Details

Type: RFC

Status: Implemented

Resolution: Done

Component/s: DM

Labels:None
Description
meas_extensions_trailedSources is a measurement plugin that characterizes fastmoving, trailed sources. Mainly, it aims to measure the length, angle, and flux. The current implementation involves two main algorithms:
 Naive: Utilizes previously measured second moments to estimate the length and angle of a trail, and compute the end points. This model is inexpensive to compute, but underestimates length.
 Vereš: A chisquared minimization using the analytic model from Vereš et al. 2012 – an axisymmetric Gaussian convolved with a line. Uses the Naive measurements and centroid as the initial parameter guess. This model assumes the flux in a given pixel is the value of the model at the center of that pixel. It estimates true length better, but is more computationally expensive than the Naive model. It also adds a flux, and centroid measurement.
Further development is ongoing. A correction to the Naive measurement is being investigated as well a third algorithm to provide a full forward model for high signaltonoise trails. The overall goal is to provide a 'smart' plugin that runs each algorithm sequentially, base on signaltonoise. Ie. For low signaltonoise, the precision of the Naive measurement will be suitable. If the signaltonoise is higher, then the next algorithm will be run, and so on.
The package is currently available at lsst/meas_extensions_trailedSourceslsstdm/meas_extensions_trailedSources. It includes unit tests for each measurement algorithm and is able to be setup along side lsst_distrib. Only dependencies outside of lsst_distrib are scipy.optimize and numpy.
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DM29795 Add meas_extensions_trailedSources to lsst_distrib
 Done

DM30633 Add meas_extensions_trailedSources as setupOptional to lsst_distrib
 Done
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Activity
Field  Original Value  New Value 

Description 
meas_extensions_trailedSources is a measurement plugin that characterizes fastmoving, trailed sources. Mainly, it aims to measure the length, angle, and flux. The current implementation involves two main algorithms:
# Naive: Utilizes previously measured second moments to estimate the length and angle of a trail, and compute the end points. This model is inexpensive to compute, but underestimates length. # Vereš: A chisquared minimization using the analytic model from Vereš et al. 2012 – an axisymmetric Gaussian convolved with a line. Uses the Naive measurements and centroid as the initial parameter guess. This model assumes the flux in a given pixel is the value of the model at the center of that pixel. It estimates true length better, but is more computationally expensive than the Naive model. It also adds a flux, and centroid measurement. Further development is ongoing. A correction to the Naive measurement is being investigated as well a third algorithm to provide a full forward model for high signaltonoise trails. The overall goal is to provide a 'smart' plugin that runs each algorithm sequentially, base on signaltonoise. Ie. For low signaltonoise, the precision of the Naive measurement will be suitable. If the signaltonoise is higher, then the next algorithm will be run, and so on. The package is currently available at [lsstdm/meas_extensions_trailedSources[https://github.com/lsstdm/meas_extensions_trailedSources]https://github.com/lsstdm/meas_extensions_trailedSources].]. It includes unit tests for each measurement algorithm and is able to be setup along side lsst_distrib. Only dependencies outside of lsst_distrib are scipy.optimize and numpy. 
meas_extensions_trailedSources is a measurement plugin that characterizes fastmoving, trailed sources. Mainly, it aims to measure the length, angle, and flux. The current implementation involves two main algorithms:
# Naive: Utilizes previously measured second moments to estimate the length and angle of a trail, and compute the end points. This model is inexpensive to compute, but underestimates length. # Vereš: A chisquared minimization using the analytic model from Vereš et al. 2012 – an axisymmetric Gaussian convolved with a line. Uses the Naive measurements and centroid as the initial parameter guess. This model assumes the flux in a given pixel is the value of the model at the center of that pixel. It estimates true length better, but is more computationally expensive than the Naive model. It also adds a flux, and centroid measurement. Further development is ongoing. A correction to the Naive measurement is being investigated as well a third algorithm to provide a full forward model for high signaltonoise trails. The overall goal is to provide a 'smart' plugin that runs each algorithm sequentially, base on signaltonoise. Ie. For low signaltonoise, the precision of the Naive measurement will be suitable. If the signaltonoise is higher, then the next algorithm will be run, and so on. The package is currently available at [lsstdm/meas_extensions_trailedSourceshttp://github.com/lsstdm/meas_extensions_trailedSources]. It includes unit tests for each measurement algorithm and is able to be setup along side lsst_distrib. Only dependencies outside of lsst_distrib are scipy.optimize and numpy. 
Status  Proposed [ 10805 ]  Flagged [ 10606 ] 
Remote Link  This issue links to "Page (Confluence)" [ 27964 ] 
Watchers  Ian Sullivan, John Parejko, KianTat Lim, Mario Juric, Meredith Rawls, Zach Langford [ Ian Sullivan, John Parejko, KianTat Lim, Mario Juric, Meredith Rawls, Zach Langford ]  Eric Bellm, Ian Sullivan, John Parejko, KianTat Lim, Mario Juric, Meredith Rawls, Zach Langford [ Eric Bellm, Ian Sullivan, John Parejko, KianTat Lim, Mario Juric, Meredith Rawls, Zach Langford ] 
Status  Flagged [ 10606 ]  Board Recommended [ 11405 ] 
Remote Link  This issue links to "Page (Confluence)" [ 28021 ] 
Remote Link  This issue links to "Page (Confluence)" [ 28091 ] 
Remote Link  This issue links to "Page (Confluence)" [ 28138 ] 
Remote Link  This issue links to "Page (Confluence)" [ 28193 ] 
Status  Board Recommended [ 11405 ]  Adopted [ 10806 ] 
Description 
meas_extensions_trailedSources is a measurement plugin that characterizes fastmoving, trailed sources. Mainly, it aims to measure the length, angle, and flux. The current implementation involves two main algorithms:
# Naive: Utilizes previously measured second moments to estimate the length and angle of a trail, and compute the end points. This model is inexpensive to compute, but underestimates length. # Vereš: A chisquared minimization using the analytic model from Vereš et al. 2012 – an axisymmetric Gaussian convolved with a line. Uses the Naive measurements and centroid as the initial parameter guess. This model assumes the flux in a given pixel is the value of the model at the center of that pixel. It estimates true length better, but is more computationally expensive than the Naive model. It also adds a flux, and centroid measurement. Further development is ongoing. A correction to the Naive measurement is being investigated as well a third algorithm to provide a full forward model for high signaltonoise trails. The overall goal is to provide a 'smart' plugin that runs each algorithm sequentially, base on signaltonoise. Ie. For low signaltonoise, the precision of the Naive measurement will be suitable. If the signaltonoise is higher, then the next algorithm will be run, and so on. The package is currently available at [lsstdm/meas_extensions_trailedSourceshttp://github.com/lsstdm/meas_extensions_trailedSources]. It includes unit tests for each measurement algorithm and is able to be setup along side lsst_distrib. Only dependencies outside of lsst_distrib are scipy.optimize and numpy. 
meas_extensions_trailedSources is a measurement plugin that characterizes fastmoving, trailed sources. Mainly, it aims to measure the length, angle, and flux. The current implementation involves two main algorithms:
# Naive: Utilizes previously measured second moments to estimate the length and angle of a trail, and compute the end points. This model is inexpensive to compute, but underestimates length. # Vereš: A chisquared minimization using the analytic model from Vereš et al. 2012 – an axisymmetric Gaussian convolved with a line. Uses the Naive measurements and centroid as the initial parameter guess. This model assumes the flux in a given pixel is the value of the model at the center of that pixel. It estimates true length better, but is more computationally expensive than the Naive model. It also adds a flux, and centroid measurement. Further development is ongoing. A correction to the Naive measurement is being investigated as well a third algorithm to provide a full forward model for high signaltonoise trails. The overall goal is to provide a 'smart' plugin that runs each algorithm sequentially, base on signaltonoise. Ie. For low signaltonoise, the precision of the Naive measurement will be suitable. If the signaltonoise is higher, then the next algorithm will be run, and so on. The package is currently available at [lsst/meas_extensions_trailedSourceshttp://github.com/lsst/meas_extensions_trailedSources]lsstdm/meas_extensions_trailedSources. It includes unit tests for each measurement algorithm and is able to be setup along side lsst_distrib. Only dependencies outside of lsst_distrib are scipy.optimize and numpy. 
Resolution  Done [ 10000 ]  
Status  Adopted [ 10806 ]  Implemented [ 11105 ] 
Remote Link  This issue links to "Page (Confluence)" [ 28803 ] 
Remote Link  This issue links to "Page (Confluence)" [ 29159 ] 
Flagging per https://developer.lsst.io/communications/rfc.html#standardproceduresthatrequireanrfc as "Adding a new package to the build".