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

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

Pengaruh Manajemen Laba terhadap Kinerja Keuangan Perusahaan melalui GCG sebagai Variabel Moderating (Studi Kasus pada Perusahaan Manufaktur di Bei Tahun 2009-2011)

The purpose of this research are to verify the impact of earning management to financial performance with good corporate governance as a moderating variable. Number of commissioner board and the existence of audit committee are use to indicate good coporate governance. The sample of research is manufacturing company that listed in Bursa Efek Indonesia for the year 2009 – 2011. T-test is used to prove all of hypothesis. The results indicate that earning management have an impact to company financial performance. Number of commissioner board can be weaken the impact of earning management to company financial performance, but the other hand, existence of audit committee can not

Penerapan Algoritma K-Nearest Neighbors Pada Analisis Sentimen Review Agen Travel

Penggunaan internet mampu memberikan pengaruh terhadap orang-orang melalui internet;kita bisa mendapat informasi, selain itu, kita juga bisa memberi pendapat positif dan negatifuntuk review tertentu. Dengan menyediakan banyak data atau informasi di internet, kamimenggunakannya untuk diproses jadi, itu akan memiliki pengetahuan baru. Berdasarkan halitu, penulis membuat penelitian, seperti klasifikasi opini dengan menganalisis sentimen melaluipendekatan penambangan teks, dalam penelitian ini; dibutuhkan suatu metode yang mampumengklasifikasikan pendapat secara akurat. Ruang lingkup penelitian ini adalah peninjauanagen perjalanan pengolahan data menggunakan algoritma K-Nearest Neighbor (K-NN) yangmenggunakan 100 review positif dan 100 review negatif dengan enam kata yang berhubungandengan sentimen yaitu: Fast, Good, Great, Buruk, Cencel, dan Tunggu. Ini memiliki buktibahwa dengan menggunakan algoritma K-Nearest Neighbor (K-NN), ia mencapai hasil akurasiterbaik dan berdasarkan perhitungan yang dinyatakan dalam aplikasi. Titik akurasi peninjauanagen perjalanan menggunakan K-Nearest Neighbor (K-NN) algoritma telah mencapai 87,00%dan titik AUC adalah 0,916, titik AUC milik kelompok Klasifikasi Excellent sehinggadinyatakan bahwa K-Nearest Neighbor (K -NN) memiliki hasil yang akurat dalammenganalisis sentimen ulasan agen perjalanan