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A sensitivity analysis of the modeling of Polycyclic Aromatic Hydrocarbon emission in galaxies

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that PAH fitting choices alter derived galaxy PAH properties by 15-20%, and that the optimal configuration—pure PAHs from the v4.00-alpha library without a redshift—yields the most accurate average values, f_i = 0.54 and…

desk verdict A careful sensitivity analysis of PAHdb modeling choices; the descriptive results and template library are solid, but the headline absolute PAH properties inherit the prior used to pick the 'optimal' configuration. read the letter →

arxiv 2412.01875 v1 pith:ETCO4SQI submitted 2024-12-02 astro-ph.GA

Alexandros Maragkoudakis , Christiaan Boersma , Pasquale Temi , Jesse D. Bregman , Louis J. Allamandola , Vincent Esposito , Alessandra Ricca , Els Peeters This is my paper · ORCID
classification astro-ph.GA
keywords PAHemissionaromaticinfraredbandsmid-infraredspectroscopygalaxyspectraldecompositiontemplatesPAHdbSpitzer-IRSJWSTMIRI-MRS
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

PAHs—polycyclic aromatic hydrocarbons, the carbon molecules that emit the aromatic infrared bands in galaxy spectra—are characterized by fitting their mid-infrared emission with a library of computed spectra. This paper tests how much the derived PAH population properties (ionization state, size, nitrogen content) depend on the modeling choices in that fitting, varying each choice one at a time. The largest swings, 15-20%, come from whether a redshift is applied to simulated band positions and from which spectral library version is used; the new v4.00-alpha library fits the full 6-15 µm spectrum and halves the average fitting uncertainty. The paper argues the optimal configuration is pure PAHs with no redshift, yielding an average galaxy PAH ionization fraction of $f_i = 0.54$ and average size of $N_C = 68$. If true, previously reported PAH trends remain valid under a linear rescaling, and the accompanying template library gives galaxy SED modelers a calibrated PAH component.

What carries the argument

The machinery is library fitting: quantum-chemically computed PAH absorption spectra are converted into emission by a cascade emission model (or a simplified calculated-temperature model), convolved with Gaussian or Lorentzian profiles at a chosen FWHM, optionally shifted by a 15 cm$^{-1}$ redshift, and fit to the PAH emission spectrum isolated by the PAHFIT decomposition. The load-bearing change is the v4.00-$\alpha$ library content, especially the newly added large, irregularly edged PAHs, which for the first time lets the fit reproduce the blue side of the 6.2 µm band and the 10-15 µm region, shifting the recovered charge and size balance. Sensitivity is quantified by Monte Carlo perturbing each spectrum 1000 times and comparing derived parameters between runs using average ratios, linear regression slopes, and Pearson correlations, together with the fitting uncertainty $\sigma_{\rm PAHdb}$.

What would settle it

Record laboratory gas-phase emission spectra of PAHs with known size and charge state across 6-15 µm and run them through the same PAHFIT-to-library-fitting pipeline; if the recovered $f_i$ and $N_C$ differ systematically from the known values, or if the lab spectra display the 15 cm$^{-1}$ anharmonic shifts that the optimal configuration omits, the claimed optimal configuration and the values $f_i = 0.54$ and $N_C = 68$ are falsified.

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Extended reading notes

Core claim

This paper establishes that the average PAH population properties reported for galaxies are not uniquely determined by the data; they shift by 15-20% under plausible modeling choices, with the largest changes caused by the choice of applying a 15 cm$^{-1}$ redshift and by the content of the spectral library. With the v4.00-$\alpha$ library, which adds large irregularly edged PAHs and nitrogen-substituted PAHs, the full 6-15 µm PAH spectrum can be modeled, including the complete 6.2 µm band that earlier library versions under-fitted; this alone halves the average fitting uncertainty. The authors select pure PAHs without a redshift as the optimal configuration, based on consistency with established band-charge assignments and with galactic nitrogen abundances, and report average values of $f_i = 0.54$ and $N_C = 68$. They also show that although each configuration changes the derived numbers, the changes follow a linear scaling, so previously reported PAH trends remain qualitatively valid.

Load-bearing premise

The load-bearing premise is that the computed spectra in the v4.00-alpha library, converted to emission with the cascade model using Gaussian profiles and no redshift, faithfully represent true astronomical PAH emission across 6-15 µm; the paper checks this with fit residuals and band-charge consistency rather than against independent laboratory emission spectra or known ground truth.

Editorial extensions

If this is right

  • Previously published PAH parameter trends, such as ionization and size differences between environments, remain qualitatively valid despite the shift in absolute values.
  • The delivered template library, parameterized by $f_i$ and $N_C$, can be plugged into galaxy SED models as a calibrated PAH emission component.
  • PAHdb fitting of JWST MIRI-MRS and Spitzer-IRS spectra gives consistent derived parameters, so results transfer across spectral resolutions.
  • The average galaxy PAH population is now characterized as substantially ionized ($f_i = 0.54$) and larger ($N_C = 68$) than the earlier values of $f_i = 0.36$ and $N_C = 55$.
  • The ~7% difference in neutral PAH fraction caused by using PAHFIT versus CAFE decomposition indicates that code choice contributes a systematic uncertainty to PAH property measurements.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the absolute values depend on library completeness, future additions to the spectral library may shift $f_i$ and $N_C$ again; the linear-scaling result suggests that relative galaxy-to-galaxy comparisons are the more stable scientific output.
  • The no-redshift conclusion is argued for the 6-15 µm region; applying it to the 3.3 µm band, where anharmonic effects are stronger, is an untested extrapolation beyond this paper.
  • A natural testable extension is to run the optimal configuration on a larger JWST sample decomposed with both PAHFIT and CAFE, quantifying how the code-induced ~7% neutral-fraction variation depends on galaxy type and radiation field.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This manuscript conducts a sensitivity analysis of PAHdb-based modeling of mid-infrared PAH emission in galaxies. Using a representative subset of 147 Spitzer-IRS galaxies from Paper I, it takes a base-run configuration (PAHdb v3.20, cascade emission model, 8 eV excitation, Gaussian profiles with FWHM 15 cm^-1, and a 15 cm^-1 redshift) and varies one element at a time: FWHM (10 cm^-1), line profile (Lorentzian), emission model (calculated temperature), redshift (omission), library version (v4.00-alpha), spectral resolution (JWST MIRI-MRS versus Spitzer-IRS, including smoothed/resampled JWST data), and decomposition code (PAHFIT versus CAFE). It reports that omitting the redshift improves fits by about 13%, that the v4.00-alpha library eliminates the long-standing under-fitting of the 6.2 um band and reduces average fitting uncertainty by roughly a factor of two, that the optimal configuration is pure PAHs with no redshift, and that this configuration gives average galaxy PAH properties of fi = 0.54 and NC = 68. It also finds that parameter variations across configurations follow linear scaling, so previously published PAH trends remain qualitatively valid, and it delivers a new library of galaxy PAH emission templates parameterized by excitation energy, NC, and fi.

Significance. The paper's main strength is its systematic mapping of configuration-induced variance. The 147-galaxy representative sample, 1000 Monte Carlo perturbations per spectrum, regression/scatter metrics for every configuration, and the public template library are concrete and reproducible deliverables. The demonstration that v4.00-alpha removes the 6.2 um fitting deficit and that redshift omission improves fits by ~13% is robust and directly useful. The linear-scaling result is important because it preserves the qualitative validity of earlier PAH trends based on v3.20 modeling. The weaker part is the absolute calibration: the optimal Case 4 configuration is selected using astrophysical priors, not by a statistically significant improvement in fit, and the headline values fi = 0.54 and NC = 68 inherit that choice. The paper is therefore best read as a sensitivity map plus a model-dependent update, rather than as a definitive new measurement of the average PAH population properties in galaxies.

major comments (3)
  1. [§4.3.1, Fig. 11, Table 4] The selection of Case 4 (pure PAHs, no redshift) as the 'optimal' configuration is not established by the fit statistics. The text states in Section 4.3.1 that 'the vast majority of galaxies are very well fitted under all four configurations with sigma_PAHdb < 0.1,' and Figure 11 shows substantial shifts in the mean of every derived parameter across Cases 1-4, with conclusion (vi) itself labeling this a fitting degeneracy. Case 4 is adopted because its per-band charge breakdown 'better represents that established in earlier work' and because the high PANH fractions in Cases 1-2 conflict with galactic nitrogen abundances; these are astrophysical priors, not data-driven discriminants. Consequently, the uncertainties quoted in Table 4 (e.g., fi = 0.54 +/- 0.10, NC = 68 +/- 4.6) are Monte Carlo fitting uncertainties within one configuration and do not include the spread across the near-equally good configurations shown in Figure 11. I request either a statistical model-comparison criterion that identifies Case 4 from the data, or a systematic error term derived from the configuration spread, together with a softening of the 'most accurate and robust determination' claim in Section 4.4 and the abstract.
  2. [§3.2.4, §4.4] The absolute values fi = 0.54 and NC = 68 depend on the unvalidated assumption that the PAHdb v4.00-alpha DFT spectra, processed with the cascade emission model, Gaussian profiles, and no redshift, faithfully represent the astronomical PAH emission spectrum over 6-15 um, including the 6.2 um band and the 10-15 um region populated by the newly added large irregular PAHs. The paper validates this mainly through reduced fit residuals and consistency with prior band-charge assignments, but it does not provide independent laboratory emission spectra, an observational ground-truth sample, or a test showing that the four configurations are distinguishable on the data (indeed Section 4.3.1 states the opposite). If the library is incomplete or systematically biased at these wavelengths, both the optimal configuration and the derived averages would shift. Please either add such an external validation or explicitly state in the abstract and Section 4.4 that fi = 0.54 and NC = 68 are conditional on the v4.00-alpha library and the adopted priors.
  3. [§4.4, Table 3] The comparison between the v3.20 base run and v4.00-alpha† in Table 4 changes three modeling choices simultaneously (library version, redshift, and PANH content), so the improvement in sigma_PAHdb from 0.28 to 0.16 and the parameter shifts cannot be attributed to the library content alone. Table 3 shows that v4.00-alpha with the base-run configuration already reduces sigma_PAHdb to about 0.56 of the v3.20 value, and the additional role of the redshift/PANH choices within v4.00-alpha is comparatively small. Please separate the library effect from the configuration effect when interpreting the origin of the improvement, for example by presenting all four v4.00-alpha configurations against the v3.20 base run in the same table.
minor comments (5)
  1. [Section 6, item (v)] There is a typo in conclusion (v): 'the the 6.2 µm PAH band' should read 'the 6.2 µm PAH band.'
  2. [Tables 5 and 6] The PAHdb uncertainties quoted for the JWST fits (e.g., +/- 0.0001) are Monte Carlo fitting uncertainties and are orders of magnitude smaller than the ~5-10% systematic differences between JWST, JSR, and IRS data and between PAHFIT and CAFE; please label them as fitting-only uncertainties to avoid overinterpreting their precision.
  3. [§4.3.1, Fig. 12] Figure 12 presents the per-band charge and composition breakdowns for a single galaxy (IRAS 05129+5128) as the basis for the sample-wide statements about Cases 1-4; please add sample-averaged breakdowns or an explicit statement of how representative this example is.
  4. [§4.2, Fig. 6] In the Lorentzian comparison, the fpure regression has slope 1.12 and intercept -0.121, so the offset is non-negligible at low fpure values; the text states that the composition is consistent between the two runs and would benefit from a comment on this systematic offset.
  5. [§3.2.4, Table 2] Case 3 is described in Table 2 as 'pure PAHs + v3.20 PANHs,' while the text defines it as 'keeping only the v3.20 PANHs by excluding the ~2000 newly added ones'; please make the table entry self-explanatory.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the sensitivity analysis compares independent fits against a fixed base run, and the Case 4 selection is prior-driven model choice rather than a self-referential derivation.

full rationale

This paper is a controlled sensitivity study rather than a derivation that presupposes its conclusions. Each modeling choice (FWHM, line profile, emission model, redshift, library version, spectral resolution, decomposition code) is evaluated empirically against a fixed v3.20 base run using Monte Carlo perturbed fits, and the reported scale factors in Table 3 are measured ratios between independent fits. The redshift comparison is an empirical goodness-of-fit result: omitting the 15 cm^-1 redshift reduces the average fitting uncertainty by about 13%, as shown in Figure 8 and Section 4.2. The selection of Case 4 as 'optimal' in Section 4.3.1 is based on two things: the measured fit improvement from omitting redshift, and consistency with external spectroscopic priors on PAH band-charge assignments (11.2 um neutral, 6-9 um cationic) and with galactic nitrogen abundances. These priors are qualitative constraints used to break fitting degeneracies; they are not the target quantities fi=0.54 and NC=68. Those target quantities are outputs of the selected fits, not inputs encoded by construction. The PAHdb v4.00-alpha spectra are DFT-computed in prior work by coauthors, but they are not fitted to the galaxy sample, and the reported improvement in matching the 6.2 um band is a direct residual comparison against the observed spectra. No equation is shown to equal its own input, and no fitted parameter is relabeled as an independent prediction. The legitimate concern raised by the paper itself—that all four configurations fit most galaxies well and expose fitting degeneracies—is a model-selection and systematic-error caveat, not circularity. Therefore the circularity score is 0.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the physical attribution of AIBs to PAH molecules, on the adequacy of DFT-computed spectra and the cascade emission model, and on prior band-charge assignments used to define the 'optimal' configuration. There are no newly invented entities. The modeling choices (FWHM, redshift, excitation energy, emission model, line profile, library composition) are treated as parameters; the sensitivity analysis varies them, but the final recommended configuration is selected in part using external priors.

free parameters (6)
  • PAH band FWHM = 15 cm^-1 (base), 10 cm^-1 (variant)
    Gaussian line-profile width in PAHdb fits; chosen from prior lab measurements, varied to test sensitivity (Section 3.2.1).
  • Anharmonic redshift = 15 cm^-1 (base), 0 cm^-1 (variant)
    Applied to calculated band positions to mimic anharmonic/hot-band effects; omission changes derived parameters by 15-20% and improves fits (Section 3.2.3).
  • Excitation photon energy = 8 eV
    Base run excitation energy; a range 6-12 eV was explored in Paper I, fixed here (Table 1).
  • Emission model = cascade (base), calculated temperature (variant)
    Choice of radiative relaxation treatment; calculated temperature model changes neutral/cation fractions dramatically (Section 3.2.2).
  • Line profile shape = Gaussian (base), Lorentzian (variant)
    Functional form of PAH bands; affects size breakdown and scatter (Section 3.2.1).
  • PAH library composition = v3.20 vs v4.00-alpha, with/without PANHs
    Database content determines which molecules can enter the fit; v4.00-alpha changes derived fi and NC substantially (Sections 3.2.4, 4.3).
assumptions (5)
  • domain assumption Aromatic infrared bands in galaxies are produced by fluorescent emission from PAHs and related species.
    Foundational attribution cited from Leger & Puget 1984 and Allamandola et al. 1985; all fitting in this paper assumes this.
  • domain assumption DFT-computed PAH absorption spectra in PAHdb, converted to emission with the cascade model, faithfully represent astronomical PAH emission.
    The database fitting method requires this; the paper validates through fit residuals only, not independent laboratory emission spectra.
  • domain assumption PAH band shapes are described by Gaussian or Lorentzian profiles with FWHM 10-15 cm^-1.
    Adopted in Section 3.2.1 based on prior laboratory measurements of small PAHs (Peeters et al. 2004).
  • domain assumption The 11.2 um band is predominantly neutral PAH and the 6-9 um bands predominantly cationic PAH.
    Used in Section 4.3.1 to reject configurations that violate this assignment; this prior is itself informed by earlier PAH modeling and laboratory work.
  • domain assumption Nitrogen abundances in galaxies are low, so PANHs should contribute little to galaxy PAH emission.
    Used in Section 4.3.1 to justify excluding nitrogenated PAHs from the optimal configuration; the paper notes cyano-PAH detections exist but argues galaxy-wide PANH fractions are unconstrained.

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Cite this review

Pith. "Pith review of A sensitivity analysis of the modeling of Polycyclic Aromatic Hydrocarbon emission in galaxies." pith.science (2026). https://pith.science/paper/ETCO4SQI

@misc{pith2026241201875,
  author       = {Pith},
  title        = {Pith review of: A sensitivity analysis of the modeling of Polycyclic Aromatic Hydrocarbon emission in galaxies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ETCO4SQI}},
  note         = {Machine review of arXiv:2412.01875}
}
abstract

We have conducted a sensitivity analysis on the mid-infrared spectral decomposition of galaxies and the modeling of the PAH emission spectrum with the NASA Ames PAH Infrared Spectroscopic Database (PAHdb) to assess the variance on the average galaxy PAH population properties under a grid of different modeling parameters. We find that the SL and SL+LL Spitzer-IRS decomposition with PAHFIT provides consistent modeling and recovery of the 5-15 $\mu$m PAH emission spectrum. For PAHdb modeling, application of a redshift to the calculated spectra to account for anharmonic effects introduces a $15\%$-$20\%$ variance on the derived parameters, while its absence improves the fits by $\sim13\%$. The 4.00-$\alpha$ release of PAHdb achieves the complete modeling of the 6-15 $\mu$m PAH spectrum, including the full 6.2 $\mu$m band, improving the average fitting uncertainty by a factor of 2. The optimal PAHdb modeling configuration requires selection of pure PAHs without applying a redshift to the bands. Although quantitatively the PAHdb-derived parameters change under different modeling configurations or database versions, their variation follows a linear scaling, with previously reported trends remaining qualitatively valid. PAHdb modeling of JWST observations, and JWST observations smoothed and resampled to the Spitzer-IRS resolution and dispersion have consistent PAHdb derived parameters. Decomposition with different codes, such as PAHFIT and CAFE, produce PAH emission spectra with noticeable variation in the 11-15~$\mu$m region, driving a $\sim7\%$ difference in the neutral PAH fraction under PAHdb modeling. A new library of galaxy PAH emission templates is delivered to be utilized in galaxy SED modeling.

Figures

Figures reproduced from arXiv: 2412.01875 by the authors.

Figure 2
Figure 2. pahfit decomposition of the combined SL+LL Spitzer -IRS spectrum of galaxy SSGSS-016. The fit (orange line) is synthesized using the following components: Dust features (light blue lines), atomic and H2 lines (magenta lines), continuum (yellow lines; the total continuum emis￾sion is shown as a purple line), and attenuation (dashed black line) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 1
Figure 1. Top panel: Representation of the sample in this work (orange points) in the galaxy main-sequence plane (SFR – M∗) with respect to the parent sample (blue points) defined in Maragkoudakis et al. (2022). Middle and bottom panel: The I6.2/I11.2 and I7.7/I11.2 PAH intensity ratio dis￾tributions respectively, of the current (orange) and parent (blue) samples, showing a good representation of the parent distributions by t… view at source ↗
Figure 3
Figure 3. Comparison of the SL and SL+LL spectra (shown in blue and red lines, respectively) of the mod (left panel) and obs (right panel) PAH spectral methods (see Section 3.1). The plotted SL+LL spectra are confined in the 5–20 µm range to aid comparison. 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 IPA H6.2/IPA H11.2 (S L + L L) rxy = 0.83 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 IPAH6.2/IPAH11.2 (SL) 0 1 2 ratio ratio… view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: Comparison of pahfit recovered PAH band strength ratios (6.2/11.2 µm, left panel; 7.7/11.2 µm, right panel) when modeling the SL+LL and SL-only spectra of galaxies. Dashed lines are lines of equality. The Pearson’s correlation coefficient rxy, along with the average ra…
Figure 5
Figure 5. Figure 5: Comparison of the PAHdb-derived PAH properties in the base run (x-axis) and the Gaussian FWHM 10 cm−1 emission profile run (y-axis). Top row: neutral PAH fraction (left), cation PAH fraction (middle), anion PAH fraction (right); Middle row: small PAH fraction (left), p…
Figure 6
Figure 6. Figure 6: Comparison of the PAHdb-derived PAH properties in the base run (x-axis) and the Lorentzian emission profile run (y-axis). Top row: neutral PAH fraction (left), cation PAH fraction (middle), anion PAH fraction (right); Middle row: small PAH fraction (left), pure PAH fra…
Figure 7
Figure 7. Figure 7: Comparison of the PAHdb-derived PAH properties in the base run (x-axis) and the calculated temperature emission model run (y-axis). Top row: neutral PAH fraction (left), cation PAH fraction (middle), anion PAH fraction (right); Middle row: small PAH fraction (left), pu…
Figure 8
Figure 8. Figure 8: Comparison of the PAHdb-derived PAH properties in the base run (x-axis) and when modeling without redshift application (y-axis). Top row: neutral PAH fraction (left), cation PAH fraction (middle), anion PAH fraction (right); Middle row: small PAH fraction (left), pure …
Figure 9
Figure 9. Figure 9: Comparison between the PAHdb v3.20 (left panel) and PAHdb v4.00-α library version (right panel) modeling of the 6–15 µm PAH spectrum of galaxy IRAS 05129+5128. A considerable improvement of the modeling of the 6.2 µm feature is now achieved with the v4.00-α library. ri…
Figure 10
Figure 10. Figure 10: Comparison of the PAHdb-derived PAH properties in the base run (x-axis) and modeling with PAHdb v4.00-α library version (y-axis) under the same configuration as in the base run. Top row: neutral PAH fraction (left), cation PAH fraction (middle), anion PAH fraction (ri…
Figure 11
Figure 11. Figure 11: The distributions of the characteristics of the PAH population derived using the PAHdb v4.00-α library for different sets of parameters (Cases 1-4; Section 3.2.4). The PAHdb v3.20 average values (vertical dashed line) and standard deviations (gray area) are shown for …
Figure 12
Figure 12. Figure 12: The PAH charge (left column) and composition (right column) breakdown for galaxy IRAS 05129+5128 under different PAHdb v4.00-α library modeling configurations. Cases 1–4 (Section 3.2.4) are shown in succession starting from the top row [PITH_FULL_IMAGE:figures/full_f…
Figure 13
Figure 13. Figure 13: MIR decomposition with pahfit and PAHdb v4.00-α for the JWST MIRI-MRS and the JWST smoothed and resampled (JSR) observations of galaxies IRAS F09111-1007 (top) and IRAS 09022-3615 (bottom). In the JWST spectra, emission lines were removed prior to pahfit fitting. Fitt…
Figure 14
Figure 14. Figure 14: MIR decomposition with cafe and PAHdb v4.00-α for the JWST MIRI-MRS spectra of galaxies IRAS F09111-1007 (top row) and IRAS 09022-3615 (bottom row) [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]
Figure 15
Figure 15. Figure 15: Comparison of the recovered PAH emission spectrum for galaxies IRAS F09111-1007 (left) and IRAS 09022-361 (right), from the decomposition of their JWST MIRI-MRS observations with pahfit and cafe codes. 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Normalized intensity 1e 2 fi=0.23, NC=…
Figure 16
Figure 16. Figure 16: Example of the new PAHdb 4.00-α library PAH emission templates. Left: Templates at fixed NC and varying fi. Right: Templates at fixed fi and varying NC . The spectra have been normalized (in Fν units) on the total integrated flux. The bottom panels show the spectral d…
Figure 17
Figure 17. Figure 17: MIR decomposition with pahfit and PAHdb v4.00-α for the Spitzer -IRS observations of galaxies IRAS F09111-1007 (top) and IRAS 09022-3615 (bottom). smoothed and resampled spectra presented in Section 4.5 [PITH_FULL_IMAGE:figures/full_fig_p023_17.png]

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Reference graph

Works this paper leans on

69 extracted references · 12 canonical work pages · cited by 1 Pith paper

  1. [1]

    2023, A&A, 677, L13, doi: 10.1051/0004-6361/202347524

    Ag´ undez, M., Marcelino, N., Tercero, B., & Cernicharo, J. 2023, A&A, 677, L13, doi: 10.1051/0004-6361/202347524

    doi:10.1051/0004-6361/202347524 2023

  2. [2]

    1996, A&A, 305, 602

    Allain, T., Leach, S., & Sedlmayr, E. 1996, A&A, 305, 602

    work page 1996

  3. [3]

    J., Hudgins, D

    Allamandola, L. J., Hudgins, D. M., & Sandford, S. A. 1999, ApJ, 511, L115, doi: 10.1086/311843

    doi:10.1086/311843 1999

  4. [4]

    J., Tielens, A

    Allamandola, L. J., Tielens, A. G. G. M., & Barker, J. R. 1985, ApJL, 290, L25, doi: 10.1086/184435

    doi:10.1086/184435 1985

  5. [5]

    Bakes, E. L. O., & Tielens, A. G. G. M. 1994, ApJ, 427, 822, doi: 10.1086/174188

    doi:10.1086/174188 1994

  6. [6]

    Bakes, E. L. O., Tielens, A. G. G. M., & Bauschlicher, Charles W., J. 2001, ApJ, 556, 501, doi: 10.1086/321501

    doi:10.1086/321501 2001

  7. [7]

    W., Boersma, C., Ricca, A., et al

    Bauschlicher, Jr., C. W., Boersma, C., Ricca, A., et al. 2010, ApJS, 189, 341, doi: 10.1088/0067-0049/189/2/341

    doi:10.1088/0067-0049/189/2/341 2010

  8. [8]

    Allamandola, L. J. 2018, ApJS, 234, 32, doi: 10.3847/1538-4365/aaa019

    doi:10.3847/1538-4365/aaa019 2018

Show all 69 references
  1. [9]

    Boersma, C., Bregman, J., & Allamandola, L. J. 2014a, ApJ, 795, 110, doi: 10.1088/0004-637X/795/2/110 —. 2015, ApJ, 806, 121, doi: 10.1088/0004-637X/806/1/121 —. 2018, ApJ, 858, 67, doi: 10.3847/1538-4357/aabcbe

    2015 doi

  2. [10]

    D., & Allamandola, L

    Boersma, C., Bregman, J. D., & Allamandola, L. J. 2013, ApJ, 769, 117, doi: 10.1088/0004-637X/769/2/117

    2013 doi

  3. [11]

    D., Allamandola, L

    Boersma, C., Bregman, J. D., Allamandola, L. J., Temi, P., & Maragkoudakis, A. 2024, ApJ, 975, 177, doi: 10.3847/1538-4357/ad7d08 24 Maragkoudakis et al

    2024 doi

  4. [12]

    W., Ricca, A., et al

    Boersma, C., Bauschlicher, Jr., C. W., Ricca, A., et al. 2014b, ApJS, 211, 8, doi: 10.1088/0067-0049/211/1/8

    doi

  5. [13]

    D., Levy, R

    Bolatto, A. D., Levy, R. C., Tarantino, E., et al. 2024, ApJ, 967, 63, doi: 10.3847/1538-4357/ad33c8

    2024 doi

  6. [14]

    Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151, doi: 10.1111/j.1365-2966.2004.07881.x

    2004

  7. [15]

    Cami, J., Bernard-Salas, J., Peeters, E., & Malek, S. E. 2010, Science, 329, 1180, doi: 10.1126/science.1192035

    2010 doi

  8. [16]

    2023, ApJL, 944, L12, doi: 10.3847/2041-8213/acac94

    Chastenet, J., Sutter, J., Sandstrom, K., et al. 2023, ApJL, 944, L12, doi: 10.3847/2041-8213/acac94

    2023 doi

  9. [17]

    2003, MNRAS, 339, 63, doi: 10.1046/j.1365-8711.2003.06154.x

    Chiappini, C., Romano, D., & Matteucci, F. 2003, MNRAS, 339, 63, doi: 10.1046/j.1365-8711.2003.06154.x

    2003

  10. [18]

    2024, A&A, 685, A75, doi: 10.1051/0004-6361/202346662

    Chown, R., Sidhu, A., Peeters, E., et al. 2024, A&A, 685, A75, doi: 10.1051/0004-6361/202346662

    2024 doi

  11. [19]

    J., & Saykally, R

    Cook, D. J., & Saykally, R. J. 1998, ApJ, 493, 793, doi: 10.1086/305156

    1998 doi

  12. [20]

    A., Candian, A., Bern´ e, O., & Tielens, A

    Croiset, B. A., Candian, A., Bern´ e, O., & Tielens, A. G. G. M. 2016, A&A, 590, A26, doi: 10.1051/0004-6361/201527714

    2016 doi

  13. [21]

    2007, ApJ, 670, 156, doi: 10.1086/521818

    Daddi, E., Dickinson, M., Morrison, G., et al. 2007, ApJ, 670, 156, doi: 10.1086/521818

    2007 doi

  14. [22]

    R., Garc ´ ıa-Bernete, I., Rigopoulou, D., et al

    Donnan, F. R., Garc ´ ıa-Bernete, I., Rigopoulou, D., et al. 2024, MNRAS, 529, 1386, doi: 10.1093/mnras/stae612

    2024 doi

  15. [23]

    T., Li, A., Hensley, B

    Draine, B. T., Li, A., Hensley, B. S., et al. 2021, ApJ, 917, 3, doi: 10.3847/1538-4357/abff51

    2021 doi

  16. [24]

    V., Kreckel, K., Sandstrom, K

    Egorov, O. V., Kreckel, K., Sandstrom, K. M., et al. 2023, ApJL, 944, L16, doi: 10.3847/2041-8213/acac92

    2023 doi

  17. [25]

    2007, A&A, 468, 33, doi: 10.1051/0004-6361:20077525

    Elbaz, D., Daddi, E., Le Borgne, D., et al. 2007, A&A, 468, 33, doi: 10.1051/0004-6361:20077525

    2007 doi

  18. [27]

    J., Ferrari, P., Buma, W

    Esposito, V. J., Ferrari, P., Buma, W. J., et al. 2024b, Molecular Physics, 122, e2261570, doi: 10.1080/00268976.2023.2261570 —. 2024c, The Journal of Chemical Physics, 160, 114312, doi: 10.1063/5.0191404

    2023

  19. [28]

    C., Tielens, A

    Galliano, F., Madden, S. C., Tielens, A. G. G. M., Peeters, E., & Jones, A. P. 2008, ApJ, 679, 310, doi: 10.1086/587051 Garc ´ ıa-Bernete, I., Rigopoulou, D., Alonso-Herrero, A., et al. 2022, A&A, 666, L5, doi: 10.1051/0004-6361/202244806

    2008 doi

  20. [29]

    2001, A&A, 370, 1030, doi: 10.1051/0004-6361:20010242

    Hony, S., Van Kerckhoven, C., Peeters, E., et al. 2001, A&A, 370, 1030, doi: 10.1051/0004-6361:20010242

    2001 doi

  21. [30]

    M., & Allamandola, L

    Hudgins, D. M., & Allamandola, L. J. 1999, ApJ, 513, L69, doi: 10.1086/311901

    1999 doi

  22. [31]

    Allamandola, L. J. 2005, ApJ, 632, 316, doi: 10.1086/432495

    2005 doi

  23. [32]

    J., & Tielens, A

    Kemper, F., Vriend, W. J., & Tielens, A. G. G. M. 2004, ApJ, 609, 826, doi: 10.1086/421339

    2004 doi

  24. [33]

    Tielens, A. G. G. M. 2021, ApJ, 918, 8, doi: 10.3847/1538-4357/ac02c6

    2021 doi

  25. [34]

    Lai, T. S. Y., Armus, L., U, V., et al. 2022, ApJL, 941, L36, doi: 10.3847/2041-8213/ac9ebf Le Page, V., Snow, T. P., & Bierbaum, V. M. 2003, ApJ, 584, 316, doi: 10.1086/345595

    2022 doi

  26. [35]

    Leger, A., & Puget, J. L. 1984, A&A, 137, L5

    1984

  27. [36]

    J., Chen, T., Candian, A., Lee, T

    Mackie, C. J., Chen, T., Candian, A., Lee, T. J., & Tielens, A. G. G. M. 2018, The Journal of Chemical Physics, 149, 134302, doi: 10.1063/1.5038725

    2018 doi

  28. [37]

    J., Candian, A., Huang, X., et al

    Mackie, C. J., Candian, A., Huang, X., et al. 2015, The Journal of Chemical Physics, 143, 224314, doi: 10.1063/1.4936779

    2015 doi

  29. [38]

    J., Candian, A., Huang, X., et al

    Mackie, C. J., Candian, A., Huang, X., et al. 2016, Journal of Chemical Physics, 145, 084313, doi: 10.1063/1.4961438

    2016 doi

  30. [39]

    2015, The Astrophysical Journal, 814, 23, doi: 10.1088/0004-637X/814/1/23

    Maltseva, E., Petrignani, A., Candian, A., et al. 2015, The Astrophysical Journal, 814, 23, doi: 10.1088/0004-637X/814/1/23

    2015 doi

  31. [40]

    J., Candian, A., et al

    Maltseva, E., Mackie, C. J., Candian, A., et al. 2018, Astronomy & Astrophysics, 610, A65, doi: 10.1051/0004-6361/201732102

    2018 doi

  32. [41]

    D., & Allamandola, L

    Maragkoudakis, A., Boersma, C., Temi, P., Bregman, J. D., & Allamandola, L. J. 2022, ApJ, 931, 38, doi: 10.3847/1538-4357/ac666f

    2022 doi

  33. [42]

    2018, MNRAS, 481, 5370, doi: 10.1093/mnras/sty2658

    Maragkoudakis, A., Ivkovich, N., Peeters, E., et al. 2018, MNRAS, 481, 5370, doi: 10.1093/mnras/sty2658

    2018 doi

  34. [43]

    2020, MNRAS, 494, 642, doi: 10.1093/mnras/staa681 —

    Maragkoudakis, A., Peeters, E., & Ricca, A. 2020, MNRAS, 494, 642, doi: 10.1093/mnras/staa681 —. 2023a, MNRAS, 520, 5354, doi: 10.1093/mnras/stad465

    2020 doi

  35. [44]

    2023b, MNRAS, 524, 3429, doi: 10.1093/mnras/stad2062

    Maragkoudakis, A., Peeters, E., Ricca, A., & Boersma, C. 2023b, MNRAS, 524, 3429, doi: 10.1093/mnras/stad2062

    doi

  36. [45]

    Maragkoudakis, A., Zezas, A., Ashby, M. L. N., & Willner, S. P. 2017, MNRAS, 466, 1192, doi: 10.1093/mnras/stw3180

    2017 doi

  37. [46]

    A., Herter, T

    Marshall, J. A., Herter, T. L., Armus, L., et al. 2007, ApJ, 670, 129, doi: 10.1086/521588

    2007 doi

  38. [47]

    L., Hudgins, D

    Mattioda, A. L., Hudgins, D. M., Boersma, C., et al. 2020, ApJS, 251, 22, doi: 10.3847/1538-4365/abc2c8

    2020 doi

  39. [48]

    A., Burkhardt, A

    McGuire, B. A., Burkhardt, A. M., Kalenskii, S., et al. 2018, Science, 359, 202, doi: 10.1126/science.aao4890

    2018 doi

  40. [49]

    A., Loomis, R

    McGuire, B. A., Loomis, R. A., Burkhardt, A. M., et al. 2021, Science, 371, 1265, doi: 10.1126/science.abb7535

    2021 doi

  41. [50]

    B., Krabbe, A

    Oliveira, C. B., Krabbe, A. C., Dors, O. L., et al. 2024, MNRAS, 531, 199, doi: 10.1093/mnras/stae1172 pah modeling sensitivity analysis 25

    2024 doi

  42. [51]

    Ossenkopf, V., Henning, T., & Mathis, J. S. 1992, A&A, 261, 567

    1992

  43. [52]

    2002, A&A, 388, 639, doi: 10.1051/0004-6361:20020416

    Pech, C., Joblin, C., & Boissel, P. 2002, A&A, 388, 639, doi: 10.1051/0004-6361:20020416

    2002 doi

  44. [53]

    W., Allamandola, L

    Peeters, E., Bauschlicher, Jr., C. W., Allamandola, L. J., et al. 2017, ApJ, 836, 198, doi: 10.3847/1538-4357/836/2/198

    2017 doi

  45. [54]

    2002, A&A, 390, 1089, doi: 10.1051/0004-6361:20020773

    Peeters, E., Hony, S., Van Kerckhoven, C., et al. 2002, A&A, 390, 1089, doi: 10.1051/0004-6361:20020773

    2002 doi

  46. [55]

    Allamandola, L. J. 2004, ApJL, 617, L65, doi: 10.1086/427186 P´ erez-Montero, E., Contini, T., Lamareille, F., et al. 2013, A&A, 549, A25, doi: 10.1051/0004-6361/201220070

    2004 doi

  47. [56]

    2019, MNRAS, 490, 5285, doi: 10.1093/mnras/stz2635

    Popesso, P., Morselli, L., Concas, A., et al. 2019, MNRAS, 490, 5285, doi: 10.1093/mnras/stz2635

    2019 doi

  48. [57]

    Tielens, A. G. G. M., & Allamandola, L. J. 2012, ApJ, 754, 75, doi: 10.1088/0004-637X/754/1/75

    2012 doi

  49. [58]

    2018, ApJ, 854, 115, doi: 10.3847/1538-4357/aaa757

    Peeters, E. 2018, ApJ, 854, 115, doi: 10.3847/1538-4357/aaa757

    2018 doi

  50. [59]

    2021, ApJ, 923, 202, doi: 10.3847/1538-4357/ac28fc

    Ricca, A., Boersma, C., & Peeters, E. 2021, ApJ, 923, 202, doi: 10.3847/1538-4357/ac28fc

    2021 doi

  51. [60]

    2024, ApJ, 968, 128, doi: 10.3847/1538-4357/ad4151

    Maragkoudakis, A. 2024, ApJ, 968, 128, doi: 10.3847/1538-4357/ad4151

    2024 doi

  52. [61]

    E., Peeters, E., & Boersma, C

    Ricca, A., Roser, J. E., Peeters, E., & Boersma, C. 2019, ApJ, 882, 56, doi: 10.3847/1538-4357/ab3124

    2019 doi

  53. [62]

    R., Garc ´ ıa-Bernete, I., et al

    Rigopoulou, D., Donnan, F. R., Garc ´ ıa-Bernete, I., et al. 2024, arXiv e-prints, arXiv:2406.11415, doi: 10.48550/arXiv.2406.11415

    2024 doi arXiv

  54. [63]

    M., Bolatto, A

    Sandstrom, K. M., Bolatto, A. D., Bot, C., et al. 2012, ApJ, 744, 20, doi: 10.1088/0004-637X/744/1/20

    2012 doi

  55. [64]

    A., Tielens, A

    Schutte, W. A., Tielens, A. G. G. M., & Allamandola, L. J. 1993, ApJ, 415, 397, doi: 10.1086/173173

    1993 doi

  56. [65]

    J., & Boersma, C

    Shannon, M. J., & Boersma, C. 2019, ApJ, 871, 124, doi: 10.3847/1538-4357/aaf562

    2019 doi

  57. [66]

    Smith, J. D. T., Draine, B. T., Dale, D. A., et al. 2007, ApJ, 656, 770, doi: 10.1086/510549

    2007 doi

  58. [67]

    Silverman, J. D. 2014, ApJS, 214, 15, doi: 10.1088/0067-0049/214/2/15

    2014 doi

  59. [68]

    Tielens, A. G. G. M. 2008, ARA&A, 46, 289, doi: 10.1146/annurev.astro.46.060407.145211

    2008

  60. [69]

    M., & Leone, S

    Williams, R. M., & Leone, S. R. 1995, ApJ, 443, 675, doi: 10.1086/175559

    1995 doi

  61. [70]

    X., Maragkoudakis, A., & Peeters, E

    Zang, R. X., Maragkoudakis, A., & Peeters, E. 2022, MNRAS, 511, 5142, doi: 10.1093/mnras/stac214

    2022 doi

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