--- title: SALT2–B22 Model for SN Ia Light Curves url: https://www.emergentmind.com/topics/salt2-b22-model type: topic --- # SALT2–B22 Model for SN Ia Light Curves The SALT2–B22 Model (commonly referred to as SALT2-B21 or "B21" in accompanying documentation) is the retrained incarnation of the SALT2 spectral energy distribution (SED) model, designed for the empirical characterization of Type Ia supernova (SN Ia) light curves within the Pantheon+ cosmological analysis and related distance-ladder measurements. The model builds upon a critical re-calibration of photometric systems, integrating updated cross-survey zeropoint solutions and the propagation of calibration uncertainties, to tightly constrain both SN Ia distances and cosmological parameters such as the Hubble constant ($H_0$) and dark energy equation of state ($w$) [2112.03864]. ## 1. Model Framework and Functional Form The SALT2–B22 model parameterizes the observed rest-frame SN flux as a function of phase and wavelength as follows: $$ F(p, \lambda) = x_0 \left[ M_0(p, \lambda) + x_1 M_1(p, \lambda) \right] \exp\left(c \cdot CL(\lambda)\right) $$ where: - $p$ is the rest-frame phase (days since $B$-band maximum), - $\lambda$ is the rest-frame wavelength (in Å), - $x_0$ is the overall flux normalization (proportional to distance$^{-2}$), - $x_1$ is the "stretch" or shape parameter, - $c$ is the color parameter (observed $B-V$ at peak), - $M_0(p, \lambda)$ and $M_1(p, \lambda)$ are the mean and first-order SED surfaces, respectively, - $CL(\lambda)$ is the color-law curve in magnitudes. The model thus generalizes the spectral evolution of SNe Ia across both photometric and spectroscopic domains, permitting robust light-curve fitting and cosmological distance estimation. ## 2. SED Surfaces and Color Law Construction $M_0(p, \lambda)$ and $M_1(p, \lambda)$ are constructed as two-dimensional cubic B-splines over a grid with: - $p \in [-20\,\textrm{d}, +50\,\textrm{d}]$ (56 nodes), - $\lambda \in [2000, 9200]$ Å (69 nodes). The knots, spline coefficients, and corresponding arrays (i.e., “M0[i][j]” and “M1[i][j]”) are contained in the HDF5-formatted B21 release files, making the surfaces machine-interpolable at arbitrary $(p, \lambda)$ values. The model is trained using a library of approximately 800 SNe Ia spanning $z \in [0.001, 1.0]$ and incorporates data from diverse photometric systems (25 telescopes/cameras, 105 filters), including Landolt, Sloan, CSP, DES, PS1, SNLS, HST, and others. The color law $CL(\lambda)$ follows the formalism of the B14 release, with $$ x = 1/\lambda \quad (\mu m^{-1}), $$ and a break near $\lambda\sim7000$ Å. Explicitly, \[ CL(\lambda) = \begin{cases} p_0 + p_1(x - x_0) + p_2(x - x_0)^2 + p_3(x - x_0)^3 & x_{\text{min}} \le x < x_{\text{break}} \\ CL(x_{\text{break}}) \left[1+\alpha (x-x_{\text{break}})\right] & x\geq x_{\text{break}} \end{cases} \] The polynomial coefficients ($\{p_0, ..., p_3\}$) and breakpoint parameter $\alpha$ are provided in the release tables; in B21/B22, these coefficients differ from B14 by a few $\times10^{-3}$, reflecting the impact of the retraining. ## 3. Photometric Cross-Calibration and Systematic Propagation A salient improvement over previous releases is the integration of the SuperCal-Fragilistic ("SuperCal") cross-calibration, which simultaneously solves for zeropoint offsets $\Delta m_f$ across all 105 filters, producing a full $105 \times 105$ zeropoint covariance matrix $C_{\text{ZPT}}$. The calibration reference is Pan-STARRS stellar photometry, with recalibration encompassing new updates to the fundamental HST CALSPEC standards (systematic shift $\sim 1.5\%$ over $\Delta\lambda = 4000$ Å). Prior to retraining, each survey’s photometry is shifted by its best-fit $\Delta m_f$. The full covariance in the zeropoints is propagated through the light-curve model retraining in the following manner: - Perform Cholesky decomposition of $C_{\text{ZPT}}$; - Generate $N=9$ correlated zeropoint realizations; - For each, shift training photometry and retrain SALT2 to derive new $M_0$, $M_1$, $CL$; - Fit the reprocessed sample to extract distances $\mu_i^{(k)}$; - Estimate the calibration-induced SN distance covariance via $$ [C_\mu]_{ij} = \sum_{k=1}^9 \left[\mu_i^{(k)} - \mu_i^{(0)}\right]\left[\mu_j^{(k)} - \mu_j^{(0)}\right] $$ This term is then added to the global cosmological covariance matrix, ensuring that calibration systematics are fundamentally coupled to cosmological parameter inference. ## 4. Differences from Previous SALT2 Releases and Performance Metrics Compared to B14 (Betoule et al. 2014) and T21, key differences in SALT2–B22 include: - Mean SED stellar surface ($M_0$) shifts of $\approx 2$–3% tilt over 3000–7000 Å, resulting in a redshift-dependent change in distance modulus ($d\mu/dz$) of 0.04 mag over $0