星形成ゼミ #369 57-68

片岡章雅 2023/12/08

PHILIPP WEBER et al.

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The formation of giant planets has traditionally been divided
into two pathways: core accretion and gravitational instability.
However, in recent years, gravitational instability has becomeless favored, primarily due to the scarcity of observations offragmented protoplanetary disks around young stars and low
occurrence rate of massive planets on very wide orbits. In thisstudy, we present a SPHERE/IRDIS polarized lightobservation of the young outbursting object V960 Mon. The
image reveals a vast structure of intricately shaped scattered
light with several spiral arms. This nding motivated a reanalysis of archival ALMA 1.3 mm data acquired just two
years after the onset of the outburst of V960 Mon. In these
data, we discover several clumps of continuum emissionaligned along a spiral arm that coincides with the scattered
light structure. We interpret the localized emission as
fragments formed from a spiral arm under gravitational
collapse. Estimating the mass of solids within these clumps to
be of several Earth masses, we suggest this observation to be
the rst evidence of gravitational instability occurring onplanetary scales. This study discusses the signi cance of thisnding for planet formation and its potential connection withthe outbursting state of V960 Mon.
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Spirals and clumps in V960 Mon: signs of planet formation via gravitational instability around an
FU Ori star?

- ターゲット：V960 Mon- 観測1- SPHERE/IRDIS による偏光観測- 渦状腕等の複雑な構造が見えた。
- 観測2- ALMA 1.3 mm のデータ再解析もした。
- 観測時期はoutburstの２年後- いくつかのクランプと上記渦状腕に沿った構造を見つけた- 議論- 渦状腕に沿ったクランプ条の構造は重力不安定による結果ではないか。
- 見積もったクランプ質量は数地球質量。
- これは重力不安定による惑星形成の証左である。

メイン画像(SPHERE)の結果
G RAVITATIONAL I NSTABILITY AROUND AN FU OR OBJECT

3

tably, this companion is co-located with the scattering structures, meaning there is no evidence of its orbit being cleared(see Fig. 1). The detection of the companion in both polarized and unpolarized intensity implies the presence of small
dust grains in its immediate vicinity or indicates significant
scattering along the line of sight.
Motivated by the spectacular scattered light image seen in
Fig. 1, we conducted a re-examination of archival ALMA
band 6 data (programme-ID: 2016.1.00209.S, PI: Takami)
previously published in Kóspál et al. (2021). We describe
our data reduction and the differences to Kóspál et al. (2021)
in Appendix A, with an exploration of the imaging parameters to test for the robustness of the observed structures.
In the left panel of Figure 2 we show the ALMA 1.3 mm
continuum image and compare it to the polarized light image
in the right panel. Both panels superimpose ALMA contours
at 3, 4, and 5 rms levels (with rms = 28 µJy beam 1 ). Remarkably, these contours reveal multiple spatially-separated
fragments of continuum emission, reaching up to 7 . Theyalign along a clockwise-opening spiral arm originating fromthe primary source. The left panel of Fig. 2 shows that theseclumps roughly coincide with the southern spiral structure
observed in scattered light. We note that although the respective observations were taken within a small temporal separation, a perfect alignment is not expected as the scattered
light image probes the illuminated surface of the structures,
while the ALMA continuum image traces emission from dust

• H-Band(λ = 1.625 μm), 偏
光イメージ• 距離d = 2189

281 pc• 伴星位置にも偏光受かった• おそらく伴星付近のダストだろう。

Figure 1. SPHERE/IRDIS linearly polarised intensity (P I) observation of V960 Mon in H-band. P I is normalized to its maximum
value (6.6 mJy arcsec 2 ) and on a logarithmic scale. The frame iscentered on the primary, which was covered by the coronagraph displayed by a black circle. The projected scale is shown in the lower
right assuming the distance of 2189 pc.

The Astrophysical Journal Letters, 806:L4 (5pp), 2015 Jun

参考：伴星のJ-Band観測(Caratti o Garatti et al. 2015)

SPHERE と ALMAの比較
4

W EBER ET AL .

• ALMA 1.3 mm連続波• 矢印の付いた箇所は赤外スパイラルと一致しているように見える。

• 適当な仮定をして、クランプの質量を見積もった。κ̲1.3mm = 2.3 cm^2 g^­1 ,T= 50 K として、固体質量が3 to 10 M , くらい。なのでガス質量に直すと 1 to 3 MJupくらい。

•

Figure 2. Comparison of ALMA band 6 continuum image in mJy beam 1 using natural weights (left panel) with SPHERE/IRDIS polarized
light image (right panel, see Fig. 1). Both images are overlaid with contours of the ALMA continuum at levels of 3, 4, and 5 rms (with rms =28µJy beam 1 ). The clean beam of the ALMA observation is shown in white in the bottom right corner of the left panel (0.00 14 ⇥ 0.00 20).The
arrows in the left panel mark the dust clumps.

sumed distances were used, the inferred clump masses would
be even smaller. Consequently, we argue that the continuum
emission effectively traces clumping occurring at scales relevant to planet formation.
4. DISCUSSION

Additionally, the presence of multiple objects in the immediate vicinity (Kóspál et al. 2021) introduces the possibility ofconsidering them as potential candidates of a past fly-by.
Kóspál et al. (2021) estimate that the mass corresponding to the unresolved emission around the primary could
be as high as 0.33 M (updated from the original value offlAccurately quantifying the impact of radiation feedback in star formation ischallenging. To address this complex problem, we employ deep learning techniques,denoising diffusion probabilistic models (DDPMs), to predict the interstellar radiationeld (ISRF) strength based on three-band dust emission at 4.5 μm, 24 μm, and 250
μm. We adopt magnetohydrodynamic simulations from the STARFORGE (STAR
FORmation in Gaseous Environments) project that model star formation and giant
molecular cloud (GMC) evolution. We generate synthetic dust emission maps
matching observed spectral energy distributions in the Monoceros R2 (MonR2) GMC.
We train DDPMs to estimate the ISRF using synthetic three-band dust emission. The
dispersion between the predictions and true values is within a factor of 0.1 for the test
set. We extended our assessment of the diffusion model to include new simulations
with varying physical parameters. While there is a consistent offset observed in theseout- of-distribution simulations, the model effectively constrains the relative intensity
to within a factor of 2. Meanwhile, our analysis reveals weak correlation between the
ISRF solely derived from dust temperature and the actual ISRF. We apply our trainedmodel to predict the ISRF in MonR2, revealing a correspondence between intense
ISRF, bright sources, and high dust emission, con rming the model’s ability to capture
ISRF variations. Our model robustly predicts radiation feedback distribution, even incomplex, poorly constrained ISRF environments like those in uenced by nearby starclusters. However, precise ISRF predictions require an accurate training dataset
mirroring the target molecular cloud’s unique physical conditions.
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Predicting the Radiation Field of Molecular Clouds using Denoising Diffusion
Probabilistic Models
Duo Xu, et al.
• 星形成における輻射フィード
バックのモデルを作るため
deep learningを使った。

• 使ったシミュレーションデー
タは STARFORGE• ３バンドのダスト放射（ 4.5
μm, 24 μm, and 250）を予測することをゴールとした• 予測値と実際の値の分散は0.1

• general introduction• 星形成のフィードバックの基本的理解• 3D-PDRというコードがあるが、構造の不定性大きい。(Bisbas et al. 2012)• ウインド/アウトフローで直接運動量を返す。
• しかもFIRのラインのマッピングは難しい（暗• アウトフローは小さい領域（~pc）• 輻射でガスを電離したりしてエネルギーを返すい）• 機械学習(例：HII領域）• Denoising Di usion Probabilistic Models• 輻射の方は数pc- 数十pc
• 観測的制約• 大質量性からの輻射場はダスト連続波で図られるが、温度不定(Bernard et al. 2010).

(DDPMs)を使う（説明はなかったので、ʼググったʼ説明は次のページに。

• この研究• DDPMsを使って、分子雲における大質量星からの輻射の強さを見積もることを目標にする• もうちょっと頑張って、[O I], [C I], and [C II]の

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FIRの ne structure line を使ってPDRモデルを使うが、PDRモデルという不定性が残る
Pound & Wol re (2023)• 教師データはMHDシミュレーション

Bard (with Gemini pro)
ディノイズ拡散確率モデル (DDPM) とは
ディノイズ拡散確率モデル (DDPM) は、近年大きな注目を集めている強力な生成モデルの一種です。画像生成、テキス
ト生成、音楽合成など、さまざまな領域でリアルで高品質なデータ生成を実現できるユニークなアプローチを提供しま• 高品質なサンプル: DDPM は、他の生成モデルと比較しても非常にリアルで高品質なデータ生成能力を備えています。

す。

• 効率的な訓練: 大規模なデータセットで効率的に訓練でき、他のモデルに比べて比較的一般的計算リソースで済み

ここでは、主要な概念を紹介します。

ます。

• 汎用性: 画像、テキスト、音声など、さまざまなデータ形式に適用できます。

拡散プロセス:
ノイズのない画像から始めて、徐々にノイズを加えていき、純粋なノイズになるまで増やすプロセスを想像してください。DDPM はこのプロセスを逆行させ、純粋なノイズから始めて、段階的に「ノイズ除去」を行い、最終的にリアルな画像が生成されるまで反復します。

DDPM の課題:• 解釈可能性: DDPM の内部構造は複雑で、特定の出力がどのように生成されたのかを理解することが困難です。
• 制御可能性: 生成されたデータのディテールを細かく制御することは困難です。

確率モデリング:• 計算コスト: 訓練は効率的ですが、推論には計算コストが高くなる可能性があり、特に高解像度の出力の場合は顕著です。

DDPM は、ノイズ除去プロセスの次のステップを予測するために、確率フレームワークを利用します。各ステップでノイズがデータにどのように影響するかという確率分布を学習し、ノイズを除去する方法を判断します。

全体として、DDPM はリアルで高品質なデータ生成のための強力かつ多用途なツールです。さらなる研究開発により、データ生成に依存するさまざまな分野を革新する可能性を秘めています。

生成能力:
参考資料:
このノイズ除去プロセスを反復的に適用することで、DDPM はリアルなデータを最初から生成できます。この生成能力• ディノイズ拡散確率モデルの徹底ガイド: 理論から実装まで: https://learnopencv.com/category/di usion-models/により、以下のような様々な用途に活用できます。

• 画像生成: 現実世界に存在しない物体、風景、さらには顔などのリアルな画像を作成します。
• テキスト生成: 詩、コード、脚本、音楽など、一貫性があり独創的なテキスト形式を生成します。

made-easy-8414298ce4da• ディノイズ拡散確率モデルへのやさしい入門: https://medium.com/@madanjumaani/what-are-di usion-modelsthe-technology-behind-ai-generated-images-cef021757a2a• 音楽合成: さまざまなスタイルの新しい音楽作品を作曲します。

• 改良されたディノイズ拡散確率モデル: https://hojonathanho.github.io/di usion/• 画像編集: 古い画像や損傷した画像を修復や強化します。
•• ソクラテス式によるディノイズ拡散確率モデル(DDPM)理解: https://towardsdatascience.com/di usion-models-

データ拡張: 他の機械学習モデルを訓練するためのデータセットのサイズと多様性を人工的に増やす

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DDPM の利点:•

教師データとして使うMHDシミュレーション• STARFORGE

Figure 1. Synthetic dust observations, including images and SEDs, for simulations at various evolutio
di↵erent feedback configurations. The first column illustrates the SEDs of the synthetic observations.
columns showcase the three-color synthetic dust images at 4.5 µm (blue), 24 µm (green), and 250 µm (red)di↵erent radiative transfer configurations. The fourth column presents the projected radiation field stren
simulations, measured in ergs/cm 3 .

• (STAR FORmation in Gaseous Environments, Grudic et al. 2021).
• 基本的に全部入りシミュレーション(stellar feedbackも色々入ってる）• 初期GMC 2e4Msunで半径10pc、初期磁場2µG、ビリアル比2。
• 質量解像度10e­3 M⊙。9Myrの進化を追った• 背景の星間輻射はG̲0 = 1.7 , 17, 170の場合をやった。(強い輻射場における結果を見たかった)• 波長方向は５ビンだけ。These bins include H-ionizing (λ < 912˚A),

Table 1. Summary of STARFORGE Simulationsa

Training &Testing
Testingts (Myr)
3.5-7.54-5.5
55
1.7-4.3b

ISRF(G0 )
1.7
1.7
17
170
1.7

Jets
Yes
No
Yes
Yes
Yes

Nsample
6750
3000
81
81
127

Notes:a
Evolutionary time, ISRF, whether protostellar jets are included, and the number of image samples.
b
These simulations are initially subjected to turbulent driving
for two crossing times, equivalent to 17.5 Myr. Furthermore,they incorporate the updated heating and cooling treatments.

far ultraviolet (912˚A< λ < 1550˚A), near ultraviolet (1550˚A< λent treatments for the stellar spectrum, a blackbody<3600˚A), optical-to-near in- frared (3600˚A<
λ and< 3μm),SED
a YSO and
SED, farat various evolutionary stages.
The figure also presents the projected radiation field avinfrared (λ > 3μm) ranges.
eraged by the radiation energy along the line-of-sight.
The synthetic dust emission SEDs are sensitive to theradiation in the optical to near-infr
emission of the cloud material in therange. In contrast, the SEDs genera
ing sources modeled as YSOs displa
from 1-10 µm. This di↵erence aris

対比する観測データ:MonR2 observations• Monoceros R2 GMC, 距離860pc, 33000Msun, >900YSOs• Spitzer IRAC at 4.5µm, MIPS at 24µm, Herschel SPIRE 250µm
A&A proofs: manuscript no. 40363corr

参考：同じ領域のHerschel 画像

Kumar et al. 2022

Fig. 1. Column density maps of the Mon R2 star-forming region at two angular resolutions, produced using getsf. The vertical colour bar is inunits of cm 2 . At a distance of 830 pc, the 11.400 resolution corresponds to a beam of 0.046 pc.

range in mass per unit length (M/L) of filaments, most remarkably that of a 1000M pc 1 filament in NGC6334 (André et al.

tion in the Milky-Way plane covered by the Herschel Hi-GAL
survey (Molinari et al. 2010) takes place in density amplified

fi6

データの概観5

Figure 2. A collection of all the synthetic dust SEDs, where the intensity of color is proportional to the number of stacked
SEDs. Blue dots represent observations in Spitzer bands (3.5 µm, 4.5 µm, 5.8 µm, 8.0 µm, 24 µm) and Herschel bands (250µm, 350 µm, 500 µm) within MonR2.

•

Figure 1. Synthetic dust observations, including images and SEDs, for simulations at various evolutionary stages and withdi↵erent feedback configurations. The first column illustrates the SEDs of the synthetic observations. The second and thirdcolumns showcase the three-color synthetic dust images at 4.5 µm (blue), 24 µm (green), and 250 µm (red) wavelengths, utilizingdi↵erent radiative transfer configurations. The fourth column presents the projected radiation field strength obtained from thesimulations, measured in ergs/cm 3 .

••Table 1. Summary of STARFORGE Simulations

Training &Testing
Testingts (Myr)
3.5-7.54-5.5
55
1.7-4.3b

ISRF(G0 )
1.7
1.7
17
170
1.7

Jets
Yes
No
Yes
Yes
Yes

a

Nsample
6750
3000
81
81
127

•

flow features, which often appear in these bands (Looneythe absence of non-thermal excitation mechanisms, such
et al. 2007; Tobin et al. 2008; Takami et al. 2010). Someas shocks, which are not included in post-processing. It
of4.5
the excess
emissionfrom shockis important to note that the presence of H2 lines at 2.22
μm,observed
24 μm,and likely
250arisesμmの３つを教師データとして、ピクセルごとのradiation
excited H2 and CO lines (Cyganowski et al. 2008), whichµm, 2.41 µm, 2.63 µm, and 3.00 µm may cause contamieldsを見積もれるかどうかトライした
are not included in our radiative transfer modeling step.
nation in the 3.6 µm bandpass. Additionally, the strong
aromatic infrared bands at 3.30 µm, 6.20 µm, 7.70 µm,
8.60 µm, 11.30 µm, and 12.70 µm can potentially con2.4. Constructing the Training Set
taminate the 3.6 µm, 5.8 µm, and 8 µm bands (Foschino•In thisなお、8µmは観測（青）をうまく再現していないが、これはPAHの放射をうまくstudy, we utilize three specific bands of dust
et al. 2019). As our project aims to infer the ISRF using
linesのコンタミがあるかも。→今emission,再現できていないから。また、3.6µmにはH2
namely 4.5 µm, 24 µm, and 250 µm, as input
a limited amount of data, it is crucial to ensure that thefor training
the machine learning model to predict
回はPAHが影響しそうな3.6
μm, the
5.8 μm,8.0 μmは使わないことにした。
synthetic
data closely
resembles the real data. To mitiradiation field at the pixel level. These bands cover bothgate this issue and achieve better performance, we havenear-infrared and far-infrared dust emission and, imporexcluded bandpasses that include H2 lines and strong
tantly, encompass information that is well-modeled by
PAH feature emission (3.6 µm, 5.8 µm, 8.0 µm) in this
正解：視線方向に積分した輻射強度マップ(64*64)
ourtraining set. Figure 2 presents a gallery of SEDs
study.
for all the synthetic data, with darker colors indicating
To enhance the model’s capability to handle real oba higher number of stacked SEDs. Observations fromservational data, which may include the presence of forethe MonR2 GMC are included in the figure for referground and background stars, we randomly introduce
インプット：３バンドのダストマップ（3*64*64）
ence,
showcasing the Spitzer bands (3.6 µm, 4.5 µm, 5.8
bright false sources that simulate such stars in the 4.5µm, 8.0 µm, 24 µm) and Herschel bands (250 µm, 350µm images. These sources exhibit a two-dimensionalµm, 500 µm). The synthetic SEDs demonstrate a broad
Gaussian intensity distribution, where the peak intenrangeofcoveragefortheobserved
data
points.
Nonethe全部で9,750 synthetic dust maps を作った。それを80%の教師データと20%のテス
sity is randomly selected as a fraction between 0.1 andless, there is an evident discrepancy in the synthetic
1 of the 99.5th percentile of the 4.5 µm images. For
ト用データにランダムに分けておいた。
SEDs,
particularly in the 8 µm emission, where it iseach synthetic dust map, we utilize the projected radinoticeably underestimated. This discrepancy is caused
ation field averaged along the line-of-sight, weighted byby strong PAH emission, indicating that the adopted
radiation energy, as the target for the machine learning
dust model does not replicate the observed PAH emistraining. In summary, the input consists of three-band

Denoisingの例

メイン結果の例
98

Figure 3. Demonstration of the di↵usion process (reverse) on a sample in the test set. In the upper row, the first panel
represents the input (condition) for the di↵usion model, while in the lower row, the first panel represents the corresponding
target (ground truth). The initial status is denoted as T1000 , which corresponds to the random Gaussian noise. The final statesof the reverse Markov chain, representing the final predictions by the di↵usion model, are indicated as T0 . The intermediate
steps of the reverse Markov chain, ranging from T = 0 to 1000, are depicted in the remaining panels.

inner parts subject to extinction and irradiation by protostars.
Figure 6 depicts the correlation between the true ISRF
and the dust temperature calculated using radmc-3d.
We observe a weak or even unclear linear trend between these two quantities. Since the plot is in log
scale, the pattern appears similar to the ISRFT Dust
vs ISRFTrue plot, but with di↵erent magnitudes in theirvalues. For reference, the one-to-one line of ISRFT Dustand ISRFTrue is also shown in Figure 6. The inferred
ISRF from the dust temperature exhibits a notable o↵-includes synthetic images created using di↵erent dust
models and entirely novel MHD simulations featuring diverse physical parameters. Since simulations can never
perfectly model observations, even when great care istaken to match physical conditions and to include relevant physical e↵ects, our training data is by definition
out-of-distribution compared to the observational data.
The tests presented here thus provide a more realistic
assessment of the prediction accuracy of the model applied to observations.

Figure 4. Predicted radiation field generated by the di↵usion model (columns 3 and 6), along with the corresponding threeband dust emission at 4.5 µm (blue), 24 µm (green), and 250 µm (red) (columns 1 and 4), and the ground truth radiation field(columns 2 and 5).

observed in the fiducial test set described in Section
3.1. This suggests that the di↵usion model can makemodel on these simulations. Despite the large di↵erencein the ISRF, the di↵usion model still produces predic-結果の続き片岡注：説明しようにも物理はない…
10

11

Figure 5. 2D histogram illustrating the correlation between the predictions of the di↵usion model and the ground truth valuesof the radiation fields.

the ground truth values for the 10 times higher ISRF
is approximately 0.25 dex, corresponding to an underestimation factor of 1.8. Similarly, the o↵set for the 100
times higher ISRF is about 0.43 dex, equivalent to anunderestimation factor of 2.7. Nevertheless, the relative
ISRF intensity is well constrained, as the predictions andthe ground truth values still exhibit a logarithmic-linear
correlation, with a dispersion of 0.5. Consequently, we
conclude that the di↵usion model is capable of providing
a reasonably accurate estimation of the radiation fieldeven when the true field is orders of magnitude di↵erent
than that of the training set. However, if a more precise
estimation of the radiation field in extremely high ISRF
regions is desired, it is advisable to retrain the di↵usion
model using an appropriate synthetic dataset.

Figure 7. Similar to Figure 5, but applied to synthetic dust images generated using di↵erent dust models. The black dottedline represents the one-to-one line, which represents perfect prediction. The blue dotted line represents the ten-to-one line,
indicating underestimation by a factor of 10. The brown dotted line represents the one-to-ten line, indicating overestimation bya factor of 10.

lations (Hopkins et al. 2023). In contrast, the fiducial
simulations used for training relied on a simpler fitting
function based on tabulated CLOUDY results (Ferland
et al. 1998), accounting for local density, temperature,and metallicity. The updated MHD simulation appliedin this assessment o↵ers a more detailed representationof heating and cooling processes, encompassing all major molecular, atomic, nebular, and continuum interactions, to better capture the thermal state of the cold ISM(Hopkins et al. 2023). This fresh batch of simulations
allows us to assess the di↵usion model’s performance
under extreme out-of-distribution conditions.
Figure 10 displays the radiation field predictions generated by the di↵usion model for these updated simulations. Despite significant di↵erences in initial conditions

actual radiation field values for these fresh simulations.
The di↵usion model still performs reasonably well but
exhibits a systematic o↵set and some variability in its
predictions. It’s worth noting that in these new simulations, the di↵usion model appears to consistently overestimate the ISRF by approximately a factor of 3. Moreover, the dispersion in the predictions is approximately
a factor of 2.
It’s possible that the updated heating and coolingmethods and/or the increased density within the clumps
and cores in the new simulations systematically lead toa decrease in the ISRF values. This is evident from thefact that the highest ground truth ISRF value in the
new simulations is considerably smaller than that in thefiducial simulations, as observed in Figure 5 and 7.

13• IRSFを10, 100倍にすると、輻射場を過小評価した（a systematic underestimation
factor of 1.8 and 2.7 for the 10 and 100
times higher ISRF ）Figure 9. Similar to Figure 5, but specifically for simulations with an ISRF of G0 = 17 (left panel) and G0 = 170 (right panel).
The black dotted line represents the one-to-one line, which represents perfect prediction. The blue dotted line represents the
ten-to-one line, indicating underestimation by a factor of 10.
15
Our findings demonstrate that the average projected
positive trend between
the dust emission at 250 µm andline-of-sight ISRF in MonR2 is notably higher than thethe predicted ISRF in the test data.
fiducial value of 1 G0 . This can be attributed to several
Typically, longer wavelength emission, such as obfactors. First, MonR2 is an active star-forming region
served by Herschel at 160 µm, 250 µm, 350 µm, and 500
where the radiation field is dominated by forming stars,µm, is commonly used to estimate the dust column density and temperature. The dust temperature, in turn,leading to a significant increase in the level of ISRF.
Additionally, our predicted ISRF is averaged along thecan be used to estimate the radiation field, as shownline-of-sight into the molecular cloud without being exin Equation 1. However, as discussed in Section 3.1
tincted by dust, providing a more accurate estimationand shown in Figure 6, there is only a limited correlaof the actual impact of radiation feedback across thetion between the ISRF and the dust temperature in thecloud. This information is crucial for further analyses
synthetic test data. Our study extends beyond theseof molecular clouds, including for investigating the inlonger wavelengths to include the analysis of shorter
wavelength emission. By considering this broader wavefluence of radiation feedback on the core mass function
or variation of turbulent properties.
length range, we obtain a more accurate estimation of
We next perform statistical analyses to investigate thethe stellar radiative feedback and dust emission.
Figure 11. Similar to Figure 5, but applied to synthetic dust images generated from new simulations with updated heating
and coolingtreatments,
as well as twocrossing times
line signifies the we
one-to-one
line,
correlation between
thepredicted
ISRF
andof turbulent
dust driving.
emis-The black dotted Finally,
investigate
the relationship between theindicating perfect predictions. The blue dotted line signifies the ten-to-one line, suggesting underestimation by a factor of 10.
brown dotted 2Dline represents
the one-to-tenillustrating
line, indicating overestimationof 10.
sion. Figure 13The
presents
histograms
this by a factorpredicted
ISRF, the column density and the dust temcorrelation at 4.5 µm, 24 µm, and 250 µm in MonR2.
perature, utilizing the column density map and the dust
The dust emission at 4.5 µm does not exhibit a clear
temperature maps derived by Pokhrel et al. (2016). Figoverall trend with the predicted ISRF. The presence
ure 14 showcases the column density map and the dustof numerous blue dots, likely foreground and/or backtemperature map of MonR2. The correlation between
ground stars unrelated to MonR2, creates a branch inthe predicted ISRF and the column density, as well as• heating/coolingの物理を変えたら輻射強度を過大評価した

Figure 11. Similar to Figure 5, but applied to synthetic dust images generated from new simulations with updated heating
and cooling treatments, as well as two crossing times of turbulent driving. The black dotted line signifies the one-to-one line,
indicating perfect predictions. The blue dotted line signifies the ten-to-one line, suggesting underestimation by a factor of 10.
The brown dotted line represents the one-to-ten line, indicating overestimation by a factor of 10.

MonR2の結果

Figure 12. Three-band dust emission at 4.5 µm(blue), 24 µm(green), and 250 µm(red) in MonR2 (left panel), alongside thepredicted radiation field by the di↵usion model (right panel). The contour lines overlaid on the dust emission maps represent
the intensity of the radiation field predicted by the di↵usion model.

synthetic test samples and real observational data. The

1. We performed radiative transfer simulations with