105.3. Forced photometry#

105_3_Forced_Photometry.r29_2_0

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: 2025-09-02
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 measures fluxes, shapes, and other properties at known source positions (e.g., from a pre-existing catalog) on a given image, without running source detection.

Related tutorials: 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 numpy as np
import astropy.units as u
from lsst.daf.butler import Butler
from lsst.pipe.tasks.measurementDriver import (
    ForcedMeasurementDriverConfig,
    ForcedMeasurementDriverTask,
)

1.2. Define parameters and functions¶

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

In [2]:

butler = Butler("dp1", collections="LSSTComCam/DP1")
assert butler 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 butler so the forced photometry can be run at the positions of already-detected objects.

In [3]:

ra = 53.076
dec = -28.110
band = 'r'

In [4]:

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

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

ref = coadd_img_refs[0]

In [5]:

exposure = butler.get("deep_coadd", dataId=ref.dataId)

objtable = butler.get("object",
                      dataId=ref.dataId,
                      storageClass="ArrowAstropy",
                      parameters={"columns": ["objectId", "coord_ra", "coord_dec", "tract",
                                              "patch",
                                              f"{band}_psfFlux", "refExtendedness"]},
                      )

The object table contains measurements from the deep_coadd images for a full tract. It is quite large, so cut it down to a only include the patch of interest to make it a manageable size.

Select objects from the same tract and patch as the loaded image.

In [6]:

pick = np.where((objtable["tract"] == ref.dataId["tract"])
                & (objtable["patch"] == ref.dataId["patch"]))

print(f"Length of patch table: {len(pick[0])}")

Length of patch table: 11123

Grab the first 5 matching entries with just their objectId and coordinates for testing. These are the only required inputs for the ForcedMeasurementDriverTask.

In [7]:

table = objtable[pick][:5].copy()["objectId", "coord_ra", "coord_dec"]
table

Out[7]:

Table length=5

objectId	coord_ra	coord_dec
int64	float64	float64
611254385447534870	53.22648161543073	-28.186979527401327
611254385447534887	53.20215945266285	-28.186654031188027
611254385447534890	53.18789858793983	-28.186399452233857
611254385447534905	53.108281109881865	-28.186148514289624
611254385447534911	53.12831647802782	-28.18576147271307

The forced measurement driver requires the input coordinates to have units attached. Attach units to the RA, Dec columns.

In [8]:

table['coord_ra'].unit = u.degree
table['coord_dec'].unit = u.degree

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 [9]:

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

Create the forced photometry driver task using the configuration.

In [10]:

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 [11]:

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

lsst.forcedMeasurementDriver INFO: Measuring 5 sources in a single band using 'ForcedMeasurementTask'

lsst.forcedMeasurementDriver.measurement INFO: Performing forced measurement on 5 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 5 records

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 [12]:

result

Out[12]:

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_instFlux	base_CircularApertureFlux_3_0_instFluxErr	base_CircularApertureFlux_3_0_flag	base_CircularApertureFlux_3_0_flag_apertureTruncated	base_CircularApertureFlux_3_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_4_5_instFlux	base_CircularApertureFlux_4_5_instFluxErr	base_CircularApertureFlux_4_5_flag	base_CircularApertureFlux_4_5_flag_apertureTruncated	base_CircularApertureFlux_4_5_flag_sincCoeffsTruncated	base_CircularApertureFlux_6_0_instFlux	base_CircularApertureFlux_6_0_instFluxErr	base_CircularApertureFlux_6_0_flag	base_CircularApertureFlux_6_0_flag_apertureTruncated	base_CircularApertureFlux_6_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_9_0_instFlux	base_CircularApertureFlux_9_0_instFluxErr	base_CircularApertureFlux_9_0_flag	base_CircularApertureFlux_9_0_flag_apertureTruncated	base_CircularApertureFlux_9_0_flag_sincCoeffsTruncated	base_CircularApertureFlux_12_0_instFlux	base_CircularApertureFlux_12_0_instFluxErr	base_CircularApertureFlux_12_0_flag	base_CircularApertureFlux_12_0_flag_apertureTruncated	base_CircularApertureFlux_17_0_instFlux	base_CircularApertureFlux_17_0_instFluxErr	base_CircularApertureFlux_17_0_flag	base_CircularApertureFlux_17_0_flag_apertureTruncated	base_CircularApertureFlux_25_0_instFlux	base_CircularApertureFlux_25_0_instFluxErr	base_CircularApertureFlux_25_0_flag	base_CircularApertureFlux_25_0_flag_apertureTruncated	base_CircularApertureFlux_35_0_instFlux	base_CircularApertureFlux_35_0_instFluxErr	base_CircularApertureFlux_35_0_flag	base_CircularApertureFlux_35_0_flag_apertureTruncated	base_CircularApertureFlux_50_0_instFlux	base_CircularApertureFlux_50_0_instFluxErr	base_CircularApertureFlux_50_0_flag	base_CircularApertureFlux_50_0_flag_apertureTruncated	base_CircularApertureFlux_70_0_instFlux	base_CircularApertureFlux_70_0_instFluxErr	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_instFlux	slot_PsfFlux_instFlux	base_PsfFlux_instFluxErr	slot_PsfFlux_instFluxErr	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	ct	ct				ct	ct				ct	ct				ct	ct				ct	ct			ct	ct			ct	ct			ct	ct			ct	ct			ct	ct																										ct	ct	ct	ct	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
0.9289773534414965	-0.4919555989453884	0	611254385447534870	0	0	12794.763762297036	12794.763762297036	3003.466269001161	3003.466269001161	508.3687438964844	6.665900707244873	False	False	False	770.1231079101562	9.93996524810791	False	False	False	955.5059814453125	13.295246124267578	False	False	False	1139.1279296875	19.969926834106445	False	False	False	1179.2709827423096	26.884258101689277	False	False	1260.2906643748283	38.04785087164237	False	False	1358.9937336146832	55.84397631440843	False	False	1718.5288484096527	78.17241098028421	False	False	3039.611535191536	111.60576319761655	False	False	6560.780028581619	156.39657352631951	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	974.6182453061164	974.6182453061164	11.364497557955273	11.364497557955273	80.06211	80.06211	1259.3179	1259.3179	1225	1225	False	False	False	False	False	False	False	0.9912035362201694	0.9912035362201694	0.0	0.0	False	False
0.928552851619991	-0.4919499179647635	0	611254385447534887	0	0	13180.666428726481	13180.666428726481	3009.729317068548	3009.729317068548	37.971256256103516	6.537532329559326	False	False	False	61.384830474853516	9.772468566894531	False	False	False	75.80436706542969	13.086181640625	False	False	False	104.4230728149414	19.707996368408203	False	False	False	184.8607995212078	26.587062346690335	False	False	322.8769664466381	37.6573457360617	False	False	1441.6865410208702	55.47213113715018	False	False	17948.585873931646	77.93020742295528	False	False	50428.610148727894	111.41405230536085	False	False	77395.19233214855	155.7412771018013	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	76.54020507067322	76.54020507067322	11.17328180985954	11.17328180985954	80.522316	80.522316	1164.1997	1164.1997	1225	1225	False	False	False	False	False	False	False	0.9915093377042407	0.9915093377042407	0.0	0.0	False	False
0.9283039525763928	-0.491945474723807	0	611254385447534890	0	0	13406.93347842264	13406.93347842264	3014.5128488849914	3014.5128488849914	185.5901336669922	6.468040943145752	False	False	False	285.6594543457031	9.704038619995117	False	False	False	326.20098876953125	13.019852638244629	False	False	False	391.8002624511719	19.607486724853516	False	False	False	468.4096273779869	26.439470770047226	False	False	583.5716409087181	37.423573852395144	False	False	1091.416959285736	54.869677380003914	False	False	2269.0049473941326	76.87333089288077	False	False	5434.982126444578	109.90134999836206	False	False	68463.42139658332	154.48411469713955	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	359.6370891157531	359.6370891157531	11.084436307905435	11.084436307905435	80.439285	80.439285	1244.6193	1244.6193	1225	1225	False	False	False	False	False	False	False	0.9920718805118075	0.9920718805118075	0.0	0.0	False	False
0.926914365442147	-0.4919410950304619	0	611254385447534905	0	0	14670.188650312628	14670.188650312628	3019.6607777124336	3019.6607777124336	154.94583129882812	6.7749176025390625	False	False	False	244.2054901123047	10.124290466308594	False	False	False	292.4091796875	13.573420524597168	False	False	False	310.5824890136719	20.43981170654297	False	False	False	301.5161562561989	27.54426059444401	False	False	374.0983957648277	38.98112749475285	False	False	409.90519720315933	57.31104598182115	False	False	1141.3614585399628	80.26401248891636	False	False	2005.0532057285309	114.69537985286343	False	False	5913.335474818945	160.59163545944864	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	296.6360079792229	296.6360079792229	11.579363562317782	11.579363562317782	80.608665	80.608665	1239.9381	1239.9381	1225	1225	False	False	False	False	False	False	False	0.9906901634557377	0.9906901634557377	0.0	0.0	False	False
0.927264048583143	-0.491934339880609	0	611254385447534911	0	0	14352.29335932013	14352.29335932013	3026.547898794748	3026.547898794748	83.66658782958984	6.598418712615967	False	False	False	145.59071350097656	9.870516777038574	False	False	False	179.47401428222656	13.228277206420898	False	False	False	223.35696411132812	19.953493118286133	False	False	False	237.26244297623634	26.952998369444664	False	False	253.3319989144802	38.17747256803523	False	False	523.4960869252682	56.23464481366406	False	False	1111.902500629425	78.79277245747345	False	False	8702.840077251196	112.62334327664705	False	False	17946.956145495176	157.77988235483815	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	167.65966984640008	167.65966984640008	11.236996979299153	11.236996979299153	79.97352	79.97352	1176.7106	1176.7106	1225	1225	False	False	False	False	False	False	False	0.9917935953031872	0.9917935953031872	0.0	0.0	False	False

Note that the table now includes PSF fluxes (base_psfFlux_instFlux) and aperture fluxes (e.g., base_CircularApertureFlux_3_0_instFlux for a 3-pixel aperture), among many other columns.

This 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.