3,266 research outputs found
Investigating Multiple Solutions in the Constrained Minimal Supersymmetric Standard Model
Recent work has shown that the Constrained Minimal Supersymmetric Standard
Model (CMSSM) can possess several distinct solutions for certain values of its
parameters. The extra solutions were not previously found by public
supersymmetric spectrum generators because fixed point iteration (the algorithm
used by the generators) is unstable in the neighbourhood of these solutions.
The existence of the additional solutions calls into question the robustness of
exclusion limits derived from collider experiments and cosmological
observations upon the CMSSM, because limits were only placed on one of the
solutions. Here, we map the CMSSM by exploring its multi-dimensional parameter
space using the shooting method, which is not subject to the stability issues
which can plague fixed point iteration. We are able to find multiple solutions
where in all previous literature only one was found. The multiple solutions are
of two distinct classes. One class, close to the border of bad electroweak
symmetry breaking, is disfavoured by LEP2 searches for neutralinos and
charginos. The other class has sparticles that are heavy enough to evade the
LEP2 bounds. Chargino masses may differ by up to around 10% between the
different solutions, whereas other sparticle masses differ at the sub-percent
level. The prediction for the dark matter relic density can vary by a hundred
percent or more between the different solutions, so analyses employing the dark
matter constraint are incomplete without their inclusion.Comment: 30 pages, 12 figures, 2 tables; v2: added discussion on speed of
shooting method, fixed typos, matches published versio
Zero-noise extrapolation for quantum-gate error mitigation with identity insertions
Quantum-gate errors are a significant challenge for achieving precision measurements on noisy intermediate-scale quantum (NISQ) computers. This paper focuses on zero-noise extrapolation (ZNE), a technique that can be implemented on existing hardware, studying it in detail and proposing modifications to existing approaches. In particular, we consider identity insertion methods for amplifying noise because they are hardware agnostic. We build a mathematical formalism for studying existing ZNE techniques and show how higher order polynomial extrapolations can be used to systematically reduce depolarizing errors. Furthermore, we introduce a method for amplifying noise that uses far fewer gates than traditional methods. This approach is compared with existing methods for simulated quantum circuits. Comparable or smaller errors are possible with fewer gates, which illustrates the potential for empowering an entirely new class of moderate-depth circuits on near term hardware
Arsenic: A Roadblock to Potential Animal Waste Management Solutions
The localization and intensification of the poultry industry over the past 50 years have incidentally created a largely ignored environmental management crisis. As a result of these changes in poultry production, concentrated animal feeding operations (CAFOs) produce far more waste than can be managed by land disposal within the regions where it is produced. As a result, alternative waste management practices are currently being implemented, including incineration and pelletization of waste. However, organic arsenicals used in poultry feed are converted to inorganic arsenicals in poultry waste, limiting the feasibility of waste management alternatives. The presence of inorganic arsenic in incinerator ash and pelletized waste sold as fertilizer creates opportunities for population exposures that did not previously exist. The removal of arsenic from animal feed is a critical step toward safe poultry waste management
Formulas and equations for finding scattering data from the Dirichlet-to-Neumann map with nonzero background potential
For the Schrodinger equation at fixed energy with a potential supported in a
bounded domain we give formulas and equations for finding scattering data from
the Dirichlet-to-Neumann map with nonzero background potential. For the case of
zero background potential these results were obtained in [R.G.Novikov,
Multidimensional inverse spectral problem for the equation
-\Delta\psi+(v(x)-Eu(x))\psi=0, Funkt. Anal. i Ego Prilozhen 22(4), pp.11-22,
(1988)]
The Machine Learning Landscape of Top Taggers
Based on the established task of identifying boosted, hadronically decaying
top quarks, we compare a wide range of modern machine learning approaches.
Unlike most established methods they rely on low-level input, for instance
calorimeter output. While their network architectures are vastly different,
their performance is comparatively similar. In general, we find that these new
approaches are extremely powerful and great fun.Comment: Yet another tagger included
Calomplification β the power of generative calorimeter models
Motivated by the high computational costs of classical simulations, machine-learned generative models can be extremely useful in particle physics and elsewhere. They become especially attractive when surrogate models can efficiently learn the underlying distribution, such that a generated sample outperforms a training sample of limited size. This kind of GANplification has been observed for simple Gaussian models. We show the same effect for a physics simulation, specifically photon showers in an electromagnetic calorimeter
The role of mutation rate variation and genetic diversity in the architecture of human disease
Background
We have investigated the role that the mutation rate and the structure of genetic variation at a locus play in determining whether a gene is involved in disease. We predict that the mutation rate and its genetic diversity should be higher in genes associated with disease, unless all genes that could cause disease have already been identified.
Results
Consistent with our predictions we find that genes associated with Mendelian and complex disease are substantially longer than non-disease genes. However, we find that both Mendelian and complex disease genes are found in regions of the genome with relatively low mutation rates, as inferred from intron divergence between humans and chimpanzees, and they are predicted to have similar rates of non-synonymous mutation as other genes. Finally, we find that disease genes are in regions of significantly elevated genetic diversity, even when variation in the rate of mutation is controlled for. The effect is small nevertheless.
Conclusions
Our results suggest that gene length contributes to whether a gene is associated with disease. However, the mutation rate and the genetic architecture of the locus appear to play only a minor role in determining whether a gene is associated with disease
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