LSST

LSST#

LSST is supported by StreamObs.

Available releases#

Current estimation of LSST performances are done using DC2 simulations (expected performances for LSST year 5), and extrapolated for year 1 to 5.

LSST DC2 Survey Files#

More information about the LSST simulations can be found in Pélissier et. all (2026).

The simulated survey#

All quantities are measured from the LSST Dark Energy Science Collaboration Data Challenge 2 (DC2) simulations, a realistic realization of the expected Rubin LSST survey performance based on five years of observations. DC2 contains both truth and measured catalogs, enabling direct characterization of survey selection effects and photometric performance.

The truth catalog contains intrinsic object properties including noiseless magnitudes, positions, and morphological parameters. Galaxies are drawn from the cosmoDC2 catalog while stars are generated from the Galfast Milky Way model. Measured catalogs are produced by passing these objects through the full LSST image simulation and data reduction pipeline, including realistic observing conditions, instrumental effects, object detection, and photometric measurements. Objects in the measured catalog are matched to their truth counterparts through positional associations, allowing direct estimation of photometric uncertainties, detection efficiencies, and classification performance.

Stellar completeness and classification#

The stellar selection function is estimated from matched truth and measured catalogs using the distance to the local magnitude limit, while the classification efficiency measures the fraction of detected stars classified as point sources using the LSST EXTENDEDNESS classifier.

The combined efficiency is the product of the detection and classification efficiencies and is used by StreamObs to probabilistically determine whether injected stars are observed.

Compact galaxies can be incorrectly classified as stars, producing an important contaminant population for stellar-stream analyses.

The galaxy contamination model is derived from true galaxies with

\[ {\rm size\_true} < 0.3\ {\rm arcsec}, \]

for which morphological star-galaxy separation becomes challenging near the survey magnitude limit.

Detection, classification and galaxy misclassification efficiencies

Detection efficiency, stellar classification efficiency, combined stellar efficiency, and galaxy contamination efficiency as a function of distance to the local magnitude limit.

Photometric errors#

Error model for LSST is taken from Tsiane et al.2025. They are derived directly from matched DC2 truth catalogs and are parameterized as a function of distance to the local magnitude limit,

\[ \Delta m_j = m_j - m_{{\rm lim},j}. \]

The photometric scatter increases rapidly near the magnitude limit and approaches a systematic floor of approximately 0.005 mag for bright sources.

LSST photometric error model

Photometric uncertainty as a function of distance to the local magnitude limit. An analytic approximation not used in StreamObs is overlaid on the DC2-derived model.

Survey depth#

Depth maps describe the spatial variation of the LSST 5σ limiting magnitude across the survey footprint.

Magnitude limits are obtained from RubinSim and propagated to StreamObs as HEALPix maps. Survey systematics are modeled through spatial variations in these limiting magnitudes, which drive both photometric uncertainties and selection functions.

LSST depth maps after 1 year of observation LSST depth maps after 4 years of observation

LSST 5σ limiting magnitude maps in the g and r bands for Year 1 and Year 4 survey configurations.

Extinction coefficients#

Dust extinction is modeled using the Schlegel et. all (1998) reddening maps.

The adopted extinction coefficients are

Filter

A_band / E(B−V)

g

3.66

r

2.70

These values are used to compute

\[ A_j = R_j E(B-V), \]

and are applied consistently when generating observed magnitudes.

Using the survey in streamobs#

Configured by config/surveys/lsst_{releases}.yaml, data in data/surveys/lsst_{releases}/:

from streamobs.surveys import SurveyFactory

survey = SurveyFactory.create_survey(
    "lsst",
    release="yr1"
)

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

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

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

Caveats#

  • Selection functions are derived from a limited DC2 calibration region and extrapolated across the full footprint through the local magnitude limit parameterization.

  • Survey systematics are modeled primarily through depth variations; PSF variations are not explicitly included.

  • DC2 simulations corresponds to 5 years of observation with LSST, thus releases up to 10 years of LSST cannot be extrapolated.

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