Rubin-LSST

Abstract:

Massive stars are predominantly born in stellar associations or clusters¹. Their radiation fields, stellar winds and supernovae strongly impact their local environment. In the first few million years of a cluster's life, massive stars are dynamically ejected and run away from the cluster at high speed². However, the production rate of dynamically ejected runaways is poorly constrained. Here we report on a sample of 55 massive runaway stars ejected from the young cluster R136 in the Large Magellanic Cloud. An astrometric analysis of Gaia data^3-5 reveals two channels of dynamically ejected runaways. The first channel ejects massive stars in all directions and is consistent with dynamical interactions during and after the birth of R136. The second channel launches stars in a preferred direction and may be related to a cluster interaction. We found that 23-33% of the most luminous stars initially born in R136 are runaways. Model predictions^2,6,7 have significantly underestimated the dynamical escape fraction of massive stars. Consequently, their role in shaping and heating the interstellar and galactic media and their role in driving galactic outflows are far more important than previously thought^8,9.

Abstract:

The upcoming Cherenkov Telescope Array (CTA) will dramatically improve the point-source sensitivity compared to the current Imaging Atmospheric Cherenkov Telescopes (IACTs). One of the key science projects of CTA will be a survey of the whole Galactic plane (GPS) using both southern and northern observatories, specifically focusing on the inner galactic region. We extend a deep learning-based image segmentation software pipeline (autosource-id) developed on Fermi-LAT data to detect and classify extended sources for the simulated CTA GPS. Using updated instrument response functions for CTA (Prod5), we test this pipeline on simulated gamma-ray sources lying in the inner galactic region (specifically 0^◦ < l < 20^◦, |b| < 3^◦) for energies ranging from 30 GeV to 100 TeV. Dividing the source extensions ranging from 0.03^◦ to 1^◦ in three different classes, we find that using a simple and light convolutional neural network it is possible to achieve a 97% global accuracy in separating the extended sources from the point-like sources. The neural net architecture including other data pre-processing codes is available online.