CMB anisotropy: deviations from Gaussianity due to non-linear gravity

Non-linear evolution of cosmological energy density fluctuations triggers deviations from Gaussianity in the temperature distribution of the cosmic microwave background. A method to estimate these deviations is proposed. N-body simulations -- in a $\Lambda$CDM cosmology -- are used to simulate the strongly non-linear evolution of cosmological structures. It is proved that these simulations can be combined with the potential approximation to calculate the statistical moments of the CMB anisotropies produced by non-linear gravity. Some of these moments are computed and the resulting values are different from those corresponding to Gaussianity.Comment: 6 latex pages with mn.sty, 3 eps figures. Accepted in MNRA

Adiabatic Invariants and Scalar Fields in a de Sitter Space-Time

The method of adiabatic invariants for time dependent Hamiltonians is applied to a massive scalar field in a de Sitter space-time. The scalar field ground state, its Fock space and coherent states are constructed and related to the particle states. Diverse quantities of physical interest are illustrated, such as particle creation and the way a classical probability distribution emerges for the system at late times.Comment: 9 pages, Latex, no figure

Primordial Gravitational Waves Enhancement

We reconsider the enhancement of primordial gravitational waves that arises from a quantum gravitational model of inflation. A distinctive feature of this model is that the end of inflation witnesses a brief phase during which the Hubble parameter oscillates in sign, changing the usual Hubble friction to anti-friction. An earlier analysis of this model was based on numerically evolving the graviton mode functions after guessing their initial conditions near the end of inflation. The current study is based on an equation which directly evolves the normalized square of the magnitude. We are also able to make a very reliable estimate for the initial condition using a rapidly converging expansion for the sub-horizon regime. Results are obtained for the energy density per logarithmic wave number as a fraction of the critical density. These results exhibit how the enhanced signal depends upon the number of oscillatory periods; they also show the resonant effects associated with particular wave numbers.Comment: 25 pages, 14 figure

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort

Breast Radiation Therapy; Machine-Learning Prediction; Acute DesquamationRaditeràpia de mama; Predicció d'aprenentatge automàtic; Descamació agudaRadioterapia de mama; Predicción de aprendizaje automático; Descamación agudaPurpose Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Methods and Materials Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. Results One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the “hero” model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. Conclusions ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan

Interaction of gravitational waves with matter

We develop a unified formalism for describing the interaction of gravitational waves with matter that clearly separates the effects of general relativity from those due to interactions in the matter. Using it, we derive a general expression for the dispersion of gravitational waves in matter in terms of correlation functions for the matter in flat spacetime. The self energy of a gravitational wave is shown to have contributions analogous to the paramagnetic and diamagnetic contributions to the self energy of an electromagnetic wave. We apply the formalism to some simple systems - free particles, an interacting scalar field, and a fermionic superfluid.Comment: NORDITA-2011-8

Wave Propagation in Stochastic Spacetimes: Localization, Amplification and Particle Creation

Here we study novel effects associated with electromagnetic wave propagation in a Robertson-Walker universe and the Schwarzschild spacetime with a small amount of metric stochasticity. We find that localization of electromagnetic waves occurs in a Robertson-Walker universe with time-independent metric stochasticity, while time-dependent metric stochasticity induces exponential instability in the particle production rate. For the Schwarzschild metric, time-independent randomness can decrease the total luminosity of Hawking radiation due to multiple scattering of waves outside the black hole and gives rise to event horizon fluctuations and thus fluctuations in the Hawking temperature.Comment: 26 pages, 1 Postscript figure, submitted to Phys. Rev. D on July 29, 199

Superadiabatic-type magnetic amplification in conventional cosmology

We consider the evolution of cosmological magnetic fields in FRW models and outline a geometrical mechanism for their superadiabatic amplification on large scales. The mechanism operates within standard electromagnetic theory and applies to FRW universes with open spatial sections. We discuss the general relativistic nature of the effect and show how it modifies the adiabatic magnetic evolution. Assuming a universe that is only marginally open today, we estimate the main features of the superadiabatically amplified residual field.Comment: Minor changes. Published versio

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan. [Abstract copyright: © 2022 The Authors.

Decaying Vacuum Energy and Deflationary Cosmology in Open and Closed Universes

We consider a nonsingular deflationary cosmological model with decaying vacuum energy density in universes of arbitrary spatial curvature. Irrespective of the value of $k$, the models are characterized by an arbitrary time scale $H_I^{-1}$ which determines the initial temperature of the universe and the largest value of the vacuum energy density, the slow decay of which generates all the presently observed matter-energy of the universe. If $H_I^{-1}$ is of the order of the Planck time, the models begin with the Planck temperature and the present day value of the cosmological constant satisfies $\Lambda_I/\Lambda_0 \simeq 10^{118}$ as theoretically suggested. It is also shown that all models allow a density parameter $\Omega_0 <2/3$ and that the age of the universe is large enough to agree with observations even with the high value of $H_0$ suggested by recent measurements.Comment: 20 pages, 2 figures (available from the authors), uses LaTe