arXiv subjects

Gijs Vermariën

Publications and source records attributed to Gijs Vermariën.

11 recordsLinked to original sources

arXiv2026

The curious case of HCO$^+$: Extreme abundances under extreme conditions

Context. HCO$^+$ is widely observed in both Galactic and extragalactic environments and typically exhibits abundances of $10^{-9}-10^{-8}$. However, recent modeling studies suggest that in environments exposed to elevated cosmic-ray ionization rates and strong thermal or mechanical processing its abundance may increase by several orders of magnitude. Aims. To interpret these predictions, we need to understand the physical conditions that produce extreme HCO$^+$ abundances and the chemical pathways that drive these enhancements. Methods. We used UCLCHEM, a gas-grain chemical code, to model the chemistry of HCO$^+$ in dense molecular, protostellar, and shocked gas under elevated cosmic-ray ionization rates ($ζ\ge 10^{-15}\,\mathrm{s^{-1}}$). Results. Extreme HCO$^+$ enhancements leading to $X$(HCO$^+$) $\gtrsim 10^{-4}$ occur only under specific combinations of temperature, density, and cosmic-ray ionization rate, primarily in protostellar and shocked gas. Increasing density generally suppresses the peak HCO$^+$ abundance, requiring higher ionization rates to produce comparable enhancements. More importantly, the extreme enhancements seem to be very dependent on the chemical network used (in our case UMIST12 versus UMIST22, with the latter leading to extreme abundances). These differences among networks arise from the removal of the destruction pathway of HCO$^+$: C + HCO$^+$ $\rightarrow$ CO + CH$^+$, and propagate to several other species including N$_2$H$^+$, H$_2$O, and H$_3$O$^{+}$.

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arXiv2026

UCLCHEM 4.0: An open source gas-grain astrochemistry simulation framework

Astrochemical modeling is a key tool for the understanding of the formation and destruction of molecules in the dense gas of the interstellar medium, as observed by modern day observational facilities. UCLCHEM is a comprehensive astrochemical modeling framework that can model the interstellar medium ranging from extra-galactic to protoplanetary disks scales. The framework consists of a core routine that solves chemical reaction networks as a function of time. The chemistry includes a description of gas and ice grain chemistry and the interactions between the two. The physical modeling includes parametrizations for modelling cloud collapse, protostellar cores and shocks as well as the ability to provide user defined inputs. This manuscript provides an overview of the physics and chemistry included in UCLCHEM, as well as the inner workings of the solver routine and the programming interface.

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arXiv2026

ALMA Central Molecular Zone Exploration Survey (ACES) I: Overview

The mass flows and energy cycles within the inner regions of galaxies exert a powerful influence on the evolution of the galaxy population. The centre of the Milky Way is the only galactic nucleus for which it is possible to resolve the physical mechanisms that drive these cycles, namely star formation and feedback, while also tracing global (>100 pc) processes which determine where and when star formation and feedback occur. We present an overview of ACES, the 'Atacama Large Millimeter/submillimeter Array (ALMA) CMZ Exploration Survey', a ~1.5" angular resolution, 0.2-3 km/s spectral resolution ALMA Band 3 (85-102 GHz), survey of the 'Central Molecular Zone' (CMZ) -- the inner-100 pc of the Galaxy (l = 359.4 deg to 0.8 deg). ACES spectral setup is tuned to observe optimal tracers of the physical, chemical, and kinematic conditions in over 70 spectral features (e.g. HCO+, HNCO, SiO, H40alpha, complex molecules) of the gas in the CMZ, to derive the properties of all potentially star-forming Galactic Centre gas, from global scales (100 pc) to dense ~0.05 pc structures that are expected to host individual star-forming cores, down to sub-sonic (<0.4 km/s) velocity resolution. In this overview paper, we provide the scientific justification for the ACES survey, explain the choice of observational setup, and describe the data legacy products. Finally, we show some of the initial ACES data which highlight the power of ACES' combination of high angular resolution, unprecedented spatial dynamic range, sensitivity, spectral resolution and spectral bandwidth as an illustration of how ACES aims to understand how global processes set the location, intensity, and timescales for star formation and feedback in the CMZ.

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arXiv2026

One-dimensional and time-dependent modelling of complex organic molecules in protostars

Complex organic molecules (COMs), the building blocks of life, have been extensively detected under various physical conditions, from quiescent clouds to star-forming regions. They therefore serve as excellent tracers for the local physical and chemical properties of these environments. Proper models that are capable of grasping the formation and destruction of COMs are crucial to understanding observations. However, given that distinct COMs may be detected from different locations and at varying times, we improve UCLCHEM - a gas-grain chemical code - to a one-dimensional, time-dependent model, tailored to protostars. In this update, we examine two stages of a protostar: the prestellar and heating stages, incorporating a simple radiative mechanism for both the internal and external radiation fields of the cloud. This approach relies on the key assumption that the dust and gas temperatures are completely coupled. Ultimately, we implement an updated version of our model to interpret observations obtained through both single-dish and interferometry under varying conditions, including a SgrB2(N1) hot core, massive Galactic clumps and a hot core in Orion. We show that our model could reproduce these observations well, highlighting that some COMs are positioned at a higher temperature in the envelope, whereas others are from the lower temperature, potentially leading to misinterpretation when using a single-point model. In a particular case of SgrB2(N1), the best model indicates that the cosmic-ray ionisation rate significantly exceeds the value typically used for the standard interstellar medium. Our model shows as an efficient computational tool particularly useful for better insights into observations of COMs.

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arXiv2025

Carbox: an end-to-end differentiable astrochemical simulation framework

Since the first observations of interstellar molecules, astrochemical simulations have been employed to model and understand its formation and destruction path- ways. With the advent of high-resolution telescopes such as JWST and ALMA, the number of detected molecules has increased significantly, thereby creating a need for increasingly complex chemical reaction networks. To model such complex systems, we have developed Carbox, a new astrochemical simulation code that leverages the modern high-performance transformation framework Jax. With Jax enabling computational efficiency and differentiability, Carbox can easily utilize GPU acceleration, be used to study sensitivity and uncertainty, and interface with advances in Scientific Machine Learning. All of these features are crucial for modeling the molecules observed by current and next-generation telescopes.

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arXiv2025

Chemical templates of the Central Molecular Zone. Shock and protostellar object signatures under Galactic Center conditions

(Abridged) The Central Molecular Zone (CMZ) of the Milky Way exhibits extreme conditions, including high gas densities, elevated temperatures, enhanced cosmic-ray ionization rates, and large-scale dynamics. Large-scale molecular surveys reveal increasing chemical and physical complexity in the CMZ. A key step to interpreting the molecular richness found in the CMZ is to build chemical templates tailored to its diverse conditions. The combined impact of high ionization, elevated temperatures, and dense gas remains insufficiently explored for observable tracers. In this study, we utilized UCLCHEM, a gas-grain time-dependent chemical model, to link physical conditions with their corresponding molecular signatures and identify key tracers of temperature, density, ionization, and shock activity. We ran a grid of models of shocks and protostellar objects representative of typical CMZ conditions, focusing on twenty-four species, including complex organic molecules. Shocked and protostellar environments show distinct evolutionary timescales ($\lesssim 10^4$ vs. $\gtrsim 10^4$ years), with 300 K emerging as a key temperature threshold for chemical differentiation. We find that cosmic-ray ionization and temperature are the main drivers of chemical trends. HCO$^+$, H$_2$CO, and CH$_3$SH trace ionization, while HCO, HCO$^+$, CH$_3$SH, CH$_3$NCO, and HCOOCH$_3$ show consistent abundance contrasts between shocks and protostellar regions over similar temperature ranges. While our models underpredict some complex organics in shocks, they reproduce observed trends for most species, supporting scenarios involving recurring shocks in Galactic Center clouds and enhanced ionization towards Sgr B2(N2). Future work should assess the role of shock recurrence and metallicity in shaping chemistry.

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arXiv2025

NeuralPDR: Neural Differential Equations as surrogate models for Photodissociation Regions

Computational astrochemical models are essential for helping us interpret and understand the observations of different astrophysical environments. In the age of high-resolution telescopes such as JWST and ALMA, the substructure of many objects can be resolved, raising the need for astrochemical modeling at these smaller scales, meaning that the simulations of these objects need to include both the physics and chemistry to accurately model the observations. The computational cost of the simulations coupling both the three-dimensional hydrodynamics and chemistry is enormous, creating an opportunity for surrogate models that can effectively substitute the chemical solver. In this work we present surrogate models that can replace the original chemical code, namely Latent Augmented Neural Ordinary Differential Equations. We train these surrogate architectures on three datasets of increasing physical complexity, with the last dataset derived directly from a three-dimensional simulation of a molecular cloud using a Photodissociation Region (PDR) code, 3D-PDR. We show that these surrogate models can provide speedup and reproduce the original observable column density maps of the dataset. This enables the rapid inference of the chemistry (on the GPU), allowing for the faster statistical inference of observations or increasing the resolution in hydrodynamical simulations of astrophysical environments.

astro-ph.GA↗

arXiv2025

Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning

Context. The outer Milky Way has a lower metallicity than our solar neighbourhood, but still many molecules are detected in the region. Molecular line ratios can serve as probes to better understand the chemistry and physics in these regions. Aims. We use interpretable machine learning to study 9 different molecular ratios, helping us understand the forward connection between the physics of these environments and the carbon and oxygen chemistries. Methods. Using a large grid of astrochemical models generated using UCLCHEM, we study the properties of molecular clouds of low oxygen and carbon initial abundance. We first try to understand the line ratios using a classical analysis. We then move on to using interpretable machine learning, namely Shapley Additive Explanations (SHAP), to understand the higher order dependencies of the ratios over the entire parameter grid. Lastly we use the Uniform Manifold Approximation and Projection technique (UMAP) as a reduction method to create intuitive groupings of models. Results. We find that the parameter space is well covered by the line ratios, allowing us to investigate all input parameters. SHAP analysis shows that the temperature and density are the most important features, but the carbon and oxygen abundances are important in parts of the parameter space. Lastly, we find that we can group different types of ratios using UMAP. Conclusions. We show the chosen ratios are mostly sensitive to changes in the carbon initial abundance, together with the temperature and density. Especially the CN/HCN and HNC/HCN ratio are shown to be sensitive to the initial carbon abundance, making them excellent probes for this parameter. Out of the ratios, only CS/SO shows a sensitivity to the oxygen abundance.

astro-ph.GA↗

arXiv2024

3D-PDR Orion dataset and NeuralPDR: Neural Differential Equations for Photodissociation Regions

We present a novel dataset of simulations of the photodissociation region (PDR) in the Orion Bar and provide benchmarks of emulators for the dataset. Numerical models of PDRs are computationally expensive since the modeling of these changing regions requires resolving the thermal balance and chemical composition along a line-of-sight into an interstellar cloud. This often makes it a bottleneck for 3D simulations of these regions. In this work, we provide a dataset of 8192 models with different initial conditions simulated with 3D-PDR. We then benchmark different architectures, focusing on Augmented Neural Ordinary Differential Equation (ANODE) based models (Code be found at https://github.com/uclchem/neuralpdr). Obtaining fast and robust emulators that can be included as preconditioners of classical codes or full emulators into 3D simulations of PDRs.

astro-ph.GA↗

arXiv2024

A broad linewidth, compact, millimeter-bright molecular emission line source near the Galactic Center

A compact source, G0.02467-0.0727, was detected in ALMA \threemm observations in continuum and very broad line emission. The continuum emission has a spectral index $α\approx3.3$, suggesting that the emission is from dust. The line emission is detected in several transitions of CS, SO, and SO$_2$ and exhibits a line width FWHM $\approx160$ \kms. The line profile appears Gaussian. The emission is weakly spatially resolved, coming from an area on the sky $\lesssim1"$ in diameter ($\lesssim10^4$ AU at the distance of the Galactic Center; GC). The centroid velocity is $v_{LSR}\approx40$-$50$ \kms, which is consistent with a location in the Galactic Center. With multiple SO lines detected, and assuming local thermodynamic equilibrium (LTE) conditions, $T_\mathrm{LTE} = 13$ K, which is colder than seen in typical GC clouds, though we cannot rule out low-density, subthermally excited, warmer gas. Despite the high velocity dispersion, no emission is observed from SiO, suggesting that there are no strong ($\gtrsim10~\mathrm{km~s}^{-1}$) shocks in the molecular gas. There are no detections at other wavelengths, including X-ray, infrared, and radio. We consider several explanations for the Millimeter Ultra-Broad Line Object (MUBLO), including protostellar outflow, explosive outflow, collapsing cloud, evolved star, stellar merger, high-velocity compact cloud, intermediate mass black hole, and background galaxy. Most of these conceptual models are either inconsistent with the data or do not fully explain it. The MUBLO is, at present, an observationally unique object.

astro-ph.GA↗

arXiv2023

A statistical and machine learning approach to the study of astrochemistry

In order to obtain a good understanding of astrochemistry, it is crucial to better understand the key parameters that govern grain-surface chemistry. For many chemical networks, these crucial parameters are the binding energies of the species. However, there exists much disagreement regarding these values in the literature. In this work, a Bayesian inference approach is taken to estimate these values. It is found that this is difficult to do in the absence of enough data. The Massive Optimised Parameter Estimation and Data (MOPED) compression algorithm is then used to help determine which species should be prioritised for future detections in order to better constrain the values of binding energies. Finally, an interpretable machine learning approach is taken in order to better understand the non-linear relationship between binding energies and the final abundances of specific species of interest.

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