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| SALT2 | |
|---|---|
| Name | SALT2 |
| Caption | Empirical light-curve model used in supernova cosmology |
| Developer | Joint Supernova Cosmology Project, Supernova Legacy Survey |
| Initial release | 2004 |
| Programming language | Python, C, Fortran (bindings) |
| Domain | Observational cosmology |
SALT2
SALT2 is an empirical spectral–temporal model used to fit Type Ia supernova photometric and spectroscopic time series for distance estimation in observational cosmology. Designed to standardize luminosities of Type Ia supernovae for Hubble-diagram analyses, the model connects rest-frame spectral energy distributions, color variations, and light-curve stretch to produce distance moduli used in measurements of cosmic expansion. SALT2 has been applied extensively by major collaborations and surveys to infer parameters related to dark energy and large-scale structure.
SALT2 was developed within collaborative projects including the Supernova Cosmology Project, the Supernova Legacy Survey, the Sloan Digital Sky Survey-II Supernova Survey, and teams associated with the High-Z Supernova Search Team. Key contributors include members from institutions such as Lawrence Berkeley National Laboratory, the European Southern Observatory, and the Centre de Physique des Particules. The model succeeded earlier empirical formalisms used by projects like the Supernova Cosmology Project and the High-Z Supernova Search Team, building on work by researchers associated with the Carnegie Supernova Project and campaigns at observatories such as Mauna Kea and Cerro Paranal. SALT2 underpins analyses reported by collaborations including the Dark Energy Survey and the Pan-STARRS1 Medium Deep Survey.
SALT2 represents a time-dependent spectral energy distribution constructed as a linear combination of a mean spectral surface and one or more components that capture principal variations observed in samples from telescopes like the Hubble Space Telescope, the Keck Observatory, and the Very Large Telescope. The model parameterizes light-curve shape through a stretch-like parameter and color through a color law that maps extinction-like effects and intrinsic color variations, informed by spectrophotometric sequences from instruments on observatories such as Apache Point Observatory and the Nordic Optical Telescope. Flux predictions are synthesized into broadband magnitudes for filter systems used by surveys including the Sloan Digital Sky Survey, the Dark Energy Survey, and the Pan-STARRS project. The formalism allows simultaneous fitting of multi-band photometry and spectra, enabling cross-calibration with photometric systems developed at institutions like the Institut d'Astrophysique de Paris and the Max Planck Institute for Astrophysics.
Training SALT2 requires heterogeneous training sets combining well-observed local supernovae from projects like the Center for Astrophysics and distant events from surveys such as the Supernova Legacy Survey and the Canada–France–Hawaii Telescope. Calibration steps reference photometric standard systems established by organizations including the National Institute of Standards and Technology and catalogs tied to observatories like Cerro Tololo Inter-American Observatory. The training pipeline accounts for instrumental response, Milky Way reddening using extinction maps produced by the Infrared Science Archive and the Two Micron All Sky Survey teams, and cross-survey offsets characterized by groups at institutions such as the Space Telescope Science Institute. Regular updates to training sets have been driven by data releases from collaborations including the Sloan Digital Sky Survey and the Dark Energy Survey Collaboration.
SALT2-derived distance moduli are central to cosmological parameter estimation in analyses by teams behind the Planck collaboration, the Dark Energy Survey, the Supernova Legacy Survey, and the Joint Light-curve Analysis. Results using SALT2 inform constraints on the cosmological constant, the equation-of-state parameter w studied by experiments such as the Baryon Oscillation Spectroscopic Survey, and the matter density probed by projects like the WiggleZ Dark Energy Survey. Combined probes frequently merge SALT2 distances with observations from the Wilkinson Microwave Anisotropy Probe, the Planck satellite, baryon acoustic oscillation measurements from the Sloan Digital Sky Survey, and weak-lensing results from the Kilo-Degree Survey.
SALT2 performs well across a range of redshifts sampled by surveys like the Supernova Legacy Survey and Pan-STARRS, but systematic uncertainties remain tied to photometric calibration, host-galaxy correlations investigated by groups at the Harvard-Smithsonian Center for Astrophysics, and potential population drift studied by teams from the Max Planck Institute for Astrophysics. The model’s empirical basis limits its extrapolation into spectral regions and epochs underrepresented in training sets, an issue pertinent for high-redshift programs using facilities such as the James Webb Space Telescope and the Roman Space Telescope. Residuals in standardized magnitudes have motivated complementary analyses by the Carnegie Supernova Project and the Nearby Supernova Factory to quantify systematic error budgets.
SALT2 is often compared to models such as MLCS2k2, SiFTO, and empirical templates derived by collaborations like the Carnegie Supernova Project. MLCS2k2, developed by teams associated with the Harvard-Smithsonian Center for Astrophysics, adopts a distinct extinction and light-curve parameterization, while SiFTO, used by the Supernova Legacy Survey, emphasizes color–stretch relations with training drawn from datasets including the Sloan Digital Sky Survey. Bayesian hierarchical models pursued by groups at Princeton University and the University of Chicago offer alternative frameworks for population inference. Comparative studies by international teams evaluate bias propagation into cosmological parameters using datasets from the Dark Energy Survey, Pan-STARRS, and the Supernova Legacy Survey.
SALT2 implementations exist in software packages maintained by collaborations such as SNANA, SNCosmo, and the SuperNova Factory, with codebases leveraging languages and tools developed at institutions like Lawrence Berkeley National Laboratory and the Space Telescope Science Institute. Released model files, training data, and light-curve fitters are distributed alongside survey data releases from the Sloan Digital Sky Survey, the Dark Energy Survey, and the Supernova Legacy Survey, supporting reproducible cosmological analyses used by teams across observatories including Cerro Tololo and Mauna Kea. Data products from SALT2-based analyses feed into public cosmology compilations authored by groups collaborating with the Planck and BOSS teams.