Roman HLWAS Survey Files

Roman HLWAS Survey Files#

This page is the data sheet for the three real-footprint Roman High Latitude Wide Area Survey (HLWAS) releases: hlwas_wide, hlwas_medium, and hlwas_all. All three reuse the DC2-derived selection-function tables (:doc:roman_dc2); the only tier-specific product is the F158 magnitude-limit map, built from the official HLWAS exposure-time maps via the exposure-scaled quasi-depth recipe (Option B) documented in :doc:selection_function_methodology. Validation figures are embedded in that methodology page’s Validation & audits section.

Survey tiers#

The HLWAS comprises four nested tiers; streamobs supports three:

Release

Footprint

F158 measured map median

Valid pixels (nside=1024)

Area

hlwas_wide

Wide tier only

26.2842 AB

~1,028,570

~3372 deg²

hlwas_medium

Medium tier only

26.2894 AB

~879,052

~2882 deg²

hlwas_all

All tiers stacked (wide + medium + deep + ultra-deep)

26.2894 AB

~1,792,907

~5878 deg²

F158 wide ≈ medium because the medium tier’s extra depth relative to wide is in other HLWAS bands, not F158 — both tiers have the same median F158 exposure time (~645 s) and therefore nearly identical F158 maglim medians under the recipe. The deep and ultra-deep tiers contribute <0.5% of hlwas_all pixels, so the stacked median stays at the wide/medium level (their deeper pixels appear locally in the map but do not shift footprint-wide statistics).

Exposure-time maps#

Exposure-time maps (F158, healsparse .hsp, nside=4096) are sourced from the spacetelescope/roman_notebooks repository (map_HLWAS-wide_F158.hsp, map_HLWAS-medium_F158.hsp, map_HLWAS-all_F158.hsp). Values are total on-sky integration time in seconds (accounting for the planned dither/pass strategy) and are used directly — no unit conversion or per-tier normalisation. The F158 exposure time is quantised in ~107.5 s single-exposure units.

Quasi-depth maps (Option B)#

The maglim maps apply the exposure-scaled quasi-depth recipe (derivation and rationale in :doc:selection_function_methodology):

depth(pix) = DC2_REF_DEPTH + 1.25 * log10( t(pix) / DC2_REF_EXPTIME )

  • DC2_REF_DEPTH ≈ 26.375 AB — median of the DC2 F158 truth-anchored maglim map (roman_dc2_maglim_f158_nside1024.fits.gz), read from the file at runtime (not hardcoded).

  • DC2_REF_EXPTIME = 770.0 s — the DC2 HLIS reference per-pixel exposure time (5.5 dithers × 140 s; Troxel et al. 2023, Sec. 3.1).

Option B anchors all tiers to the same DC2 truth-anchored reference (not to per-tier STScI ETC depths of mixed vintage), so the maps land exactly on the DC2-relative delta_mag convention the shared tables need, and inter-tier comparisons are self-consistent. The HLWAS medians (~26.28–26.29) sit slightly below the DC2 reference because the typical HLWAS exposure (~645 s) is shorter than the DC2 reference (770 s): 26.375 + 1.25 × log10(645/770) ≈ 26.28.

Measured map medians#

Release

t_median (s)

DC2_REF_DEPTH

DC2_REF_EXPTIME

measured median (AB)

hlwas_wide

645.1

26.375

770.0

26.2842

hlwas_medium

645.1

26.375

770.0

26.2894

hlwas_all

645.1

26.375

770.0

26.2894

Maps are written at nside=1024 (RING, float32) to match the DC2 maps, by scripts/roman/build_hlwas_maglim_maps.py, to data/surveys/roman_hlwas_<tier>/ (gitignored). For reference, the STScI community-defined HLWAS median 5σ point-source depths are F158 26.2 (wide) and 26.4 (medium); these differ from F158-only because the medium tier’s reported depth includes additional bands.

Selection-function tables#

The completeness and photo-error tables are identical to roman_dc2 and live under data/surveys/roman_dc2/; the build script symlinks them into each tier’s data directory so the loader finds them at the default path:

File

Description

roman_stellar_efficiency_cutf158.csv

F158 detection + classification efficiency vs delta_mag

roman_photoerror_f158_catalog.csv

Median reported magerr vs delta_mag (S/N cut)

roman_photoerror_f158.csv

Truth-based scatter of (obs − true) vs delta_mag (noise draw)

See :doc:roman_dc2 for these products and :doc:selection_function_methodology for how they are derived.

Configuration#

Each tier is a YAML in config/surveys/: roman_hlwas_wide.yaml, roman_hlwas_medium.yaml, roman_hlwas_all.yaml (releases hlwas_wide, hlwas_medium, hlwas_all). All use F158 only (F106/F129 HLWAS maps are not yet built; F184 is excluded per the DC2 documentation). Extinction coefficients and the saturation threshold are copied from roman_dc2.yaml (same instrument). The delta_saturation (= saturation − map median) is keyed to each tier’s measured median:

Release

map_median (AB)

delta_saturation

hlwas_wide

26.2842

−9.2842

hlwas_medium

26.2894

−9.2894

hlwas_all

26.2894

−9.2894

The column namespace per release is {name}_{release}: roman_hlwas_wide, roman_hlwas_medium, roman_hlwas_all.

Using the surveys in streamobs#

from streamobs.surveys import Survey

survey_wide = Survey.load('roman', release='hlwas_wide')
maglim = survey_wide.get_maglim('F158', pixel=123456)
eff    = survey_wide.get_completeness('F158', mag=25.0, maglim=maglim)
err    = survey_wide.get_photo_error('F158', magnitude=25.0, maglim=maglim)

Regenerating the maps#

conda activate streamobs
python scripts/roman/build_hlwas_maglim_maps.py

This reads the healsparse exposure-time maps, reads DC2_REF_DEPTH from the DC2 F158 maglim map at runtime, applies the Option B recipe, writes the three nside=1024 maglim maps, and symlinks the DC2 CSV files into each tier’s data directory.

Caveats#

  • The quasi-depth recipe assumes photon-noise-limited exposures and a uniform PSF; real HLWAS depths vary with detector gaps, background, and read noise in the shortest exposures.

  • The DC2-derived completeness/photo-error tables characterise the deep DC2 reference simulation; their use for HLWAS relies on the selection function depending on magnitude only through delta_mag = mag − maglim.

  • The hlwas_all map includes deep/ultra-deep pixels (t up to ~23,600 s; local maglim up to ~29.5 AB) where the shallow DC2 selection function may be unreliable.

  • ETC-derived or truth-anchored depth maps for the real HLWAS are a future upgrade that will supersede this quasi-depth approach.

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