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6 results for “PCA filtering”
PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTDOC Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTDOC instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTGJON Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTGJON instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
Raw Data and PCA Filtering of Apache Point Observatory NMSU 1m StellaCam Observations of LCROSS
<p>This archive contains the raw data and data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the StellaCam instrument on the Apache Point Observatory NMSU 1m telescope.</p> <p>Full details about the raw data are available in Chanover, N. J. et al. Results from the NMSU-NASA Marshall Space Flight Center LCROSS observational campaign. <em>J. Geophys. Res. (Planets)</em> <strong>116</strong>, E08003 (2011). <a href="https://doi.org/10.1029/2010JE003761">https://doi.org/10.1029/2010JE003761</a></p> <p>We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of Apache Point Observatory 3.5m Agile Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Agile instrument on the Apache Point Observatory 3.5m telescope. We use principal component analysis (PCA) filtering both to improve the coregistration of the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of MMT Observatory 6.5m CCD47 Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the CCD47 instrument on the MMT Observatory 6.5m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of Tortugas Mountain Observatory 0.6m Goodrich Observations of LCROSS
<p>This archive contains the data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Goodrich instrument on the Tortugas Mountain Observatory 0.6m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
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