J/A+A/707/A206 Python Code AMARU (Bernal+, 2026)
Automated model selection for the spectral fitting of large samples of active
galactic nucleus spectra.
Bernal S., Sanchez-Saez P., Arevalo P., Avila F., Bauer F.E.,
Caceres-Burgos P., Lira P., Martinez-Aldama M.L., Sotomayor B.
<Astron. Astrophys. 707, A206 (2026)>
=2026A&A...707A.206B 2026A&A...707A.206B (SIMBAD/NED BibCode)
ADC_Keywords: Models ; Active gal. nuclei ; Spectroscopy
Keywords: methods: data analysis - techniques: spectroscopic - galaxies: active
Abstract:
We developed an algorithm to automatically recommend and selected the
best model to fit active galactic nucleus (AGN) spectra in the
ultraviolet/optical wavelength range, enhancing the efficiency of
fitting large samples of AGN spectra by replacing the visual
inspection and manual selection of the best model.
We employed the Penalized PiXel-Fitting (pPXF) software to fit AGN
spectra using a complete model that includes: narrow and broad
emission lines (NELs and BELs), Balmer continuum, Balmer high-order
emission lines (H8-H50), FeII pseudo-continuum, AGN continuum, and
stellar populations for objects with z<1; we call this model-1. The
fit residuals were analyzed using the discrete wavelet transform
(DWT), looking for deviations in the DWT coefficients above some
empirically determined threshold value. When deviations were detected
in regions of interest of a spectrum (i.e., around Hα, Hβ,
MgII, CIV, and [OIII]λ4959, 5007), the significance of the
residuals and kinematics were used to recommend (or not) the addition
of an extra fitting component for the model. When a new model is
recommended, we compared the new fit and the previous one using the
root-mean-square (RMS) difference and F-test.
The final results of the developed algorithm are the selection of the
best-fit model and the corresponding fit results. We validated the
results of the algorithm using a sample of 800 SDSS AGN spectra. Each
object was fitted using model-1 and the results were visually
inspected by three human validators. The validators also recommended
(or not) the addition of the same additional components that the
algorithm is equipped to recommend.
Comparing the recommendation of each validator and the algorithm, the
median coincidence fraction is 0.83-0.88 for different threshold
values. These values are comparable to the median coincidence fraction
of 0.90 between human validators. The computational time of the model
recommendation routine was <1s for 90 percent of the objects, compared
to ~=60s for visual inspection during the validation exercise.
The presented algorithm is an efficient and effective tool for the
spectral fitting of large samples of AGN spectra with options for
improvement and applications for specific studies.
Description:
We present the novel code for Automatic Multiscale Analysis for
Recommendation of AGN Models, AMARU. The principles have guided
its development:
modularity (it is easy to add new modules, and we encourage and
support such developments, or swap modules modelling the same
component), clarity (the code is easy to use and to understand), and
efficiency (it runs quickly and is parallelised to take advantage of
modern processors with multiple cores), both for the developers and
for the users.
File Summary:
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ReadMe 80 . This file
amaru.tar 512 12038 Python Code AMARU
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Acknowledgements:
Santiago Bernal, sbernal(at)das.uchile.cl
(End) Patricia Vannier [CDS] 25-Feb-2026