39 research outputs found
The Influence of Large-Scale Structure on Halo Shapes and Alignments
Alignments of galaxy clusters (the Binggeli effect), as well as of galaxies
themselves have long been studied both observationally and theoretically. Here
we test the influence of large-scales structures and tidal fields on the shapes
and alignments of cluster-size and galaxy-size dark matter halos. We use a
high-resolution N-body simulation of a CDM universe, together with the
results of Colberg et al. (2005), who identified filaments connecting pairs of
clusters. We find that cluster pairs connected by a filament are strongly
aligned with the cluster-cluster axis, whereas unconnected ones are not. For
smaller, galaxy-size halos, there also is an alignment signal, but its strength
is independent of whether the halo is part of an obvious large-scale structure.
Additionally, we find no measureable dependence of galaxy halo shape on
membership of a filament. We also quantify the influence of tidal fields and
find that these do correlate strongly with alignments of halos. The alignments
of most halos are thus caused by tidal fields, with cluster-size halos being
strongly aligned through the added mechanism of infall of matter from
filaments.Comment: 8 pages, 6 figures, accepted for publication in MNRA
SPHRAY: A Smoothed Particle Hydrodynamics Ray Tracer for Radiative Transfer
We introduce SPHRAY, a Smoothed Particle Hydrodynamics (SPH) ray tracer
designed to solve the 3D, time dependent, radiative transfer (RT) equations for
arbitrary density fields. The SPH nature of SPHRAY makes the incorporation of
separate hydrodynamics and gravity solvers very natural. SPHRAY relies on a
Monte Carlo (MC) ray tracing scheme that does not interpolate the SPH particles
onto a grid but instead integrates directly through the SPH kernels. Given
initial conditions and a description of the sources of ionizing radiation, the
code will calculate the non-equilibrium ionization state (HI, HII, HeI, HeII,
HeIII, e) and temperature (internal energy/entropy) of each SPH particle. The
sources of radiation can include point like objects, diffuse recombination
radiation, and a background field from outside the computational volume. The MC
ray tracing implementation allows for the quick introduction of new physics and
is parallelization friendly. A quick Axis Aligned Bounding Box (AABB) test
taken from computer graphics applications allows for the acceleration of the
raytracing component. We present the algorithms used in SPHRAY and verify the
code by performing all the test problems detailed in the recent Radiative
Transfer Comparison Project of Iliev et. al. The Fortran 90 source code for
SPHRAY and example SPH density fields are made available on a companion website
(www.sphray.org).Comment: 17 pages, 16 figures, submitted to MNRAS, comments welcome. source
code, high res. figures and examples can be found at http://www.sphray.or
A many-analysts approach to the relation between religiosity and well-being
The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates
A Many-analysts Approach to the Relation Between Religiosity and Well-being
The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β = 0.120). For the second research question, this was the case for 65% of the teams (median reported β = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks
based on a few demonstrations or natural language instructions. While these
capabilities have led to widespread adoption, most LLMs are developed by
resource-rich organizations and are frequently kept from the public. As a step
towards democratizing this powerful technology, we present BLOOM, a
176B-parameter open-access language model designed and built thanks to a
collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer
language model that was trained on the ROOTS corpus, a dataset comprising
hundreds of sources in 46 natural and 13 programming languages (59 in total).
We find that BLOOM achieves competitive performance on a wide variety of
benchmarks, with stronger results after undergoing multitask prompted
finetuning. To facilitate future research and applications using LLMs, we
publicly release our models and code under the Responsible AI License