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In this paper we present PyCF3, a python client for the cosmological distance-velocity calculator CosmicFlow-3. The project has a cache and retry system designed with the objective of reducing the stress on the server and mitigating the waiting times of the users in the calculations. We also address Quality Assurance code standards and availability of the code.

The availability of fast to evaluate and reliable predictive models is highly relevant in multi-query scenarios where evaluating some quantities in real, or near-real-time becomes crucial. As a result, reduced-order modelling techniques have gained traction in many areas in recent years. We introduce Arby, an entirely data-driven Python package for building reduced order or surrogate models. In contrast to standard approaches, which involve solving partial differential equations, Arby is entirely data-driven. The package encompasses several tools for building and interacting with surrogate models in a user-friendly manner. Furthermore, fast model evaluations are possible at a minimum computational cost using the surrogate model. The package implements the Reduced Basis approach and the Empirical Interpolation Method along a classic regression stage for surrogate modelling. We illustrate the simplicity in using Arby to build surrogates through a simple toy model: a damped pendulum. Then, for a real case scenario, we use Arby to describe CMB temperature anisotropies power spectra. On this multi-dimensional setting, we find that out from an initial set of $80,000$ power spectra solutions with $3,000$ multipole indices each, could be well described at a given tolerance error, using just a subset of $84$ solutions., Comment: 10 pages, 8 figures

To search for optical counterparts to gravitational waves, it is crucial to develop an efficient follow-up method that allows for both a quick telescopic scan of the event localization region and search through the resulting image data for plausible optical transients. We present a method to detect these transients based on an artificial neural network. We describe the architecture of two networks capable of comparing images of the same part of the sky taken by different telescopes. One image corresponds to the epoch in which a potential transient could exist; the other is a reference image of an earlier epoch. We use data obtained by the Dr. Cristina V. Torres Memorial Astronomical Observatory and archival reference images from the Sloan Digital Sky Survey. We trained a convolutional neural network and a dense layer network on simulated source samples and tested the trained networks on samples created from real image data. Autonomous detection methods replace the standard process of detecting transients, which is normally achieved by source extraction of a difference image followed by human inspection of the detected candidates. Replacing the human inspection component with an entirely autonomous method would allow for a rapid and automatic follow-up of interesting targets of opportunity. The method will be further tested on telescopes participating in the Transient Optical Robotic Observatory of the South Collaboration.

We present an algorithm implemented in the astroalign Python module for image registration in astronomy. Our module does not rely on WCS information and instead matches 3-point asterisms (triangles) on the images to find the most accurate linear transformation between the two. It is especially useful in the context of aligning images prior to stacking or performing difference image analysis. Astroalign can match images of different point-spread functions, seeing, and atmospheric conditions., Comment: 4 pages, 2 figures, Python package

We present the results of the optical follow-up, conducted by the TOROS collaboration, of gravitational wave events detected during the Advanced LIGO-Virgo second observing run (Nov 2016 -- Aug 2017). Given the limited field of view ($\sim100\arcmin$) of our observational instrumentation we targeted galaxies within the area of high localization probability that were observable from our sites. We analyzed the observations using difference imaging, followed by a Random Forest algorithm to discriminate between real and bogus transients. For all three events that we respond to, except GW170817, we did not find any bona fide optical transient that was plausibly linked with the observed gravitational wave event. Our observations were conducted using telescopes at Estaci\'{o}n Astrof\'{\i}sica de Bosque Alegre, Cerro Tololo Inter-American Observatory, and the Dr. Cristina V. Torres Memorial Astronomical Observatory. Our results are consistent with the LIGO-Virgo detections of a binary black hole merger (GW170104) for which no electromagnetic counterparts were expected, as well as a binary neutron star merger (GW170817) for which an optical transient was found as expected., Comment: 9 pages, 4 figures

Data processing pipelines represent an important slice of the astronomical software library that include chains of processes that transform raw data into valuable information via data reduction and analysis. In this work we present Corral, a Python framework for astronomical pipeline generation. Corral features a Model-View-Controller design pattern on top of an SQL Relational Database capable of handling: custom data models; processing stages; and communication alerts, and also provides automatic quality and structural metrics based on unit testing. The Model-View-Controller provides concept separation between the user logic and the data models, delivering at the same time multi-processing and distributed computing capabilities. Corral represents an improvement over commonly found data processing pipelines in Astronomy since the design pattern eases the programmer from dealing with processing flow and parallelization issues, allowing them to focus on the specific algorithms needed for the successive data transformations and at the same time provides a broad measure of quality over the created pipeline. Corral and working examples of pipelines that use it are available to the community at https://github.com/toros-astro., Comment: 19 pages, 3 figures, accepted for publication at Astronomy and Computing. Code available at https://github.com/toros-astro/corral

We present the results of the optical follow-up conducted by the TOROS collaboration of the first gravitational-wave event GW150914. We conducted unfiltered CCD observations (0.35-1 micron) with the 1.5-m telescope at Bosque Alegre starting ~2.5 days after the alarm. Given our limited field of view (~100 square arcmin), we targeted 14 nearby galaxies that were observable from the site and were located within the area of higher localization probability. We analyzed the observations using two independent implementations of difference-imaging algorithms, followed by a Random-Forest-based algorithm to discriminate between real and bogus transients. We did not find any bona fide transient event in the surveyed area down to a 5-sigma limiting magnitude of r=21.7 mag (AB). Our result is consistent with the LIGO detection of a binary black hole merger, for which no electromagnetic counterparts are expected, and with the expected rates of other astrophysical transients., Comment: ApJ Letters, in press

Five and six dimensional static, spherically symmetric, asymptotically Euclidean black holes, are unstable under gravitational perturbations if their mass is lower than a critical value set by the string tension. The instability is due to the Gauss-Bonnet correction to Einstein's equations, and was found in a previous work on linear stability of Einstein-Gauss-Bonnet black holes with constant curvature horizons in arbitrary dimensions. We study the unstable cases and calculate the values of the critical masses. The results are relevant to the issue of black hole production in high energy collisions., Comment: 9 pages, 2 figures