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Stellar Metallicity (log Z/Z_⊙)¶
Stellar metallicity sets the UV-optical SED shape: metal-poor stars are
hotter and bluer (less line blanketing), while metal-rich stars have
stronger absorption features and redder continua. This plot sweeps
met_logzsol from \(-2\) (0.01 \(Z_\odot\)) to \(0.2\)
(1.6 \(Z_\odot\)) at fixed SFH and dust.
from pathlib import Path
import jax
import matplotlib.pyplot as plt
jax.config.update("jax_enable_x64", True)
from tengri import Fixed, Parameters, SEDModel, load_ssp_data
from tengri.analysis.plotting import setup_style, sweep_parameter
setup_style()
def _find_ssp():
"""Find SSP data file in standard locations."""
name = "ssp_prsc_miles_chabrier_wNE_logGasU-3.0_logGasZ0.0.h5"
for p in [
Path("data") / name,
Path("../data") / name,
Path("../../data") / name,
Path("../../../data") / name,
]:
if p.exists():
return str(p)
return None
SSP_PATH = _find_ssp()
if SSP_PATH is None:
raise FileNotFoundError("SSP data not found — skipping example")
ssp = load_ssp_data(SSP_PATH)
# --- Build model: intermediate-age galaxy with modest dust ---
spec = Parameters(
sfh_tsnorm_log_peak_sfr=Fixed(0.5),
sfh_tsnorm_peak_lbt_gyr=Fixed(2.0),
sfh_tsnorm_width_gyr=Fixed(1.0),
sfh_tsnorm_skew=Fixed(0.0),
sfh_tsnorm_trunc=Fixed(8.0),
met_logzsol=Fixed(-0.3), # Will sweep this
dust_tau_bc=Fixed(0.3),
dust_tau_diff=Fixed(0.2),
dust_slope=Fixed(-0.7),
redshift=Fixed(0.1),
)
model = SEDModel(spec, ssp)
# --- Sweep stellar metallicity ---
values = [-2.0, -1.5, -1.0, -0.5, -0.3, 0.0, 0.2]
# # The sweep_parameter helper creates a single SEDModel instance and calls
# # model.predict_rest_sed(...) in a loop. JAX JIT compilation is cached
# # automatically via tengri's persistent compilation cache (enabled at
# # import time), so repeated forward model calls reuse the compiled kernel.
fig, ax = sweep_parameter(
model,
"met_logzsol",
values,
cmap="viridis",
label_fmt=r"$\log Z/Z_\odot$ = {:.1f}",
wave_range=(1000, 12000),
)
ax.set_title(r"Stellar Metallicity: $\log\,Z/Z_\odot$ sweep", fontsize=12)
ax.set_ylabel(r"$\lambda F_\lambda$ (normalized at 5500 Å)")
ax.set_ylim(0, 7.5e4)
plt.tight_layout()
plt.savefig("plot_logzsol_sweep.png", dpi=150, bbox_inches="tight")
plt.show()