The Square Kilometre Array (SKA)

Abstract:

New deep learning techniques present promising new analysis methods for Imaging Atmospheric Cherenkov Telescopes (IACTs) such as the upcoming Cherenkov Telescope Array (CTA). In particular, the use of Convolutional Neural Networks (CNNs) could provide a direct event classification method that uses the entire information contained within the Cherenkov shower image, bypassing the need to Hillas parameterise the image and allowing fast processing of the data. Existing work in this field has utilised images of the integrated charge from IACT camera photomultipliers, however the majority of current and upcoming generation IACT cameras have the capacity to read out the entire photosensor waveform following a trigger. As the arrival times of Cherenkov photons from Extensive Air Showers (EAS) at the camera plane are dependent upon the altitude of their emission and the impact distance from the telescope, these waveforms contain information potentially useful for IACT event classification. In this test-of-concept simulation study, we investigate the potential for using these camera pixel waveforms with new deep learning techniques as a background rejection method, against both proton and electron induced EAS. We find that a means of utilising their information is to create a set of seven additional 2-dimensional pixel maps of waveform parameters, to be fed into the machine learning algorithm along with the integrated charge image. Whilst we ultimately find that the only classification power against electrons is based upon event direction, methods based upon timing information appear to out-perform similar charge based methods for gamma/hadron separation. We also review existing methods of event classifications using a combination of deep learning and timing information in other astroparticle physics experiments.

Abstract:

Using high-resolution Hubble Space Telescope imaging data, we perform a visual morphological classification of ∼36 000 galaxies at z < 1 in the deep extragalactic visible legacy survey/cosmological evolution survey region. As the main goal of this study, we derive the stellar mass function (SMF) and stellar mass density (SMD) sub-divided by morphological types. We find that visual morphological classification using optical imaging is increasingly difficult at z > 1 as the fraction of irregular galaxies and merger systems (when observed at rest-frame UV/blue wavelengths) dramatically increases. We determine that roughly two-thirds of the total stellar mass of the Universe today was in place by z ∼ 1. Double-component galaxies dominate the SMD at all epochs and increase in their contribution to the stellar mass budget to the present day. Elliptical galaxies are the second most dominant morphological type and increase their SMD by ∼2.5 times, while by contrast, the pure-disc population significantly decreases by ∼85 per cent⁠. According to the evolution of both high- and low-mass ends of the SMF, we find that mergers and in situ evolution in discs are both present at z < 1, and conclude that double-component galaxies are predominantly being built by the in situ evolution in discs (apparent as the growth of the low-mass end with time), while mergers are likely responsible for the growth of ellipticals (apparent as the increase of intermediate/high-mass end).

Abstract:

We examine the 1.4 GHz radio luminosities of galaxies arising from star formation and active galactic nuclei (AGNs) within the state-of-the-art cosmological hydrodynamic simulation SIMBA. SIMBA grows black holes via gravitational torque limited accretion from cold gas and Bondi accretion from hot gas, and employs AGN feedback including jets at low Eddington ratios. We define a population of radio loud AGNs (RLAGNs) based on the presence of ongoing jet feedback. Within RLAGN, we define high and low excitation radio galaxies (HERGs and LERGs) based on their dominant mode of black hole accretion: torque limited accretion representing feeding from a cold disc, or Bondi representing advection-dominated accretion from a hot medium. SIMBA predicts good agreement with the observed radio luminosity function (RLF) and its evolution, overall as well as separately for HERGs and LERGs. Quiescent galaxies with AGN-dominated radio flux dominate the RLF at ≳1022−23 W Hz−1, while star formation dominates at lower radio powers. Overall, RLAGNs have higher black hole accretion rates and lower star formation rates than non-RLAGN at a given stellar mass or velocity dispersion, but have similar black hole masses. SIMBA predicts an LERG number density of 8.53 Mpc−3, ∼10× higher than for HERGs, broadly as observed. While LERGs dominate among most massive galaxies with the largest black holes and HERGs dominate at high specific star formation rates, they otherwise largely populate similar-sized dark matter haloes and have similar host galaxy properties. SIMBA thus predicts that deeper radio surveys will reveal an increasing overlap between the host galaxy demographics of HERGs and LERGs.