Searching for eV-scale sterile neutrinos with eight years of atmospheric neutrinos at the IceCube neutrino telescope

We report in detail on searches for eV-scale sterile neutrinos, in the context of a 3+1 model, using eight years of data from the IceCube neutrino telescope. By analyzing the reconstructed energies and zenith angles of 305,735 atmospheric $\nu_\mu$ and $\bar{\nu}_\mu$ events we construct confidence intervals in two analysis spaces: $\sin^2 (2\theta_{24})$ vs. $\Delta m^2_{41}$ under the conservative assumption $\theta_{34}=0$; and $\sin^2(2\theta_{24})$ vs. $\sin^2 (2\theta_{34})$ given sufficiently large $\Delta m^2_{41}$ that fast oscillation features are unresolvable. Detailed discussions of the event selection, systematic uncertainties, and fitting procedures are presented. No strong evidence for sterile neutrinos is found, and the best-fit likelihood is consistent with the no sterile neutrino hypothesis with a p-value of 8\% in the first analysis space and 19\% in the second.Comment: This long-form paper is a companion to the letter "An eV-scale sterile neutrino search using eight years of atmospheric muon neutrino data from the IceCube Neutrino Observatory". v2: update other experiments contours on results plo

Paper Trails: Following the Money

In many recent elections, the candidates who raise the most money have a better shot at winning, so candidates must raise millions of dollars to win an election. A top question to consider in all elections: Where is the money coming from? Posting about the financing behind federal elections from In All Things - an online hub committed to the claim that the life, death, and resurrection of Jesus Christ has implications for the entire world. http://inallthings.org/paper-trails-following-the-money

An eV-scale sterile neutrino search using eight years of atmospheric muon neutrino data from the IceCube Neutrino Observatory

The results of a 3+1 sterile neutrino search using eight years of data from the IceCube Neutrino Observatory are presented. A total of 305,735 muon neutrino events are analyzed in reconstructed energy-zenith space to test for signatures of a matter-enhanced oscillation that would occur given a sterile neutrino state with a mass-squared differences between 0.01\,eV$^2$ and 100\,eV$^2$. The best-fit point is found to be at $\sin^2(2\theta_{24})=0.10$ and $\Delta m_{41}^2 = 4.5{\rm eV}^2$, which is consistent with the no sterile neutrino hypothesis with a p-value of 8.0\%.Comment: 11 pages, 5 figures. This letter is supported by the long-form paper "Searching for eV-scale sterile neutrinos with eight years of atmospheric neutrinos at the IceCube neutrino telescope," also appearing on arXiv. Digital data release available at: https://github.com/icecube/HE-Sterile-8year-data-releas

高等専門学校用共通教材「新素材IV.複合材料編」の評価調査の結果(中扉)

The presence of a population of point sources in a data set modifies the underlying neutrino-count statistics from the Poisson distribution. This deviation can be exactly quantified using the non-Poissonian template fitting technique, and in this work we present the first application of this approach to the IceCube high-energy neutrino data set. Using this method, we search in 7 yr of IceCube data for point-source populations correlated with the disk of the Milky Way, the Fermi bubbles, the Schlegel, Finkbeiner, and Davis dust map, or with the isotropic extragalactic sky. No evidence for such a population is found in the data using this technique, and in the absence of a signal, we establish constraints on population models with source-count distribution functions that can be described by a power law with a single break. The derived limits can be interpreted in the context of many possible source classes. In order to enhance the flexibility of the results, we publish the full posterior from our analysis, which can be used to establish limits on specific population models that would contribute to the observed IceCube neutrino flux

Simulation of SVPWM Based Multivariable Control Method for a DFIG Wind Energy System

This paper deals with a variable speed device toproduce electrical energy on a power network based on adoubly-fed induction machine used in generating mode(DFIG) in wind energy system by using SVPWM powertransfer matrix. This paper presents a modeling and controlapproach which uses instantaneous real and reactive powerinstead of dq components of currents in a vector controlscheme. The main features of the proposed model comparedto conventional models in the dq frame of reference arerobustness and simplicity of realization. The sequential loopclosing technique is adopted to design a multivariable controlsystem including six compensators for a DFIG wind energysystem to capture the maximum wind power and to inject therequired reactive power to the generator. In this paperSVPWM method is used for better controlling of converters.It also provides fault ride through method to protect theconverter during a fault. The time-domain simulation of thestudy system is presented by using MATLAB Simulink to testthe system robustness, to validate the proposed model and toshow the enhanced tracking capability

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

16 pagesPrior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac

Cosmic ray spectrum and composition from PeV to EeV using 3 years of data from IceTop and IceCube

We report on measurements of the all-particle cosmic ray energy spectrum and composition in the PeV to EeV energy range using 3 years of data from the IceCube Neutrino Observatory. The IceTop detector measures cosmic ray induced air showers on the surface of the ice, from which the energy spectrum of cosmic rays is determined by making additional assumptions about the mass composition. A separate measurement is performed when IceTop data are analyzed in coincidence with the high-energy muon energy loss information from the deep in-ice IceCube detector. In this measurement, both the spectrum and the mass composition of the primary cosmic rays are simultaneously reconstructed using a neural network trained on observables from both detectors. The performance and relative advantages of these two distinct analyses are discussed, including the systematic uncertainties and the dependence on the hadronic interaction models, and both all-particle spectra as well as individual spectra for elemental groups are presented