31,575 research outputs found
Evolution and Nucleosynthesis of Massive Stars and Related Nuclear Uncertainties
Properties of atomic nuclei important for the prediction of astrophysical
reaction rates are reviewed. In the first part, a recent simulation of
evolution and nucleosynthesis of stars between 15 and 25 solar masses is
presented. This study is used to illustrate the required nuclear input as well
as to give examples of the sensitivity to certain rates. The second part
focusses on the prediction of nuclear rates in the statistical model
(Hauser-Feshbach) and direct capture (DWBA). Some of the important ingredients
are addressed. Discussed in more detail are approaches to predict level
densities, parity distributions, and optical alpha+nucleus potentials.Comment: Invited talk at 17th Int. Nucl. Phys. Conf. of the EPS "Nuclear
Physics in Astrophysics", Debrecen, Hungary, 2002 (new version: fixed typo in
alpha potential parameters; note: the parameters are incorrect in the NPA
paper
Basic Parameter Estimation of Binary Neutron Star Systems by the Advanced LIGO/Virgo Network
Within the next five years, it is expected that the Advanced LIGO/Virgo
network will have reached a sensitivity sufficient to enable the routine
detection of gravitational waves. Beyond the initial detection, the scientific
promise of these instruments relies on the effectiveness of our physical
parameter estimation capabilities. The majority of this effort has been towards
the detection and characterization of gravitational waves from compact binary
coalescence, e.g. the coalescence of binary neutron stars. While several
previous studies have investigated the accuracy of parameter estimation with
advanced detectors, the majority have relied on approximation techniques such
as the Fisher Matrix. Here we report the statistical uncertainties that will be
achievable for optimal detection candidates (SNR = 20) using the full parameter
estimation machinery developed by the LIGO/Virgo Collaboration via Markov-Chain
Monte Carlo methods. We find the recovery of the individual masses to be
fractionally within 9% (15%) at the 68% (95%) credible intervals for equal-mass
systems, and within 1.9% (3.7%) for unequal-mass systems. We also find that the
Advanced LIGO/Virgo network will constrain the locations of binary neutron star
mergers to a median uncertainty of 5.1 deg^2 (13.5 deg^2) on the sky. This
region is improved to 2.3 deg^2 (6 deg^2) with the addition of the proposed
LIGO India detector to the network. We also report the average uncertainties on
the luminosity distances and orbital inclinations of ideal detection candidates
that can be achieved by different network configurations.Comment: Second version: 15 pages, 9 figures, accepted in Ap
Constraining scalar resonances with top-quark pair production at the LHC
Constraints on models which predict resonant top-quark pair production at the
LHC are provided via a reinterpretation of the Standard Model (SM) particle
level measurement of the top-anti-top invariant mass distribution,
. We make use of state-of-the-art Monte Carlo event simulation to
perform a direct comparison with measurements of in the
semi-leptonic channels, considering both the boosted and the resolved regime of
the hadronic top decays. A simplified model to describe various scalar
resonances decaying into top-quarks is considered, including CP-even and
CP-odd, color-singlet and color-octet states, and the excluded regions in the
respective parameter spaces are provided.Comment: 34 pages, 17 figure
Comparison of Gravitational Wave Detector Network Sky Localization Approximations
Gravitational waves emitted during compact binary coalescences are a
promising source for gravitational-wave detector networks. The accuracy with
which the location of the source on the sky can be inferred from gravitational
wave data is a limiting factor for several potential scientific goals of
gravitational-wave astronomy, including multi-messenger observations. Various
methods have been used to estimate the ability of a proposed network to
localize sources. Here we compare two techniques for predicting the uncertainty
of sky localization -- timing triangulation and the Fisher information matrix
approximations -- with Bayesian inference on the full, coherent data set. We
find that timing triangulation alone tends to over-estimate the uncertainty in
sky localization by a median factor of for a set of signals from
non-spinning compact object binaries ranging up to a total mass of , and the over-estimation increases with the mass of the system. We
find that average predictions can be brought to better agreement by the
inclusion of phase consistency information in timing-triangulation techniques.
However, even after corrections, these techniques can yield significantly
different results to the full analysis on specific mock signals. Thus, while
the approximate techniques may be useful in providing rapid, large scale
estimates of network localization capability, the fully coherent Bayesian
analysis gives more robust results for individual signals, particularly in the
presence of detector noise.Comment: 11 pages, 7 Figure
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Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting
In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measure are used to quantify the quality of ensemble prediction. Copyright 2006 by the American Geophysical Union
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