TY - JOUR A1 - Miller, Amy E. A1 - Cioni, Maria-Rosa L. A1 - de Grijs, Richard A1 - Sun, Ning-Chen A1 - Bell, Cameron P. M. A1 - Choudhury, Samyaday A1 - Ivanov, Valentin D. A1 - Marconi, Marcella A1 - Oliveira, Joana M. A1 - Petr-Gotzens, Monika A1 - Ripepi, Vincenzo A1 - van Loon, Jacco Th. T1 - The VMC survey - XLVII. Turbulence-controlled hierarchical star formation in the large magellanic cloud JF - Monthly notices of the Royal Astronomical Society N2 - We perform a statistical clustering analysis of upper main-sequence stars in the Large Magellanic Cloud (LMC) using data from the Visible and Infrared Survey Telescope for Astronomy survey of the Magellanic Clouds. We map over 2500 young stellar structures at 15 significance levels across similar to 120 square degrees centred on the LMC. The structures have sizes ranging from a few parsecs to over 1 kpc. We find that the young structures follow power-law size and mass distributions. From the perimeter-area relation, we derive a perimeter-area dimension of 1.44 +/- 0.20. From the mass-size relation and the size distribution, we derive two-dimensional fractal dimensions of 1.50 +/- 0.10 and 1.61 +/- 0.20, respectively. We find that the surface density distribution is well represented by a lognormal distribution. We apply the Larson relation to estimate the velocity dispersions and crossing times of these structures. Our results indicate that the fractal nature of the young stellar structures has been inherited from the gas clouds from which they form and that this architecture is generated by supersonic turbulence. Our results also suggest that star formation in the LMC is scale-free from 10 to 700 pc. KW - methods: statistical KW - stars: early-type KW - stars: formation KW - galaxies: individual: Magellanic Clouds KW - galaxies: stellar content KW - galaxies: structure Y1 - 2022 U6 - https://doi.org/10.1093/mnras/stac508 SN - 0035-8711 SN - 1365-2966 VL - 512 IS - 1 SP - 1196 EP - 1213 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Lange, J. A1 - Pohl, Martin T1 - The average GeV-band emission from gamma-ray bursts JF - Astronomy and astrophysics : an international weekly journal N2 - Aims. We analyze the emission in the 0.3-30 GeV energy range of gamma-ray bursts detected with the Fermi Gamma-ray Space Telescope. We concentrate on bursts that were previously only detected with the Gamma-Ray Burst Monitor in the keV energy range. These bursts will then be compared to the bursts that were individually detected with the Large Area Telescope at higher energies. Methods. To estimate the emission of faint GRBs we used nonstandard analysis methods and sum over many GRBs to find an average signal that is significantly above background level. We used a subsample of 99 GRBs listed in the Burst Catalog from the first two years of observation. Results. Although most are not individually detectable, the bursts not detected by the Large Area Telescope on average emit a significant flux in the energy range from 0.3 GeV to 30 GeV, but their cumulative energy fluence is only 8% of that of all GRBs. Likewise, the GeV-to-MeV flux ratio is less and the GeV-band spectra are softer. We confirm that the GeV-band emission lasts much longer than the emission found in the keV energy range. The average allsky energy flux from GRBs in the GeV band is 6.4 x 10(-4) erg cm(-2) yr(-1) or only similar to 4% of the energy flux of cosmic rays above the ankle at 10(18.6) eV. KW - methods: statistical KW - surveys KW - gamma-ray burst: general Y1 - 2013 U6 - https://doi.org/10.1051/0004-6361/201220652 SN - 0004-6361 VL - 551 IS - 1 PB - EDP Sciences CY - Les Ulis ER - TY - JOUR A1 - Bernardi, Rafael L. A1 - Berdja, Amokrane A1 - Dani Guzman, Christian A1 - Torres-Torriti, Miguel A1 - Roth, Martin M. T1 - Restoration of images with a spatially varying PSF of the T80-S telescope optical model using neural networks JF - Monthly notices of the Royal Astronomical Society N2 - Most image restoration methods in astronomy rely upon probabilistic tools that infer the best solution for a deconvolution problem. They achieve good performances when the point spread function (PSF) is spatially invariant in the image plane. However, this condition is not always satisfied in real optical systems. We propose a new method for the restoration of images affected by static and anisotropic aberrations using Deep Neural Networks that can be directly applied to sky images. The network is trained using simulated sky images corresponding to the T80-S Telescope optical model, a 80-cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system. Once trained, the network can be used directly on sky images, outputting a corrected version of the image that has a constant and known PSF across its field of view. The method is to be tested on the T80-S Telescope. We present the method and results on synthetic data. KW - methods: statistical KW - techniques: image processing Y1 - 2021 U6 - https://doi.org/10.1093/mnras/stab3400 SN - 0035-8711 SN - 1365-2966 VL - 510 IS - 3 SP - 4284 EP - 4294 PB - Oxford Univ. Press CY - Oxford ER -