arXiv subjects

Anna McLeod

Publications and source records attributed to Anna McLeod.

2 recordsLinked to original sources

arXiv2026

The Effect of External Photoevaporation on the Disk Fraction in M17

A major obstacle to improving models of planet formation is understanding how the local environment influences the lifetime of the disks in which they form. The spread in observed disk lifetimes is caused by effects both observational (e.g., target selection, survey sensitivity) and physical (e.g., disk destruction by internal and external photoevaporation); however, the degree to which each plays a role remains poorly constrained. Isolating the impact of external photoevaporation on the disk lifetime benefits from the inclusion of low-mass ($\lesssim0.5$ M$_{\odot}$) YSOs, for which this effect is most predominant. In this work, we measure the inner disk fraction from JHK excess in the ~6000 M$_{\odot}$, ~1 Myr-old star-forming region M17. Using VLT/HAWK-I, we perform a deep photometric survey of an ~8$^{\prime}\times$8$^{\prime}$ field towards the region. The ~4 times greater sensitivity and ~2-3 times higher resolution than previous surveys of M17 reveal 10,339 sources. We select cluster members using the Massive Young Star-Forming Complex Study in Infrared and X-ray (MYStIX) catalog and find a disk fraction of 28$\pm$2%: the first X-ray-selected disk fraction measurement in M17 to include low-mass YSOs, and only the second such measurement in any high-mass star-forming region. After correcting for observational biases, we find no correlation between disk fraction and incident UV flux within M17, likely due to dynamical mixing within the region. However, when compared to other regions of similar age, we find lower disk fractions in regions with higher UV fields, suggesting that external photoevaporation decreases the average disk lifetime.

astro-ph.SR↗

arXiv2023

A Machine Learning Approach to Galactic Emission-Line Region Classification

Diagnostic diagrams of emission-line ratios have been used extensively to categorize extragalactic emission regions; however, these diagnostics are occasionally at odds with each other due to differing definitions. In this work, we study the applicability of supervised machine-learning techniques to systematically classify emission-line regions from the ratios of certain emission lines. Using the Million Mexican Model database, which contains information from grids of photoionization models using \texttt{cloudy}, and from shock models, we develop training and test sets of emission line fluxes for three key diagnostic ratios. The sets are created for three classifications: classic \hii{} regions, planetary nebulae, and supernova remnants. We train a neural network to classify a region as one of the three classes defined above given three key line ratios that are present both in the SITELLE and MUSE instruments' band-passes: [{\sc O\,iii}]$\lambda5007$/H$β$, [{\sc N\,ii}]$\lambda6583$/H$α$, ([{\sc S\,ii}]$\lambda6717$+[{\sc S\,ii}]$\lambda6731$)/H$α$. We also tested the impact of the addition of the [{\sc O\,ii}]$\lambda3726,3729$/[{\sc O\,iii}]$\lambda5007$ line ratio when available for the classification. A maximum luminosity limit is introduced to improve the classification of the planetary nebulae. Furthermore, the network is applied to SITELLE observations of a prominent field of M33. We discuss where the network succeeds and why it fails in certain cases. Our results provide a framework for the use of machine learning as a tool for the classification of extragalactic emission regions. Further work is needed to build more comprehensive training sets and adapt the method to additional observational constraints.

astro-ph.GA↗