Search

We estimate the biases caused by the coherent deflection of cosmic rays due to the Galactic magnetic field (GMF) in maximum likelihood analyses for searches of ultrahigh-energy cosmic-ray sources in the literature. We simulate mock event data sets with a set of assumptions for the starburst galaxy source model, coherent deflection by a GMF model, and mixed-mass composition. We then conduct a maximum likelihood analysis without accounting for the GMF in the same manner as previous studies. We find that the anisotropic fraction f _ani is estimated to be systematically lower than the true value. We estimate the true parameters that are compatible with the best-fit parameters that were reported, and find that except for a narrow region with a large anisotropic fraction and a small separation angular scale, a wide parameter space is still compatible with the experimental results. We also develop a maximum likelihood method that takes the GMF model into account and confirm in Monte Carlo simulations that we can estimate the true parameters within a 1 σ contour under the ideal condition that we know the event-by-event mass and the GMF.

This study aimed to develop a new convolutional neural network (CNN) method for estimating the specific binding ratio (SBR) from only frontal projection images in single-photon emission-computed tomography using [123I]ioflupane. We created five datasets to train two CNNs, LeNet and AlexNet: (1) 128FOV used a 0° projection image without preprocessing, (2) 40FOV used 0° projection images cropped to 40 × 40 pixels centered on the striatum, (3) 40FOV training data doubled by data augmentation (40FOV_DA, left-right reversal only), (4) 40FOVhalf, and (5) 40FOV_DAhalf, split into left and right (20 × 40) images of 40FOV and 40FOV_DA to separately evaluate the left and right SBR. The accuracy of the SBR estimation was assessed using the mean absolute error, root mean squared error, correlation coefficient, and slope. The 128FOV dataset had significantly larger absolute errors compared to all other datasets (p < 0. 05). The best correlation coefficient between the SBRs using SPECT images and those estimated from frontal projection images alone was 0.87. Clinical use of the new CNN method in this study was feasible for estimating the SBR with a small error rate using only the frontal projection images collected in a short time.