arXiv subjects

B. L. Crompvoets

Publications and source records attributed to B. L. Crompvoets.

3 recordsLinked to original sources

arXiv2026

Introducing SESHAT: A Tool for Object Classification from JWST Catalogs

JWST's exquisite data have opened the doors to new possibilities in detecting broad classes of astronomical objects, but also to new challenges in classifying those objects. In this work, we introduce SESHAT, the Stellar Evolutionary Stage Heuristic Assessment Tool for the identification of Young Stellar Objects, field stars (main sequence through asymptotic giant branch), brown dwarfs, white dwarfs, and galaxies, from any JWST photometry. This identification is done using the machine learning method XGBoost to analyze thousands of rows of synthetic photometry, modified at run-time to match the filters available in the data to be classified. We validate this tool on real data of both star-forming regions and cosmological fields, and find we are able to reproduce the observed classes of objects to a minimum of 85\% recall across every class, with all available data, without additional information on the ellipticity or spatial distribution of the objects. Furthermore, this tool can be used to test the filter choices for JWST proposals by verifying whether the chosen filters are sufficient to identify the desired class of objects. SESHAT is released as a Python package to the community for general use.

astro-ph.IM↗

arXiv2024

Climbing the Cliffs: Classifying YSOs in the Cosmic Cliffs JWST Data using a Probabilistic Random Forest

Among the first observations released to the public from the James Webb Space Telescope (JWST) was a section of the star-forming region NGC 3324 known colloquially as the "Cosmic Cliffs." We build a photometric catalog of the region and test the ability of using the Probabilistic Random Forest machine learning method to identify its Young Stellar Objects (YSOs). We find 450 candidate YSOs (cYSOs) out of 19~497 total objects within the field, 413 of which are cYSOs not found in previous works. These classifications are verified with several different metrics, including recall and precision. Using the obtained probabilities of objects being YSOs, we employ a Monte Carlo approach to determine the surface density of cYSOs in the Cosmic Cliffs, which we find to be largely coincident with column densities derived from Herschel data, up to a column density of 1.37 $\times$ 10$^{22}$ cm$^{-2}$. The newly determined number and spatial distribution of YSOs in the Cosmic Cliffs demonstrate that JWST is far more capable of detecting YSOs in dusty regions than Spitzer. Comparisons of the observed colors and brightness of faint cYSOs with those of pre-main-sequence models suggest JWST has detected a significant population of sub-stellar YSOs in the Cosmic Cliffs. The size of this population further suggests previous estimates of star formation efficiencies in molecular clouds have been systematically low.

astro-ph.SR↗

arXiv2022

OSSOS XXV: Large Populations and Scattering-Sticking in the Distant Transneptunian Resonances

There have been 77 TNOs discovered to be librating in the distant transneptunian resonances (beyond the 2:1 resonance, at semimajor axes greater than 47.7~AU) in four well-characterized surveys: the Outer Solar System Origins Survey (OSSOS) and three similar prior surveys. Here we use the OSSOS Survey Simulator to measure their intrinsic orbital distributions using an empirical parameterized model. Because many of the resonances had only one or very few detections, $j$:$k$ resonant objects were grouped by $k$ in order to have a better basis for comparison between models and reality. We also use the Survey Simulator to constrain their absolute populations, finding that they are much larger than predicted by any published Neptune migration model to date; we also find population ratios that are inconsistent with published models, presenting a challenge for future Kuiper Belt emplacement models. The estimated population ratios between these resonances are largely consistent with scattering-sticking predictions, though further discoveries of resonant TNOs with high-precision orbits will be needed to determine whether scattering-sticking can explain the entire distant resonant population or not.

astro-ph.EP↗