Preliminary Results from the Caltech Core-Collapse Project (CCCP)

We present preliminary results from the Caltech Core-Collapse Project (CCCP), a large observational program focused on the study of core-collapse SNe. Uniform, high-quality NIR and optical photometry and multi-epoch optical spectroscopy have been obtained using the 200'' Hale and robotic 60'' telescopes at Palomar, for a sample of 50 nearby core-collapse SNe. The combination of both well-sampled optical light curves and multi-epoch spectroscopy will enable spectroscopically and photometrically based subtype definitions to be disentangled from each other. Multi-epoch spectroscopy is crucial to identify transition events that evolve among subtypes with time. The CCCP SN sample includes every core-collapse SN discovered between July 2004 and September 2005 that was visible from Palomar, found shortly (< 30 days) after explosion (based on available pre-explosion photometry), and closer than ~120 Mpc. This complete sample allows, for the first time, a study of core-collapse SNe as a population, rather than as individual events. Here, we present the full CCCP SN sample and show exemplary data collected. We analyze available data for the first ~1/3 of the sample and determine the subtypes of 13 SNe II based on both light curve shapes and spectroscopy. We discuss the relative SN II subtype fractions in the context of associating SN subtypes with specific progenitor stars.Comment: To appear in the proceedings of the meeting "The Multicoloured Landscape of Compact Objects and their Explosive Origins", Cefalu, Italy, June 2006, to be published by AIP, Eds. L. Burderi et a

Spatio-Temporal Low Count Processes with Application to Violent Crime Events

There is significant interest in being able to predict where crimes will happen, for example to aid in the efficient tasking of police and other protective measures. We aim to model both the temporal and spatial dependencies often exhibited by violent crimes in order to make such predictions. The temporal variation of crimes typically follows patterns familiar in time series analysis, but the spatial patterns are irregular and do not vary smoothly across the area. Instead we find that spatially disjoint regions exhibit correlated crime patterns. It is this indeterminate inter-region correlation structure along with the low-count, discrete nature of counts of serious crimes that motivates our proposed forecasting tool. In particular, we propose to model the crime counts in each region using an integer-valued first order autoregressive process. We take a Bayesian nonparametric approach to flexibly discover a clustering of these region-specific time series. We then describe how to account for covariates within this framework. Both approaches adjust for seasonality. We demonstrate our approach through an analysis of weekly reported violent crimes in Washington, D.C. between 2001-2008. Our forecasts outperform standard methods while additionally providing useful tools such as prediction intervals

Longitudinal multivariate tensor- and searchlight-based morphometry using permutation testing

Tensor based morphometry [1] was used to detect statistically significant regions of neuroanatomical change over time in a comparison between 36 probable Alzheimer's Disease patients and 20 age- and sexmatched controls. Baseline and twelve-month repeat Magnetic Resonance images underwent tied spatial normalisation [10] and longitudinal high-dimensional warps were then estimated. Analyses involved univariate and multivariate data derived from the longitudinal deformation fields. The most prominent findings were expansion of the fluid spaces, and contraction of the hippocampus and temporal region. Multivariate measures were notably more powerful, and have the potential to identify patterns of morphometric difference that would be overlooked by conventional mass-univariate analysis