Magnetic white dwarfs in the Early Data Release of the Sloan Digital Sky Survey

We have identified 7 new magnetic DA white dwarfs in the Early Data Release of the Sloan Digital Sky Survey. Our selection strategy has also recovered all the previously known magnetic white dwarfs contained in the SDSS EDR, KUV03292+0035 and HE0330-0002. Analysing the SDSS fibre spectroscopy of the magnetic DA white dwarfs with our state-of-the-art model spectra, we find dipole field strengths 1.5<=B_d<=63MG and effective temperatures 8500<=Teff<=39000K. As a conservative estimate, we expect that the complete SDSS will increase the number of known magnetic white dwarfs by a factor 3.Comment: 7 pages, 4 figures, accepted for publication in Astronomy & Astrophysic

Present-day plate motions

A data set comprising 110 spreading rates, 78 transform fault azimuths and 142 earthquake slip vectors was inverted to yield a new instantaneous plate motion model, designated RM2. The mean averaging interval for the relative motion data was reduced to less than 3 My. A detailed comparison of RM2 with angular velocity vectors which best fit the data along individual plate boundaries indicates that RM2 performs close to optimally in most regions, with several notable exceptions. On the other hand, a previous estimate (RM1) failed to satisfy an extensive set of new data collected in the South Atlantic Ocean. It is shown that RM1 incorrectly predicts the plate kinematics in the South Atlantic because the presently available data are inconsistent with the plate geometry assumed in deriving RM1. It is demonstrated that this inconsistency can be remedied by postulating the existence of internal deformation with the Indian plate, although alternate explanations are possible

Particle Gibbs with Ancestor Sampling

Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). We present a novel PMCMC algorithm that we refer to as particle Gibbs with ancestor sampling (PGAS). PGAS provides the data analyst with an off-the-shelf class of Markov kernels that can be used to simulate the typically high-dimensional and highly autocorrelated state trajectory in a state-space model. The ancestor sampling procedure enables fast mixing of the PGAS kernel even when using seemingly few particles in the underlying SMC sampler. This is important as it can significantly reduce the computational burden that is typically associated with using SMC. PGAS is conceptually similar to the existing PG with backward simulation (PGBS) procedure. Instead of using separate forward and backward sweeps as in PGBS, however, we achieve the same effect in a single forward sweep. This makes PGAS well suited for addressing inference problems not only in state-space models, but also in models with more complex dependencies, such as non-Markovian, Bayesian nonparametric, and general probabilistic graphical models

On ℓ\ell-torsion in class groups of number fields

For each integer $\ell \geq 1$, we prove an unconditional upper bound on the size of the $\ell$-torsion subgroup of the class group, which holds for all but a zero-density set of field extensions of $\mathbb{Q}$ of degree $d$, for any fixed $d \in \{2,3,4,5\}$ (with the additional restriction in the case $d=4$ that the field be non-$D_4$). For sufficiently large $\ell$ (specified explicitly), these results are as strong as a previously known bound that is conditional on GRH. As part of our argument, we develop a probabilistic "Chebyshev sieve," and give uniform, power-saving error terms for the asymptotics of quartic (non-$D_4$) and quintic fields with chosen splitting types at a finite set of primes.Comment: 27 pages, updated from v1 with minor edits to expositio

BaBar B Decay Results

Data from the first run of the BaBar detector at the PEP II accelerator are presented. Measurements of many rare B decay modes are now possible using the large data sets currently being collected by BaBar. An overview of analysis techniques and results on data collected in 2000 are described.Comment: 16 pages, 35 figures. Submission to Lepton-Photon 2001 Rome conference proceeding

Application of the Hilbert-Huang Transform to the Search for Gravitational Waves

We present the application of a novel method of time-series analysis, the Hilbert-Huang Transform, to the search for gravitational waves. This algorithm is adaptive and does not impose a basis set on the data, and thus the time-frequency decomposition it provides is not limited by time-frequency uncertainty spreading. Because of its high time-frequency resolution it has important applications to both signal detection and instrumental characterization. Applications to the data analysis of the ground and space based gravitational wave detectors, LIGO and LISA, are described

Mutualism, Parasitism, and Evolutionary Adaptation

Our investigations concern the role of symbiosis as an enabling mechanism in evolutionary adaptation. Previous work has illustrated how the formation of mutualist groups can guide genetic variation so as to enable the evolution of ultimately independent organisms that would otherwise be unobtainable. The new experiments reported here show that this effect applies not just in genetically related organisms but may also occur from symbiosis between distinct species. In addition, a new detail is revealed: when the symbiotic group members are drawn from two separate species only one of these species achieves eventual independence and the other remains parasitic. It is nonetheless the case that this second species, formerly mutualistic, was critical in enabling the independence of the first. We offer a biological example that is suggestive of the effect and discuss the implications for evolving complex organisms, natural and artificial