal. In Section [sec:models] we introduce our numerical models and compare the mock observations derived from the models to the true observational data. In Section [sec:discuss] we discuss our results in the context of the search for M31-like planes elsewhere in the Universe. Throughout our analysis, we employ a Λ cold dark matter (ΛCDM) cosmology with WMAP7+BAO+H0 parameters ΩΛ = 0.73, Ωm = 0.27, and h = 0.70 , and unless otherwise noted all logarithms are base 10.

In the Local Group, almost all satellite dwarf galaxies that are within the virial radius of the Milky Way (MW) and M31 exhibit strong environmental influence. The orbital histories of these satellites provide the key to understanding the role of the MW/M31 halo, lower-mass groups, and cosmic reionization on the evolution of dwarf galaxies. We examine the virial-infall histories of satellites with $\mstar=10^{3-9} \msun$ using the ELVIS suite of cosmological zoom-in dissipationless simulations of 48 MW/M31-like halos. Satellites at z = 0 fell into the MW/M31 halos typically $5-8 \gyr$ ago at z = 0.5 − 1. However, they first fell into any host halo typically $7-10 \gyr$ ago at z = 0.7 − 1.5. This difference arises because many satellites experienced “group preprocessing” in another host halo, typically of $\mvir \sim 10^{10-12} \msun$, before falling into the MW/M31 halos. Satellites with lower-mass and/or those closer to the MW/M31 fell in earlier and are more likely to have experienced group preprocessing; half of all satellites with $\mstar < 10^6 \msun$ were preprocessed in a group. Infalling groups also drive most satellite-satellite mergers within the MW/M31 halos. Finally, _none_ of the surviving satellites at z = 0 were within the virial radius of their MW/M31 halo during reionization (z > 6), and only <4% were satellites of any other host halo during reionization. Thus, effects of cosmic reionization versus host-halo environment on the formation histories of surviving dwarf galaxies in the Local Group occurred at distinct epochs and are separable in time.

INTRODUCTION Our goal is efficient and robust numerical evaluation of Fourier integrals of the form f() = N_n(a,b) \int\, d^n k\, e^{+b i (\cdot)}\, () \quad , \quad () = _n(a,b) \int\, d^n r\, e^{-b i (\cdot)}\,f() \; , with normalization factors N_n(a,b) = |b|^{n/2} (2\pi)^{-n(1+a)/2} \quad , \quad _n(a,b) = |b|^{n/2} (2\pi)^{-n(1-a)/2} \; , where the constants a and b establish our choice of Fourier convention[1]. We focus on two- and three-dimensional (n = 2, 3) transforms of functions that can be adequately represented with a small number of (not necessarily low order) multipoles. Specifically, for n = 2, we expand f(r,\varphi_r) = ^{+\infty} f_m(r)\,\Phi_m(\varphi_r) \quad , \quad (k,\varphi_k) = ^{+\infty} (k)\,\Phi_m(\varphi_k) \; , using the polar basis functions \Phi_m(\varphi) \equiv {}\, e^{i m \varphi} with orthonormality[2] (δD and δ are the Dirac and Kronecker delta functions, respectively): ^{+\infty} \Phi_m(\varphi)\Phi^\ast_m(\varphi') = \delta_D(\varphi-\varphi') and \int_0^{2\pi} d\varphi\, \Phi_m(\varphi) ^\ast(\varphi) = \; . Similarly, for n = 3, we expand f(r,\theta_r,\varphi_r) = ^{\infty}^{+\ell} f_{\ell m}(r)\, Y_{\ell m}(\theta_r,\varphi_r) \quad, \quad (k,\theta_k,\varphi_k) = ^{\infty}^{+\ell} }(k)\, Y_{\ell m}(\theta_k,\varphi_k) \; , using the spherical-harmonic basis functions[3] (with associated Legendre polynomials Pℓm) Y_{\ell m}(\theta,\varphi) \equiv {2}{(\ell+m)!}}\, P_{\ell}^m(\cos\theta) \Phi_m(\varphi) with orthonormality[4] ^{\infty}^{+\ell} Y_{\ell m}(\theta,\varphi)Y_{\ell m}^\ast(\theta',\varphi') = \delta_D(\cos\theta-\cos\theta') \delta_D(\varphi-\varphi') and[5] \int d\Omega\, Y_{\ell m}(\theta,\varphi) Y_{\ell' m'}^\ast(\theta,\varphi) = \; . In the special case of a three-dimensional $f()$ that is cylindrically symmetric, _i.e._ has no ϕr dependence, only m = 0 terms contribute to the multipole expansion equation [eqn:multipole3]. Since Y_{\ell 0}(\theta,\varphi) = {4\pi}}\,L_{\ell}(\mu) with Lℓ the Legendre polynomial and μ ≡ cosθ, it is then convenient to replace equation [eqn:multipole3] with the equivalent expansion f(r,\mu_r) = ^\infty f^{(\mu)}_{\ell}(r) L_{\ell}(\mu_r) \quad , \quad (k,\mu_k) = ^\infty ^{(\mu)}_{\ell}(k) L_{\ell}(\mu_k) \; , in which the coefficient functions are simply rescaled f^{(\mu)}_{\ell}(r) = {4\pi}}\,f_{\ell 0}(r) \quad , \quad ^{(\mu)}_{\ell}(k) = {4\pi}}\,}(k) \; . [1] Use a = 1 and b = 1 for the convention of references . [2] http://dlmf.nist.gov/1.17#E12 [3] http://dlmf.nist.gov/14.30#E1 [4] http://dlmf.nist.gov/1.17#E25 [5] http://dlmf.nist.gov/14.30#E8

ABSTRACT At many universities, graduates students, researchers, and faculty engage in research projects without having a keen grasp of statistical concepts and methods. We introduce a novel application titled Johnny Takes on Stats in Informatics, in an attempt to succinctly and clearly define relevant concepts in statistics that become crucial to conducting research in an accurate manner. We draw on storytelling narratives, such as those used in Rapunsel (www.rapunsel.org) and Alice 3D (www.alice.org), while integrating a JS application designed to walk researchers through the process of using on-line tools (here we use Google Trends) and data set files to analyze statistical information found on-line. We cite potential causes of concern in using data sets from Google Trends (and similar data set providers) and discuss future considerations for statistical learning applications such as Johnny Takes on Stats in Informatics.