<scp>PDFchem</scp>: A new fast method to determine ISM properties and infer environmental parameters using probability distributions

Bisbas, Thomas G.; Dishoeck, E. F. van; Hu, Chia-Yu; Schruba, Andreas

doi:10.1093/mnras/stac3487

“…In addition to that and inspired by hydrodynamical simulations that indicate the existence of a connection between the local (effective) A V and the local total number density, we use a variable density slab, which represents the A V -n H relation discussed in Bisbas et al (2019) and Gaches et al (2022). This slab covers densities from 10 2 to 10 5.5 cm −3 and is found to estimate reasonably well the average local PDR conditions of three-dimensional clouds (Bisbas et al 2023). It is thus interesting to explore whether it can reproduce the observed abundances of species.…”

Section: Pdr Modelingmentioning

confidence: 99%

Dependence of Chemical Abundance on the Cosmic-Ray Ionization Rate in IC 348

Luo

¹

,

Zhang

²

,

Bisbas

³

et al. 2023

ApJ

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Ions (e.g., H 3 + , H2O+) have been used extensively to quantify the cosmic-ray ionization rate (CRIR) in diffuse sightlines. However, measurements of the CRIR in low-to-intermediate density gas environments are rare, especially when background stars are absent. In this work, we combine molecular line observations of CO, OH, CH, and HCO+ in the star-forming cloud IC 348, and chemical models to constrain the value of the CRIR and study the response of the distribution of chemical abundances. The cloud boundary is found to have an A V of approximately 4 mag. From the interior to the exterior of the cloud, the observed 13CO line intensities drop by an order of magnitude. The calculated average abundance of 12CO (assuming 12C/13C=65) is (1.2 ± 0.9) × 10−4, which decreases by a factor of 6 from the interior to the outside regions. The average abundance of CH (3.3 ± 0.7 × 10−8) is in good agreement with previous findings in diffuse and translucent clouds (A V < 5 mag). However, we did not find a decline in CH abundance in regions of high extinction (A V ≃ 8 mag) as previously reported in Taurus. By comparing the observed molecular abundances and chemical models, we find a decreasing trend of the CRIR as A V increases. The inferred CRIR of ζ cr = (4.7 ± 1.5) × 10−16 s−1 at low A V is consistent with H 3 + measurements toward two nearby massive stars.

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“…The abundance ratio between HCO + and 12 CO (hereafter CO if not specified) is a tracer of the CR ionization rate (e.g., Zhou et al 2022;Bisbas et al 2023). To further test this notion in the SNR W28, we calculate the column density of HCO + and CO and their abundance ratio.…”

Section: Column Densities and Abundance Ratio Between 12 Co And Hco +mentioning

confidence: 99%

“…The J = 1-0 lines of HCO + , HCN, and HNC molecules are typical tracers of dense gas (e.g., Shirley 2015). The abundance ratio between HCO + and other specific molecules, for example, CO (e.g., Zhou et al 2018;Bisbas et al 2023), has been proposed to be a tracer of CR ionization (e.g., Albertsson et al 2018), while the line ratio ( ) ( ) I I HCN HNC serves as a tracer of gas kinetic temperature (e.g., Hacar et al 2020). Therefore, observation of these three lines toward W28 provides information on the physical and chemical effects of W28 on its adjacent MCs.…”

Section: Introductionmentioning

confidence: 99%

Shock and Cosmic-Ray Chemistry Associated with the Supernova Remnant W28

Tu 涂,

Chen 陈,

Zhou 周

et al. 2024

ApJ

1

0

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Supernova remnants (SNRs) exert a strong influence on the physics and chemistry of the nearby molecular clouds through shock waves and the cosmic rays (CRs) they accelerate. To investigate the SNR–cloud interaction in the prototype interacting SNR W28 (G6.4−0.1), we present new observations of the HCO+, HCN , and HNC J = 1–0 lines, supplemented by archival data of CO isotopes, N2H+ and H13CO+. We compare the spatial distribution and spectral line profiles of different molecular species. Using the local thermodynamic equilibrium assumption, we obtain an abundance ratio of N(HCO+)/N(CO) ∼10−4 in the northeastern shocked cloud, which is higher by an order of magnitude than the values in unshocked clouds. This can be accounted for by the chemistry jointly induced by shock and CRs, with the physical parameters previously obtained from observations: preshock density n H ∼ 2 × 105 cm−3, CR ionization rate ζ = 2.5 × 10−15 s−1, and shock velocity V s = 15–20 km s−1. Toward a point outside the northeastern boundary of W28 with a known high CR ionization rate, we estimate the abundance ratio N(HCO+)/N(N2H+) ≈ 0.6–3.3, which can be reproduced by a chemical simulation if a high density n H ∼ 2 × 105 cm−3 is adopted.

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“…The traditional approach of estimating the number density of GMCs is based on observations of column density and certain assumptions on the geometry of the clouds, for example, a cylindrical geometry for filamentary structures or spherical geometry for dense cores (André et al 2014). Bisbas et al (2021Bisbas et al ( , 2023 proposed an empirical power law to convert the observed column density to the mean number density of GMCs based on the MHD simulations from Wu et al (2017), which works decently but with noticeable scatter. Another method to constrain the number density of GMCs is utilizing density "probes," such as cyanoacetylene Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.…”

Section: Introductionmentioning

confidence: 99%

Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds

Xu

¹

,

Tan

²

,

Hsu

³

et al. 2023

ApJ

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We introduce the state-of-the-art deep-learning denoising diffusion probabilistic model as a method to infer the volume or number density of giant molecular clouds (GMCs) from projected mass surface density maps. We adopt magnetohydrodynamic simulations with different global magnetic field strengths and large-scale dynamics, i.e., noncolliding and colliding GMCs. We train a diffusion model on both mass surface density maps and their corresponding mass-weighted number density maps from different viewing angles for all the simulations. We compare the diffusion model performance with a more traditional empirical two-component and three-component power-law fitting method and with a more traditional neural network machine-learning approach. We conclude that the diffusion model achieves an order-of-magnitude improvement on the accuracy of predicting number density compared to that by other methods. We apply the diffusion method to some example astronomical column density maps of Taurus and the infrared dark clouds G28.37+0.07 and G35.39-0.33 to produce maps of their mean volume densities.

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PDFchem: A new fast method to determine ISM properties and infer environmental parameters using probability distributions

Cited by 16 publications

References 210 publications

Dependence of Chemical Abundance on the Cosmic-Ray Ionization Rate in IC 348

Dependence of Chemical Abundance on the Cosmic-Ray Ionization Rate in IC 348

Shock and Cosmic-Ray Chemistry Associated with the Supernova Remnant W28

Denoising Diffusion Probabilistic Models to Predict the Density of Molecular Clouds

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