Using Astrometry to Deblend Microlensing Events

We discuss the prospect of deblending microlensing events by observing astrometric shifts of the lensed stars. Since microlensing searches are generally performed in very crowded fields, it is expected that stars will be confusion limited rather than limited by photon statistics. By performing simulations of events in crowded fields, we find that if we assume a dark lens and that the lensed star obeys a power law luminosity function, $n(L)\propto L^{-\beta}$, over half the simulated events show a measurable astrometric shift. Our simulations included 20000 stars in a $256\times 256$ Nyquist sampled CCD frame. For $\beta=2$, we found that 58% of the events were significantly blended $(F_{\ast}/F_{tot}\leq 0.9)$, and of those, 73% had a large astrometric shift $(\geq 0.5 pixels)$. Likewise, for $\beta=3$, we found that 85% of the events were significantly blended, and that 85% of those had large shifts. Moreover, the shift is weakly correlated to the degree of blending, suggesting that it may be possible not only to detect the existence of a blend, but also to deblend events statistically using shift information.Comment: 24 pages, 7 postscript Figure

Comparing the Ancient Star Formation Histories of the Magellanic Clouds

We present preliminary results from a new HST archival program aimed at tightly constraining the ancient (>4 Gyr ago) star formation histories (SFHs) of the field populations of the SMC and LMC. We demonstrate the quality of the archival data by constructing HST/WFPC2-based color-magnitude diagrams (CMDs; M_{F555W} ~ +8) for 7 spatially diverse fields in the SMC and 8 fields in the LMC. The HST-based CMDs are >2 magnitudes deeper than any from ground based observations, and are particularly superior in high surface brightness regions, e.g., the LMC bar, which contain a significant fraction of star formation and are crowding limited from ground based observations. To minimize systematic uncertainties, we derive the SFH of each field using an identical maximum likelihood CMD fitting technique. We then compute an approximate mass weighted average SFH for each galaxy. We find that both galaxies lack a dominant burst of early star formation, which suggests either a suppression or an under-fueling of early star formation. From 10-12 Gyr ago, the LMC experienced a period of enhanced stellar mass growth relative to the SMC. Similar to some previous studies, we find two notable peaks in the SFH of the SMC at ~4.5 and 9 Gyr ago, which could be due to repeated close passages with the LMC, implying an interaction history that has persisted for at least 9 Gyr. We find little evidence for strong periodic behavior in the lifetime SFHs of both MCs, suggesting that repeated encounters with the Milky Way are unlikely. Beginning ~3.5 Gyr ago, both galaxies show increases in their SFHs, in agreement with previous studies, and thereafter, track each other remarkably well. (abridged)Comment: 9 pages, 5 Figures, Accepted for Publication in MNRA

Sleep, circadian rhythms, and the pathogenesis of Alzheimer Disease

Disturbances in the sleep–wake cycle and circadian rhythms are common symptoms of Alzheimer Disease (AD), and they have generally been considered as late consequences of the neurodegenerative processes. Recent evidence demonstrates that sleep–wake and circadian disruption often occur early in the course of the disease and may even precede the development of cognitive symptoms. Furthermore, the sleep–wake cycle appears to regulate levels of the pathogenic amyloid-beta peptide in the brain, and manipulating sleep can influence AD-related pathology in mouse models via multiple mechanisms. Finally, the circadian clock system, which controls the sleep–wake cycle and other diurnal oscillations in mice and humans, may also have a role in the neurodegenerative process. In this review, we examine the current literature related to the mechanisms by which sleep and circadian rhythms might impact AD pathogenesis, and we discuss potential therapeutic strategies targeting these systems for the prevention of AD

Adaptive filtering of radar images for autofocus applications

Autofocus techniques are being designed at the Jet Propulsion Laboratory to automatically choose the filter parameters (i.e., the focus) for the digital synthetic aperture radar correlator; currently, processing relies upon interaction with a human operator who uses his subjective assessment of the quality of the processed SAR data. Algorithms were devised applying image cross-correlation to aid in the choice of filter parameters, but this method also has its drawbacks in that the cross-correlation result may not be readily interpretable. Enhanced performance of the cross-correlation techniques of JPL was hypothesized given that the images to be cross-correlated were first filtered to improve the signal-to-noise ratio for the pair of scenes. The results of experiments are described and images are shown