SITCOMTN-124

Metrics and Plots Available in ComCam On-Sky Data#

Abstract

This technote explores the metrics/plots generated by the LSST Science Pipelines for Data Preview 1. We describe the methods used to aggregate these plots and metrics, list metrics and plots created by each task, and provide examples of each metricBundle / Plot produced.

Intro#

This document was originally created as part of the Operations Rehearsal 3 and updated during the preparation of Data Preview 1. It is not intended to be a definitive source/documentation for plots and metrics. Instead, the hope is that this document can help others learn how to determine what plots/metrics exist and how to access them.

To facilitate this we describe the methods we used to find, access and understand the data products created when running the data reduction pipeline (DRP). This incudes:

Which collections to use for registry queries
Which metrics/plots exist in these collections
The tasks and configuration used to generate these metrics/plots
How to retrieve plots in the notebook aspect of the Rubin Science Platform
How to find plots in the plot navigator

Then we list out the tasks in the Nightly Validation Pipeline that generate metrics and plots. For each task we link the pipeline yaml that configures the task (tagged to w_2025_04) and examples of the metricMeasurementBundles and Plots created by each task. This should allow for a quick exploration of the data products produced as a part of this pipeline.

For each metricBundle we list an example dataId used, the metricBundle’s name, its atools docstring, any additional notes, and an example result. We split these into Visit-level metrics and Tract-level metrics.

Finally, for each plot datasetType we show an example version of the plot, again split into Visit-level plots and Tract-level metrics.

The code used to generate the plots and figures for this technote can be found in the notebook atools_metrics_plots_census.ipynb

Finding and accessing data products#

In this section we describe the ways we determined which metrics and plots were created by the Data Release Processing (DRP) runs.

For more information on how metrics and plots are created or how to run your own pipeline see the tutorial in the analysis_tools documentation.

Which collections to use#

The data reduction campaigns are listed in the “LSSTComCam Intermittent Cumulative DRP Runs” confluence page. On that page we can see the following information for the collection containing the w_2025_04 intermittent cumulative DRP reductions:

repo = /repo/main
collection = LSSTComCam/runs/DRP/DP1/w_2025_04/DM-48556

What tools/metrics/plots exist#

To see what metrics exist in a given butler collection (note that this assumes the butler has been initialized with the desired repo, the registry extracted via registry = butler.registry, and the collection name assigned to the variable collection), execute the following. This code block finds all datasetTypes with storageClass of MetricMeasurementBundle.

metrics_datasets = []

for datasetType in registry.queryDatasetTypes():
    if registry.queryDatasets(datasetType, collections=collection).any(execute=False, exact=False):
        if datasetType.storageClass_name == 'MetricMeasurementBundle':
            print(datasetType)
            metrics_datasets.append(datasetType)

A similar query with MetricMeasurementBundle replaced by Plot will show you all of the plot datasetTypes that have been defined for the given repo/collection. Some examples are shown below:

DatasetType('matchedVisitCore_metrics', {instrument, skymap, tract}, MetricMeasurementBundle)
DatasetType('matchedVisitCore_g_stellarAstrometricResidualStdDevDec_FocalPlanePlot', {instrument, skymap, tract}, Plot)
DatasetType('preSourceTableCore_skySourceFlux_HistPlot', {band, instrument, physical_filter, visit}, Plot)
DatasetType('objectTableExtended_r_ap12PsfSky_SkyPlot', {skymap, tract}, Plot)

This query compiles a list of datasetType objects, which include the butler dimensions necessary to retrieve the dataset (the items included within the curly braces above; for example, to retrieve matchedVisitCore_metrics from the butler, one would need to specify the desired instrument, skymap, and tract in the dataId passed to the butler). An example of how to retrieve one of these:

did_tract = {'tract': 5063, 'skymap': 'lsst_cells_v1', 'band': 'g', 'instrument': 'LSSTComCam'}

matchedVisitCoreMetrics = butler.get('matchedVisitCore_metrics', dataId=did_tract, collections=collection)

What was configured and run in the pipeline#

Consider the following pipeline task definition for analyzePreSourceTableCore:

  analyzePreSourceTableCore:
    class: lsst.analysis.tools.tasks.SourceTableVisitAnalysisTask
    config:
      connections.outputName: preSourceTableCore
      atools.skyFluxVisitStatisticMetric: SkyFluxStatisticMetric
      atools.skyFluxVisitStatisticMetric.applyContext: VisitContext
      atools.skySourceSky: SkySourceSkyPlot
      atools.skySourceFlux: SkySourceHistPlot
      python: |
        from lsst.analysis.tools.atools import *
        from lsst.analysis.tools.contexts import *

In a pipeline task definition (e.g., analyzePreSourceTableCore), the output will be called

{connections.outputName}_metrics for metric bundles, and
{connections.outputName}_atool.name_plotType for plots

An example from the above config file:

{connections.outputName}=preSourceTableCore
atool.name=SkySourceSkyPlot
plotType=SkyPlot

will produce the datasetType preSourceTableCore_SkySourceSky_SkyPlot.

In the pipeline for nightly validation, analyzePreSourceTableCore is defined as:

imports:
  - $DRP_PIPE_DIR/pipelines/LSSTComCamSim/DRP-ops-rehearsal-3.yaml
tasks:
  analyzePreSourceTableCore:
    class: lsst.analysis.tools.tasks.SourceTableVisitAnalysisTask
    config:
      connections.data: preSourceTable_visit
      connections.inputName: preSourceTable_visit
      connections.outputName: preSourceTableCore

This slightly reconfigures the version of “analyzePreSourceTableCore” that is imported from DRP-ops-rehearsal-3.yaml, which in turn imports from pipelines/_ingredients. The reconfiguration simply changes the input dataset.

Extracting the exact configuration from butler#

Above we described the general way to see which tasks were run and how they were configured, but given a specific task there is an associated configuration file in the butler. For example, for the analyzePreSourceTableCore task there is a configuration file with the datasetType of analyzePreSourceTableCore_config that can be obtained with butler.get and shows the exact configuration of the task.

Retrieving plots from the butler#

A quick test in a notebook will show that you cannot use butler.get to retrieve plots from the butler. To retrieve them, you will instead use the butler.retrieveArtifacts method. If you know the name of the plot dataset you wish to retrieve, an appropriate dataId, the butler has been initialized with the desired repo and the registry extracted via registry = butler.registry, and the collection name assigned to the variable collection, you can use:

plot_name = 'preSourceTableCore_skySourceFlux_HistPlot'
output_filepath = './plots/'
plot_dataset = registry.queryDatasets(plot_name, dataId=dataId)
butler.retrieveArtifacts(plot_dataset, output_filepath,
                         preserve_path=False, overwrite=True)

The two required inputs for butler.retrieveArtifacts are an iterable of DatasetRefs (in this case, obtained using registry.queryDatasets) and the path to the output destination. If you set the optional keyword preserve_path to False, the files will simply be placed in the destination directory. If this is left at its default value of True, the original path will be retained (and will contain many deeply-nested directories). Note also that overwrite=False by default.

A less optimal workaround is to retrieve the plot using:

plot_name = 'preSourceTableCore_skySourceFlux_HistPlot'
ref = butler.registry.findDataset(plot_name, dataId, collections=collections)
uri = butler.getURI(ref)
image_bytes = uri.read()

Then it can be saved (though it is preferable to use butler.retrieveArtifacts for that purpose):

file_name= uri.basename()
with open(file_name, 'wb') as file:
  file.write(image_bytes)

or displayed in a notebook:

from IPython.display import Image
Image(image_bytes)

Using the plot navigator#

The rubin plot navigator can be accessed at https://usdf-rsp.slac.stanford.edu/plot-navigator and is described in DMTN-237. This interface allows the user to quickly browse plots that are stored in the butler. To find/select first identify a collection, after clicking the collection you will be givien a list of plots that exist in that collection and can futher select the type of plot you would like to view (e.g. objectTableCore_wPerpPSFP_ColorColorFitPlot).

Nightly validation pipeline#

https://github.com/lsst/drp_pipe/blob/main/pipelines/LSSTComCamSim/nightly-validation-ops-rehearsal-3.yaml

The nightly validation pipeline described here was run during Operations Rehearsal 3.

Visit-level tasks#

This table has 3 columns: Task, datasetType, and StorageClass. The task entry links to the pipeline yaml responsible for the task configuration. The datasetType links to either a description of the metric bundle or an example plot generated as part of the nightly validation run. The StorageClass is either Plot or MetricMeasurementBundle.

Task (link to pipeline yaml)	datasetType	StorageClass
calibrate	calexpSummary_metrics	MetricMeasurementBundle

analyzeMatchedVisitCore	matchedVisitCore_metrics	MetricMeasurementBundle
	matchedvisitcore-{band}-stellarphotometricrepeatability-histplot	Plot
	matchedvisitcore-{band}-stellarastrometricresidualstddevra-focalplaneplot	Plot
	matchedvisitcore-{band}-stellarphotometricresiduals-focalplaneplot	Plot
	matchedvisitcore-{band}-stellarastrometricresidualsdec-focalplaneplot	Plot
	matchedvisitcore-{band}-stellarastrometricresidualsra-focalplaneplot	Plot
	matchedvisitcore-{band}-stellarastrometricresidualstddevdec-focalplaneplot	Plot
	matchedvisitcore-{band}-stellarastrometricrepeatability2-histplot	Plot
	matchedvisitcore-{band}-stellarastrometricrepeatability1-histplot	Plot
	matchedvisitcore-{band}-stellarastrometricrepeatability3-histplot	Plot
	matchedvisitcore-{band}-stellarastrometricselfrepeatabilitydec-histplot	Plot
	matchedvisitcore-{band}-stellarastrometricselfrepeatabilityra-histplot	Plot

analyzePreSourceTableCore	preSourceTableCore_metrics	MetricMeasurementBundle
	presourcetablecore-skysourceflux-histplot	Plot

Tract-level tasks#

Task (link to pipeline yaml)	datasetType	StorageClass
analyzeDiaSourceTableTract	diaSourceTableTract_metrics	MetricMeasurementBundle
	diasourcetabletract-streakdiasourceplot-diaskyplot	Plot

validateObjectTableCore	objectTableColumnValidate_metrics	MetricMeasurementBundle

analyzeObjectTableCore	objectTableCore_metrics	MetricMeasurementBundle
	objecttablecore-{band}-skyobjectsky-skyplot	Plot
	objecttablecore-xperppsfp-colorcolorfitplot	Plot
	objecttablecore-{band}-shapesizefractionaldiff-scatterplotwithtwohists	Plot
	objecttablecore-xperpcmodel-colorcolorfitplot	Plot
	objecttablecore-{band}-e1diff-scatterplotwithtwohists	Plot
	objecttablecore-wperppsfp-colorcolorfitplot	Plot
	objecttablecore-wperpcmodel-colorcolorfitplot	Plot
	objecttablecore-{band}-skyobjectflux-histplot	Plot
	objecttablecore-{band}-e2diff-scatterplotwithtwohists	Plot
	objecttablecore-{band}-cmodelbulgesizevscmodelbulgemag-scatterplotwithtwohists	Plot
	objecttablecore-{band}-psfcmodelscatter-scatterplotwithtwohists	Plot
	objecttablecore-{band}-cmodeldisksizevscmodeldiskmag-scatterplotwithtwohists	Plot
	objecttablecore-{band}-shapesizedetradiusvspsfmag-scatterplotwithtwohists	Plot
	objecttablecore-{band}-shapesizedetradiusvscmodelmag-scatterplotwithtwohists	Plot

analyzeObjectTableExtended	objectTableExtended_metrics	MetricMeasurementBundle
	objecttableextended-{band}-ap12psfsky-skyplot	Plot
	objecttableextended-{band}-psfcmodelsky-skyplot	Plot

propertyMapTract	propertymaptract-healsparsemap-deepcoadd-dcr-e1-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-dcr-dra-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-psf-size-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-psf-maglim-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-dcr-ddec-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-sky-noise-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-dcr-e2-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-exposure-time-sum-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-psf-e1-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-psf-e2-weighted-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-epoch-mean-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-epoch-min-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-epoch-max-propertymapplot	Plot
	propertymaptract-healsparsemap-deepcoadd-sky-background-weighted-mean-propertymapplot	Plot

Visit-level metrics#

Below we list out the metrics associated with each metric bundle at a visit level.

calexpSummary_metrics#

dataId used in examples:

did_visit_det = {'visit':7024040300001, 'skymap':'ops_rehersal_prep_2k_v1', 'instrument':'LSSTComCamSim', 'detector':0}

metricBundle: calexpSummaryMetrics (source)

Docstring: Calibrated exposure summary statistics to load from the butler

Notes: These metrics are computed outside of analysis_tools in the computeExposureSummaryStats pipetask (source),

which has the following docstring: Task to compute exposure summary statistics.

This task computes various quantities suitable for DPDD and other downstream processing at the detector centers, including:

psfSigma
psfArea
psfIxx
psfIyy
psfIxy
ra
dec
zenithDistance
zeroPoint
skyBg
skyNoise
meanVar
raCorners
decCorners
astromOffsetMean
astromOffsetStd

These additional quantities are computed from the stars in the detector:

psfStarDeltaE1Median
psfStarDeltaE2Median
psfStarDeltaE1Scatter
psfStarDeltaE2Scatter
psfStarDeltaSizeMedian
psfStarDeltaSizeScatter
psfStarScaledDeltaSizeScatter

These quantities are computed based on the PSF model and image mask to assess the robustness of the PSF model across a given detector (against, e.g., extrapolation instability):

maxDistToNearestPsf
psfTraceRadiusDelta

These quantities are computed to assess depth:

effTime
effTimePsfSigmaScale
effTimeSkyBgScale
effTimeZeroPointScale

metric name	value	units
psfSigma	1.506499	pix
psfArea	34.678724
psfIxx	2.258628	pix
psfIyy	2.281597	pix
psfIxy	0.049743	pix
ra	124.617999	deg
dec	-15.026213	deg
zenithDistance	17.825242	deg
zeroPoint	31.271871	mag
skyBg	1703.767764	mag
skyNoise	34.471789	mag
meanVar	1175.081472	mag
astromOffsetMean	0.005423	arcsec
astromOffsetStd	0.003085	arcsec
nPsfStar	419.000000
psfStarDeltaE1Median	-0.000103	pix
psfStarDeltaE2Median	-0.000645	pix
psfStarDeltaE1Scatter	0.008924	pix
psfStarDeltaE2Scatter	0.009482	pix
psfStarDeltaSizeMedian	0.000991	pix
psfStarDeltaSizeScatter	0.007213	pix
psfStarScaledDeltaSizeScatter	0.004785	pix
psfTraceRadiusDelta	0.012334	pix
maxDistToNearestPsf	419.527519	deg
effTime	12.083753	s
effTimePsfSigmaScale	1.127983	s
effTimeSkyBgScale	0.344765	s
effTimeZeroPointScale	1.035749	s

matchedVisitCore_metrics#

dataId used in examples:

did_tract = {'tract':5063, 'skymap': 'lsst_cells_v1', 'band': 'g', 'instrument':'LSSTComCam'}

These metrics are for a set of “matched visits,” which is created by taking all objects from the isolated_star_sources table in a given tract, then matching all of the sourceTable catalogs to accumulate all measurements for each source. These collections can be used for repeatability measurements.

Note that although these metrics use information from individual visits, their Butler dimension is tract because they aggregate measurements from all visits contributing to a tract.

metricBundle: stellarAstrometricRepeatability1 (source)

Docstring: Calculate the AMx, ADx, AFx metrics and make histograms showing the data used to compute the metrics.

Notes: AMx, ADx, and AFx (where x=[1, 2, 3]) are defined in the Observatory System Specifications (OSS) as requirement OSS-REQ-0388. AMx is a measure of astrometric repeatability on xx arcminute scales, where xx=[5, 20, 200] for x=[1, 2, 3]. ADx defines thresholds for outliers, and AFx captures the percentage of outliers exceeding the ADx outlier limit.

Defined with signal-to-noise (S/N) cuts of 50 < S/N < 50000 and a magnitude range of 17 < mag < 21.5.

stellarAstrometricRepeatability1 measures repeatability on 5 arcminute scales.

metric name	value	units
u_AM1	16.649138	mas
u_AF1	22.201517	%
u_AD1	28.129798	mas
g_AM1	15.628324	mas
g_AF1	24.470774	%
g_AD1	30.426507	mas
r_AM1	13.416911	mas
r_AF1	13.611736	%
r_AD1	22.404970	mas
i_AM1	12.607847	mas
i_AF1	11.067706	%
i_AD1	20.722490	mas
z_AM1	12.461857	mas
z_AF1	11.519684	%
z_AD1	21.046111	mas
y_AM1	12.761201	mas
y_AF1	12.620278	%
y_AD1	21.692446	mas

metricBundle: stellarAstrometricRepeatability2 (source)

Docstring: Calculate the AMx, ADx, AFx metrics and make histograms showing the data used to compute the metrics.

Notes: stellarAstrometricRepeatability2 measures repeatability on 20 arcminute scales.

metric name	value	units
u_AM2	15.659163	mas
u_AF2	20.873564	%
u_AD2	27.267193	mas
g_AM2	14.838544	mas
g_AF2	23.823734	%
g_AD2	30.171673	mas
r_AM2	12.428093	mas
r_AF2	11.682237	%
r_AD2	21.131835	mas
i_AM2	11.615206	mas
i_AF2	9.139840	%
i_AD2	19.391778	mas
z_AM2	11.338962	mas
z_AF2	9.349920	%
z_AD2	19.546127	mas
y_AM2	11.638790	mas
y_AF2	10.039435	%
y_AD2	20.025457	mas

metricBundle: stellarAstrometricRepeatability3 (source)

Docstring: Calculate the AMx, ADx, AFx metrics and make histograms showing the data used to compute the metrics.

Notes: stellarAstrometricRepeatability3 measures repeatability on 200 arcminute scales. There are no measurements for these metrics currently, because we do not have datasets spanning the requisite 200-arcminute scale.

metric name	value	units
u_AM3	nan	mas
u_AF3	nan	%
u_AD3	nan	mas
g_AM3	nan	mas
g_AF3	nan	%
g_AD3	nan	mas
r_AM3	nan	mas
r_AF3	nan	%
r_AD3	nan	mas
i_AM3	nan	mas
i_AF3	nan	%
i_AD3	nan	mas
z_AM3	nan	mas
z_AF3	nan	%
z_AD3	nan	mas
y_AM3	nan	mas
y_AF3	nan	%
y_AD3	nan	mas

metricBundle: stellarAstrometricSelfRepeatabilityDec (source)

Docstring: Calculate the median position RMS of point sources.

Notes: The median of the per-source RMS positional scatter in Declination. Defined with a magnitude selection of 17 < mag < 21.5.

metric name	value	units
u_dmL2AstroErr_Dec	15.141275	mas
g_dmL2AstroErr_Dec	9.422350	mas
r_dmL2AstroErr_Dec	9.224946	mas
i_dmL2AstroErr_Dec	8.756453	mas
z_dmL2AstroErr_Dec	10.908766	mas
y_dmL2AstroErr_Dec	10.780756	mas

metricBundle: stellarAstrometricSelfRepeatabilityRA (source)

Docstring: Calculate the median position RMS of point sources.

Notes: The median of the per-source RMS positional scatter in Right Ascension. Defined with a magnitude selection of 17 < mag < 21.5.

metric name	value	units
u_dmL2AstroErr_RA	16.201780	mas
g_dmL2AstroErr_RA	15.396698	mas
r_dmL2AstroErr_RA	10.670404	mas
i_dmL2AstroErr_RA	10.214307	mas
z_dmL2AstroErr_RA	11.032055	mas
y_dmL2AstroErr_RA	11.842188	mas

metricBundle: stellarPhotometricRepeatability (source)

Docstring: Compute photometric repeatability from multiple measurements of a set of stars. First, a set of per-source quality criteria are applied. Second, the individual source measurements are grouped together by object index and per-group quantities are computed (e.g., a representative S/N for the group based on the median of associated per-source measurements). Third, additional per-group criteria are applied. Fourth, summary statistics are computed for the filtered groups.

Notes: The median and outlier fraction of the per-source RMS magnitude scatter. Defined for stars with S/N > 200. Metrics with “stellarPhotRepeatStdev” in their names correspond to PA1 defined in the OSS as requirement OSS-REQ-0387. Metrics with “stellarPhotRepeatOutlierFraction” correspond to PF1 (the fraction of outliers exceeding the threshold PA2) from the same requirement, with PA2 (the outlier threshold) set to 15 mmag by default. The “_ct” metrics simply capture the number of stars that went into the calculation.

metric name	value	units
u_stellarPhotRepeatStdev	10.082369	mmag
u_stellarPhotRepeatOutlierFraction	8.125000	%
u_ct	160.0	ct
g_stellarPhotRepeatStdev	11.278285	mmag
g_stellarPhotRepeatOutlierFraction	5.925926	%
g_ct	540.0	ct
r_stellarPhotRepeatStdev	9.653192	mmag
r_stellarPhotRepeatOutlierFraction	7.682292	%
r_ct	768.0	ct
i_stellarPhotRepeatStdev	6.544506	mmag
i_stellarPhotRepeatOutlierFraction	0.360144	%
i_ct	833.0	ct
z_stellarPhotRepeatStdev	7.944945	mmag
z_stellarPhotRepeatOutlierFraction	1.505646	%
z_ct	797.0	ct
y_stellarPhotRepeatStdev	8.168943	mmag
y_stellarPhotRepeatOutlierFraction	0.319489	%
y_ct	313.0	ct

metricBundle: stellarPhotometricRepeatabilityCalib (source)

metric name	value	units
u_stellarPhotRepeatStdev	7.905896	mmag
u_stellarPhotRepeatOutlierFraction	1.492537	%
u_ct	134.0	ct
g_stellarPhotRepeatStdev	9.951525	mmag
g_stellarPhotRepeatOutlierFraction	5.023923	%
g_ct	418.0	ct
r_stellarPhotRepeatStdev	11.208904	mmag
r_stellarPhotRepeatOutlierFraction	8.910891	%
r_ct	505.0	ct
i_stellarPhotRepeatStdev	3.709744	mmag
i_stellarPhotRepeatOutlierFraction	0.000000	%
i_ct	492.0	ct
z_stellarPhotRepeatStdev	3.154263	mmag
z_stellarPhotRepeatOutlierFraction	0.000000	%
z_ct	423.0	ct
y_stellarPhotRepeatStdev	6.970876	mmag
y_stellarPhotRepeatOutlierFraction	0.000000	%
y_ct	149.0	ct

metricBundle: stellarPhotometricResiduals (source)

Docstring: Plot mean photometric residuals as a function of the position on the focal plane. First, a set of per-source quality criteria are applied. Second, the individual source measurements are grouped together by object index and the per-group magnitude is computed. The residuals between the individual sources and these magnitudes are then used to construct a plot showing the mean residual as a function of the focal-plane position.

Notes: In addition to producing plots of the residuals as a function of focal plane position, the median, standard deviation (“Stdev”), and the median absolute deviation \(\sigma_{MAD}\) (“SigmaMad”) are reported for the tract. Because these are residuals about the median, the median residual should be 0 by construction.

metric name	value	units
u_photResidTractSigmaMad	7.106819	mmag
u_photResidTractStdev	14.064299	mmag
u_photResidTractMedian	0.000000	mmag
g_photResidTractSigmaMad	8.182756	mmag
g_photResidTractStdev	10.718751	mmag
g_photResidTractMedian	0.000000	mmag
r_photResidTractSigmaMad	5.673041	mmag
r_photResidTractStdev	6.913324	mmag
r_photResidTractMedian	0.000000	mmag
i_photResidTractSigmaMad	5.870441	mmag
i_photResidTractStdev	6.884908	mmag
i_photResidTractMedian	0.000000	mmag
z_photResidTractSigmaMad	5.960165	mmag
z_photResidTractStdev	8.674804	mmag
z_photResidTractMedian	0.000000	mmag
y_photResidTractSigmaMad	7.328806	mmag
y_photResidTractStdev	8.854357	mmag
y_photResidTractMedian	0.000000	mmag

metricBundle: stellarPhotometricResidualsCalib (source)

metric name	value	units
u_photResidTractSigmaMad	6.159587	mmag
u_photResidTractStdev	10.387054	mmag
u_photResidTractMedian	0.000000	mmag
g_photResidTractSigmaMad	7.016645	mmag
g_photResidTractStdev	9.551593	mmag
g_photResidTractMedian	0.000000	mmag
r_photResidTractSigmaMad	4.761156	mmag
r_photResidTractStdev	6.989406	mmag
r_photResidTractMedian	0.000000	mmag
i_photResidTractSigmaMad	5.027914	mmag
i_photResidTractStdev	7.205964	mmag
i_photResidTractMedian	0.000000	mmag
z_photResidTractSigmaMad	4.924935	mmag
z_photResidTractStdev	7.766163	mmag
z_photResidTractMedian	0.000000	mmag
y_photResidTractSigmaMad	7.862110	mmag
y_photResidTractStdev	9.141854	mmag
y_photResidTractMedian	0.000000	mmag

sourceObjectTable_metrics#

dataId used in examples:

did_visit_det = {'visit': 2024120500075, 'skymap': 'lsst_cells_v1', 'instrument': 'LSSTComCam', 'detector': 4}

metricBundle: astromColorDiffMetrics (source)

Docstring: Calculate the AB1 and ABF1 metrics from the difference between the target and reference catalog coordinates.

metric name	value	units
AB1_RA	13.946738	mas
ABF1_RA	14.849624	%
AB1_Dec	8.169414	mas
ABF1_Dec	2.067669	%
AB1_tot	16.971670	mas
ABF1_tot	24.812030	%

preSourceTableCore_metrics#

dataId used in examples:

did_visit_det = {'visit': 2024120500075, 'skymap': 'lsst_cells_v1', 'instrument': 'LSSTComCam', 'detector': 4}

metricBundle: skyFluxVisitStatisticMetric source

Docstring: Calculate sky flux statistics. This uses the 9-pixel aperture flux for sky sources/objects, and returns multiple statistics on the measured fluxes. Note that either visitContext (measurement on sourceTable) or coaddContext (measurement on objectTable) must be specified.

metric name	value	units
medianSky	-2.348488	nJy
meanSky	-7.034565	nJy
stdevSky	618.708919	nJy
sigmaMADSky	600.762855	nJy

Note that the default visit (7024040300001) used for this document did not have an entry for preSourceTableCore_metrics, so these values are for visit 7024040300002.

Tract-level metrics#

diaSourceTableTract_metrics#

dataId used in examples:

did_tract = {'tract': 5063, 'skymap': 'lsst_cells_v1', 'band': 'g', 'instrument': 'LSSTComCam'}

metricBundle: NumDiaSources (source)

Docstring: Count all DiaSources that do not have known bad/quality flags.

metric name	value	units
numDiaSources	96643.0	ct

metricBundle: NumStreakCenterDiaSources (source)

Docstring: Count DiaSources that have the STREAK flag in the center of the source.

metric name	value	units
numStreakCenterDiaSources	115.0	ct

metricBundle: NumStreakDiaSources (source)

Docstring: Count DiaSources that fall in a STREAK flag footprint region.

metric name	value	units
numStreakDiaSources	116.0	ct

objectTableColumnValidate_metrics#

dataId used in examples:

did_tract = {'tract': 5063, 'skymap': 'lsst_cells_v1', 'band': 'g', 'instrument': 'LSSTComCam'}

metricBundle: validCmodelFluxMetric (source)

Docstring: Calculate the fraction of values in a column that have valid numerical values (i.e., not NaN), and that fall within the specified “reasonable” range for the values.

metric name	value	units
u_validFracColumn	44.901217	%
u_nanFracColumn	42.851855	%
g_validFracColumn	90.825567	%
g_nanFracColumn	5.225247	%
r_validFracColumn	89.916261	%
r_nanFracColumn	6.306434	%
i_validFracColumn	89.704590	%
i_nanFracColumn	5.970174	%
z_validFracColumn	86.220004	%
z_nanFracColumn	6.772176	%
y_validFracColumn	67.222752	%
y_nanFracColumn	15.830500	%

metricBundle: validPsfFluxMetric (source)

Docstring: Calculate the fraction of values in a column that have valid numerical values (i.e., not NaN), and that fall within the specified “reasonable” range for the values.

metric name	value	units
u_validFracColumn	46.466084	%
u_nanFracColumn	40.227490	%
g_validFracColumn	93.893007	%
g_nanFracColumn	1.868023	%
r_validFracColumn	92.928092	%
r_nanFracColumn	2.955407	%
i_validFracColumn	92.475233	%
i_nanFracColumn	2.573486	%
z_validFracColumn	88.955097	%
z_nanFracColumn	3.368476	%
y_validFracColumn	69.016903	%
y_nanFracColumn	12.810026	%

objectTableCore_metrics#

dataId used in examples:

did_tract = {'tract': 5063, 'skymap': 'lsst_cells_v1', 'band': 'g', 'instrument': 'LSSTComCam'}

metricBundle: blendMetrics (source)

Docstring: Calculate metrics based on the performance of the deblender for blends with multiple children

metric name	value	units
numBlends	46065.000000
meanBlendIterations	16.823771
meanBlendLogL	-1053145.375000

metricBundle: coaddInputCount (source)

Docstring: skyPlot and associated metrics indicating the number of exposures that have gone into creating a coadd.

metric name	value	units
u_inputCount_median	5.0	ct
u_inputCount_sigmaMad	4.4	ct
u_inputCount_mean	5.9	ct
g_inputCount_median	109.0	ct
g_inputCount_sigmaMad	89.0	ct
g_inputCount_mean	102.1	ct
r_inputCount_median	130.0	ct
r_inputCount_sigmaMad	100.8	ct
r_inputCount_mean	120.0	ct
i_inputCount_median	85.0	ct
i_inputCount_sigmaMad	72.6	ct
i_inputCount_mean	83.2	ct
z_inputCount_median	95.0	ct
z_inputCount_sigmaMad	69.7	ct
z_inputCount_mean	88.0	ct
y_inputCount_median	17.0	ct
y_inputCount_sigmaMad	10.4	ct
y_inputCount_mean	15.6	ct

metricBundle: coaddPatchCount (source)

Docstring: An atool to count the patches in a tract. Counts the patches that make it into the objectTable_tract.

metric name	value	units
u_patchCount	30.000000
g_patchCount	47.000000
r_patchCount	46.000000
i_patchCount	47.000000
z_patchCount	45.000000
y_patchCount	40.000000

metricBundle: e1Diff (source)

Docstring: The difference between E1 and E1 PSF plotted on a scatter plot. Used in coaddQualityCore.

Notes: Statistics of residual of e1 ellipticity measurement of sources relative to the PSF.

metric name	value	units
u_highSNStars_median	-0.006962	pix
u_highSNStars_sigmaMad	0.012748	pix
u_highSNStars_count	7.0	ct
u_lowSNStars_median	0.000510	pix
u_lowSNStars_sigmaMad	0.006357	pix
u_lowSNStars_count	137.0	ct
g_highSNStars_median	0.001289	pix
g_highSNStars_sigmaMad	0.003274	pix
g_highSNStars_count	458.0	ct
g_lowSNStars_median	0.000393	pix
g_lowSNStars_sigmaMad	0.004051	pix
g_lowSNStars_count	1522.0	ct
r_highSNStars_median	0.000526	pix
r_highSNStars_sigmaMad	0.001800	pix
r_highSNStars_count	638.0	ct
r_lowSNStars_median	0.000351	pix
r_lowSNStars_sigmaMad	0.002744	pix
r_lowSNStars_count	1954.0	ct
i_highSNStars_median	0.000453	pix
i_highSNStars_sigmaMad	0.001744	pix
i_highSNStars_count	680.0	ct
i_lowSNStars_median	0.000210	pix
i_lowSNStars_sigmaMad	0.002965	pix
i_lowSNStars_count	2218.0	ct
z_highSNStars_median	-0.000107	pix
z_highSNStars_sigmaMad	0.002300	pix
z_highSNStars_count	610.0	ct
z_lowSNStars_median	-0.000013	pix
z_lowSNStars_sigmaMad	0.003097	pix
z_lowSNStars_count	1917.0	ct
y_highSNStars_median	0.000569	pix
y_highSNStars_sigmaMad	0.002821	pix
y_highSNStars_count	102.0	ct
y_lowSNStars_median	0.000041	pix
y_lowSNStars_sigmaMad	0.003921	pix
y_lowSNStars_count	772.0	ct

metricBundle: e2Diff (source)

Docstring: The difference between E2 and E2 PSF plotted on a scatter plot for coadd level data. Used in coaddQualityCore.

Notes: Statistics of residual of e2 ellipticity measurement of sources relative to the PSF.

metric name	value	units
u_highSNStars_median	-0.007902	pix
u_highSNStars_sigmaMad	0.011632	pix
u_highSNStars_count	7.0	ct
u_lowSNStars_median	0.000784	pix
u_lowSNStars_sigmaMad	0.005373	pix
u_lowSNStars_count	137.0	ct
g_highSNStars_median	0.000637	pix
g_highSNStars_sigmaMad	0.002228	pix
g_highSNStars_count	458.0	ct
g_lowSNStars_median	0.000124	pix
g_lowSNStars_sigmaMad	0.003238	pix
g_lowSNStars_count	1522.0	ct
r_highSNStars_median	0.000508	pix
r_highSNStars_sigmaMad	0.001917	pix
r_highSNStars_count	638.0	ct
r_lowSNStars_median	0.000388	pix
r_lowSNStars_sigmaMad	0.002888	pix
r_lowSNStars_count	1954.0	ct
i_highSNStars_median	0.000883	pix
i_highSNStars_sigmaMad	0.002213	pix
i_highSNStars_count	680.0	ct
i_lowSNStars_median	0.000855	pix
i_lowSNStars_sigmaMad	0.003093	pix
i_lowSNStars_count	2218.0	ct
z_highSNStars_median	-0.000148	pix
z_highSNStars_sigmaMad	0.001759	pix
z_highSNStars_count	610.0	ct
z_lowSNStars_median	-0.000071	pix
z_lowSNStars_sigmaMad	0.002598	pix
z_lowSNStars_count	1917.0	ct
y_highSNStars_median	-0.000236	pix
y_highSNStars_sigmaMad	0.002834	pix
y_highSNStars_count	102.0	ct
y_lowSNStars_median	-0.000439	pix
y_lowSNStars_sigmaMad	0.003815	pix
y_lowSNStars_count	772.0	ct

metricBundle: isolatedDeblenderMetrics (source)

Docstring: Calculate metrics based on the performance of the deblender for parents with only a single child peak.

metric name	value	units
numIsolated	67992.000000
meanIsolatedIterations	16.082113
meanIsolatedLogL	-677.472961

metricBundle: parentDeblenderMetrics (source)

Docstring: Calculate metrics based on the performance of the deblender

metric name	value	units
numParents	114061.000000
numDeblendFailed	0.000000
numIncompleteData	45524.000000
numDetectedPeaks	363411.000000
numDeblendedChildren	359466.000000

metricBundle: psfCModelScatter (source)

Docstring: Creates a scatterPlot showing the difference between PSF and CModel mags

Notes: Metrics give summary statistics of the PSF-cmodel magnitudes.

metric name	value	units
u_psf_cModel_diff_median	1.062645	mmag
u_psf_cModel_diff_sigmaMad	4.325282	mmag
u_psf_cModel_diff_mean	10.745125	mmag
g_psf_cModel_diff_median	-2.305251	mmag
g_psf_cModel_diff_sigmaMad	2.320611	mmag
g_psf_cModel_diff_mean	-1.821309	mmag
r_psf_cModel_diff_median	-1.147710	mmag
r_psf_cModel_diff_sigmaMad	2.572899	mmag
r_psf_cModel_diff_mean	-0.521258	mmag
i_psf_cModel_diff_median	-0.576176	mmag
i_psf_cModel_diff_sigmaMad	2.773069	mmag
i_psf_cModel_diff_mean	-0.551983	mmag
z_psf_cModel_diff_median	-1.466141	mmag
z_psf_cModel_diff_sigmaMad	2.297925	mmag
z_psf_cModel_diff_mean	-1.435888	mmag
y_psf_cModel_diff_median	-2.427449	mmag
y_psf_cModel_diff_sigmaMad	2.106005	mmag
y_psf_cModel_diff_mean	-1.936034	mmag

metricBundle: shapeSizeFractionalDiff (source)

Docstring: The difference between shape and shape PSF plotted on a scatter plot for coadd level data. Used in coaddQualityCore.

Notes: Fractional size residuals (\(S/S_{PSF} - 1\)). Stars with “highSN” have S/N > 2700, and “lowSN” have S/N > 500.

metric name	value	units
u_highSNStars_median	-0.018624	pix
u_highSNStars_sigmaMad	0.028057	pix
u_highSNStars_count	7.0	ct
u_lowSNStars_median	-0.001748	pix
u_lowSNStars_sigmaMad	0.006542	pix
u_lowSNStars_count	137.0	ct
g_highSNStars_median	0.000799	pix
g_highSNStars_sigmaMad	0.005744	pix
g_highSNStars_count	458.0	ct
g_lowSNStars_median	0.000275	pix
g_lowSNStars_sigmaMad	0.006641	pix
g_lowSNStars_count	1522.0	ct
r_highSNStars_median	-0.000390	pix
r_highSNStars_sigmaMad	0.003146	pix
r_highSNStars_count	638.0	ct
r_lowSNStars_median	0.000291	pix
r_lowSNStars_sigmaMad	0.003772	pix
r_lowSNStars_count	1954.0	ct
i_highSNStars_median	-0.001118	pix
i_highSNStars_sigmaMad	0.003354	pix
i_highSNStars_count	680.0	ct
i_lowSNStars_median	-0.000326	pix
i_lowSNStars_sigmaMad	0.003610	pix
i_lowSNStars_count	2218.0	ct
z_highSNStars_median	-0.000394	pix
z_highSNStars_sigmaMad	0.003151	pix
z_highSNStars_count	610.0	ct
z_lowSNStars_median	0.000573	pix
z_lowSNStars_sigmaMad	0.003387	pix
z_lowSNStars_count	1917.0	ct
y_highSNStars_median	-0.004150	pix
y_highSNStars_sigmaMad	0.004891	pix
y_highSNStars_count	102.0	ct
y_lowSNStars_median	-0.000406	pix
y_lowSNStars_sigmaMad	0.003624	pix
y_lowSNStars_count	772.0	ct

metricBundle: skippedDeblenderMetrics (source)

Docstring: Calculate metrics based on blends skipped by the deblender

metric name	value	units
numSkippedBlends	4.000000
numBlendParentTooBig	4.000000
numBlendTooManyPeaks	0.000000
numBlendTooManyMasked	0.000000
numSkippedPeaks	3945.000000

metricBundle: skyFluxStatisticMetric (source)

metric name	value	units
u_medianSky	-22.670816	nJy
u_meanSky	-44.955825	nJy
u_stdevSky	458.312329	nJy
u_sigmaMADSky	358.909370	nJy
g_medianSky	2.320502	nJy
g_meanSky	5.908797	nJy
g_stdevSky	122.467253	nJy
g_sigmaMADSky	65.140802	nJy
r_medianSky	-0.373212	nJy
r_meanSky	2.401479	nJy
r_stdevSky	217.256946	nJy
r_sigmaMADSky	89.284979	nJy
i_medianSky	-9.153765	nJy
i_meanSky	-8.731332	nJy
i_stdevSky	225.413986	nJy
i_sigmaMADSky	132.290373	nJy
z_medianSky	-38.847815	nJy
z_meanSky	-37.734689	nJy
z_stdevSky	468.570872	nJy
z_sigmaMADSky	234.272551	nJy
y_medianSky	-178.689968	nJy
y_meanSky	-230.084323	nJy
y_stdevSky	1662.990931	nJy
y_sigmaMADSky	1127.054293	nJy

metricBundle: wPerpCModel (source)

Docstring: No docstring

Notes: Width of stellar locus in (r-i) vs. (g-r) as defined in Ivezic et al. 2004, measured with cmodel magnitudes.

metric name	value	units
wPerp_cModelFlux_sigmaMAD	14.941928	mmag
wPerp_cModelFlux_median	0.408690	mmag

metricBundle: wPerpPSFP (source)

Docstring: No docstring

Notes: Width of the blue portion of the stellar locus in (r-i) vs. (g-r) as defined in Ivezic et al. 2004, measured with PSF magnitudes.

metric name	value	units
wPerp_psfFlux_sigmaMAD	14.965038	mmag
wPerp_psfFlux_median	0.354766	mmag

metricBundle: xPerpCModel (source)

Docstring: No docstring

Notes: Width of the red portion of the stellar locus in (r-i) vs. (g-r) as defined in Ivezic et al. 2004, measured with cmodel magnitudes.

metric name	value	units
xPerp_cModelFlux_sigmaMAD	56.216178	mmag
xPerp_cModelFlux_median	1.497274	mmag

metricBundle: xPerpPSFP (source)

Docstring: No docstring

Notes: Width of the red portion of the stellar locus in (r-i) vs. (g-r) as defined in Ivezic et al. 2004, measured with PSF magnitudes.

metric name	value	units
xPerp_psfFlux_sigmaMAD	55.733773	mmag
xPerp_psfFlux_median	0.950084	mmag

metricBundle: yPerpCModel (source)

Docstring: No docstring

metric name	value	units
yPerp_cModelFlux_sigmaMAD	9.130749	mmag
yPerp_cModelFlux_median	-0.721818	mmag

metricBundle: yPerpPSF (source)

Docstring: No docstring

metric name	value	units
yPerp_psfFlux_sigmaMAD	8.156364	mmag
yPerp_psfFlux_median	-0.542668	mmag

Note the erroneous “P” at the end of “wPerpPSFP” and “xPerpPSFP”. This is due to a typo in the pipelines code.

objectTableExtended_metrics#

dataId used in examples:

did_tract = {'tract': 5063, 'skymap': 'lsst_cells_v1', 'band': 'g', 'instrument': 'LSSTComCam'}

metricBundle: ap12PsfSky (source)

Docstring: No docstring

Notes: Difference between magnitudes in a 12-pixel aperture and PSF magnitudes. Metrics summarize rolled-up statistics of the plotted quantities.

metric name	value	units
g_ap12_psf_diff_median	-2.497167	mmag
g_ap12_psf_diff_sigmaMad	7.382115	mmag
g_ap12_psf_diff_mean	-3.173155	mmag
r_ap12_psf_diff_median	-1.285786	mmag
r_ap12_psf_diff_sigmaMad	4.182110	mmag
r_ap12_psf_diff_mean	-3.415648	mmag
i_ap12_psf_diff_median	-1.689628	mmag
i_ap12_psf_diff_sigmaMad	4.563705	mmag
i_ap12_psf_diff_mean	-3.558899	mmag

Visit-level plots#

matchedVisitCore#

_images/matchedvisitcore-%7Bband%7D-stellarphotometricresiduals-focalplaneplot.png — Fig. 3 **Plot Name**: matchedVisitCore_g_stellarPhotometricResiduals_FocalPlanePlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricresidualsdec-focalplaneplot.png — Fig. 4 **Plot Name**: matchedVisitCore_g_stellarAstrometricResidualsDec_FocalPlanePlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricresidualsra-focalplaneplot.png — Fig. 5 **Plot Name**: matchedVisitCore_g_stellarAstrometricResidualsRA_FocalPlanePlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricresidualstddevdec-focalplaneplot.png — Fig. 6 **Plot Name**: matchedVisitCore_g_stellarAstrometricResidualStdDevDec_FocalPlanePlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricrepeatability1-histplot.png — Fig. 7 **Plot Name**: matchedVisitCore_g_stellarAstrometricRepeatability1_HistPlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricrepeatability2-histplot.png — Fig. 8 **Plot Name**: matchedVisitCore_g_stellarAstrometricRepeatability2_HistPlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricrepeatability3-histplot.png — Fig. 9 **Plot Name**: matchedVisitCore_g_stellarAstrometricRepeatability3_HistPlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricselfrepeatabilitydec-histplot.png — Fig. 10 **Plot Name**: matchedVisitCore_g_stellarAstrometricSelfRepeatabilityDec_HistPlot#

Associated metrics: matchedVisitCore_metrics

_images/matchedvisitcore-%7Bband%7D-stellarastrometricselfrepeatabilityra-histplot.png — Fig. 11 **Plot Name**: matchedVisitCore_g_stellarAstrometricSelfRepeatabilityRA_HistPlot#

Associated metrics: matchedVisitCore_metrics

preSourceTableCore#

Tract-level plots#

diaSourceTableTract#

objectTableCore#

_images/objecttablecore-xperppsfp-colorcolorfitplot.png — Fig. 16 **Plot Name**: objectTableCore_xPerpPSFP_ColorColorFitPlot#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-xperpcmodel-colorcolorfitplot.png — Fig. 17 **Plot Name**: objectTableCore_xPerpCModel_ColorColorFitPlot#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-wperppsfp-colorcolorfitplot.png — Fig. 18 **Plot Name**: objectTableCore_wPerpPSFP_ColorColorFitPlot#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-wperpcmodel-colorcolorfitplot.png — Fig. 19 **Plot Name**: objectTableCore_wPerpCModel_ColorColorFitPlot#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-shapesizefractionaldiff-scatterplotwithtwohists.png — Fig. 20 **Plot Name**: objectTableCore_g_shapeSizeFractionalDiff_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-shapesizedetradiusvspsfmag-scatterplotwithtwohists.png — Fig. 21 **Plot Name**: objectTableCore_g_shapeSizeDetRadiusVsPsfMag_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-shapesizedetradiusvscmodelmag-scatterplotwithtwohists.png — Fig. 22 **Plot Name**: objectTableCore_g_shapeSizeDetRadiusVsCmodelMag_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-e1diff-scatterplotwithtwohists.png — Fig. 23 **Plot Name**: objectTableCore_g_e1Diff_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-e2diff-scatterplotwithtwohists.png — Fig. 24 **Plot Name**: objectTableCore_g_e2Diff_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-cmodelbulgesizevscmodelbulgemag-scatterplotwithtwohists.png — Fig. 25 **Plot Name**: objectTableCore_g_cModelBulgeSizeVsCmodelBulgeMag_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-cmodeldisksizevscmodeldiskmag-scatterplotwithtwohists.png — Fig. 26 **Plot Name**: objectTableCore_g_cModelDiskSizeVsCmodelDiskMag_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

_images/objecttablecore-%7Bband%7D-psfcmodelscatter-scatterplotwithtwohists.png — Fig. 27 **Plot Name**: objectTableCore_g_psfCModelScatter_ScatterPlotWithTwoHists#

Associated metrics: objectTableCore_metrics

objectTableExtended#

PropertyMapTract#

_images/propertymaptract-healsparsemap-deepcoadd-psf-size-weighted-mean-propertymapplot.png — Fig. 32 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_psf_size_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-psf-maglim-weighted-mean-propertymapplot.png — Fig. 33 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_psf_maglim_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-sky-noise-weighted-mean-propertymapplot.png — Fig. 34 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_sky_noise_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-psf-e1-weighted-mean-propertymapplot.png — Fig. 35 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_psf_e1_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-psf-e2-weighted-mean-propertymapplot.png — Fig. 36 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_psf_e2_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-dcr-e1-weighted-mean-propertymapplot.png — Fig. 37 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_dcr_e1_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-dcr-e2-weighted-mean-propertymapplot.png — Fig. 38 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_dcr_e2_weighted_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-exposure-time-sum-propertymapplot.png — Fig. 39 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_exposure_time_sum_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-epoch-mean-propertymapplot.png — Fig. 40 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_epoch_mean_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-epoch-min-propertymapplot.png — Fig. 41 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_epoch_min_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-epoch-max-propertymapplot.png — Fig. 42 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_epoch_max_PropertyMapPlot#

_images/propertymaptract-healsparsemap-deepcoadd-sky-background-weighted-mean-propertymapplot.png — Fig. 43 **Plot Name**: propertyMapTract_HealSparseMap_deepCoadd_sky_background_weighted_mean_PropertyMapPlot#