805 research outputs found
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, , derived from stacked
pairwise spectroscopy of clusters with richness . Using 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 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 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 of SDSS redMaPPer clusters. Employing the velocity
bias constraints of Guo et al. (2015), we find with and .
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
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