Note
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Diffuse ISM Optical Depth (τ_diff)¶
The diffuse ISM attenuation affects all stellar light (not just young stars). Higher τ_diff reddens the optical continuum and weakens the 4000 Å break, a signature of aging stellar populations.
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 SWEEP_CMAPS, 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: typical star-forming galaxy ---
spec = Parameters(
sfh_tsnorm_log_peak_sfr=Fixed(1.0),
sfh_tsnorm_peak_lbt_gyr=Fixed(2.0), # Peak ~2 Gyr ago
sfh_tsnorm_width_gyr=Fixed(1.5),
sfh_tsnorm_skew=Fixed(0.2),
sfh_tsnorm_trunc=Fixed(3.0),
met_logzsol=Fixed(-0.3),
dust_tau_bc=Fixed(0.5), # Modest birth cloud dust
dust_tau_diff=Fixed(0.3), # Will sweep this
dust_slope=Fixed(-0.7), # Calzetti-like
redshift=Fixed(0.1),
)
model = SEDModel(spec, ssp)
# --- Sweep τ_diff ---
values = [0.0, 0.3, 0.7, 1.5, 3.0]
# # 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,
"dust_tau_diff",
values,
cmap=SWEEP_CMAPS["dust"],
label_fmt=r"$\tau_{{diff}}$ = {:.1f}",
wave_range=(1000, 10000),
)
ax.set_ylim(0, 1.5e5)
ax.set_title("Diffuse ISM Dust: Impact on Galaxy Attenuation", fontsize=12)
ax.set_ylabel(r"$\lambda F_\lambda$ (normalized at 5500 Å)")
plt.tight_layout()
plt.savefig("plot_tau_diff_sweep.png", dpi=150, bbox_inches="tight")
plt.show()