The Synthetic-Oversampling Method: Using Photometric Colors to Discover Extremely Metal-Poor Stars

Extremely metal-poor (EMP) stars ([Fe/H] < -3.0 dex) provide a unique window into understanding the first generation of stars and early chemical enrichment of the Universe. EMP stars are exceptionally rare, however, and the relatively small number of confirmed discoveries limits our ability to exploit these near-field probes of the first ~500 Myr after the Big Bang. Here, a new method to photometrically estimate [Fe/H] from only broadband photometric colors is presented. I show that the method, which utilizes machine-learning algorithms and a training set of ~170,000 stars with spectroscopically measured [Fe/H], produces a typical scatter of ~0.29 dex. This performance is similar to what is achievable via low-resolution spectroscopy, and outperforms other photometric techniques, while also being more general. I further show that a slight alteration to the model, wherein synthetic EMP stars are added to the training set, yields the robust identification of EMP candidates. In particular, this synthetic-oversampling method recovers ~20% of the EMP stars in the training set, at a precision of ~0.05. Furthermore, ~65% of the false positives from the model are very metal-poor stars ([Fe/H] < -2.0 dex). The synthetic-oversampling method is biased towards the discovery of warm (~F-type) stars, a consequence of the targeting bias from the SDSS/SEGUE survey. This EMP selection method represents a significant improvement over alternative broadband optical selection techniques. The models are applied to >12 million stars, with an expected yield of ~600 new EMP stars, which promises to open new avenues for exploring the early universe.Comment: 15 pages, 7 figures, to be submitted to Ap

Using GPS as a reference frame for SAR images applied to a post eruptive period for Okmok Volcano, Aleutian Islands, Alaska

While high spatial coverage makes InSAR a popular tool to study active volcanoes its use can possess challenges for certain environments. Volcanoes along Alaska's Aleutian chain are difficult targets for InSAR as their seasonal snow cover causes decorrelation close to the volcanic caldera, their exposed location in the North Pacific renders them prone to severe atmospheric phase artifacts, and their location on small islands prevents the selection of suitable reference points necessary for deformation analysis. Existing GPS networks define a known reference frame in which SAR is better understood. Okmok volcano is one of the most active volcanoes in the Aleutian Island Chain and shows significant non-linear deformation behavior as it progresses through its eruption cycles. A stack of L-band imagery acquired by the SAR sensor PALSAR on board the JAXA Advanced Land Observing Satellite produced a post eruption deformation time series between August 2008 and October 2010. This data along with a merged DEM comprised of AirSAR SRTM and Worldview-1 stereo pair data, and GPS data from 3 continuous and 3 post eruption campaign sites was used for this study. In this research, a comparison and combination of InSAR and GPS time-series data will be presented aimed at the following research goals: 1) What is the accuracy and precision of InSAR-derived deformation estimates in such challenging environments; 2) How accurate can the deformation of the InSAR reference point be estimated from a joint analysis of InSAR and GPS deformation signals; 3) How non-linear volcanic deformation can be constrained by the measurements of a local GPS network and support the identification of residual atmospheric signals in InSAR-derived deformation time series. Further research into the combination of GPS and InSAR applied to the nonlinear aspect of volcanic deformation can enhance geodetic modeling of the volcano and associated eruption processes

Stoves

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