Context. The construction of catalogues of galaxies and the a posteriori study of galaxy properties in relation to their environment have been hampered by scarce redshift information. The new 3-dimensional (3D) surveys permit small, faint, physically bound satellites to be distinguished from a background-projected galaxy population, giving a more comprehensive 3D picture of the surroundings. Aims. We aim to provide representative samples of isolated galaxies, isolated pairs, and isolated triplets for testing galaxy evolution and secular processes in low density regions of the local Universe, as well as to characterise their local and large-scale environments. Methods. We used spectroscopic data from the tenth data release of the Sloan Digital Sky Survey (SDSS-DR10) to automatically and homogeneously compile catalogues of 3702 isolated galaxies, 1240 isolated pairs, and 315 isolated triplets in the local Universe (z ≤ 0.080). To quantify the effects of their local and large-scale environments, we computed the projected density and the tidal strength for the brightest galaxy in each sample. Results. We find evidence of isolated pairs and isolated triplets that are physically bound at projected separations up to d ≤ 450 kpc with radial velocity difference ∆ ≤ 160 km s −1 , where the effect of the companion typically accounts for more than 98% of the total tidal strength affecting the central galaxy. For galaxies in the catalogues, we provide their positions, redshifts, and degrees of relation with their physical and large-scale environments. The catalogues are publicly available to the scientific community. Conclusions. For isolated galaxies, isolated pairs, and isolated triplets, there is no difference in their degree of interaction with the large-scale structure (up to 5 Mpc), which may suggest that they have a common origin in their formation and evolution. We find that most of them belong to the outer parts of filaments, walls, and clusters, and generally differ from the void population of galaxies.

Context. To understand the evolution of galaxies, it is necessary to have a reference sample where the effect of the environment is minimized and quantified. In the framework of the AMIGA project (Analysis of the interstellar Medium of Isolated GAlaxies), we present a revision of the environment for galaxies in the Catalogue of Isolated Galaxies (CIG, Karachentseva 1973, Astrof. Issledovaniia Byu. Spec. Ast. Obs., 8, 3) using the ninth data release of the Sloan Digital Sky Survey (SDSS-DR9). Aims. The aims of this study are to refine the photometric-based AMIGA sample of isolated galaxies and to provide an improvement of the quantification of the isolation degree with respect to previous works, using both photometry and spectroscopy. Methods. We developed an automatic method to search for neighbours within a projected area of 1 Mpc radius centred on each primary galaxy to revise the CIG isolation criteria introduced by Karachentseva (1973). The local number density at the fifth nearest neighbour and the tidal strength affecting the CIG galaxy were estimated to quantify the isolation degree. Results. Of the 636 CIG galaxies considered in the photometric study, 426 galaxies fulfil the CIG isolation criteria within 1 Mpc, taking into account projected neighbours. Of the 411 CIG galaxies considered in the spectroscopic study, 347 galaxies fulfil the CIG isolation criteria when a criterion about redshift difference is added. The available redshifts allow us to reject background neighbours and thus improve the photometric assessment. On average, galaxies in the AMIGA sample show lower values in the local number density and the tidal strength parameters than galaxies in denser environments such as pairs, triplets, compact groups, and clusters. Conclusions. For the first time, the environment and the isolation degree of AMIGA galaxies are quantified using digital data. The use of the SDSS database permits one to identify fainter and smaller-size satellites than in previous AMIGA works. The AMIGA sample is improved by this study, because we reduced the sample of isolated galaxies used in previous AMIGA works by about 20%. The availability of the spectroscopic data allows us to check the validity of the CIG isolation criteria, which is not fully efficient. About 50% of the neighbours considered as potential companions in the photometric study are in fact background objects. We also find that about 92% of the neighbour galaxies that show recession velocities similar to the corresponding CIG galaxy are not considered by the CIG isolation criteria as potential companions, which may have a considerable influence on the evolution of the central CIG galaxy.

The analysis and combination of data from different gamma-ray instruments involves the use of collaboration proprietary software and case-bycase methods. The effort of defining a common data format for high-level data, namely event lists and instrument response functions (IRFs), has recently started for very-high-energy gamma-ray instruments, driven by the upcoming Cherenkov Telescope Array (CTA). In this work we implemented this prototypical data format for a small set of MAGIC, VERITAS, FACT, and H.E.S.S. Crab nebula observations, and we analyzed them with the open-source gammapy software package. By combining data from Fermi-LAT, and from four of the currently operating imaging atmospheric Cherenkov telescopes, we produced a joint maximum likelihood fit of the Crab nebula spectrum. Aspects of the statistical errors and the evaluation of systematic uncertainty are also commented upon, along with the release format of spectral measurements. The results presented in this work are obtained using open-access on-line assets that allow for a long-term reproducibility of the results.

Context. The basic properties of galaxies can be affected by both nature (internal processes) or nurture (interactions and effects of environment). Deconvolving the two effects is an important current effort in astrophysics. Observed properties of a sample of isolated galaxies should be mainly the result of internal (natural) evolution. It follows that nurture-induced galaxy evolution can only be understood through a comparative study of galaxies in different environments. Aims. We take a first look at SDSS (g − r) colors of galaxies in the AMIGA sample, which consists of many of the most isolated galaxies in the local Universe. This alerted us at the same time to the pitfalls of using automated SDSS colors. Methods. We focused on median values for the principal morphological subtypes found in the AMIGA sample (E/S0 and Sb-Sc) and compared them with equivalent measures obtained for galaxies in denser environments. Results. We find a weak tendency for AMIGA spiral galaxies to be redder than objects in close pairs. We find no clear difference when we compared this with galaxies in other (e.g. group) environments. However, the (g − r) color of isolated galaxies shows a Gaussian distribution, as might be expected assuming nurture-free evolution. We find a smaller median absolute deviation in colors for isolated galaxies compared to both wide and close pairs. The majority of the deviation on median colors for spiral subtypes is caused by a color-luminosity correlation. Surprisingly, isolated and non-isolated early-type galaxies show similar (g − r). We see little evidence for a green valley in our sample because most spirals redder than (g − r) = 0.7 have spurious colors. Conclusions. The redder colors of AMIGA spirals and lower color dispersions for AMIGA subtypes -compared with close pairsare likely caused by a more passive star formation in very isolated galaxies.

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Gammapy is a Python package for high-level gamma-ray data analysis built on Numpy, Scipy and Astropy. It enables us to analyze gamma-ray data and to create sky images, spectra and lightcurves, from event lists and instrument response information, and to determine the position, morphology and spectra of gamma-ray sources. So far Gammapy has mostly been used to analyze data from H.E.S.S. and Fermi-LAT, and is now being used for the simulation and analysis of observations from the Cherenkov Telescope Array (CTA). We have proposed Gammapy as a prototype for the CTA science tools. This contribution gives an overview of the Gammapy package and project and shows an analysis application example with simulated CTA data.

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Gammapy is a Python package for high-level gamma-ray data analysis built on Numpy, Scipy and Astropy. It enables us to analyze gamma-ray data and to create sky images, spectra and lightcurves, from event lists and instrument response information, and to determine the position, morphology and spectra of gamma-ray sources. So far Gammapy has mostly been used to analyze data from H.E.S.S. and Fermi-LAT, and is now being used for the simulation and analysis of observations from the Cherenkov Telescope Array (CTA). We have proposed Gammapy as a prototype for the CTA science tools. This contribution gives an overview of the Gammapy package and project and shows an analysis application example with simulated CTA data.