Measuring Extinction Curves of Lensing Galaxies

We critique the method of constructing extinction curves of lensing galaxies using multiply imaged QSOs. If one of the two QSO images is lightly reddened or if the dust along both sightlines has the same properties then the method works well and produces an extinction curve for the lensing galaxy. These cases are likely rare and hard to confirm. However, if the dust along each sightline has different properties then the resulting curve is no longer a measurement of extinction. Instead, it is a measurement of the difference between two extinction curves. This "lens difference curve'' does contain information about the dust properties, but extracting a meaningful extinction curve is not possible without additional, currently unknown information. As a quantitative example, we show that the combination of two Cardelli, Clayton, & Mathis (CCM) type extinction curves having different values of R(V) will produce a CCM extinction curve with a value of R(V) which is dependent on the individual R(V) values and the ratio of V band extinctions. The resulting lens difference curve is not an average of the dust along the two sightlines. We find that lens difference curves with any value of R(V), even negative values, can be produced by a combination of two reddened sightlines with different CCM extinction curves with R(V) values consistent with Milky Way dust (2.1 < R(V) < 5.6). This may explain extreme values of R(V) inferred by this method in previous studies. But lens difference curves with more normal values of R(V) are just as likely to be composed of two dust extinction curves with R(V) values different than that of the lens difference curve. While it is not possible to determine the individual extinction curves making up a lens difference curve, there is information about a galaxy's dust contained in the lens difference curves.Comment: 15 pages, 4 figues, ApJ in pres

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In-Network Outlier Detection in Wireless Sensor Networks

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy usage,(3) only uses single hop communication thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption.Comment: Extended version of a paper appearing in the Int'l Conference on Distributed Computing Systems 200