Study of electron anti-neutrinos associated with gamma-ray bursts using KamLAND

We search for electron anti-neutrinos ($\overline{\nu}_e$) from long and short-duration gamma-ray bursts~(GRBs) using data taken by the KamLAND detector from August 2002 to June 2013. No statistically significant excess over the background level is found. We place the tightest upper limits on $\overline{\nu}_e$ fluence from GRBs below 7 MeV and place first constraints on the relation between $\overline{\nu}_e$ luminosity and effective temperature.Comment: 16 pages and 5 figure

KamLAND Sensitivity to Neutrinos from Pre-Supernova Stars

In the late stages of nuclear burning for massive stars (M>8~M_{\sun}), the production of neutrino-antineutrino pairs through various processes becomes the dominant stellar cooling mechanism. As the star evolves, the energy of these neutrinos increases and in the days preceding the supernova a significant fraction of emitted electron anti-neutrinos exceeds the energy threshold for inverse beta decay on free hydrogen. This is the golden channel for liquid scintillator detectors because the coincidence signature allows for significant reductions in background signals. We find that the kiloton-scale liquid scintillator detector KamLAND can detect these pre-supernova neutrinos from a star with a mass of 25~M_{\sun} at a distance less than 690~pc with 3$\sigma$ significance before the supernova. This limit is dependent on the neutrino mass ordering and background levels. KamLAND takes data continuously and can provide a supernova alert to the community.Comment: 19 pages, 6 figures, 1 tabl

Importance Sampling for Objetive Funtion Estimations in Neural Detector Traing Driven by Genetic Algorithms

To train Neural Networks (NNs) in a supervised way, estimations of an objective function must be carried out. The value of this function decreases as the training progresses and so, the number of test observations necessary for an accurate estimation has to be increased. Consequently, the training computational cost is unaffordable for very low objective function value estimations, and the use of Importance Sampling (IS) techniques becomes convenient. The study of three different objective functions is considered, which implies the proposal of estimators of the objective function using IS techniques: the Mean-Square error, the Cross Entropy error and the Misclassification error criteria. The values of these functions are estimated by IS techniques, and the results are used to train NNs by the application of Genetic Algorithms. Results for a binary detection in Gaussian noise are provided. These results show the evolution of the parameters during the training and the performances of the proposed detectors in terms of error probability and Receiver Operating Characteristics curves. At the end of the study, the obtained results justify the convenience of using IS in the training

Three-dimensional magnetic resonance imaging of the anterolateral ligament of the knee: an evaluation of intact and anterior cruciate ligament–deficient knees from the scientific anterior cruciate ligament network international (SANTI) Study Group

Purpose: The aim of this study was to determine the visualisation rate of the ALL in uninjured and ACL deficient knees when using 3D-MRI. In addition, it was sought to characterize the spectrum of ALL injury in acute and chronically ACL deficient knees, and also to determine the inter and intra-observer reliability of a 3D-MRI classification of ALL injury. Methods: 100 knees underwent 3D-MRI (60 with ACL rupture and 40 non-injured knees). The ALL was evaluated by two blinded orthopaedic surgeons. The ALL was classified as Type A: continuous, clearly defined low-signal band, Type B: with warping, thinning, or iso-signal changes, Type C: without clear continuity. Comparison between acute (<1 month) and chronically ACL injured knees was evaluated as well as intra and inter-observer reliability. Results: Complete visualisation of the full path of the ALL was achieved in all non-injured knees. In the ACL injured group, 24 acutely injured knees were imaged: 87.5% showed evidence of injury (3 knees were normal/Type A (12.5%), 18 Type B (75.0%), and 3 Type C (12.5%)). 36 knees chronically ACL injured knees were imaged: 55.6% showed evidence of injury (16 Type A (44.4%), 18 Type B (50.0%), and 2 Type C (5.6%)). The difference in the rate of injury between the two groups was significant (p = 0.03). Multivariate analysis demonstrated that the delay from ACL injury to MRI was the only factor (negatively) associated with the rate of injury to the ALL. Inter- and intra-observer reliability of the classification of ALL type were good (kappa 0.86 and 0.93 respectively). Conclusion: 3D-MRI allows full visualisation of the ALL in all knees. The rate of injury to the ALL in acutely ACL injured knees identified on 3D-MRI is higher than previous reports using standard MRI techniques. This rate is significantly higher than the rate of injury to the ALL identified in chronically ACL injured knees. Level of Evidence: IV, Diagnostic, case control study