The visual inspection of image and catalog data continues to be a valuable aspect of astronomical data analysis. As the scale of astronomical image and catalog data continues to grow, visualizing the data becomes increasingly difficult. In this work, we introduce FitsMap, a simple, lightweight tool for visualizing astronomical image and catalog data. FitsMap uses well-understood image tiling techniques and a novel catalog tiling technique to serve gigapixel images with catalogs containing tens of millions of sources using only a simple web server. Further, the web-based visualizations can be viewed performantly on mobile devices. FitsMap is implemented in Python and is open source (https://github.com/ryanhausen/fitsmap).

Abstract We test the possibility of using a convolutional neural network to infer the inclination angle of a black hole directly from the incomplete image of the black hole’s shadow in the u v -plane. To this end, we develop a proof-of-concept network and use it to explicitly find how the error depends on the degree of coverage, type of input and coverage pattern. We arrive at a typical error of 10° at a level of absolute coverage 1% (for a pattern covering a central part of the u v -plane), 0.3% (pattern covering the central part and the periphery, the 0.3% referring to the central part only), and 14% (uniform pattern). These numbers refer to a network that takes both amplitude and phase of the visibility function as inputs. We find that this type of network works best in terms of the error itself and its distribution for different angles. In addition, the same type of network demonstrates similarly good performance on highly blurred images mimicking sources nearing being unresolved. In terms of coverage, the magnitude of the error does not change much as one goes from the central pattern to the uniform one. We argue that this may be due to the presence of a typical scale which can be mostly learned by the network from the central part alone.

Here we present Halo-FDCA, a robust open source Python package for modeling and estimating total flux densities of radio (mini) halos in galaxy clusters. Radio halos are extended ( ~200 - 1500 kpc in size) synchrotron emitting sources found in galaxy clusters that trace the presence of cosmic rays and magnetic fields in the intracluster medium (ICM). These sources are centrally located and have a low surface brightness. Their exact origin is still unknown but they are likely related to cosmic rays being re-accelerated in-situ by merger or sloshing driven ICM turbulence. The presented algorithm combines the numerical power of the Markov Chain Monte Carlo routine and multiple theoretical models to estimate the total radio flux density of a radio halo from a radio image and its associated uncertainty. This method introduces a flexible analytic fitting procedure to replace existing simplistic manual measurements prone to biases and inaccuracies. It allows to easily determine the properties of the emission and is particularly suitable for future studies of large samples of clusters.