Amina Diop | Searcharxiv

arXiv2026

First Detection of HC5N in a Class II Disk around TW Hya

Over the last decade of ALMA's operation the molecular inventory of protoplanetary disks has expanded rapidly, revealing a diverse set of nitrogen-bearing organics and carbon-chain molecules that trace both prebiotic chemistry and fundamental disk physics. Despite this progress, detections of larger species such as cyanopolyynes have remained limited, leaving larger carbon-chain chemistry in Class II disks largely unconstrained. Here, we report the first detection of HC5N toward the TW Hya protoplanetary disk, representing the largest cyanopolyyne identified to date in a Class II system. We derive a HC5N column density for two rotational transitions J = 41-40 and J = 37-36, N_T ~ 10^12 cm^-2 for assumed T_rot = 20-50 K and optically thin emission in LTE. We compare HC5N and HC3N formation mechanisms and analyze the HC3N/HC5N ratio. We use a chemical model to estimate the expected abundance and emitting layer of HC5N in a TW Hya-like disk. Although HC5N emission is spatially unresolved, measured column densities suggest an origin in the warm molecular layer where CN-based pathways are active. This detection extends the known carbon-chain chemistry in Class II disks and demonstrates that long cyanopolyynes can form and persist in planet-forming environments.

astro-ph.SR↗

arXiv2023

Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine Learning Techniques

Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties. Often multiple factors are intertwined, impacting the abundances of both simple and complex species. We present a new approach to understanding these chemical and physical interdependencies using machine learning. Specifically we explore the case of CO modeled under the conditions of a generic disk and build an explanatory regression model to study the dependence of CO spatial density on the gas density, gas temperature, cosmic ray ionization rate, X-ray ionization rate, and UV flux. Our findings indicate that combinations of parameters play a surprisingly powerful role in regulating CO compared to any singular physical parameter. Moreover, in general, we find the conditions in the disk are destructive toward CO. CO depletion is further enhanced in an increased cosmic ray environment and in disks with higher initial C/O ratios. These dependencies uncovered by our new approach are consistent with previous studies, which are more modeling intensive and computationally expensive. Our work thus shows that machine learning can be a powerful tool not only for creating efficient predictive models, but also for enabling a deeper understanding of complex chemical processes.

astro-ph.EP↗