DELVE#

DELVE is supported by StreamObs.

Available releases#

Release	Bands	Footprint	Reference band	Median reference-band depth
`dr3_gold` (DELVE DR3 Gold)	griz	17,099 deg² (shipped maglim map)	g	24.373

In the DES footprint, DELVE DR3 Gold is DES Y6 Gold — the two surveys share imaging there — so this release is built to be mutually consistent with DES on that overlap, and the overlap is the primary cross-check (see Validation under Creation below).

Products#

All of the selection-function products are derived from the DELVE Balrog synthetic-source-injection catalogue (BalrogOfTheStars_Catalog_V4.hdf5, 62,922,015 injected rows) by scripts/des/balrog_selection_function.py --survey delve. The method, the evidence behind each choice and the runbook are in Selection functions from Balrog (DECam surveys); see Creation below for the per-release summary. The derivation takes ~35 minutes wall time and ~48 GB peak RAM on the full V4 catalogue.

File	Contents
`delve_dr3_gold_maglim_{g,r,i,z}_nside128.fits.gz`	truth-anchored S/N = 5 depth maps
`delve_dr3_gold_stellar_efficiency_cutg.csv`	stellar detection + classification efficiency vs `delta_mag`
`delve_dr3_gold_photoerror_g.csv`	sample photo-error curve — truth scatter, drives the noise draw (reference band)
`delve_dr3_gold_photoerror_g_catalog.csv`	catalog photo-error curve — reported `magerr`, drives the S/N cut (reference band)
`delve_dr3_gold_photoerror_g_nocut.csv`	sample, no S/N cut — the noise draw for forced-photometry bands
`delve_dr3_gold_photoerror_g_catalog_nocut.csv`	catalog, no S/N cut — reported `magerr` for forced-photometry bands
`delve_dr3_gold_galaxy_misclass_cutg.csv`	fraction of detected true galaxies classified as point sources
`delve_dr3_gold_photoerror_g{,_catalog}{,_nocut}_raw.csv`	pre-afterburner provenance
`delve_dr3_gold_audit.json`	counts, anchors and convention flags for the run

Reference band is g. The completeness and photo-error curves are keyed to delta_mag = mag_true − maglim(pixel) and applied band-independently, so colour is carried by the per-band depth maps rather than by separate per-band tables.

Depth and bands#

Truth-anchored medians:

band	truth-anchored depth
g	24.373
r	23.962
i	23.389
z	22.951

Footprint of the shipped maglim map is 17,099 deg², at nside 128 (delve_dr3_gold_maglim_{g,r,i,z}_nside128.fits.gz). Each band’s depth is mosaicked from two input map files (DR3.2 and DR3.1.1+3.1.2), which are exactly disjoint halves of the footprint (0.00% overlap, 99.76% union); this is handled internally by the reducer and is transparent to a product user.

The V4 Balrog injects griz only, matching DES Y6, so no u or Y product is derivable and none is shipped.

DELVE DR3 Gold truth-anchored depth histograms

The four anchor shifts all share a sign, which is the coherence check that validates the anchor. Medians quoted on the histograms are of the written map, which masks pixels deviating more than 1.5 mag from the band median, so they sit ~0.02 below the anchor values quoted below. The sky map shows the two disjoint DR3 halves that are mosaicked into each band.

Photometric errors#

Four curves ship, not two. The reference band g uses the sample/catalog pair, which is measured on the detected population, since g’s own photometry is conditioned on its own detection. Every other band (r, i, z) is forced photometry — measured at the g position, not conditioned on its own detection — and uses the _nocut pair instead.

Survey.get_photo_error(band=...) picks the matching pair automatically and raises rather than guessing if the required curve for a non-reference band is not loaded, instead of silently applying the detected-population curve to forced photometry.

Using it in streamobs#

Configured by config/surveys/delve_dr3_gold.yaml, data in data/surveys/delve_dr3_gold/:

from streamobs.surveys import SurveyFactory

survey = SurveyFactory.create_survey(
    "delve",
    release="dr3_gold"
)

maglim = survey.get_maglim("g", pixel=pix)

completeness = survey.get_completeness(
    "g",
    mag,
    maglim
)

photo_error = survey.get_photo_error(
    "g",
    mag,
    maglim
)

For a forced-photometry band, pass that band to get_photo_error (e.g. survey.get_photo_error("r", mag, maglim)) — it automatically resolves to the _nocut curve rather than the reference-band curve.

Caveats#

EXT_XGB — what a real DR3 Gold user would actually cut on — cannot be evaluated on Balrog, for the same reason as DES Y6 (App. A.2 of Bechtol et al. 2025): the required features are never measured for injected sources. DELVE ships bdf_extended_class_dr3gold instead, which is exactly reproducible from BDF_T/BDF_S2N but is a different selection from an EXT_XGB cut. DES handles this gap with a trained surrogate plus deconvolution; no equivalent surrogate is shipped for DELVE.
There is no external validation of the DELVE star classification comparable to the SPLASH-SXDF check done for DES. (That check validated completeness but could not measure contamination either — see DES.)
Galaxy misclassification is noise-dominated brightward of delta_mag ≈ −4. The true-galaxy counts per bin get small there and the rate swings wildly; treat the curve as reliable only faintward of that.
The efficiency table starts shallower on the bright side than DES’s. DELVE’s table begins at delta_mag = −5.0 (mag_g = 19.375), 3.4 mag shallower than DES’s −8.4 (mag_g = 16.625). Stars brighter than g ≈ 19.4 are held flat at the table’s brightest value (detection_eff = 0.90) by streamobs’ bright-edge hold — treat completeness for very bright stars as indicative rather than measured.

Creation#

How the injections work#

The selection-function products are derived from the DELVE Balrog synthetic-source-injection catalogue (BalrogOfTheStars_Catalog_V4.hdf5, 62,922,015 injected rows) by scripts/des/balrog_selection_function.py --survey delve. The method, the evidence behind each choice and the full runbook are in Selection functions from Balrog (DECam surveys); this page carries only the per-release summary. The derivation takes ~35 minutes wall time and ~48 GB peak RAM on the full V4 catalogue.

Regenerate the figures on this page with python scripts/des/build_delve_survey_doc_figs.py. There is deliberately no surrogate-confusion figure and no external-validation figure, unlike DES: DELVE needs no EXT_XGB surrogate, and no SPLASH-equivalent truth catalogue overlaps the footprint.

Depth-map derivation#

The shift applied to each input map:

band	input map median	truth-anchored	shift
g	24.178	24.373	+0.196
r	23.675	23.962	+0.287
i	23.204	23.389	+0.185
z	22.560	22.951	+0.391

Each band’s depth is mosaicked from two input map files (DR3.2 and DR3.1.1+3.1.2), which are exactly disjoint halves of the footprint (0.00% overlap, 99.76% union); passing only one silently drops ~half the injections and produces an all-zero efficiency table. See Selection functions from Balrog (DECam surveys) for the mosaicking details.

The shifts are smooth and all the same sign — the coherence check that validates the anchor, same as DES. Unlike DES, whose shifts are all negative, DELVE’s are all positive: the input maps are slightly optimistic relative to what the injections actually recover.

Pixels deviating more than 1.5 mag from their band median are masked when the maps are written: g 29,872 (0.60%), r 49,822 (1.02%), i 30,806 (0.63%), z 25,535 (0.51%).

Star/galaxy classification#

classification_eff describes the bdf_extended_class_dr3gold, 0 ≤ EXT ≤ 1 selection. Unlike DES this needs no surrogate, no deconvolution and no deep-field truth join: bdf_extended_class_dr3gold needs only BDF_T and BDF_S2N, both of which are measured for injections, and truth labels come from truth_STAR, which ships per row. The classifier reuses the DES Y6 Gold interpolation nodes, so DES and DELVE are classified identically — that is what makes the two releases comparable in delta_mag space.

Counts behind the curve: 13,141,646 true stars binned; 8,361,275 detected (63.6%); 7,140,687 classified (85.4% of detected). The bright-end detection plateau sits at 0.901, and the combined (classification × detection) efficiency crosses 50% at delta_mag = −0.144. The efficiency table spans delta_mag = −5.0 to +2.5 (mag_g 19.375 to 26.875), 31 rows.

As of 2026-09-04, the V4 catalogue also carries a persisted bdf_extended_class_dr3gold int8 column, computed by the same vendored function the reducer uses, with provenance recorded in the dataset attrs. Values run 0–4 plus a −9 sentinel; the distribution is 32.2% point source (0–1), 30.3% extended (2–4), 37.5% sentinel — the sentinel fraction is dominated by the ~21% undetected injections, whose meas_bdf_* fields are all 0.0 and so fail the s2n > 0 test.

Stellar detection and classification efficiency for the 0 ≤ bdf_extended_class_dr3gold ≤ 1 selection, with the galaxy misclassification rate on the same axes. The bright-end detection plateau sits at 0.901 rather than near unity because the per-object quality flags (meas_flags, meas_bdf_flags) are applied in the efficiency numerator — the Roman/LSST convention. The shaded region marks where the misclassification curve is noise-dominated; see Caveats.

Photometric-error derivation#

The sample/catalog pair is measured on the detected population and applies to the reference band g. The _nocut pair is measured without the reference-band S/N cut and applies to r, i and z, which are forced at the g position and so are not conditioned on their own detection.

The two pairs are identical brightward of the depth (22 bins agree to within 1e-6) and diverge only faintward, where the S/N cut truncates the detected sample: its measured scatter turns over and falls while the _nocut curve keeps rising, up to 0.38 dex apart. Using the detected curve for forced photometry would understate faint-band errors.

Per-tile zero points were measured for 1,499 tiles (reference offset +0.0225, spread (16–84)/2 = 0.1758). 329 tiles deviated by more than 0.05 mag and were rejected, not corrected — matching DES’s own treatment of this class of artifact — dropping 8,503,446 of the 62,922,015 injected rows. Rejecting rather than correcting improved the error-inflation factor (truth scatter / reported error) from 2.00 to 1.50; the shipped value is 1.502. Because the curves are delta_mag-keyed and the imaging is homogeneous within the survey, they extrapolate to the full footprint — the maglim maps ship unmasked.

The bright-end cut removes bins with delta_mag < −3.25 (23 of 64 raw bins, leaving 41). The truth-scatter histogram has 0.005 mag bins, so a binned sigma can only take multiples of 0.0025; brightward of delta_mag ≈ −3.26 the curve is pinned to that grid and reports the bin width rather than the scatter. The first bin reaching sigma = 0.020 — the same floor the cleaned DES curve has — is delta_mag = −3.256. See scripts/des/delve_photoerror_corrections.yaml for the full rationale.

The cleaned curve floors at 0.020 mag, which makes sys_error: 0.005 safe (3.1% in quadrature) — exactly as for DES.

DELVE DR3 Gold photometric error model

All four photo-error curves. Solid is the detected-population pair used for the reference band g; dashed is the _nocut pair used for the forced-photometry bands r, i and z. They agree brightward of the depth and separate only faintward, where the S/N cut truncates the detected sample and its measured scatter turns over rather than continuing to rise. The lower panel is the error-inflation factor, ~1.5 near the limit.

Validation#

Since DELVE DR3 Gold is DES Y6 Gold in the DES footprint, comparing the two releases in delta_mag space is the primary validation for this release — it needs no sky overlap, unlike a positional cross-match. Over −4 < delta_mag < 0:

the combined efficiency curves agree to a median absolute difference of 0.063 (max 0.292);
the photo-error curves agree to 0.020 dex.

Derivation-level limitations#

classification_eff turns up faintward of delta_mag ≈ 1.75 (0.29 → 0.42 by 2.5). This is small-N noise, and is harmless because the faint clamp (DET_EFF_DELTA_MAX = 1.0) zeroes detection_eff and classification_detection_eff for delta_mag > 1 regardless.

Questions about these files can be addressed to Peter Ferguson.

DELVE

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