Hintze Centre for Astrophysical Surveys

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.

Abstract:

During the early stages of galaxy evolution, a significant fraction of galaxies undergo a transitional phase between the “blue nugget” systems, which arise from the compaction of large, active star-forming disks, and the “red nuggets,” which are red and passive compact galaxies. These objects are typically only observable with space telescopes, and detailed studies of their size, mass, and stellar population parameters have been conducted on relatively small samples. Strong gravitational lensing can offer a new opportunity to study them in detail, even with ground-based observations. In this study, we present the first six bona fide samples of strongly lensed post-blue nugget (pBN) galaxies, which were discovered in the Kilo Degree Survey. By using the lensing-magnified luminosity from optical and near-infrared bands, we have derived robust structural and stellar population properties of the multiple images of the background sources. The pBN galaxies have very small sizes of R eff < 1.3 kpc, high mass density inside 1 kpc of log(Σ1/M⊙kpc−2)>9.3 , and low specific star formation rates of log(sSFRGyr-1)≲0 , The size–mass and Σ1–mass relations of this sample are consistent with those of the red nuggets, while their sSFR is close to the lower end of compact star-forming blue nugget systems at the same redshift, suggesting a clear evolutionary link between them.

Abstract:

We study how bar strength and bar kinematics affect star formation in different regions of the bar by creating radial profiles of EW[Hα] and Dn4000 using data from Sloan Digital Sky Survey-IV Mapping Nearby Galaxies at Apache Point Observatory (MaNGA). Bars in galaxies are classified as strong or weak using Galaxy Zoo DESI, and they are classified as fast and slow bars using the Tremaine–Weinberg method on stellar kinematic data from the MaNGA survey. In agreement with previous studies, we find that strong bars in star-forming (SF) galaxies have enhanced star formation in their center and beyond the bar-end region, while star formation is suppressed in the arms of the bar. This is not found for weakly barred galaxies, which have very similar radial profiles to unbarred galaxies. In addition, we find that slow bars in SF galaxies have significantly higher star formation along the bar than fast bars. However, the global star formation rate is not significantly different between galaxies with fast and slow bars. This suggests that the kinematics of the bar do not affect star formation globally, but changes where star formation occurs in the galaxy. Thus, we find that a bar will influence its host the most if it is both strong and slow.