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A first taste of MEAD (Measuring Extinction and Abundances of Dust) -- I. Diffuse Milky Way interstellar dust extinction features in JWST infrared spectra

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper reports that the 10 µm silicate extinction feature correlates with Mg, Fe, and O in dust, giving a grain stoichiometry of Mg:Fe:O = 1.1:1:11.2.

desk verdict New JWST diffuse-ISM spectra and a plausible first correlation between 10 um silicate strength and dust-phase Mg/Fe/O columns, but the derived Mg:Fe:O = 1.1:1:11.2 is not yet supported because the slopes come from free-intercept fits with positive offsets and no propagated uncertainties. read the letter →

arxiv 2412.14378 v1 pith:GQNHOTLV submitted 2024-12-18 astro-ph.GA

Marjorie Decleir , Karl D. Gordon , Karl A. Misselt , Burcu Günay , Julia Roman-Duval , Sascha T. Zeegers This is my paper · ORCID
classification astro-ph.GA
keywords interstellardustextinctionsilicategrainsinfraredspectroscopyelementalabundancesMilkyWaydiffuseISMJWSTgrainmodels
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

This paper aims to show that the 10 µm silicate extinction feature in the infrared spectra of nine diffuse Milky Way sightlines can be tied directly to the composition of the dust that produces it. Combining JWST near- and mid-infrared spectra with gas-phase column densities from the ultraviolet, the authors find strong correlations between the feature strength and the amounts of magnesium, iron, and oxygen locked in dust, and they convert the fitted slopes into an average grain stoichiometry of Mg:Fe:O = 1.1:1:11.2. That number contrasts with current dust models, which assume roughly four oxygen atoms per metal rather than the roughly eleven implied here. The same spectra also show, tentatively, the 3.4 and 6.2 µm hydrocarbon bands and a 3 µm water-ice feature in sightlines with $A(V)\le2.5$, which would be first detections of these materials in purely diffuse gas. If the correlations hold up, extinction features become a direct probe of dust stoichiometry rather than just a qualitative fingerprint.

What carries the argument

The central object is the 10 µm silicate extinction feature, produced by Si–O stretching in dust grains, measured as an optical depth $\tau(\lambda)$ after multiplying the observed flux by $\lambda^2$ to flatten the stellar Rayleigh–Jeans continuum and fitting a line to two narrow windows (7.9–8.1 and 12.6–12.8 µm). The feature is fitted with a skewed Gaussian profile, whose peak optical depth $\tau(\lambda_0)$ serves as the feature strength. The quantitative bridge to composition is the depletion relation $N(X)_{\rm dust} = N({\rm H})\,[N(X)_{\rm ref}/N({\rm H})] - N(X)_{\rm gas}$, which converts ultraviolet gas-phase measurements into how much of each element is in the dust. The load-bearing identity is that dividing the fitted slopes of $\tau(\lambda_0)$ versus $N({\rm Mg})_{\rm dust}$, $N({\rm Fe})_{\rm dust}$, and $N({\rm O})_{\rm dust}$ reproduces the average Mg:Fe:O ratio of the silicate grains.

What would settle it

Measure full NIR–MIR extinction curves for the same nine sightlines using stellar atmosphere models instead of local linear continua and recompute $\tau(\lambda_0)$; if the slopes against $N({\rm Mg}, {\rm Fe}, {\rm O})_{\rm dust}$ change substantially, the reported stoichiometry is an artifact of the continuum normalization.

Watch

Extended reading notes

Core claim

For the first time, the paper connects the measured strength of the 10 µm silicate band to independently derived column densities of Mg, Fe, and O in dust along the same lines of sight. It reports very strong correlations for all three elements, so the feature scales with the amount of these elements in the solid phase. Dividing the slopes of the linear fits yields an average stoichiometry Mg:Fe:O = 1.1:1:11.2, substantially more oxygen-rich than the 1:1:4 assumed by current grain models; the authors note the excess could be oxygen in a separate, correlated carrier such as ice on grain surfaces. They also show the feature strength correlates with $A(V)$ and even better with $A(1500\,\AA)$, consistent with silicate grains dominating the extinction near 1500 Å, and they document sightline-to-sightline variation in the feature's peak wavelength that indicates different silicate types in different environments.

Load-bearing premise

The result rests on the assumption that a straight line fit across two short continuum windows at 7.9–8.1 and 12.6–12.8 µm defines the true continuum under the 10 µm band; if that local continuum is biased, all optical depths, slopes, and the derived Mg:Fe:O ratio shift together.

Editorial extensions

If this is right

  • If the correlations are real, the 10 µm band can be used as a column-density indicator for Mg, Fe, and O in dust, not only as a qualitative silicate tracer.
  • The oxygen-rich ratio Mg:Fe:O = 1.1:1:11.2 would require grain models to place roughly 2–3 times more oxygen per metal in the solid phase, either in silicates or in a companion O-rich material such as ice.
  • The better correlation of feature strength with $A(1500\,\AA)$ than with $A(V)$ supports the model prediction that silicate grains dominate extinction around 1500 Å.
  • Confirmed hydrocarbon and ice features at $A(V)\le2.5$ would imply that these materials are native to diffuse gas, not just condensed in dense clouds, changing where and how carbon and water budgets in the ISM are closed.
  • Sightline-to-sightline shifts in peak wavelength imply that a single cosmic silicate recipe does not work; any successful dust model must produce composition variation across diffuse environments.

Reading between the lines

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

  • If the positive intercept seen in all three slope fits is taken literally, the assumption of a linear zero-intercept relation between feature strength and dust column density is suspect; a testable extension is to push the same analysis to lower and higher $A(V)$ sightlines to see whether the intercept vanishes.
  • Adding silicon column densities to this slope-ratio scheme would discriminate olivine from pyroxene and tell whether the oxygen excess is inside silicates or in a separate oxygen-bearing carrier such as ice.
  • The same slope-ratio technique could be applied to the 20 µm silicate bending feature once spectra are good enough, providing an independent check on whether the 11.2 oxygen value is a property of the silicate material or of the continuum normalization.
  • Because the continuum choice directly moves all three slopes together, the stoichiometry should be re-derived once stellar-atmosphere continuum models are applied; a shift in $A(V)/\tau(\lambda_0)$ toward the literature value near 18 would likely lower the inferred oxygen excess.
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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. Decleir et al. present JWST NIRCam grism and MIRI MRS spectra of nine diffuse Milky Way sightlines (A(V)=1.2-2.5), measure the 10 um silicate feature with a skewed Gaussian profile, and study its correlations with A(V), A(1500 A), hydrogen columns, and literature-based dust column densities of Mg, Fe, and O. They report an average silicate stoichiometry Mg:Fe:O = 1.1:1:11.2, tentative detections of 3.4 and 6.2 um hydrocarbon features, and a 3 um ice feature toward HD073882 and tentatively in the sample average.

Significance. The paper opens a valuable observational window by combining JWST mid-infrared spectroscopy with elemental abundance measurements in the same low-A(V) sightlines. The reported correlations between silicate feature strength and dust-column densities of Mg, Fe, and O are empirical and use independent literature columns, so they are not circular. If the quantitative stoichiometry survives improved analysis, it would challenge current dust models that assume Mg:Fe:O ~ 1:1:4. The paper's strengths include the public release of spectra and analysis code, explicit handling of instrumental artifacts (MRS leak, PSF extraction, stellar lines), and the comparison of feature strengths to four modern grain models.

major comments (3)
  1. [Sec. 4.4, Fig. 9, Table 3] The headline stoichiometry Mg:Fe:O = 1.1:1:11.2 is obtained by dividing the slopes of free-intercept linear fits to tau(lambda0) versus N(Mg,Fe,O)_dust. These fits return positive intercepts of roughly 0.04-0.05 in tau (e.g., y=2.7e-19x+0.04 in Fig. 9), which the authors themselves acknowledge as unrealistic. Because the slopes are strongly coupled to the intercept, the reported ratio is not supported by the current fits; a zero-intercept fit would shift the ratio toward roughly 1:1:7-8 based on the plotted points. The paper should present the physically motivated zero-intercept slopes as the primary result, propagate the tau and column-density uncertainties into the slopes and the stoichiometry, and test the sensitivity of the ratio to the choice of continuum windows in Sec. 3.1.
  2. [Sec. 4.2.1 and Sec. 3.1] The paper notes that the average A(V)/tau(lambda0)=23 is larger than literature values (e.g., 18.2 in Gao et al. 2010 and about 13 in Gordon et al. 2021) and that this may indicate an underestimate of tau caused by the local line-continuum normalization. Since the same tau values drive the slopes in Fig. 9, a systematic error in the continuum level shifts all slopes and the derived stoichiometry coherently. The authors should quantify this systematic at least by varying the continuum definition (for example, a power-law continuum or alternative anchor windows) or by using the model spectra already computed in Sec. 4.4 to estimate the bias.
  3. [Sec. 4.4, Table 3] The fits in Fig. 9 use only seven black data points, yet Table 3 reports the slopes without any uncertainties. Without confidence intervals the reader cannot determine whether the data slope tau/N(O)=2.6e-20 is statistically distinguishable from, for example, the Y24 model value of 3.4e-20, nor whether the derived Mg:Fe:O ratio is compatible with the model value of 1:1:4. The stoichiometric comparison to models requires error propagation that includes the covariance among the tau measurements and the uncertainties in the dust column densities from Eq. (8).
minor comments (5)
  1. [Sec. 3.1, Eq. (1)] Equation (1) defines tau = ln(1/F_norm) but does not explicitly state that F_norm is the observed spectrum divided by the fitted continuum; please make this explicit.
  2. [Sec. 3.4] The quoted 20-sigma detection of the 3 um feature toward HD073882 and the 7-sigma average detection are statistical only; the text also mentions possible stellar-line contamination and a possible instrumental artifact. Please state explicitly that the reported significance does not include these systematic effects.
  3. [Fig. 9 and Table 3] The fit equations shown in the corners of Fig. 9 should be accompanied by their uncertainties, and the figure caption should state clearly which sightlines (the gray points) are excluded from the fits shown.
  4. [Sec. 4.2 and Sec. 4.3] With only seven to nine sightlines, the reported Spearman rank coefficients would be more informative if accompanied by p-values or a statement of the effective sample size, particularly for the null correlations in Sec. 4.2.2.
  5. [Sec. 2.3] The MRS spectral leak at about 12.3 um is subtracted as part of the reduction; because the silicate-feature continuum anchor at 12.6-12.8 um is close to this feature, please comment on the residual uncertainty in the continuum placement after the leak correction.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the central correlations and stoichiometric ratio rest on independent JWST feature measurements and literature column densities, not on equations that encode the target result.

full rationale

The paper's central claim — correlations between the 10 um silicate feature strength and dust-column densities of Mg, Fe, and O, plus a derived stoichiometry Mg:Fe:O = 1.1:1:11.2 — is built from two independent measurement streams. The feature strength tau(lambda0) is measured from JWST NIRCam/MIRI spectra and a skewed-Gaussian profile fit (Sec. 3.1), while N(X)_dust is computed by Eq. 8 from N(H) (Van De Putte et al. 2023) and gas-phase columns from Ritchey et al. (2023) and Jenkins (2009). Neither quantity is defined in terms of the other, and no equation is constructed to reproduce the reported stoichiometry; the ratio is obtained by dividing slopes of independent linear fits (Table 3). The acknowledged positive intercepts of those fits (Sec. 4.4), the local-continuum normalization caveat (Sec. 4.2.1), and the absence of propagated slope uncertainties are statistical and modeling limitations, not circular reductions. Self-citations (Gordon et al. 2021, Decleir et al. 2022, Gordon et al. 2009) are used as archival comparison data or for sample properties; the load-bearing abundance inputs are external literature measurements. The model comparisons use external grain models (D03, ZDA04, HD23, Y24) evaluated with the same fitting procedure, which is a consistency test rather than a fitted input. Therefore no circular step is identified; the score reflects only the presence of non-load-bearing self-citations.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The central results rest on four unproven measurement assumptions: the straight-line local continuum, the solar-reference depletion columns, the silicate attribution of the 10 um band plus all O in silicates, and the diffuse-ISM definition. No new entities are introduced. The many MCMC profile parameters and the linear regression slopes in Fig. 9 are fitted quantities; the latter directly determine the headline stoichiometry.

free parameters (5)
  • Skewed Gaussian profile parameters per sightline (B, xi, omega, alpha) = Table 2: lambda0 9.47-9.90 um, tau(lambda0) 0.051-0.101, FWHM 1.73-1.94 um, area 0.094-0.196 um, alpha 1.22-2.95
    The 10 um feature strength and shape are characterized by MCMC fits of Eq. 5; these fitted values are the dependent quantities in all correlations.
  • Local continuum line fit parameters (slope, intercept) per sightline around 7.9-8.1 and 12.6-12.8 um = not tabulated
    The feature strength is sensitive to this line; the paper notes that a different normalization can change tau by factors up to 3.
  • Slopes of tau vs N(Mg)_dust, N(Fe)_dust, N(O)_dust = 2.7e-19, 2.9e-19, 2.6e-20 (plus intercepts around 0.04-0.05)
    Dividing these slopes yields the reported stoichiometry; no uncertainties are propagated.
  • Scaling factor applied to Chiar et al. (2013) profile for 3.4 um comparison = 2.5
    Chosen by eye to match the average feature strength; affects the shape comparison plot, not the peak tau value.
  • Wavelength windows for continuum and 11.1-12.1 um mask = 7.9-8.1 and 12.6-12.8 um; masked 11.1-12.1 um
    Hand-selected windows and exclusion region set the zero level of the optical depth and therefore all feature strengths.
assumptions (4)
  • domain assumption After multiplying by lambda squared, the stellar continuum under each feature is well approximated by a straight line over the chosen windows.
    Invoked in Sec. 3.1 for the 10 um feature and in Secs. 3.3-3.4 for the 3.4/6.2/3 um features; if false, all optical depths are biased.
  • domain assumption N(X)_dust = N(H)[N(X)_ref/N(H)] - N(X)_gas with solar reference abundances from Jenkins (2009).
    Eq. 8 in Sec. 4.4; the dust-phase column densities and hence slopes/stoichiometry inherit any error in the reference abundances and gas column measurements.
  • domain assumption The 10 um feature is carried by Mg- and Fe-rich silicates, so ratios of the fitted slopes measure silicate stoichiometry.
    Sec. 4.4; the paper notes the alternative that Mg, Fe, O are in other correlated dust phases, which would change the interpretation.
  • domain assumption Sightlines with A(V) <= 2.5 and no strong ice feature are 'diffuse' even when some show weak 3 um absorption.
    Sec. 1 defines diffuse by absence of large amounts of ice; the HD073882 detection then blurs this definition.
invented entities (1)
  • None
    purpose: No new particles, forces, dimensions, or conserved quantities are introduced.
    The 11-12 um feature is an observed excess, not a postulated entity; its possible carriers are drawn from prior literature.

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Cite this review

Pith. "Pith review of A first taste of MEAD (Measuring Extinction and Abundances of Dust) -- I. Diffuse Milky Way interstellar dust extinction features in JWST infrared spectra." pith.science (2026). https://pith.science/paper/GQNHOTLV

@misc{pith2026241214378,
  author       = {Pith},
  title        = {Pith review of: A first taste of MEAD (Measuring Extinction and Abundances of Dust) -- I. Diffuse Milky Way interstellar dust extinction features in JWST infrared spectra},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GQNHOTLV}},
  note         = {Machine review of arXiv:2412.14378}
}
abstract

We present the initial results of MEAD (Measuring Extinction and Abundances of Dust), with a focus on the dust extinction features observed in our JWST near- and mid-infrared spectra of nine diffuse Milky Way sightlines ($1.2 \leq A(V) \leq 2.5$). For the first time, we find strong correlations between the 10 $\mu$m silicate feature strength and the column densities of Mg, Fe and O in dust. This is consistent with the well-established theory that Mg- and Fe-rich silicates are responsible for this feature. We obtained an average stoichiometry of the silicate grains in our sample of Mg:Fe:O = 1.1:1:11.2, constraining the grain composition. We find variations in the feature properties, indicating that different sightlines contain different types of silicates. In the average spectrum of our sample, we tentatively detect features around 3.4 and 6.2 $\mu$m, which are likely caused by aliphatic and aromatic/olefinic hydrocarbons, respectively. If real, to our knowledge, this is the first detection of hydrocarbons in purely diffuse sightlines with $A(V) \leq 2.5$, confirming the presence of these grains in diffuse environments. We detected a 3 $\mu$m feature toward HD073882, and tentatively in the sample average, likely caused by water ice (or solid-state water trapped on silicate grains). If confirmed, to our knowledge, this is the first detection of ice in purely diffuse sightlines with $A(V) \leq 2.5$, supporting previous findings that these molecules can exist in the diffuse ISM.

Figures

Figures reproduced from arXiv: 2412.14378 by the authors.

Figure 1
Figure 1. NIRCam and MIRI spectra (rebinned to a resolution λ/∆λ = 400) for all nine stars in MEAD that were observed with JWST, multiplied by λ 2 to flatten out the decreasing Rayleigh-Jeans tail of the stellar spectrum at these wavelengths, normalized to the mean flux*λ 2 between 5 and 7.5 µm, and ordered from flattest (bottom) to steepest (top) spectrum for convenient visualization. taking the 50th percentile of the poster… view at source ↗
Figure 2
Figure 2. Silicate features, ordered from weakest (bottom) to strongest (top). The skewed Gaussian fits are shown in red. with δ = α √ 1 + α2 (7) Given that the PDF in Eq. 2 is normalized, the ampli￾tude parameter B is equal to the integrated area under the fitted profile. Finally, we will consider the shape pa￾rameter α as a measure of the asymmetry. A positive value for α corresponds to a profile that is right skewed (i.e.,… view at source ↗
Figure 3
Figure 3. 3.4 µm (left) and 6.2 µm (right) hydrocarbon features, normalized to A(V ) for all sightlines (in color). The average is shown in black, and the standard error of the mean in red. The purple dotted line shows the feature for the Quintuplet as fit by Chiar et al. (2013) (C13). In the left plot, we divided the C13 profile by a factor of 2.5. The two sightlines with noisy MIRI spectra are not included in the right plot… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: 3 µm feature normalized to A(V ) for HD073882 (left), and for the rest of the sample (right) (in color). The average is shown in black, and the standard error of the mean in red. In dashed and dotted lines, we show the re-normalized profiles from Decleir et al. (2022) …
Figure 5
Figure 5. Figure 5: Observed correlations between silicate feature properties. The gray data points correspond to the two sightlines with a noisy MIRI spectrum. The Spearman’s rank correlation coefficients are shown in the corner of each panel (in black for only the black data points, and…
Figure 6
Figure 6. Figure 6: Peak wavelength, λ0, vs. peak optical depth, τ (λ0), compared to literature measurements from Gordon et al. (2021) (in green). The gray data points correspond to the two sightlines with a noisy MIRI spectrum. The Spear￾man’s rank correlation coefficients are shown in t…
Figure 7
Figure 7. Figure 7: Silicate feature properties compared to A(V ) and A(1500 ˚A), with literature measurements from Gordon et al. (2021) added in the right panels (in green). again, indicates that silicates cannot be responsible for all the optical extinction. Different sightlines have re…
Figure 8
Figure 8. Figure 8: Silicate feature peak optical depth compared to hydrogen column densities and molecular hydrogen fraction. 1 2 3 N(Mg)dust 1e17 0.025 0.050 0.075 0.100 0.125 ( 0) = 1.00 = 0.70 y=2.7e-19x+0.04 1 2 3 N(Fe)dust 1e17 = 1.00 = 0.94 y=2.9e-19x+0.04 0 1 2 N(O)dust 1e18 = 1.0…
Figure 9
Figure 9. Figure 9: Silicate feature peak optical depth compared to column densities of Mg, Fe and O in dust, taken from Ritchey et al. (2023) (or Jenkins (2009)). The black line is a fit to the black data points, with its equation given in the top right corner of each panel. The colored …

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Reference graph

Works this paper leans on

81 extracted references · 19 canonical work pages · cited by 1 Pith paper

  1. [1]

    J., Whittet, D

    Adamson, A. J., Whittet, D. C. B., & Duley, W. W. 1990, MNRAS, 243, 400

    1990

  2. [2]

    R., et al

    Argyriou, I., Glasse, A., Law, D. R., et al. 2023, A&A, 675, A111, doi: 10.1051/0004-6361/202346489 10 https://doi.org/10.5281/zenodo.14286122 11 https://github.com/mdecleir/mead/releases/tag/v1.0.0 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A...

    doi:10.1051/0004-6361/202346489 2023

  3. [3]

    2014, The Skew-Normal and Related Families, IMS monographs (Cambridge University Press)

    Azzalini, A., & Capitanio, A. 2014, The Skew-Normal and Related Families, IMS monographs (Cambridge University Press). https://books.google.com/books?id=-tkaAgAAQBAJ

    work page 2014

  4. [4]

    C., Hubeny, I., & Rauch, T

    Bohlin, R. C., Hubeny, I., & Rauch, T. 2020, AJ, 160, 21, doi: 10.3847/1538-3881/ab94b4

    doi:10.3847/1538-3881/ab94b4 2020

  5. [5]

    C., Krick, J

    Bohlin, R. C., Krick, J. E., Gordon, K. D., & Hubeny, I. 2022, AJ, 164, 10, doi: 10.3847/1538-3881/ac6fe1

    doi:10.3847/1538-3881/ac6fe1 2022

  6. [6]

    C., M´ esz´ aros, S., Fleming, S

    Bohlin, R. C., M´ esz´ aros, S., Fleming, S. W., et al. 2017, AJ, 153, 234, doi: 10.3847/1538-3881/aa6ba9

    doi:10.3847/1538-3881/aa6ba9 2017

  7. [7]

    C., Savage, B

    Bohlin, R. C., Savage, B. D., & Drake, J. F. 1978, ApJ, 224, 132, doi: 10.1086/156357

    doi:10.1086/156357 1978

  8. [8]

    D., Leroy, A

    Bolatto, A. D., Leroy, A. K., Jameson, K., et al. 2011, ApJ, 741, 12, doi: 10.1088/0004-637X/741/1/12

    doi:10.1088/0004-637x/741/1/12 2011

Show all 81 references
  1. [9]

    Boogert, A. C. A., Gerakines, P. A., & Whittet, D. C. B. 2015, ARA&A, 53, 541, doi: 10.1146/annurev-astro-082214-122348

    2015 doi

  2. [10]

    D., Hayward, T

    Bregman, J. D., Hayward, T. L., & Sloan, G. C. 2000, ApJL, 544, L75, doi: 10.1086/317294

    2000 doi

  3. [11]

    R., Strazzulla, G., Baratta, G., & Colangeli, L

    Brucato, J. R., Strazzulla, G., Baratta, G., & Colangeli, L. 2004, A&A, 413, 395, doi: 10.1051/0004-6361:20031574

    2004 doi

  4. [12]

    A., Clayton, G

    Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245, doi: 10.1086/167900

    1989 doi

  5. [13]

    Y., Li, A., & Zhou, C

    Chen, T., Xiao, C. Y., Li, A., & Zhou, C. T. 2022, MNRAS, 509, 5231, doi: 10.1093/mnras/stab3175

    2022 doi

  6. [14]

    E., & Tielens, A

    Chiar, J. E., & Tielens, A. G. G. M. 2001, ApJL, 550, L207, doi: 10.1086/319631 —. 2006, ApJ, 637, 774, doi: 10.1086/498406

    2001 doi

  7. [15]

    2013, ApJ, 770, 78, doi: 10.1088/0004-637X/770/1/78

    Ricca, A. 2013, ApJ, 770, 78, doi: 10.1088/0004-637X/770/1/78

    2013 doi

  8. [16]

    E., Tielens, A

    Chiar, J. E., Tielens, A. G. G. M., Whittet, D. C. B., et al. 2000, ApJ, 537, 749, doi: 10.1086/309047

    2000 doi

  9. [17]

    M., & Herbst, E

    Cuppen, H. M., & Herbst, E. 2007, ApJ, 668, 294, doi: 10.1086/521014

    2007 doi

  10. [18]

    Day, K. L. 1974, ApJL, 192, L15, doi: 10.1086/181578 —. 1979, ApJ, 234, 158, doi: 10.1086/157482

    1974 doi

  11. [19]

    2024a, JWST NIRCam grism and MIRI MRS spectra for the MEAD sightlines, 1.0, Zenodo, doi: 10.5281/zenodo.14286122 —

    Decleir, M. 2024a, JWST NIRCam grism and MIRI MRS spectra for the MEAD sightlines, 1.0, Zenodo, doi: 10.5281/zenodo.14286122 —. 2024b, mdecleir/mead: v1.0.0, v1.0.0, Zenodo, doi: 10.5281/zenodo.14291651

    doi

  12. [20]

    D., Andrews, J

    Decleir, M., Gordon, K. D., Andrews, J. E., et al. 2022, ApJ, 930, 15, doi: 10.3847/1538-4357/ac5dbe

    2022 doi

  13. [21]

    M., Fujiyoshi, T., et al

    Do-Duy, T., Wright, C. M., Fujiyoshi, T., et al. 2020, MNRAS, 493, 4463, doi: 10.1093/mnras/staa396

    2020 doi

  14. [22]

    1995, A&A, 300, 503

    Mutschke, H. 1995, A&A, 300, 503

    1995

  15. [23]

    1986, Ap&SS, 128, 47, doi: 10.1007/BF00656018

    Dorschner, J., & Henning, T. 1986, Ap&SS, 128, 47, doi: 10.1007/BF00656018

    1986 doi

  16. [24]

    Draine, B. T. 1984, ApJL, 277, L71, doi: 10.1086/184206 —. 2003a, ARA&A, 41, 241, doi: 10.1146/annurev.astro.41.011802.094840 —. 2003b, ApJ, 598, 1017, doi: 10.1086/379118 —. 2016, ApJ, 831, 109, doi: 10.3847/0004-637X/831/1/109

    1984 arXiv

  17. [25]

    2001, A&A, 378, 228, doi: 10.1051/0004-6361:20011196

    Fabian, D., Henning, T., J¨ ager, C., et al. 2001, A&A, 378, 228, doi: 10.1051/0004-6361:20011196

    2001 doi

  18. [26]

    Fitzpatrick, E. L. 1999, PASP, 111, 63, doi: 10.1086/316293

    1999 doi

  19. [27]

    L., & Massa, D

    Fitzpatrick, E. L., & Massa, D. 1990, ApJS, 72, 163, doi: 10.1086/191413

    1990 doi

  20. [28]

    Clayton, G. C. 2019, ApJ, 886, 108, doi: 10.3847/1538-4357/ab4c3a

    2019 doi

  21. [29]

    2016, ApJ, 830, 71, doi: 10.3847/0004-637X/830/2/71

    Koch, I. 2016, ApJ, 830, 71, doi: 10.3847/0004-637X/830/2/71

    2016 doi

  22. [30]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067

    2013 doi

  23. [31]

    W., & Li, A

    Gao, J., Jiang, B. W., & Li, A. 2010, Earth, Planets and Space, 62, 63, doi: 10.5047/eps.2008.12.006

    2010 doi

  24. [32]

    C., et al

    Gasman, D., Argyriou, I., Sloan, G. C., et al. 2023, A&A, 673, A102, doi: 10.1051/0004-6361/202245633

    2023 doi

  25. [33]

    L., Whittet, D

    Gibb, E. L., Whittet, D. C. B., Schutte, W. A., et al. 2000, ApJ, 536, 347, doi: 10.1086/308940

    2000 doi

  26. [34]

    R., Dartois, E., & Mu˜ noz Caro, G

    Godard, M., Geballe, T. R., Dartois, E., & Mu˜ noz Caro, G. M. 2012, A&A, 537, A27, doi: 10.1051/0004-6361/201117197

    2012 doi

  27. [35]

    Gordon, K. D. 2024, Journal of Open Source Software, 9, 7023, doi: 10.21105/joss.07023

    2024 doi

  28. [36]

    D., Cartledge, S., & Clayton, G

    Gordon, K. D., Cartledge, S., & Clayton, G. C. 2009, ApJ, 705, 1320, doi: 10.1088/0004-637X/705/2/1320

    2009 doi

  29. [37]

    D., Clayton, G

    Gordon, K. D., Clayton, G. C., Decleir, M., et al. 2023, ApJ, 950, 86, doi: 10.3847/1538-4357/accb59

    2023 doi

  30. [38]

    D., Misselt, K

    Gordon, K. D., Misselt, K. A., Bouwman, J., et al. 2021, ApJ, 916, 33, doi: 10.3847/1538-4357/ac00b7

    2021 doi

  31. [39]

    P., Kelly, D

    Greene, T. P., Kelly, D. M., Stansberry, J., et al. 2017, Journal of Astronomical Telescopes, Instruments, and Systems, 3, 035001, doi: 10.1117/1.JATIS.3.3.035001

    2017 doi

  32. [40]

    A., Gehrz, R

    Hackwell, J. A., Gehrz, R. D., & Woolf, N. J. 1970, Nature, 227, 822, doi: 10.1038/227822a0

    1970 doi

  33. [41]

    S., Gudennavar, S

    Haris, U., Parvathi, V. S., Gudennavar, S. B., et al. 2016, AJ, 151, 143, doi: 10.3847/0004-6256/151/6/143

    2016 doi

  34. [42]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    2020 doi

  35. [43]

    2010, ARA&A, 48, 21, doi: 10.1146/annurev-astro-081309-130815

    Henning, T. 2010, ARA&A, 48, 21, doi: 10.1146/annurev-astro-081309-130815

    2010 doi

  36. [44]

    S., & Draine, B

    Hensley, B. S., & Draine, B. T. 2020, ApJ, 895, 38, doi: 10.3847/1538-4357/ab8cc3 —. 2023, ApJ, 948, 55, doi: 10.3847/1538-4357/acc4c2 20 Decleir et al

    2020 doi

  37. [45]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    2007 doi

  38. [46]

    1996, AJ, 112, 235, doi: 10.1086/118005

    Imanishi, M., Sasaki, Y., Goto, M., et al. 1996, AJ, 112, 235, doi: 10.1086/118005

    1996 doi

  39. [47]

    Jenkins, E. B. 2009, ApJ, 700, 1299, doi: 10.1088/0004-637X/700/2/1299

    2009 doi

  40. [48]

    2011, ApJL, 741, L9, doi: 10.1088/2041-8205/741/1/L9

    Jing, D., He, J., Brucato, J., et al. 2011, ApJL, 741, L9, doi: 10.1088/2041-8205/741/1/L9

    2011 doi

  41. [49]

    P., Fanciullo, L., K¨ ohler, M., et al

    Jones, A. P., Fanciullo, L., K¨ ohler, M., et al. 2013, A&A, 558, A62, doi: 10.1051/0004-6361/201321686

    2013 doi

  42. [50]

    2017, A&A, 602, A46, doi: 10.1051/0004-6361/201630225

    Verstraete, L. 2017, A&A, 602, A46, doi: 10.1051/0004-6361/201630225

    2017 doi

  43. [51]

    J., & Tielens, A

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

    2004 doi

  44. [52]

    S., Sofia, U

    Parvathi, V. S., Sofia, U. J., Murthy, J., & Babu, B. R. S. 2012, ApJ, 760, 36, doi: 10.1088/0004-637X/760/1/36

    2012 doi

  45. [53]

    Tielens, A. G. G. M., & Sellgren, K. 1994, ApJ, 437, 683, doi: 10.1086/175031

    1994 doi

  46. [54]

    2021, Nature Astronomy, 5, 78, doi: 10.1038/s41550-020-01214-x

    Potapov, A., Bouwman, J., J¨ ager, C., & Henning, T. 2021, Nature Astronomy, 5, 78, doi: 10.1038/s41550-020-01214-x

    2021 doi

  47. [55]

    2024, ApJ, 965, 48, doi: 10.3847/1538-4357/ad2c07

    Potapov, A., J¨ ager, C., Mutschke, H., & Henning, T. 2024, ApJ, 965, 48, doi: 10.3847/1538-4357/ad2c07

    2024 doi

  48. [56]

    G., Adamson, A

    Rawlings, M. G., Adamson, A. J., & Whittet, D. C. B. 2003, MNRAS, 341, 1121, doi: 10.1046/j.1365-8711.2003.06389.x

    2003

  49. [57]

    M., Jenkins, E

    Ritchey, A. M., Jenkins, E. B., Shull, J. M., et al. 2023, ApJ, 952, 57, doi: 10.3847/1538-4357/acda25

    2023 doi

  50. [58]

    Rocha, W. R. M., Rachid, M. G., McClure, M. K., He, J., & Linnartz, H. 2024, A&A, 681, A9, doi: 10.1051/0004-6361/202347437

    2024 doi

  51. [59]

    2014, MNRAS, 441, 1017, doi: 10.1093/mnras/stu510

    Rowlands, K., Dunne, L., Dye, S., et al. 2014, MNRAS, 441, 1017, doi: 10.1093/mnras/stu510

    2014 doi

  52. [60]

    A., Allamandola, L

    Sandford, S. A., Allamandola, L. J., Tielens, A. G. G. M., et al. 1991, ApJ, 371, 607, doi: 10.1086/169925

    1991 doi

  53. [61]

    A., van der Hucht, K

    Schutte, W. A., van der Hucht, K. A., Whittet, D. C. B., et al. 1998, A&A, 337, 261

    1998

  54. [62]

    2024, ApJ, 963, 120, doi: 10.3847/1538-4357/ad1dd8

    Shao, Z., & Jiang, B. 2024, ApJ, 963, 120, doi: 10.3847/1538-4357/ad1dd8

    2024 doi

  55. [63]

    K., Barlow, M

    Speck, A. K., Barlow, M. J., & Skinner, C. J. 1997, MNRAS, 288, 431, doi: 10.1093/mnras/288.2.431

    1997 doi

  56. [64]

    K., Whittington, A

    Speck, A. K., Whittington, A. G., & Hofmeister, A. M. 2011, ApJ, 740, 93, doi: 10.1088/0004-637X/740/2/93

    2011 doi

  57. [65]

    P., & Donn, B

    Stecher, T. P., & Donn, B. 1965, ApJ, 142, 1681, doi: 10.1086/148461

    1965 doi

  58. [66]

    2010, ApJL, 712, L16, doi: 10.1088/2041-8205/712/1/L16

    Steglich, M., J¨ ager, C., Rouill´ e, G., et al. 2010, ApJL, 712, L16, doi: 10.1088/2041-8205/712/1/L16

    2010 doi

  59. [67]

    2023, ApJ, 953, 53, doi: 10.3847/1538-4357/acd53c

    Sun, F., Egami, E., Pirzkal, N., et al. 2023, ApJ, 953, 53, doi: 10.3847/1538-4357/acd53c

    2023 doi

  60. [68]

    Bregman, J., & Witteborn, F. C. 1996, ApJ, 461, 210, doi: 10.1086/177049 Van De Putte, D., Cartledge, S. I. B., Gordon, K. D.,

    1996 doi

  61. [69]

    C., & Roman-Duval, J

    Clayton, G. C., & Roman-Duval, J. 2023, ApJ, 944, 33, doi: 10.3847/1538-4357/ac9902

    2023 doi

  62. [70]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

    2020 doi

  63. [71]

    V., & Henning, T

    Voshchinnikov, N. V., & Henning, T. 2010, A&A, 517, A45, doi: 10.1051/0004-6361/200912817

    2010 doi

  64. [72]

    C., & Draine, B

    Weingartner, J. C., & Draine, B. T. 2001, ApJ, 548, 296, doi: 10.1086/318651

    2001 doi

  65. [73]

    W., Glasse, A., et al

    Wells, M., Pel, J. W., Glasse, A., et al. 2015, PASP, 127, 646, doi: 10.1086/682281

    2015 doi

  66. [74]

    Whittet, D. C. B. 2015, ApJ, 811, 110, doi: 10.1088/0004-637X/811/2/110 —. 2022, Dust in the Galactic Environment (Third Edition), doi: 10.1088/2514-3433/ac7204

    2015 doi

  67. [75]

    Whittet, D. C. B., Duley, W. W., & Martin, P. G. 1990, MNRAS, 244, 427

    1990

  68. [76]

    Whittet, D. C. B., Boogert, A. C. A., Gerakines, P. A., et al. 1997, ApJ, 490, 729, doi: 10.1086/304914

    1997 doi

  69. [77]

    M., Do Duy, T., & Lawson, W

    Wright, C. M., Do Duy, T., & Lawson, W. 2016, MNRAS, 457, 1593, doi: 10.1093/mnras/stw041

    2016 doi

  70. [78]

    P., Guillet, V., et al

    Ysard, N., Jones, A. P., Guillet, V., et al. 2024, A&A, 684, A34, doi: 10.1051/0004-6361/202348391

    2024 doi

  71. [79]

    Bromley, S. T. 2023, Faraday Discussions, 245, 609, doi: 10.1039/D3FD00055A

    2023 doi

  72. [80]

    Zubko, V., Dwek, E., & Arendt, R. G. 2004, ApJS, 152, 211, doi: 10.1086/382351

    2004 doi

  73. [81]

    2021, ApJS, 257, 63, doi: 10.3847/1538-4365/ac2cc3

    Zuo, W., Li, A., & Zhao, G. 2021, ApJS, 257, 63, doi: 10.3847/1538-4365/ac2cc3

    2021 doi

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