105.3. Forced photometry#

105_3_Forced_Photometry

105.3. Forced Photometry¶

For the Rubin Science Platform at data.lsst.cloud.
Data Release: Data Preview 1
Container Size: large
LSST Science Pipelines version: r29.2.0
Last verified to run: 2025-11-03
Repository: github.com/lsst/tutorial-notebooks

Learning objective: Measure sources from an image via forced photometry using IDs and coordinates.

LSST data products: deep_coadd, object table

Packages: lsst.pipe.tasks, lsst.daf.butler

Credit: Originally developed by the Rubin Community Science team working with Erfan Nourbakhsh. Please consider acknowledging them if this notebook is used for the preparation of journal articles, software releases, or other notebooks.

Get Support: Everyone is encouraged to ask questions or raise issues in the Support Category of the Rubin Community Forum. Rubin staff will respond to all questions posted there.

1. Introduction¶

This notebook performs forced photometry using the Data Preview 1 (DP1) data products on an input image, given a set of input IDs and RA/Dec coordinates. The ForcedMeasurementDriverTask serves as a convenience function, allowing users to measure sources without running a full-scale pipetask command or dealing with additional pipeline setup. This task can measure fluxes, shapes, and other properties at known source positions (e.g., from a pre-existing catalog) on a given image, without running source detection. (Note that for brevity we disable shape measurement in this notebook.)

Related tutorials: 100-level Detect and measure sources

1.1. Import packages¶

Import numpy, a fundamental package for scientific computing with arrays in Python (numpy.org). From the lsst package, we import Butler to access the LSST data repository, which allows us to retrieve images and catalogs using data IDs. Also import ForcedMeasurementDriverConfig and ForcedMeasurementDriverTask, which are mid-level config and task classes used to set up and execute forced photometry.

In [1]:

import matplotlib.pyplot as plt
import numpy as np
import astropy.units as u
from lsst.daf.butler import Butler
from lsst.pipe.tasks.measurementDriver import (
    ForcedMeasurementDriverConfig,
    ForcedMeasurementDriverTask,
)
from lsst.rsp import get_tap_service
from lsst.utils.plotting import set_rubin_plotstyle
set_rubin_plotstyle()

1.2. Define parameters and functions¶

In [2]:

def fix_flux_columns(table):
    """Rename the columns in an output table from the ForcedMeasurementDriverTask.

    This task is necessary to rename "instFlux" columns to "flux" and units from
    "ct" to "nJy".

    Parameters
    ----------
    table : Astropy Table
        An Astropy table resulting from executing the ForcedMeasurementDriver

    Outputs
    -------
    table : Astropy Table
        Transformed version of the input table, with columns and units updated.
    """

    cols_with_instflux = [col for col in np.array(table.colnames) if 'instFlux' in col]

    for col in cols_with_instflux:
        new_colname = str.replace(col, 'instFlux', 'flux')
        table.rename_column(col, new_colname)
        table[new_colname].unit = u.nJy

    return table

Create an instance of the butler, and assert that it exists.

In [3]:

butler = Butler("dp1", collections="LSSTComCam/DP1")
assert butler is not None

Instantiate the TAP service and assert that it exists.

In [4]:

service = get_tap_service("tap")
assert service is not None

2. Load an image and table¶

Identify coadded images overlapping a particular sky position and select one to run forced photometry on. Also load the corresponding object table using the TAP service so that forced photometry can be run at the positions of already-detected objects.

In [5]:

ra = 53.1
dec = -28.1
band = 'r'

In [6]:

query = f"band='{band}' AND \
          patch.region OVERLAPS POINT({ra}, {dec})"

coadd_img_refs = butler.query_datasets('deep_coadd', where=query)

ref = coadd_img_refs[0]

In [7]:

exposure = butler.get(ref)

Extract a list of objects from the Object table in a small radius near the position of interest. These will be used both as an input list for forced measurement, and as a comparison for the results.

In [8]:

query = f"""SELECT TOP 50 objectId, coord_ra, coord_dec, {band}_psfMag, {band}_psfFlux
        FROM dp1.Object
        WHERE CONTAINS(POINT('ICRS', coord_ra, coord_dec),
        CIRCLE('ICRS', {ra}, {dec}, 0.1)) = 1
        AND refExtendedness < 0.5
        AND {band}_psfMag < 26 AND {band}_psfMag > 17
        AND tract = {ref.dataId['tract']}
        AND patch = {ref.dataId['patch']}"""

job = service.submit_job(query)
job.run()
job.wait(phases=['COMPLETED', 'ERROR'])
print('Job phase is', job.phase)
if job.phase == 'ERROR':
    job.raise_if_error()
assert job.phase == 'COMPLETED'
objtable = job.fetch_result().to_table()

Job phase is COMPLETED

Option to examine the objtable:

In [9]:

# objtable

3. Run the forced measurement driver¶

Configure the forced measurement driver to use base_PsfFlux as the algorithm for the PSF flux slot, and base_TransformedCentroidFromCoord for centroids transformed from the reference catalog. Set other config parameters to disable shape measurement, avoid replacing other detected footprints with noise, and enable aperture correction for the selected flux slot. These are simply examples of how to set configuration options, and are not required to run the task.

Note: base_PsfFlux and base_TransformedCentroidFromCoord are included by default in the measurement plugins, so there's no need to add them manually to config.measurement.plugins.names. They’ll be picked up automatically.

In [10]:

config = ForcedMeasurementDriverConfig()
config.measurement.slots.psfFlux = "base_PsfFlux"
config.measurement.slots.centroid = "base_TransformedCentroidFromCoord"
config.measurement.slots.shape = None
config.measurement.doReplaceWithNoise = False
config.doApCorr = True

Option: examine the configuration.

In [11]:

# config

Create the forced photometry driver task using the configuration.

In [12]:

driver = ForcedMeasurementDriverTask(config=config)

Run the task using the input table of source positions and IDs, the deep_coadd exposure image, and required parameters: column names for ID, RA, and Dec, plus a PSF footprint scaling factor (used to create synthetic footprints since detection is skipped in forced photometry).

In [13]:

result = driver.runFromAstropy(
    objtable,
    exposure,
    id_column_name="objectId",
    ra_column_name="coord_ra",
    dec_column_name="coord_dec",
    psf_footprint_scaling=3.0,
)

lsst.forcedMeasurementDriver INFO: Measuring 50 sources in a single band using 'ForcedMeasurementTask'

lsst.forcedMeasurementDriver.measurement INFO: Performing forced measurement on 50 sources

lsst.forcedMeasurementDriver INFO: Applying aperture corrections to a single band

lsst.forcedMeasurementDriver.applyApCorr INFO: Applying aperture corrections to 1 instFlux fields

lsst.forcedMeasurementDriver INFO: Finished processing for a single band; output catalog has 88 fields and 50 records

Because the ForcedMeasurementDriverTask relies on code that is typically used in early stages of data processing, it assumes that fluxes are instrumental fluxes and appends the suffixes _instFlux and _instFluxErr to the output columns. It also sets the units for these columns to "ct" (counts). Because we know that we are measuring on calibrated images whose pixel values are in nJy (to confirm this, execute exposure.metadata['BUNIT'] in a code cell to see what the units are), we rename the columns and set their units to nJy. The code that causes this behavior will be updated in the future to use keywords from the input image to set the units and suffixes.

In [14]:

result = fix_flux_columns(result)

Examine the resulting Astropy table containing the measured sources. Each row corresponds to a record from the input table, with columns for source ID, RA, and Dec, along with additional measurement fields defined by the configuration.

In [15]:

result[:5]

Out[15]:

Table length=5

coord_ra	coord_dec	parent	objectId	parentObjectId	deblend_nChild	base_TransformedCentroidFromCoord_x	slot_Centroid_x	base_TransformedCentroidFromCoord_y	slot_Centroid_y	base_CircularApertureFlux_3_0_flux	base_CircularApertureFlux_3_0_fluxErr	base_CircularApertureFlux_3_0_flag	base_CircularApertureFlux_3_0_flag_apertureTruncated	base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_4_5_flux	base_CircularApertureFlux_4_5_fluxErr	base_CircularApertureFlux_4_5_flag	base_CircularApertureFlux_4_5_flag_apertureTruncated	base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated	base_CircularApertureFlux_6_0_flux	base_CircularApertureFlux_6_0_fluxErr	base_CircularApertureFlux_6_0_flag	base_CircularApertureFlux_6_0_flag_apertureTruncated	base_CircularApertureFlux_6_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_9_0_flux	base_CircularApertureFlux_9_0_fluxErr	base_CircularApertureFlux_9_0_flag	base_CircularApertureFlux_9_0_flag_apertureTruncated	base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_12_0_flux	base_CircularApertureFlux_12_0_fluxErr	base_CircularApertureFlux_12_0_flag	base_CircularApertureFlux_12_0_flag_apertureTruncated	base_CircularApertureFlux_17_0_flux	base_CircularApertureFlux_17_0_fluxErr	base_CircularApertureFlux_17_0_flag	base_CircularApertureFlux_17_0_flag_apertureTruncated	base_CircularApertureFlux_25_0_flux	base_CircularApertureFlux_25_0_fluxErr	base_CircularApertureFlux_25_0_flag	base_CircularApertureFlux_25_0_flag_apertureTruncated	base_CircularApertureFlux_35_0_flux	base_CircularApertureFlux_35_0_fluxErr	base_CircularApertureFlux_35_0_flag	base_CircularApertureFlux_35_0_flag_apertureTruncated	base_CircularApertureFlux_50_0_flux	base_CircularApertureFlux_50_0_fluxErr	base_CircularApertureFlux_50_0_flag	base_CircularApertureFlux_50_0_flag_apertureTruncated	base_CircularApertureFlux_70_0_flux	base_CircularApertureFlux_70_0_fluxErr	base_CircularApertureFlux_70_0_flag	base_CircularApertureFlux_70_0_flag_apertureTruncated	base_PixelFlags_flag	base_PixelFlags_flag_offimage	base_PixelFlags_flag_edge	base_PixelFlags_flag_nodata	base_PixelFlags_flag_interpolated	base_PixelFlags_flag_saturated	base_PixelFlags_flag_cr	base_PixelFlags_flag_bad	base_PixelFlags_flag_suspect	base_PixelFlags_flag_edgeCenter	base_PixelFlags_flag_nodataCenter	base_PixelFlags_flag_interpolatedCenter	base_PixelFlags_flag_saturatedCenter	base_PixelFlags_flag_crCenter	base_PixelFlags_flag_badCenter	base_PixelFlags_flag_suspectCenter	base_PixelFlags_flag_edgeCenterAll	base_PixelFlags_flag_nodataCenterAll	base_PixelFlags_flag_interpolatedCenterAll	base_PixelFlags_flag_saturatedCenterAll	base_PixelFlags_flag_crCenterAll	base_PixelFlags_flag_badCenterAll	base_PixelFlags_flag_suspectCenterAll	base_PsfFlux_flux	slot_PsfFlux_flux	base_PsfFlux_fluxErr	slot_PsfFlux_fluxErr	base_PsfFlux_area	slot_PsfFlux_area	base_PsfFlux_chi2	slot_PsfFlux_chi2	base_PsfFlux_npixels	slot_PsfFlux_npixels	base_PsfFlux_flag	slot_PsfFlux_flag	base_PsfFlux_flag_noGoodPixels	slot_PsfFlux_flag_noGoodPixels	base_PsfFlux_flag_edge	slot_PsfFlux_flag_edge	base_InvalidPsf_flag	base_PsfFlux_apCorr	slot_PsfFlux_apCorr	base_PsfFlux_apCorrErr	slot_PsfFlux_apCorrErr	base_PsfFlux_flag_apCorr	slot_PsfFlux_flag_apCorr
rad	rad					pix	pix	pix	pix	nJy	nJy				nJy	nJy				nJy	nJy				nJy	nJy				nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy																										nJy	nJy	nJy	nJy	pix	pix			pix	pix
float64	float64	int64	int64	int64	int32	float64	float64	float64	float64	float64	float64	bool	bool	bool	float64	float64	bool	bool	bool	float64	float64	bool	bool	bool	float64	float64	bool	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	float64	float64	float64	float64	float32	float32	float32	float32	int32	int32	bool	bool	bool	bool	bool	bool	bool	float64	float64	float64	float64	bool	bool
0.9284357235248085	-0.48955837921319045	0	611254385447554194	0	0	13284.999720410628	13284.999720410628	5476.550237359781	5476.550237359781	267.4403991699219	6.649595737457275	False	False	False	374.1120910644531	9.9679536819458	False	False	False	451.2291259765625	13.329604148864746	False	False	False	487.3237609863281	20.0559024810791	False	False	False	516.9873642921448	27.010765596179162	False	False	592.7185564339161	38.2286436199333	False	False	603.4480983316898	56.16045435014221	False	False	1389.1180970072746	78.58883392020425	False	False	13701.634986788034	112.41619821442139	False	False	16783.954358518124	157.08954869334747	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	504.0876477231298	504.0876477231298	11.369714815728049	11.369714815728049	79.67882	79.67882	1257.2571	1257.2571	1225	1225	False	False	False	False	False	False	False	0.9926227225688778	0.9926227225688778	0.0	0.0	False	False
0.9282775317464668	-0.48958578850659457	0	611254385447554203	0	0	13429.012542012175	13429.012542012175	5448.402083947918	5448.402083947918	3343.945068359375	7.2206573486328125	False	False	False	4737.42138671875	10.542635917663574	False	False	False	5486.0849609375	13.848408699035645	False	False	False	6099.17724609375	20.4603328704834	False	False	False	6305.931492388248	27.35771190483854	False	False	6662.3800202310085	38.54057906635318	False	False	7185.683129191399	56.36777529446366	False	False	7783.820784300566	78.82544614627501	False	False	8451.208186686039	112.51447026545274	False	False	9201.015788793564	157.2583420106997	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	6281.169383703591	6281.169383703591	12.350997682091442	12.350997682091442	79.54622	79.54622	3122.0657	3122.0657	1225	1225	False	False	False	False	False	False	False	0.9921914160616393	0.9921914160616393	0.0	0.0	False	False
0.9272024046327516	-0.4896004729817264	0	611254385447554186	0	0	14407.610681273925	14407.610681273925	5433.802739179831	5433.802739179831	88.51903533935547	6.66691255569458	False	False	False	135.90452575683594	9.968441009521484	False	False	False	175.01821899414062	13.363798141479492	False	False	False	319.04559326171875	20.158843994140625	False	False	False	598.8098386824131	27.13246037468635	False	False	1046.1342959702015	38.48990233327417	False	False	1406.0898782610893	56.581044689317096	False	False	2301.9651289880276	79.24108602004041	False	False	5359.633947879076	113.17686730896857	False	False	11312.44118487835	158.60750134349462	False	False	False	False	False	False	True	False	True	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	184.70560611225693	184.70560611225693	11.356577852522133	11.356577852522133	79.86682	79.86682	4223.6074	4223.6074	1225	1225	False	False	False	False	False	False	False	0.9918108526120145	0.9918108526120145	0.0	0.0	False	False
0.9284095641568397	-0.4894710389109419	0	611254385447554528	0	0	13308.733371473894	13308.733371473894	5566.65502189172	5566.65502189172	333.40179443359375	6.633640766143799	False	False	False	468.37109375	9.903467178344727	False	False	False	528.0891723632812	13.254477500915527	False	False	False	563.399658203125	19.955745697021484	False	False	False	635.1309954226017	26.90392806061721	False	False	1356.178539097309	38.09122859411815	False	False	2647.178950548172	56.01577089468925	False	False	4299.581887036562	78.40007987818127	False	False	4872.77668350935	111.81898504143693	False	False	7012.317384690046	156.45652682607104	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	618.2581842060997	618.2581842060997	11.297752410121362	11.297752410121362	79.86561	79.86561	12541.634	12541.634	1225	1225	False	False	False	False	False	False	False	0.9920744321600977	0.9920744321600977	0.0	0.0	False	False
0.9270212468925674	-0.4893837934693033	0	611254385447554460	0	0	14572.45284106942	14572.45284106942	5657.331678510893	5657.331678510893	140.5730743408203	6.664852619171143	False	False	False	206.09010314941406	9.961759567260742	False	False	False	232.52064514160156	13.341032028198242	False	False	False	276.97552490234375	20.12602424621582	False	False	False	323.38783636689186	27.205066957785725	False	False	399.21373549103737	38.52455134455442	False	False	1026.6242810189724	56.70204525695046	False	False	4801.452009677887	79.51730125655827	False	False	35313.71744328737	114.02059146456531	False	False	64719.08585858345	159.92814099699558	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	260.36672335234454	260.36672335234454	11.320431535259344	11.320431535259344	79.50919	79.50919	1224.4935	1224.4935	1225	1225	False	False	False	False	False	False	False	0.9918253476111658	0.9918253476111658	0.0	0.0	False	False

The table now includes PSF fluxes (base_psfFlux_flux) and aperture fluxes (e.g., base_CircularApertureFlux_3_0_flux for a 3-pixel aperture), among many other columns.

Most of the output columns are similar to columns that exist in the Source table -- refer to the schema for the DP1 Source table and the 200-level tutorial notebook on the Source catalog for descriptions.

Each flux measurement has an associated flux error and one or more flag columns to allow filtering of bad measurements. Additionally, there are many pixel-based flags (denoted base_PixelFlags_*) highlighting possible issues with the data for each source.

Columns that begin with slot_ are duplicates of other columns in the table. These exist as a way of telling algorithms in the Science Pipelines which is the "preferred" measurement for things like positions, shapes, and fluxes. Because there are multiple ways of measuring shapes (for example), this lets processing algorithms know which of these to select. Note that the specific measurements populating (some of) the slots were configured above, for example when we specified config.measurement.slots.psfFlux = "base_PsfFlux".

The forced measurement functionality is limited to a handful of measurement plugins (for example, model-fitting algorithms will not work with this task because they require more ancillary information than it is set up to receive). Nonetheless, it offers a quick and easy way to extract measurements at arbitrary positions.

3.1 Compare the forced measurements to the Object table¶

Make a plot showing the ratio of the measured PSF fluxes from the forced measurement task to the psfFlux for the same objects in the Object table. Plot these as a function of magnitude, using Astropy units to convert slot_PsfFlux_flux from the result table to AB magnitudes.

In [16]:

meas_mag = (result['slot_PsfFlux_flux'].value*u.nJy).to(u.ABmag)

fig, ax = plt.subplots(figsize=(6, 4))

ax.plot(meas_mag, result['slot_PsfFlux_flux']/objtable['r_psfFlux'], 'k.')
ax.hlines(1.0, 17.3, 26.3, linestyle=':', color='gray')
ax.minorticks_on()
ax.set_xlim(17.3, 26.3)
ax.set_xlabel('measured PSF mag')
ax.set_ylabel('flux ratio (forced/Object)')

plt.tight_layout()
plt.show()

No description has been provided for this image

4. Forced measurement on a visit image¶

The forced measurement task can be run on any ExposureF object. To demonstrate this, retrieve a visit_image and execute the driver to extract forced photometry on that image. The appropriate input catalog for visit_image measurements is the Source table.

In [17]:

query = f"band='{band}' AND \
          visit.region OVERLAPS POINT({ra}, {dec})"

visit_img_refs = butler.query_datasets('visit_image', where=query)

ref = visit_img_refs[0]

In [18]:

exposure = butler.get(ref)

In [19]:

query = f"""SELECT TOP 50 sourceId, ra, dec, psfFlux
        FROM dp1.Source
        WHERE CONTAINS(POINT('ICRS', ra, dec),
        CIRCLE('ICRS', {ra}, {dec}, 0.1)) = 1
        AND extendedness < 0.5
        AND visit = {ref.dataId['visit']}
        AND detector = {ref.dataId['detector']}"""

job = service.submit_job(query)
job.run()
job.wait(phases=['COMPLETED', 'ERROR'])
print('Job phase is', job.phase)
if job.phase == 'ERROR':
    job.raise_if_error()
assert job.phase == 'COMPLETED'
srctable = job.fetch_result().to_table()

Job phase is COMPLETED

In [20]:

config = ForcedMeasurementDriverConfig()
config.measurement.slots.psfFlux = "base_PsfFlux"
config.measurement.slots.centroid = "base_TransformedCentroidFromCoord"
config.measurement.slots.shape = None
config.measurement.doReplaceWithNoise = False
config.doApCorr = True

In [21]:

driver = ForcedMeasurementDriverTask(config=config)

In [22]:

src_result = driver.runFromAstropy(
    srctable,
    exposure,
    id_column_name="sourceId",
    ra_column_name="ra",
    dec_column_name="dec",
    psf_footprint_scaling=3.0,
)

lsst.forcedMeasurementDriver INFO: Measuring 50 sources in a single band using 'ForcedMeasurementTask'

lsst.forcedMeasurementDriver.measurement INFO: Performing forced measurement on 50 sources

lsst.forcedMeasurementDriver INFO: Applying aperture corrections to a single band

lsst.forcedMeasurementDriver.applyApCorr INFO: Applying aperture corrections to 1 instFlux fields

lsst.forcedMeasurementDriver INFO: Finished processing for a single band; output catalog has 88 fields and 50 records

In [23]:

src_result = fix_flux_columns(src_result)

In [24]:

src_result[:5]

Out[24]:

Table length=5

coord_ra	coord_dec	parent	objectId	parentObjectId	deblend_nChild	base_TransformedCentroidFromCoord_x	slot_Centroid_x	base_TransformedCentroidFromCoord_y	slot_Centroid_y	base_CircularApertureFlux_3_0_flux	base_CircularApertureFlux_3_0_fluxErr	base_CircularApertureFlux_3_0_flag	base_CircularApertureFlux_3_0_flag_apertureTruncated	base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_4_5_flux	base_CircularApertureFlux_4_5_fluxErr	base_CircularApertureFlux_4_5_flag	base_CircularApertureFlux_4_5_flag_apertureTruncated	base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated	base_CircularApertureFlux_6_0_flux	base_CircularApertureFlux_6_0_fluxErr	base_CircularApertureFlux_6_0_flag	base_CircularApertureFlux_6_0_flag_apertureTruncated	base_CircularApertureFlux_6_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_9_0_flux	base_CircularApertureFlux_9_0_fluxErr	base_CircularApertureFlux_9_0_flag	base_CircularApertureFlux_9_0_flag_apertureTruncated	base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_12_0_flux	base_CircularApertureFlux_12_0_fluxErr	base_CircularApertureFlux_12_0_flag	base_CircularApertureFlux_12_0_flag_apertureTruncated	base_CircularApertureFlux_17_0_flux	base_CircularApertureFlux_17_0_fluxErr	base_CircularApertureFlux_17_0_flag	base_CircularApertureFlux_17_0_flag_apertureTruncated	base_CircularApertureFlux_25_0_flux	base_CircularApertureFlux_25_0_fluxErr	base_CircularApertureFlux_25_0_flag	base_CircularApertureFlux_25_0_flag_apertureTruncated	base_CircularApertureFlux_35_0_flux	base_CircularApertureFlux_35_0_fluxErr	base_CircularApertureFlux_35_0_flag	base_CircularApertureFlux_35_0_flag_apertureTruncated	base_CircularApertureFlux_50_0_flux	base_CircularApertureFlux_50_0_fluxErr	base_CircularApertureFlux_50_0_flag	base_CircularApertureFlux_50_0_flag_apertureTruncated	base_CircularApertureFlux_70_0_flux	base_CircularApertureFlux_70_0_fluxErr	base_CircularApertureFlux_70_0_flag	base_CircularApertureFlux_70_0_flag_apertureTruncated	base_PixelFlags_flag	base_PixelFlags_flag_offimage	base_PixelFlags_flag_edge	base_PixelFlags_flag_nodata	base_PixelFlags_flag_interpolated	base_PixelFlags_flag_saturated	base_PixelFlags_flag_cr	base_PixelFlags_flag_bad	base_PixelFlags_flag_suspect	base_PixelFlags_flag_edgeCenter	base_PixelFlags_flag_nodataCenter	base_PixelFlags_flag_interpolatedCenter	base_PixelFlags_flag_saturatedCenter	base_PixelFlags_flag_crCenter	base_PixelFlags_flag_badCenter	base_PixelFlags_flag_suspectCenter	base_PixelFlags_flag_edgeCenterAll	base_PixelFlags_flag_nodataCenterAll	base_PixelFlags_flag_interpolatedCenterAll	base_PixelFlags_flag_saturatedCenterAll	base_PixelFlags_flag_crCenterAll	base_PixelFlags_flag_badCenterAll	base_PixelFlags_flag_suspectCenterAll	base_PsfFlux_flux	slot_PsfFlux_flux	base_PsfFlux_fluxErr	slot_PsfFlux_fluxErr	base_PsfFlux_area	slot_PsfFlux_area	base_PsfFlux_chi2	slot_PsfFlux_chi2	base_PsfFlux_npixels	slot_PsfFlux_npixels	base_PsfFlux_flag	slot_PsfFlux_flag	base_PsfFlux_flag_noGoodPixels	slot_PsfFlux_flag_noGoodPixels	base_PsfFlux_flag_edge	slot_PsfFlux_flag_edge	base_InvalidPsf_flag	base_PsfFlux_apCorr	slot_PsfFlux_apCorr	base_PsfFlux_apCorrErr	slot_PsfFlux_apCorrErr	base_PsfFlux_flag_apCorr	slot_PsfFlux_flag_apCorr
rad	rad					pix	pix	pix	pix	nJy	nJy				nJy	nJy				nJy	nJy				nJy	nJy				nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy			nJy	nJy																										nJy	nJy	nJy	nJy	pix	pix			pix	pix
float64	float64	int64	int64	int64	int32	float64	float64	float64	float64	float64	float64	bool	bool	bool	float64	float64	bool	bool	bool	float64	float64	bool	bool	bool	float64	float64	bool	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	float64	float64	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	bool	float64	float64	float64	float64	float32	float32	float32	float32	int32	int32	bool	bool	bool	bool	bool	bool	bool	float64	float64	float64	float64	bool	bool
0.9261081148182645	-0.4911686627122309	0	600320187643727228	0	0	3101.66259961815	3101.66259961815	2683.004464538525	2683.004464538525	10807.9931640625	117.04840087890625	False	False	False	14554.33984375	161.17442321777344	False	False	False	16430.48828125	204.00741577148438	False	False	False	18135.875	290.233642578125	False	False	False	18850.2669319883	380.44648697216655	False	False	18621.223867726512	530.2929337173343	False	False	20332.8076853659	772.9056019654408	False	False	20894.15279313596	1076.531081938482	False	False	22019.16297424445	1536.5094034569463	False	False	27885.559588468866	2152.805710172363	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	17778.756447556774	17778.756447556774	180.18195906181134	180.18195906181134	60.060238	60.060238	721.8461	721.8461	625	625	False	False	False	False	False	False	False	0.9809244686116884	0.9809244686116884	0.0	0.0	False	False
0.9258628462972498	-0.49177969085351353	0	600320187643725537	0	0	2438.7283028993475	2438.7283028993475	2607.271619806805	2607.271619806805	717.3385620117188	94.7679443359375	False	False	False	1011.271240234375	140.94180297851562	False	False	False	1028.5889892578125	187.99290466308594	False	False	False	1389.0892333984375	282.6224060058594	False	False	False	1775.736476920545	379.8526842567531	False	False	2398.3982537165284	538.3793392462661	False	False	2547.437992397696	791.151575585714	False	False	1871.5431990595534	1106.7699811746106	False	False	20.762026413343847	1579.610098628831	False	False	2603.195472717285	2211.8687754055977	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	1316.499294189713	1316.499294189713	144.42200007852327	144.42200007852327	64.09392	64.09392	659.8952	659.8952	625	625	False	False	False	False	False	False	False	0.9835306768964074	0.9835306768964074	0.0	0.0	False	False
0.9258579443251841	-0.4918512094624013	0	600320187643725545	0	0	2365.981628818359	2365.981628818359	2619.4701984414346	2619.4701984414346	9540.904296875	116.2877197265625	False	False	False	12732.375	161.72169494628906	False	False	False	14447.5498046875	206.31500244140625	False	False	False	15925.21484375	296.0376892089844	False	False	False	16502.007598618045	391.39138390243903	False	False	16231.319842783734	545.2871911938946	False	False	16306.222371668555	794.2562894202924	False	False	15509.101887841709	1108.6240937283785	False	False	15566.423372515594	1582.4464097529465	False	False	15454.48386008956	2212.987169375934	False	False	False	False	False	False	True	False	False	True	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	16252.081965620302	16252.081965620302	185.0742737417989	185.0742737417989	64.46643	64.46643	687.1295	687.1295	625	625	False	False	False	False	False	False	False	0.9838525809503976	0.9838525809503976	0.0	0.0	False	False
0.9258520844953266	-0.4915129280229249	0	600320187643725492	0	0	2704.1706055077134	2704.1706055077134	2536.087617595198	2536.087617595198	468.6375427246094	92.76402282714844	False	False	False	559.5792236328125	138.5949249267578	False	False	False	545.8077392578125	184.9916229248047	False	False	False	924.0810546875	278.5931701660156	False	False	False	1575.320604648441	374.6779156372959	False	False	1685.3772354964167	530.4033398668362	False	False	1297.7036626692861	779.924690034393	False	False	2840.970360888168	1092.0769221143248	False	False	4930.439350276254	1559.53851909711	False	False	11492.966477772105	2183.545614546594	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	811.9116635594163	811.9116635594163	139.62530431615218	139.62530431615218	62.738995	62.738995	581.6456	581.6456	625	625	False	False	False	False	False	False	False	0.9821524445255163	0.9821524445255163	0.0	0.0	False	False
0.9260258826062737	-0.49092172614552126	0	600320187643725502	0	0	3332.699980399892	3332.699980399892	2553.201562890704	2553.201562890704	458.43310546875	91.42707061767578	False	False	False	700.7069091796875	136.46255493164062	False	False	False	885.3251342773438	182.42738342285156	False	False	False	1039.4859619140625	274.1864013671875	False	False	False	1458.4040450565517	369.48824955448566	False	False	1501.442922835471	522.4254263048504	False	False	3339.7937393246684	768.1923238428856	False	False	9380.934817486908	1075.9891955318915	False	False	19759.50555419922	1538.1169736551235	False	False	51215.99873912055	2154.6620911456776	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	False	717.0489159549701	717.0489159549701	132.85099720890076	132.85099720890076	59.464394	59.464394	619.6509	619.6509	625	625	False	False	False	False	False	False	False	0.9790756961724103	0.9790756961724103	0.0	0.0	False	False

4.1 Compare the forced measurements to the Source table¶

Make a plot showing the ratio of the measured PSF fluxes from the forced measurement task to the psfFlux for the same objects in the Source table. Plot these as a function of magnitude, using Astropy units to convert slot_PsfFlux_flux from the result table to AB magnitudes.

In [25]:

meas_mag = (src_result['slot_PsfFlux_flux'].value*u.nJy).to(u.ABmag)

plt.plot(meas_mag, src_result['slot_PsfFlux_flux']/srctable['psfFlux'], 'k.')
plt.hlines(1.0, 17.3, 26.3, linestyle=':', color='gray')
plt.minorticks_on()
plt.xlim(17.3, 26.3)
plt.xlabel('measured PSF mag')
plt.ylabel('flux ratio (forced/sourceTable)')

plt.tight_layout()
plt.show()

In [ ]: