83,007 research outputs found
Dark matter annihilation and decay in dwarf spheroidal galaxies: The classical and ultrafaint dSphs
Dwarf spheroidal (dSph) galaxies are prime targets for present and future
gamma-ray telescopes hunting for indirect signals of particle dark matter. The
interpretation of the data requires careful assessment of their dark matter
content in order to derive robust constraints on candidate relic particles.
Here, we use an optimised spherical Jeans analysis to reconstruct the
`astrophysical factor' for both annihilating and decaying dark matter in 21
known dSphs. Improvements with respect to previous works are: (i) the use of
more flexible luminosity and anisotropy profiles to minimise biases, (ii) the
use of weak priors tailored on extensive sets of contamination-free mock data
to improve the confidence intervals, (iii) systematic cross-checks of binned
and unbinned analyses on mock and real data, and (iv) the use of mock data
including stellar contamination to test the impact on reconstructed signals.
Our analysis provides updated values for the dark matter content of 8
`classical' and 13 `ultrafaint' dSphs, with the quoted uncertainties directly
linked to the sample size; the more flexible parametrisation we use results in
changes compared to previous calculations. This translates into our ranking of
potentially-brightest and most robust targets---viz., Ursa Minor, Draco,
Sculptor---, and of the more promising, but uncertain targets---viz., Ursa
Major 2, Coma---for annihilating dark matter. Our analysis of Segue 1 is
extremely sensitive to whether we include or exclude a few marginal member
stars, making this target one of the most uncertain. Our analysis illustrates
challenges that will need to be addressed when inferring the dark matter
content of new `ultrafaint' satellites that are beginning to be discovered in
southern sky surveys.Comment: 19 pages, 14 figures, submitted to MNRAS. Supplementary material
available on reques
Integrating and Ranking Uncertain Scientific Data
Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates
The uncertain representation ranking framework for concept-based video retrieval
Concept based video retrieval often relies on imperfect and uncertain concept detectors. We propose a general ranking framework to define effective and robust ranking functions, through explicitly addressing detector uncertainty. It can cope with multiple concept-based representations per video segment and it allows the re-use of effective text retrieval functions which are defined on similar representations. The final ranking status value is a weighted combination of two components: the expected score of the possible scores, which represents the risk-neutral choice, and the scores’ standard deviation, which represents the risk or opportunity that the score for the actual representation is higher. The framework consistently improves the search performance in the shot retrieval task and the segment retrieval task over several baselines in five TRECVid collections and two collections which use simulated detectors of varying performance
Data-driven satisficing measure and ranking
We propose an computational framework for real-time risk assessment and
prioritizing for random outcomes without prior information on probability
distributions. The basic model is built based on satisficing measure (SM) which
yields a single index for risk comparison. Since SM is a dual representation
for a family of risk measures, we consider problems constrained by general
convex risk measures and specifically by Conditional value-at-risk. Starting
from offline optimization, we apply sample average approximation technique and
argue the convergence rate and validation of optimal solutions. In online
stochastic optimization case, we develop primal-dual stochastic approximation
algorithms respectively for general risk constrained problems, and derive their
regret bounds. For both offline and online cases, we illustrate the
relationship between risk ranking accuracy with sample size (or iterations).Comment: 26 Pages, 6 Figure
Probabilistic performance estimators for computational chemistry methods: Systematic Improvement Probability and Ranking Probability Matrix. I. Theory
The comparison of benchmark error sets is an essential tool for the
evaluation of theories in computational chemistry. The standard ranking of
methods by their Mean Unsigned Error is unsatisfactory for several reasons
linked to the non-normality of the error distributions and the presence of
underlying trends. Complementary statistics have recently been proposed to
palliate such deficiencies, such as quantiles of the absolute errors
distribution or the mean prediction uncertainty. We introduce here a new score,
the systematic improvement probability (SIP), based on the direct system-wise
comparison of absolute errors. Independently of the chosen scoring rule, the
uncertainty of the statistics due to the incompleteness of the benchmark data
sets is also generally overlooked. However, this uncertainty is essential to
appreciate the robustness of rankings. In the present article, we develop two
indicators based on robust statistics to address this problem: P_{inv}, the
inversion probability between two values of a statistic, and \mathbf{P}_{r},
the ranking probability matrix. We demonstrate also the essential contribution
of the correlations between error sets in these scores comparisons
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