Line Forest¶
Line Forest measures equivalent widths and absolute line strengths for a comprehensive set of spectral lines in BOSS spectra. It uses trained neural network models to predict line properties from windowed spectral regions.
What it does¶
Line Forest provides measurements for individual spectral lines including:
Equivalent width
Absolute line strength
Detection significance
Uncertainty estimates (as percentiles from Monte Carlo resampling)
How it works¶
Spectral preparation: The observed spectrum is converted to log10(flux) space. Bad pixels (non-finite, negative, or high-error) are cleaned: flux values are set to 1 and uncertainties are capped at 5 times the median error.
Line measurement: For each spectral line in the target list:
A window centered on the line (converted from air to vacuum wavelength) is extracted and resampled onto a uniform grid of 128 steps using spline interpolation.
The windowed spectrum is passed through a pre-trained TensorFlow neural network model that predicts equivalent width, absolute strength, and a detection statistic.
Monte Carlo uncertainties: The measurement is repeated 100 times with Gaussian noise added to the spectrum (scaled by the flux uncertainty). The distribution of measurements across these realizations provides percentile-based uncertainties (16th, 50th, 84th percentiles).
Detection filtering: A line is only reported if:
The detection statistic exceeds 0.5 in absolute value (initial detection).
The detection rate across Monte Carlo realizations exceeds 30% (
detection_raw > 0.3).
Two model types: Lines use one of two neural network models depending on window size:
hlines.model: Used for broader lines (200 A windows), including Balmer and Paschen series hydrogen lines, Ca H&K.zlines.model: Used for narrower lines (50 A windows), including metal lines and helium lines.
Lines measured¶
Line Forest measures lines from the following species and series:
Hydrogen Balmer series¶
H-alpha (6562.8 A), H-beta (4861.3 A), H-gamma (4340.5 A), H-delta (4101.7 A), H-epsilon (3970.1 A), H-8 through H-17 (3889–3697 A)
Hydrogen Paschen series¶
Pa-7 (10049.5 A), Pa-8 (9546.1 A), Pa-9 (9229.1 A), Pa-10 through Pa-17 (9015–8467 A)
Calcium¶
Ca II triplet (8498.0, 8542.1, 8662.1 A), Ca K (3933.7 A), Ca H (3968.5 A)
Helium¶
He I (4471.5, 5015.7, 5875.6, 6678.2 A), He II (4685.7 A)
Other species¶
N II (6548.1, 6583.5 A), S II (6716.4, 6730.8 A), Fe II (5018.4, 5169.0, 5197.6, 6432.7 A), O I (5577.3, 6300.3, 6363.8 A), O II (3727.4 A), O III (4363.9, 4958.9, 5006.8 A), Li I (6707.8 A)
Output fields¶
For each line X (e.g., h_alpha, ca_ii_8662, li_i):
Field |
Description |
|---|---|
|
Equivalent width (Angstroms; negative = emission, positive = absorption) |
|
Absolute line strength |
|
Detection statistic from the neural network (values > 0.5 indicate detection) |
|
Fraction of Monte Carlo realizations where the line was detected |
|
16th, 50th, 84th percentile of equivalent width from Monte Carlo |
|
16th, 50th, 84th percentile of absolute strength from Monte Carlo |
Fields are null when the line is not detected or falls outside the spectral coverage.
Key caveats¶
Line Forest requires TensorFlow and pre-trained neural network models. The models were trained on BOSS spectra and may not generalize to spectra from other instruments.
The detection threshold (|detection_stat| > 0.5 and detection_raw > 0.3) is a heuristic. Marginal detections should be treated with care.
Wavelengths in the line list are given in air; they are converted to vacuum internally using the Ciddor (1996) formula.
Some lines (particularly high-order Balmer and Paschen lines) may be blended. The neural network approach captures the blended profile, but the reported equivalent widths may not correspond to isolated single-line measurements.
Lines near the edges of the BOSS wavelength coverage (especially the blue end below 3700 A and the red end beyond 10000 A) may have degraded S/N and less reliable measurements.