Electrode level Monte Carlo model of radiation damage effects on astronomical CCDs

Current optical space telescopes rely upon silicon Charge Coupled Devices (CCDs) to detect and image the incoming photons. The performance of a CCD detector depends on its ability to transfer electrons through the silicon efficiently, so that the signal from every pixel may be read out through a single amplifier. This process of electron transfer is highly susceptible to the effects of solar proton damage (or non-ionizing radiation damage). This is because charged particles passing through the CCD displace silicon atoms, introducing energy levels into the semi-conductor bandgap which act as localized electron traps. The reduction in Charge Transfer Efficiency (CTE) leads to signal loss and image smearing. The European Space Agency's astrometric Gaia mission will make extensive use of CCDs to create the most complete and accurate stereoscopic map to date of the Milky Way. In the context of the Gaia mission CTE is referred to with the complementary quantity Charge Transfer Inefficiency (CTI = 1-CTE). CTI is an extremely important issue that threatens Gaia's performances. We present here a detailed Monte Carlo model which has been developed to simulate the operation of a damaged CCD at the pixel electrode level. This model implements a new approach to both the charge density distribution within a pixel and the charge capture and release probabilities, which allows the reproduction of CTI effects on a variety of measurements for a large signal level range in particular for signals of the order of a few electrons. A running version of the model as well as a brief documentation and a few examples are readily available at http://www.strw.leidenuniv.nl/~prodhomme/cemga.php as part of the CEMGA java package (CTI Effects Models for Gaia).Comment: Accepted by MNRAS on 13 February 2011. 15 pages, 7 figures and 5 table

Moving in time: simulating how neural circuits enable rhythmic enactment of planned sequences

Many complex actions are mentally pre-composed as plans that specify orderings of simpler actions. To be executed accurately, planned orderings must become active in working memory, and then enacted one-by-one until the sequence is complete. Examples include writing, typing, and speaking. In cases where the planned complex action is musical in nature (e.g. a choreographed dance or a piano melody), it appears to be possible to deploy two learned sequences at the same time, one composed from actions and a second composed from the time intervals between actions. Despite this added complexity, humans readily learn and perform rhythm-based action sequences. Notably, people can learn action sequences and rhythmic sequences separately, and then combine them with little trouble (Ullén & Bengtsson 2003). Related functional MRI data suggest that there are distinct neural regions responsible for the two different sequence types (Bengtsson et al. 2004). Although research on musical rhythm is extensive, few computational models exist to extend and inform our understanding of its neural bases. To that end, this article introduces the TAMSIN (Timing And Motor System Integration Network) model, a systems-level neural network model capable of performing arbitrary item sequences in accord with any rhythmic pattern that can be represented as a sequence of integer multiples of a base interval. In TAMSIN, two Competitive Queuing (CQ) modules operate in parallel. One represents and controls item order (the ORD module) and the second represents and controls the sequence of inter-onset-intervals (IOIs) that define a rhythmic pattern (RHY module). Further circuitry helps these modules coordinate their signal processing to enable performative output consistent with a desired beat and tempo.Accepted manuscrip

A Block Minorization--Maximization Algorithm for Heteroscedastic Regression

The computation of the maximum likelihood (ML) estimator for heteroscedastic regression models is considered. The traditional Newton algorithms for the problem require matrix multiplications and inversions, which are bottlenecks in modern Big Data contexts. A new Big Data-appropriate minorization--maximization (MM) algorithm is considered for the computation of the ML estimator. The MM algorithm is proved to generate monotonically increasing sequences of likelihood values and to be convergent to a stationary point of the log-likelihood function. A distributed and parallel implementation of the MM algorithm is presented and the MM algorithm is shown to have differing time complexity to the Newton algorithm. Simulation studies demonstrate that the MM algorithm improves upon the computation time of the Newton algorithm in some practical scenarios where the number of observations is large

Parallelization of a Dynamic Monte Carlo Algorithm: a Partially Rejection-Free Conservative Approach

We experiment with a massively parallel implementation of an algorithm for simulating the dynamics of metastable decay in kinetic Ising models. The parallel scheme is directly applicable to a wide range of stochastic cellular automata where the discrete events (updates) are Poisson arrivals. For high performance, we utilize a continuous-time, asynchronous parallel version of the n-fold way rejection-free algorithm. Each processing element carries an lxl block of spins, and we employ the fast SHMEM-library routines on the Cray T3E distributed-memory parallel architecture. Different processing elements have different local simulated times. To ensure causality, the algorithm handles the asynchrony in a conservative fashion. Despite relatively low utilization and an intricate relationship between the average time increment and the size of the spin blocks, we find that for sufficiently large l the algorithm outperforms its corresponding parallel Metropolis (non-rejection-free) counterpart. As an example application, we present results for metastable decay in a model ferromagnetic or ferroelectric film, observed with a probe of area smaller than the total system.Comment: 17 pages, 7 figures, RevTex; submitted to the Journal of Computational Physic