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  1. Data Management
  2. DM-12584

clarify the role of fake source implantation in Level 1 detection efficiencies

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      Description

      The purpose of this ticket is to resolve whether or not the implantation of fake sources will be done as part of the Level 1 detection efficiency characterization, or if an alternate method will be sufficient for the science needs. K.-T. says the computational system is sized to accommodate it. If this issue remains unclear, perhaps a study that assess the options, their computational costs, and their scientific payoff should be done so that DM can better assess the need for fake sources and allocating staffing hours as necessary. We note that this is not a question limited to Special Programs, but it is possible that this capability is a feature that a user-proposed program might expect in the processing pipeline.

      Section 4.9 of DMTN-065 summarizes the existing requirements on DM to produce detection efficiencies, copied here for convenience:

      • The DPDD (LSE-163) does not have any specific data product related to detection efficiencies, but Section 3.2 ”Image Characterization Data” does specify that ”Each processed image .. will record information on the pixel variance ... as well as the per-pixel masks ... These will allow the users to determine the validity and usefullness of each pixel in estimating the flux density recorded in that area of the sky”.

      • The DMSR (LSE-61), Section 1.2.11 ”Level 1 Data Quality Report Definition” (ID: DMS-REQ0097): ”The DMS shall produce a Level 1 Data Quality Report that contains indicators of data quality that result from running the DMS pipelines, including at least ... detection efficiency for point sources vs. mag for each utilized filter.” However, this is a nightly data quality assessment and not a per-image product.

      • The DMAD (LDM-151), Section 5.6.3 ”MakeSelectionMaps”, states that this calibration step ”is responsible for producing multi-scale maps that describe LSST’s depth and efficiency at detecting different classes of object. The details of what metrics will be mapped, the format and scale of the maps (e.g. hierarchical pixelizations vs. polygons), and the way the metrics will be computed are all unknown”. It also states that this must be extendable to Level 3, but that ”the details of what DM will provide still needs to be clarified to the community”, and notes that the reprocessing time for fake plants could be prohibitive. (Section 3 ”Alert Production” also specifies that in LDM-151 ”we do not address estimation of the selection function for alert generation through the injection of simulated sources ... Source detection thresholds can be estimated through the use of sky sources”.)

      Additional context for this ticket can be found in Section 4.9 of the DMTN study on DM and Special Programs, available at https://dmtn-065.lsst.io/

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            mgraham Melissa Graham created issue -
            ebellm Eric Bellm made changes -
            Field Original Value New Value
            Summary clarify the roll of fake source implantation in Level 1 detection efficiencies clarify the role of fake source implantation in Level 1 detection efficiencies
            swinbank John Swinbank made changes -
            Team DM Science [ 12218 ]
            mgraham Melissa Graham made changes -
            Description The purpose of this ticket is to resolve whether or not the implantation of fake sources will be done as part of the Level 1 detection efficiency characterization, or if an alternate method will be sufficient for the science needs. K.-T. says the computational system is sized to accommodate it. If this issue remains unclear, perhaps a study that assess the options, their computational costs, and their scientific payoff should be done so that DM can better assess the need for fake sources and allocating staffing hours as necessary. We note that this is not a question limited to Special Programs, but it is possible that this capability is a feature that a user-proposed program might expect in the processing pipeline.

            The nominal deadline has been set to 1.5 months prior to the expected call for Special Programs white paper proposals, but need not be resolved by then. Additional context for this ticket can be found in Section 4.9 of the DMTN study on DM and Special Programs, available at https://github.com/lsst-dmsst/DM_SP_study/blob/master/ms.pdf
            The purpose of this ticket is to resolve whether or not the implantation of fake sources will be done as part of the Level 1 detection efficiency characterization, or if an alternate method will be sufficient for the science needs. K.-T. says the computational system is sized to accommodate it. If this issue remains unclear, perhaps a study that assess the options, their computational costs, and their scientific payoff should be done so that DM can better assess the need for fake sources and allocating staffing hours as necessary. We note that this is not a question limited to Special Programs, but it is possible that this capability is a feature that a user-proposed program might expect in the processing pipeline.

            The nominal deadline has been set to 1.5 months prior to the expected call for Special Programs white paper proposals, but need not be resolved by then. Additional context for this ticket can be found in Section 4.9 of the DMTN study on DM and Special Programs, available at https://dmtn-065.lsst.io/
            mgraham Melissa Graham made changes -
            Assignee Mario Juric [ mjuric ] Eric Bellm [ ebellm ]
            mgraham Melissa Graham made changes -
            Due Date 15/Feb/18 31/Dec/18
            lguy Leanne Guy made changes -
            Labels dm-sst SpecialPrograms dm-sst
            mgraham Melissa Graham made changes -
            Description The purpose of this ticket is to resolve whether or not the implantation of fake sources will be done as part of the Level 1 detection efficiency characterization, or if an alternate method will be sufficient for the science needs. K.-T. says the computational system is sized to accommodate it. If this issue remains unclear, perhaps a study that assess the options, their computational costs, and their scientific payoff should be done so that DM can better assess the need for fake sources and allocating staffing hours as necessary. We note that this is not a question limited to Special Programs, but it is possible that this capability is a feature that a user-proposed program might expect in the processing pipeline.

            The nominal deadline has been set to 1.5 months prior to the expected call for Special Programs white paper proposals, but need not be resolved by then. Additional context for this ticket can be found in Section 4.9 of the DMTN study on DM and Special Programs, available at https://dmtn-065.lsst.io/
            The purpose of this ticket is to resolve whether or not the implantation of fake sources will be done as part of the Level 1 detection efficiency characterization, or if an alternate method will be sufficient for the science needs. K.-T. says the computational system is sized to accommodate it. If this issue remains unclear, perhaps a study that assess the options, their computational costs, and their scientific payoff should be done so that DM can better assess the need for fake sources and allocating staffing hours as necessary. We note that this is not a question limited to Special Programs, but it is possible that this capability is a feature that a user-proposed program might expect in the processing pipeline.

            Section 4.9 of DMTN-065 summarizes the existing requirements on DM to produce detection efficiencies, copied here for convenience:

            • The DPDD (LSE-163) does not have any specific data product related to detection efficiencies, but Section 3.2 ”Image Characterization Data” does specify that ”Each processed image .. will record information on the pixel variance ... as well as the per-pixel masks ... These will allow the users to determine the validity and usefullness of each pixel in estimating the flux density recorded in that area of the sky”.

            • The DMSR (LSE-61), Section 1.2.11 ”Level 1 Data Quality Report Definition” (ID: DMS-REQ0097): ”The DMS shall produce a Level 1 Data Quality Report that contains indicators of data quality that result from running the DMS pipelines, including at least ... detection efficiency for point sources vs. mag for each utilized filter.” However, this is a nightly data quality assessment and not a per-image product.

            • The DMAD (LDM-151), Section 5.6.3 ”MakeSelectionMaps”, states that this calibration step ”is responsible for producing multi-scale maps that describe LSST’s depth and efficiency at detecting different classes of object. The details of what metrics will be mapped, the format and scale of the maps (e.g. hierarchical pixelizations vs. polygons), and the way the metrics will be computed are all unknown”. It also states that this must be extendable to Level 3, but that ”the details of what DM will provide still needs to be clarified to the community”, and notes that the reprocessing time for fake plants could be prohibitive. (Section 3 ”Alert Production” also specifies that in LDM-151 ”we do not address estimation of the selection function for alert generation through the injection of simulated sources ... Source detection thresholds can be estimated through the use of sky sources”.)

            Additional context for this ticket can be found in Section 4.9 of the DMTN study on DM and Special Programs, available at [https://dmtn-065.lsst.io/]
            mgraham Melissa Graham made changes -
            Risk Score 0
            mgraham Melissa Graham made changes -
            Link This issue is parent task of DM-19308 [ DM-19308 ]
            lguy Leanne Guy made changes -
            Epic Link DM-20827 [ 373834 ]

              People

              • Assignee:
                ebellm Eric Bellm
                Reporter:
                mgraham Melissa Graham
                Watchers:
                Eric Bellm, John Swinbank, Krzysztof Findeisen, Melissa Graham
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                  Summary Panel