Statistical Analysis of PAHs as a Tracer of Anomalous Microwave Emission Using DIRBE Data

Alan Kogut; Brandon Hensley; Danielle Sponseller; David T. Chuss

REVIEW 4 major objections 4 minor 34 references

Statistical Analysis of PAHs as a Tracer of Anomalous Microwave Emission Using DIRBE Data

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that the 3.3 µm PAH emission feature traced by DIRBE correlates with anomalous microwave emission better than 857 GHz thermal dust does in 17% of 98 Planck sources—and in 37% of the 27 high-significance detections.

desk verdict A careful extension of AME–PAH spatial correlation to 98 sources; the 17%/37% preference for PAHs is a real feature of the data as processed, but it is conditional on the Commander AME template. read the letter →

arxiv 2509.03611 v1 pith:PKWHNAPI submitted 2025-09-03 astro-ph.CO astro-ph.GA

Danielle Sponseller , David T. Chuss , Brandon Hensley , Alan Kogut This is my paper · ORCID

classification astro-ph.COastro-ph.GA

keywords anomalousmicrowaveemissionpolycyclicaromatichydrocarbons3.3micronDIRBEspinningdustthermalspatialcorrelationinterstellarmedium

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 asks whether polycyclic aromatic hydrocarbons—small carbon molecules that glow at 3.3 microns—are the material behind anomalous microwave emission (AME), an unexplained microwave glow seen toward interstellar dust. The authors map the 3.3 µm PAH feature from archival COBE/DIRBE data, then compare, source by source, how well AME tracks that PAH map versus how well it tracks the usual far-infrared dust map at 857 GHz. Across 98 compact Planck AME sources, thermal dust is the better tracer for most, but 17% of sources are better matched by PAHs; among the 27 sources with the most secure AME detections, 37% favor PAHs. The authors do not claim this proves PAHs are the AME carrier, only that neither tracer works everywhere and that local interstellar conditions likely shape which tracer wins.

What carries the argument

The load-bearing object is the PAH 3.3 µm emission map, produced per pixel by linear least-squares fitting of DIRBE bands 1–4 against three basis functions: the Faint Source Model starlight spectrum, the interplanetary-dust zodiacal model, and the band-integrated PAH emission spectrum from a model interstellar dust SED. The comparison metric is the Spearman rank correlation coefficient between AME at 30 GHz (evaluated from the Planck Commander two-component spinning-dust template), the PAH map, and the 857 GHz thermal dust map, with bootstrap uncertainties and preference significance η_pref = (r_AP − r_AD)/√(σ_AP² + σ_AD²). The PAH basis function isolates the 3.3 µm feature; η_pref decides w

What would settle it

Re-run the same 98-source analysis with an AME map constructed from a different spectral assumption (e.g., a single-component spinning-dust template or a QUIJOTE-led free-free-separated map) and compare which sources prefer PAHs; if the PAH-preferring set changes or vanishes, the claim is an artifact of the Commander template. A targeted observation of one high-significance PAH-preferring source (e.g., ρ-Ophiuchus) that distinguishes spinning-dust dipole emission from free-free at the AME peak would directly test whether the spatial correlation corresponds to the proposed carrier.

Watch

Extended reading notes

Core claim

Central claim: emission from small PAHs, isolated through the 3.3 µm C–H feature, is a statistically significant spatial tracer of AME for a meaningful minority of sources, and a large minority of the cleanest detections. Per-pixel PAH maps from DIRBE bands 1–4 (linear least-squares decomposition against starlight, zodiacal light, and PAH basis functions) are compared by Spearman correlation with AME at 30 GHz and 857 GHz dust in 4°×4° patches. Among 98 Planck AME sources, 17% prefer PAHs, nine at ≥2σ; among 27 high-significance detections, 37% prefer PAHs, seven at ≥2σ. With QUIJOTE-based significances, 39% of significant sources prefer PAHs. Conclusion: neither tracer suffices alone; envir

Load-bearing premise

The reference AME map is built from a component-separation model that assumes a specific two-component spinning-dust spectrum; if the true AME spectrum differs, flux can be shuffled among AME, free-free, and synchrotron so the correlations may reflect the assumed template rather than the real AME distribution.

Editorial extensions

If this is right

A full-sky extension of the same DIRBE-based PAH mapping can test whether diffuse high-latitude AME also prefers PAHs, where free-free contamination is smaller.
Convolving the maps to the Planck beam and degrading to Nside=128 does not change the preferred-tracer counts, so the result is not an artifact of oversampling the one-degree AME map.
With QUIJOTE-based detection significances, the number of significant AME sources rises from 27 to 43 and the PAH-preferring fraction stays near 39%, meaning the 37% figure is stable under a more reliable free-free separation.
Higher-resolution 3.3 µm observations will sharpen the test: if PAH preference strengthens at finer scales, unresolved dust–PAH separation explains why the all-source fraction is only 17%.

Reading between the lines

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

Editorial inference: the 17%-to-37% jump likely tracks AME detection cleanliness. A direct test is to regress η_pref on free-free fraction and Galactic latitude; if PAH preference is concentrated in low free-free regions, contamination suppresses the all-source fraction.
Editorial inference: the 857 GHz 'thermal dust' map itself contains PAH and hot-vibrational dust emission, so it is not a pure large-grain tracer. A temperature-corrected dust column map from multi-band Planck fits would make the dust-vs-PAH contest cleaner and could shift some sources.
Editorial inference: if PAH emission mechanisms are environment-dependent, the 3.3 µm feature alone may misrepresent PAH column density in photodissociation regions; combining it with 7.7/11.3 µm PAH bands could raise the PAH-preference fraction in the same sample.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this paper does what it says – it correlates the DIRBE 3.3 µm PAH map with the Planck AME map in all 98 compact sources and reports that a notable minority prefer PAH to dust as a tracer. The 17% (and 37% for high-SNR) figure is a genuine feature of the data products used. But the AME map is not an observed sky; it's the Commander component-separation output assuming a two-component spinning-dust spectrum. If that template is wrong, free-free or synchrotron leakage can mimic PAH correlation, especially in PDR/star-forming regions. The authors know this – they say in Section 5 the AME map is 'likely biased' – but the headline number tends to float free of that caveat.

What's new and good: This is the first per-source spatial correlation analysis of the 3.3 µm feature across the full Planck sample. The DIRBE fitting procedure (bands 1-4, starlight/zodi/PAH basis) is sensible and the residuals look fine. The bootstrap uncertainties and the beam-degradation check are standard but done properly. The paper is also honest about the QUIJOTE update: it only changes detection significance, not the spatial map.

Soft spots: The main one is the Commander dependency. The stress-test note is right that this is load-bearing. I'd also flag that the PAH basis comes from a single fiducial size distribution (Hensley & Draine 2023); if small PAHs are destroyed or enhanced in different environments, the amplitude map could be biased even if the correlation is stable. The 4-degree patch size is arbitrary but they tested beam effects. None of these are fatal – the paper is explicit about them – but they mean the result is conditional, not definitive.

Who this is for: people working on AME carriers and foregrounds. It's a useful survey-level constraint, not a smoking gun. I'd send it to a referee, asking for a clear statement that the fractions depend on the Commander model and a discussion of whether an independent AME map (e.g., QUIJOTE or an alternative component separation) would change the percentages.

Referee Report

4 major / 4 minor

Summary. The paper uses DIRBE bands 1–4 to construct maps of the 3.3 μm PAH emission feature via a three-component linear least-squares fit (starlight, zodiacal, PAH), then computes Spearman rank correlations between this PAH map, the Planck 857 GHz thermal dust map, and the Planck Commander AME map evaluated at 30 GHz. The analysis is applied to 4°×4° patches around 98 Planck AME sources, plus a larger patch around λ-Orionis. The central quantitative claims are that 17% of all sources are better correlated with PAHs than with thermal dust, and that this fraction rises to 37% for the 27 high-significance Planck detections (Table 2). The paper includes bootstrap uncertainties, a beam-smoothing robustness test, and a secondary analysis using QUIJOTE detection significances.

Significance. If the results hold, this is the largest systematic spatial-correlation study of AME versus PAH 3.3 μm emission to date, and it provides concrete source lists for follow-up by higher-resolution facilities such as SPHEREx and QUIJOTE. The paper's strengths are its use of publicly archived DIRBE data, transparent statistical methodology, bootstrap error estimates, and explicit acknowledgment of the main limitation: the reference AME map is not an observed sky product but the output of a component-separation model. The finding that PAH-tracing regions are preferentially at high latitude and among high-SNR AME detections is interesting and falsifiable. The main risk is that the headline percentages are conditional on the Commander AME map, whose spectral model is the very spinning-dust hypothesis under test.

major comments (4)

[Section 2 and Section 5] The central result (Table 2; 17% and 37%) is computed against the Planck Commander AME map at 30 GHz, which is derived by fitting a two-component spinning-dust spectral template. As the authors note in Section 5, this map is 'likely biased due to complications in separating it from other components, such as free-free emission.' This bias is load-bearing: PAH 3.3 μm emission and free-free are both enhanced in photodissociation regions, so a spurious AME–free-free correlation can masquerade as an AME–PAH correlation. The QUIJOTE comparison updates only the detection significance, not the spatial AME map. Please quantify the sensitivity of the 17%/37% fractions to the AME map choice, for example by repeating the correlation analysis with an independent AME map (e.g., a GNILC or a different component-separation output) or by injecting simulated free-free leakage into the Commander map.
[Section 3, Eq. (1)–(6)] The PAH map itself is the output of a 4-band, 3-component linear fit whose basis functions are model-dependent: the PAH SED is taken from the Hensley & Draine (2023) fiducial grain-size distribution, the starlight basis from the FSM model, and the zodiacal basis from the Kelsall et al. IPD model. Residuals of 2–3% per pixel do not quantify template error. Since the PAH map is the independent tracer, systematic template errors could propagate directly into the rank correlations. Please show that the headline preferences are robust to plausible variations in the PAH SED template, the starlight model, and the zodiacal subtraction, or compare the derived 3.3 μm maps with an independent PAH tracer such as WISE 12 μm for a subset of regions.
[Equation (7) and Table 1] The preference significance η_pref = (r_AP − r_AD) / sqrt(σ_AP^2 + σ_AD^2) treats r_AP and r_AD as independent, but they are measured from the same AME map and the same set of pixels, so their bootstrap errors are correlated. This affects the counts of 'strong preference' sources (9 with η_pref ≥ 2, 69 with η_pref ≤ −2, Table 2). The bootstrap resampling already available in pymccorrelation could be used to estimate the uncertainty on the difference r_AP − r_AD directly, avoiding the independence assumption. Please recompute the significance counts with this covariance taken into account.
[Section 4, patch definition] The correlation analysis is performed on 4°×4° patches without any background subtraction or local mean removal. Spearman coefficients over ~300 pixels can be driven by a large-scale gradient common to all three maps rather than by source-level spatial association. The comparison of r_AP with r_AD partially mitigates this because the common large-scale component affects both coefficients, but the interpretation of the numbers as 'tracer preference' still assumes that the patch-scale morphology is dominated by the AME source. I recommend adding a test with a high-pass filter or background-subtracted maps, or at least a discussion of how the 4° patch choice affects the r_AP versus r_AD comparison.

minor comments (4)

[Abstract and Section 4] The abstract reports '17% of the AME sources are better correlated' without specifying that this is a raw r_AP > r_AD comparison, not a significance-thresholded result. Please state the threshold (raw vs. ≥2σ) in the abstract to avoid overinterpretation.
[Section 4, paragraph 2] Typo: 'r-Ophiuchus' should be 'ρ-Ophiuchus'.
[Figure 3 caption] The legend order in the figure ('PAH, Zodi, Starlight, Model') does not match the plotted line order described in the caption; please align them for clarity.
[Table 1 footnote] The note says 'Boldface is used to indicate sources which were flagged ...', but the table as typeset does not show boldface. Please ensure the table file renders the boldface or add a separate column.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PAH and AME maps are independently constructed; correlation counts are empirical.

full rationale

The paper's central claim is a set of correlation comparisons between a DIRBE-derived PAH 3.3 micron map and the Planck Commander AME map at 30 GHz, using the Planck 857 GHz map as the dust tracer. The PAH map is produced by an independent least-squares spectral fit to DIRBE bands 1-4; the AME map is a component-separation product from Planck Collaboration et al. (2016a). The reported 17% and 37% fractions are counts of sources where the Spearman coefficient r_AP exceeds r_AD, not fitted parameters or predicted quantities. The AME map does assume a two-component spinning-dust spectral template, which is related to the physical hypothesis under study, but the spatial morphology of the fitted AME amplitude is not constructed from the PAH map, so the correlation test is not circular by construction. The paper explicitly acknowledges that the AME map may be biased by free-free contamination, which is a systematic limitation rather than a circular reduction. Self-citations (e.g., Hensley & Draine 2023, Chuss et al. 2022) provide input spectral models and prior methodology, but the central correlation analysis does not reduce to these citations. No equation-level equivalence or fitted-input-as-prediction is present.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on two externally constructed products: the Planck Commander AME map (with fitted spinning-dust spectral parameters) and the Hensley and Draine PAH SED template. It also relies on hand-chosen analysis parameters (mask threshold, patch size) and on assumptions about the fidelity of starlight and zodiacal models. No new physical entities are introduced.

free parameters (4)

AME two-component spinning-dust spectral parameters = Planck Commander best-fit amplitudes and peak shifts for two spinning-dust components
The 30 GHz AME map used as the reference is reconstructed from these fitted values; errors propagate into every correlation.
PAH 3.3 micron spectral template = Hensley and Draine (2023) astrodust model SED
The PAH amplitude in each pixel is the projection of four DIRBE bands onto this assumed spectral shape; errors in the template map directly into the PAH map.
Point-source mask threshold = 0.5 MJy/sr above local background in DIRBE Band 1
Chosen by hand to remove bright stars; changing it changes which pixels enter the correlations.
Correlation patch size = 4 x 4 degrees (17 x 17 degrees for lambda Orionis)
Chosen for the correlation analysis; Spearman coefficients depend on the included background area.

assumptions (5)

domain assumption DIRBE ZSMA maps have residual zodiacal emission that is linearly modeled by the IPD spectrum.
Section 3: the zodi component is fit as a linear basis function after ZSMA subtraction.
domain assumption The FSM starlight model correctly predicts diffuse starlight in DIRBE bands 1 through 4.
Section 3: starlight basis is taken from the FSM model; errors here would bias the PAH amplitude.
domain assumption The Planck 857 GHz map traces thermal dust from large grains with no significant PAH contamination.
Used as the thermal dust tracer in the correlation comparison.
domain assumption The Planck Commander two-component spinning-dust template is a valid parameterization of AME in all 98 sources.
Section 2: the AME map is evaluated from these templates; the authors note it is not based on understanding of the underlying physics.
ad hoc to paper Spearman correlations over 4 x 4 degree patches without background subtraction capture source-level spatial association.
The patch size and lack of background removal are choices in this paper; diffuse large-scale structure could dominate the ranks.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Statistical Analysis of PAHs as a Tracer of Anomalous Microwave Emission Using DIRBE Data." pith.science (2026). https://pith.science/paper/PKWHNAPI

@misc{pith2026250903611,
  author       = {Pith},
  title        = {Pith review of: Statistical Analysis of PAHs as a Tracer of Anomalous Microwave Emission Using DIRBE Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PKWHNAPI}},
  note         = {Machine review of arXiv:2509.03611}
}

abstract

We use archival data from the Diffuse Infrared Background Experiment (DIRBE) to map the polycyclic aromatic hydrocarbon (PAH) 3.3 $\mu$m emission feature and analyze its correlation with AME in 98 compact sources identified by the Planck collaboration. We find that while FIR thermal dust emission continues to be a better tracer of AME in most of the considered regions, 17% of the AME sources are better correlated with emission from small PAHs as traced by DIRBE. Furthermore, of the 27 sources which were identified as highly significant AME detections in the Planck analysis, 37% prefer PAHs as an AME tracer. Further work is required to understand to what extent local interstellar conditions are affecting PAH emission mechanisms and to reveal the underlying carriers of AME.

Figures

Figures reproduced from arXiv: 2509.03611 by the authors.

**Figure 1.** A representative PAH emission spectrum is shown as a thin, blue line. DIRBE Bands 1–4 are shown in gray. Band 3 contains the prominent 3.3 µm emission feature while the adjacent bands contain only continuum emission from PAHs, allowing this feature to be isolated. The band-integrated PAH spectrum used to perform the linear least-squares fitting is shown as a thicker blue line. The zodiacal emission and starlight spe… view at source ↗

**Figure 2.** Maps of λ-Orionis, the Perseus Molecular Cloud, and ρ-Ophiuchus are shown in the top, middle, and bottom rows, respectively. The left column is the AME at 30 GHz, the middle shows our map of PAH 3.3 µm emission, and the right column shows thermal dust emission as traced by the Planck 857 GHz map degraded to the resolution of the DIRBE maps (Nside = 256). Masked point sources are shown in gray [PITH_FULL_IMAGE:figur… view at source ↗

**Figure 3.** Examples of spectral fits in λ-Orionis, the Perseus Molecular Cloud, and ρ-Ophiuchus are shown in the top, middle, and bottom panels, respectively. Each panel shows the fit for a randomly-chosen unmasked pixel. DIRBE data and error bars are shown as well, however the error bars are smaller than the marker points. The root-mean-square fractional residuals are on the order of 2–3% in each case. The locations of the pi… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Scatter plots for λ-Orionis, the Perseus Molecular Cloud, and ρ-Ophiuchus (top, middle, and bottom rows, respectively). The AME-PAH correlations are shown in the left column while the AME-dust correlations are shown on the right. The Spearman rank correlation coeffici…

**Figure 5.** Figure 5: Results of bootstrap resampling to estimate the uncertainty in measured correlation coefficients for the Perseus Molecular Cloud. The minimal overlap between the AME-PAH and AME-Dust correlation coefficient distributions shows that the preference for PAHs as a tracer …

**Figure 6.** Figure 6: shows the map of AME at 30 GHz obtained using the Planck model parameters along with the locations of the 98 sources. Sources which are better correlated with PAHs than with thermal dust emission are shown in red. The PAH-correlated sources indicated in red include t…

**Figure 7.** Figure 7: A histogram of the significance at which PAHs are the preferred tracer for AME. The absolute magnitude indicates the statistical significance, and a positive (negative) value indicates that PAHs (large dust grains) are the best tracer. A notable fraction of sources pr…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

34 extracted references · 26 canonical work pages

[1]

H., Chluba, J., Hill, J

Abitbol, M. H., Chluba, J., Hill, J. C., & Johnson, B. R. 2017, Monthly Notices of the Royal Astronomical Society, 471, 1126 Ali-Ha ¨ ımoud, Y., Hirata, C. M., & Dickinson, C. 2009, Monthly Notices of the Royal Astronomical Society, 395, 1055

work page 2017
[2]

2020, MNRAS, 495, 3482

Arce-Tord, C., Vidal, M., Casassus, S., et al. 2020, MNRAS, 495, 3482

work page 2020
[3]

C., Onaka, T., Galliano, F., et al

Bell, A. C., Onaka, T., Galliano, F., et al. 2019, Publications of the Astronomical Society of Japan, 71

work page 2019
[4]

2021, MNRAS, 502, 589

Casassus, S., Vidal, M., Arce-Tord, C., et al. 2021, MNRAS, 502, 589

work page 2021
[5]

T., Hensley, B

Chuss, D. T., Hensley, B. S., Kogut, A. J., et al. 2022, The Astrophysical Journal, 940, 59

work page 2022
[6]

Curran, P. A. 2014, arXiv e-prints, arXiv:1411.3816

arXiv 2014
[7]

2011, MNRAS, 418, L35

Dickinson, C., Peel, M., & Vidal, M. 2011, MNRAS, 418, L35

2011
[8]

2018, New Astronomy Reviews, 80, 1 Dor´ e, O., Bock, J., Ashby, M., et al

Dickinson, C., Ali-Ha ¨ ımoud, Y., Barr, A., et al. 2018, New Astronomy Reviews, 80, 1 Dor´ e, O., Bock, J., Ashby, M., et al. 2014, arXiv e-prints, arXiv:1412.4872

arXiv 2018

Show all 34 references

[9]

T., & Hensley, B

Draine, B. T., & Hensley, B. 2013, The Astrophysical Journal, 765, 159

2013
[10]

T., & Lazarian, A

Draine, B. T., & Lazarian, A. 1998, The Astrophysical Journal, 508, 157 —. 1999, The Astrophysical Journal, 512, 740

1998
[11]

K., Jewell, J

Eriksen, H. K., Jewell, J. B., Dickinson, C., et al. 2008, ApJ, 676, 10 G´ enova-Santos, R., Rubi˜ no-Mart ´ ın, J. A., Rebolo, R., et al. 2015, MNRAS, 452, 4169 G´ enova-Santos, R., Rubi˜ no-Mart ´ ın, J. A., Pel´ aez-Santos, A., et al. 2017, MNRAS, 464, 4107

2008
[12]

M., Hivon, E., Banday, A

Gorski, K. M., Hivon, E., Banday, A. J., et al. 2005, The Astrophysical Journal, 622, 759

2005
[13]

E., Dickinson, C., Cleary, K

Harper, S. E., Dickinson, C., Cleary, K. A., et al. 2025, MNRAS, 536, 2914

2025
[14]

S., & Draine, B

Hensley, B. S., & Draine, B. T. 2023, The Astrophysical Journal, 948, 55

2023
[15]

S., Draine, B

Hensley, B. S., Draine, B. T., & Meisner, A. M. 2016, The Astrophysical Journal, 827, 45

2016
[16]

J., et al

Herman, D., Hensley, B., Andersen, K. J., et al. 2023, A&A, 675, A15

2023
[17]

Jones, A. P. 2009, A&A, 506, 797

2009
[18]

L., Franz, B

Kelsall, T., Weiland, J. L., Franz, B. A., et al. 1998, The Astrophysical Journal, 508, 44

1998
[19]

J., Bennett, C

Kogut, A., Banday, A. J., Bennett, C. L., et al. 1996, ApJ, 460, 1

1996
[20]

M., Readhead, A

Leitch, E. M., Readhead, A. C. S., Pearson, T. J., & Myers, S. T. 1997, The Astrophysical Journal, 486, L23

1997
[21]

Li, A., & Draine, B. T. 2001, ApJ, 554, 778

2001
[22]

J., & Morris, M

Maddalena, R. J., & Morris, M. 1987, ApJ, 323, 179

1987
[23]

S., Mezger, P

Mathis, J. S., Mezger, P. G., & Panagia, N. 1983, A&A, 500, 259

1983
[24]

2007, A&A, 468, 171

Meny, C., Gromov, V., Boudet, N., et al. 2007, A&A, 468, 171

2007
[25]

G., Mathis, J

Mezger, P. G., Mathis, J. S., & Panagia, N. 1982, A&A, 105, 372

1982
[26]

2020, ApJL, 900, L40

Nashimoto, M., Hattori, M., Poidevin, F., & G´ enova-Santos, R. 2020, ApJL, 900, L40

2020
[27]

2012, A&A, 543, A103 Planck Collaboration, Ade, P

Paradis, D., Dobashi, K., Shimoikura, T., et al. 2012, A&A, 543, A103 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2011, A&A, 536, A20 —. 2014, A&A, 565, A103 Planck Collaboration, Adam, R., Ade, P. A. R., et al. 2016a, A&A, 594, A10 Planck Collaboration, Ade, P. A...

2012
[28]

T., Rubi˜ no Mart ´ ın, J

Poidevin, F., G´ enova-Santos, R. T., Rubi˜ no Mart ´ ın, J. A., et al. 2023, Monthly Notices of the Royal Astronomical Society, 519, 3481 15

2023
[29]

Flannery, B. P. 1992, Numerical recipes in FORTRAN. The art of scientific computing (Cambridge University Press)

1992
[30]

C., Ricci, C., Aalto, S., et al

Privon, G. C., Ricci, C., Aalto, S., et al. 2020, ApJ, 893, 149

2020
[31]

Wehus, I. K. 2016, Monthly Notices of the Royal Astronomical Society, 458, 2032 Rubi˜ no-Mart ´ ın, J. A., Guidi, F., G´ enova-Santos, R. T., et al. 2023, MNRAS, 519, 3383

2016
[32]

T., Flagey, N., Paladini, R., et al

Tibbs, C. T., Flagey, N., Paladini, R., et al. 2011, Monthly Notices of the Royal Astronomical Society, 418, 1889

2011
[33]

Tielens, A. G. G. M. 2008, ARA&A, 46, 289

2008
[34]

C., & Draine, B

Weingartner, J. C., & Draine, B. T. 2001, ApJS, 134, 263

2001

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

H., Chluba, J., Hill, J

Abitbol, M. H., Chluba, J., Hill, J. C., & Johnson, B. R. 2017, Monthly Notices of the Royal Astronomical Society, 471, 1126 Ali-Ha ¨ ımoud, Y., Hirata, C. M., & Dickinson, C. 2009, Monthly Notices of the Royal Astronomical Society, 395, 1055

work page 2017

[2] [2]

2020, MNRAS, 495, 3482

Arce-Tord, C., Vidal, M., Casassus, S., et al. 2020, MNRAS, 495, 3482

work page 2020

[3] [3]

C., Onaka, T., Galliano, F., et al

Bell, A. C., Onaka, T., Galliano, F., et al. 2019, Publications of the Astronomical Society of Japan, 71

work page 2019

[4] [4]

2021, MNRAS, 502, 589

Casassus, S., Vidal, M., Arce-Tord, C., et al. 2021, MNRAS, 502, 589

work page 2021

[5] [5]

T., Hensley, B

Chuss, D. T., Hensley, B. S., Kogut, A. J., et al. 2022, The Astrophysical Journal, 940, 59

work page 2022

[6] [6]

Curran, P. A. 2014, arXiv e-prints, arXiv:1411.3816

arXiv 2014

[7] [7]

2011, MNRAS, 418, L35

Dickinson, C., Peel, M., & Vidal, M. 2011, MNRAS, 418, L35

2011

[8] [8]

2018, New Astronomy Reviews, 80, 1 Dor´ e, O., Bock, J., Ashby, M., et al

Dickinson, C., Ali-Ha ¨ ımoud, Y., Barr, A., et al. 2018, New Astronomy Reviews, 80, 1 Dor´ e, O., Bock, J., Ashby, M., et al. 2014, arXiv e-prints, arXiv:1412.4872

arXiv 2018