Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2607.17795

DESTINY: a new binding-energy-resolved astrochemical framework. Self-Consistent Competitiveness using Branched Absorbing Markov Chains

Maria Groyne·Cedric Baijot·Michaël De Becker
View original source ↗Read source document ↗Export metadata ↓

Abstract

Under cryogenic interstellar conditions, the amorphous structure of interstellar ice results in binding-energy distributions (BEDs) per species. However, only few studies attempted their inclusion in astrochemical models. This paper introduces DESTINY, a deterministic astrochemical framework designed to incorporate BEDs while self-consistently accounting for the competition among activated surface processes. The framework is currently constrained to a monolayer. Surface processes initiated by surface species are reformulated using a trial-frequency-capped formalism represented through branched absorbing Markov chains. The ordinary differential equations (ODE) system is redefined based on normalized effective probabilities. Preliminary results based on a reduced surface network are discussed. To isolate the effects of the probabilistic reformulation from those induced by BED discretizations, DESTINY is benchmarked against Nautilus, a single-BE rate-equation based open source code. In the single-BE limit, DESTINY reproduces the behavior of Nautilus for most species. The largest deviations are obtained for CH$_{x = [2,4]}$ ; these are traced to a different treatment of the H$_2$ encounter effect, impacting both H$_2$ surface exploration and desorption efficiencies within the DESTINY framework. Introducing BEDs redistributes species among adsorption sites of different depths, altering the balance between diffusion, desorption, and reactions. Significant effects are found for H, H$_2$, NH$_x$, NO, CH$_x$, CO and H$_x$CO. Preliminary results showed that the self-consistent treatment of the H$_2$ encounter effect coupled with the explicit treatment of BEDs can substantially modify grain-surface chemistry. Further framework extensions are expected in the near future.

Explore related subjects

astro-ph.IM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Maria Groyne, Cedric Baijot, Michaël De Becker. 2026-07-20. DESTINY: a new binding-energy-resolved astrochemical framework. Self-Consistent Competitiveness using Branched Absorbing Markov Chains. https://arxiv.org/abs/2607.17795

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Observations of Binary Stars with the 1.3-m Devasthal Fast Optical Telescope Using Speckle Interferometry: An Attempt

We present a feasibility study of implementing optical interferometry and speckle techniques with the 1.3-m Devasthal Fast Optical Telescope (DFOT) at Aryabhatta Research Institute of Observational Sciences (ARIES), which is currently dedicated to photometric observations. Using the sCMOS camera at the DFOT backend, we perform interferometric speckle observations of six binary stars. Standard Speckle Interferometry algorithms are applied to analyze the recorded speckle images. While this study does not aim to achieve the diffraction limit of DFOT or address a full science-driven resolution case, it serves as a crucial testbed for instrumentation, data acquisition, and analysis of Speckles with DFOT. Although the individual speckle patterns exhibit significant positional variations from frame to frame, the Speckle Interferometry analysis successfully recovers the characteristic three-peak structure of the wide binaries, allowing their relative separations and position angles to be determined, demonstrating the viability of the approach. The measured angular relative separations of $γ$ Leo, $γ$ Vir, and $ζ$ Her are $4.744'' \pm 0.012''$, $3.387'' \pm 0.027''$, and $1.582'' \pm 0.026''$, respectively, with corresponding projected position angles of $126.94^{\circ} \pm 0.14^\circ$, $171.57^{\circ} \pm 0.33^\circ$, and $81.06^{\circ} \pm 0.91^\circ$. These measurements are consistent with previously reported values in the literature, which provides strong motivation for more systematic observations and future implementation of optical interferometry techniques at meter-class telescopes.

astro-ph.IM↗
arXiv2026

Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks

Gravitational-wave detectors such as LIGO, Virgo, and KAGRA are highly sensitive instruments susceptible to many noise sources. Short-duration transient noise events, known as glitches, pose a particular challenge for data analysis pipelines, as they can mimic or obscure astrophysical signals. We present GlitchGAN, a class-conditional generative model built on the Conditional Derivative GAN (cDVGAN) architecture, capable of synthesizing seven glitch types from LIGO's third observing run (O3) directly in the time domain. GlitchGAN generalizes effectively, learning to reproduce a diverse glitch space consistent with high-quality DeepExtractor reconstructions, and can generate hybrid glitch morphologies by interpolating across its class-conditioning vector. It generates 1000 glitches in under 22 seconds on a CPU, suitable for detector simulations, mock data challenges, and pipeline validation. Synthetic glitches are validated using the Gravity Spy classifier and UMAP embeddings, both showing strong agreement with real data. To probe residual distributional differences, we train a separate holdout GlitchGAN model and use a downstream CNN to distinguish held-out real glitches from synthetic ones: detectability is high in a clean representation but drops substantially once both populations are injected into realistic detector noise, the condition under which they would typically be used. Despite this, GlitchGAN-generated glitches remain practically useful: augmenting real training sets with synthetic samples matches simple duplication of real data when data is abundant, and increasingly outperforms it as real data becomes scarce. Finally, we highlight a limitation of magnitude-only spectrograms: magnitude Q-transform classifiers can confidently misclassify physically unrealistic glitches from less robust models, underscoring the need for validation methods that preserve phase information.

astro-ph.IM↗
arXiv2026

SGN: A python framework for stream-processing pipelines

We present the Stream Graph Navigator (SGN), a lightweight Python framework for building streaming data applications. In SGN, stream-processing pipelines are built by connecting computational components into directed acyclic graphs that run within an event loop. The time-series extension of the SGN library, SGN-TS, introduces signal processing methods to handle time series data. Together, SGN and SGN-TS provide the foundation for SGNL, a matched-filtering gravitational-wave search pipeline, and are being adopted by multiple projects across the low-latency gravitational-wave data analysis infrastructure as an extensible and maintainable framework for future gravitational-wave observations.

astro-ph.IM↗