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Register a new userA new finite-field mass reconstruction algorithm
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A new method for the reconstruction of the projected mass distribution of clusters of galaxies from the image distortion of background galaxies is discussed. This method is essentially equivalent to the one we developed previously, i.e., the noise-filtering method, but has several practical advantages: (1) It is much easier to implement; (2) it can be easily applied to wide-field images, since the constraints on the number of gridpoints are much weaker than for the previous method, and (3) it can be easily generalized to more complicated field geometries, such as that of the Wide Field Planetary Camera 2 (WFPC2) onboard HST. We have tested the performance of our new inversion method (for which a FORTRAN-77 implementation is available from the authors) using simulated data, demonstrating that it fares very favourably.
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A method is described to model the magnetic field in the vicinity of constellations of multiple satellites using field and plasma current measurements. This quadratic model has the properties that the divergence is zero everywhere and matches the measured values of the magnetic field and its curl (current) at each spacecraft, and thus extends the linear curlometer method to second order. It is able to predict the topology of the field lines near magnetic structures, such as near reconnecting regions or flux ropes, and allows a tracking of the motion of these structures relative to the spacecraft constellation. Comparisons to PIC simulations estimate the model accuracy. Reconstruction of two electron diffusion regions show the expected field line structure. The model can be applied to other small-scale phenomena (bow shock, waves of commensurate wavelength), and can be modified to reconstruct also the electric field, allowing tracing of particle trajectories.
The DArk Matter Particle Explorer (DAMPE) is one of the four satellites within the Strategic Pioneer Research Program in Space Science of the Chinese Academy of Science (CAS). The Silicon-Tungsten Tracker (STK), which is composed of 768 singled-sided silicon microstrip detectors, is one of the four subdetectors in DAMPE, providing track reconstruction and charge identification for relativistic charged particles. The charge response of DAMPE silicon microstrip detectors is complicated, depending on the incident angle and impact position. A new charge reconstruction algorithm for the DAMPE silicon microstrip detector is introduced in this paper. This algorithm can correct the complicated charge response, and was proved applicable by the ion test beam.
We propose a new mass reconstruction technique for SUSY processes at the LHC. The idea is to completely solve the kinematics of the SUSY cascade decay by using the assumption that the selected events satisfy the same mass shell conditions of the sparticles involved in the cascade decay. Using this technique, we study the measurement of the mass of the bottom squarks in the cascade decay of the gluino. Based on the final state including two high p_T leptons and two b-jets, we investigate different possible approaches to the mass reconstruction of the gluino and the two bottom squarks. In particular we evaluate the performance of different algorithms in discriminating two bottom squark states with a mass difference as low as 5%.
The existence of large volumes of time series data in many applications has motivated data miners to investigate specialized methods for mining time series data. Clustering is a popular data mining method due to its powerful exploratory nature and its usefulness as a preprocessing step for other data mining techniques. This article develops two novel clustering algorithms for time series data that are extensions of a crisp c-shapes algorithm. The two new algorithms are heuristic derivatives of fuzzy c-means (FCM). Fuzzy c-Shapes plus (FCS+) replaces the inner product norm in the FCM model with a shape-based distance function. Fuzzy c-Shapes double plus (FCS++) uses the shape-based distance, and also replaces the FCM cluster centers with shape-extracted prototypes. Numerical experiments on 48 real time series data sets show that the two new algorithms outperform state-of-the-art shape-based clustering algorithms in terms of accuracy and efficiency. Four external cluster validity indices (the Rand index, Adjusted Rand Index, Variation of Information, and Normalized Mutual Information) are used to match candidate partitions generated by each of the studied algorithms. All four indices agree that for these finite waveform data sets, FCS++ gives a small improvement over FCS+, and in turn, FCS+ is better than the original crisp c-shapes method. Finally, we apply two tests of statistical significance to the three algorithms. The Wilcoxon and Friedman statistics both rank the three algorithms in exactly the same way as the four cluster validity indices.
A novel and detailed convergence analysis is presented for a greedy algorithm that was previously introduced for operator reconstruction problems in the field of quantum mechanics. This algorithm is based on an offline/online decomposition of the reconstruction process and on an ansatz for the unknown operator obtained by an a priori chosen set of linearly independent matrices. The presented convergence analysis focuses on linear-quadratic (optimization) problems governed by linear differential systems and reveals the strong dependence of the performance of the greedy algorithm on the observability properties of the system and on the ansatz of the basis elements. Moreover, the analysis allows us to use a precise (and in some sense optimal) choice of basis elements for the linear case and led to the introduction of a new and more robust optimized greedy reconstruction algorithm. This optimized approach also applies to nonlinear Hamiltonian reconstruction problems, and its efficiency is demonstrated by numerical experiments.
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