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Register a new userLUCI: A Python package for SITELLE spectral analysis
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High-resolution optical integral field units (IFUs) are rapidly expanding our knowledge of extragalactic emission nebulae in galaxies and galaxy clusters. By studying the spectra of these objects -- which include classic HII regions, supernova remnants, planetary nebulae, and cluster filaments -- we are able to constrain their kinematics (velocity and velocity dispersion). In conjunction with additional tools, such as the BPT diagram, we can further classify emission regions based on strong emission-line flux ratios. LUCI is a simple-to-use python module intended to facilitate the rapid analysis of IFU spectra. LUCI does this by integrating well-developed pre-existing python tools such as astropy and scipy with new machine learning tools for spectral analysis (Rhea et al. 2020). Furthermore, LUCI provides several easy-to-use tools to access and fit SITELLE data cubes.
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With the advent of the Large Array Telescope (LAT) on board the Fermi satellite, a new window on the Universe has been opened. Publicly available, the Fermi-LAT data come together with an analysis software named ScienceTools (ST, http://fermi.gsfc.nasa.gov/ssc/data/analysis/software/) which can be run through a Python interface. Nevertheless, for the user, the ST can be hard to run and imply several steps. Users already contributed with scripts for a specific task but no tool allowing a complete analysis is currently available. We present a Python package called {tt Enrico}, designed to facilitate the data analysis. Using only configuration files and front end tools from the command line, the user can easily perform/reproduce an entire Fermi analysis and make plots for publications. It also include new features like debug plots, pipeline execution on one or several CPUs, downloading of the Fermi data or the generation of a sky model from the Fermi catalogue. {tt Enrico} is an open-source project currently available for download at url{https://github.com/gammapy/enrico}
MUSE (Multi Unit Spectroscopic Explorer) is an integral-field spectrograph mounted on the Very Large Telescope (VLT) in Chile and made available to the European community since October 2014. The Centre de Recherche Astrophysique de Lyon has developed a dedicated software to help MUSE users analyze the reduced data. In this paper we introduce MPDAF, the MUSE Python Data Analysis Framework, based on several well-known Python libraries (Numpy, Scipy, Matplotlib, Astropy) which offers new tools to manipulate MUSE-specific data. We present different examples showing how this Python package may be useful for MUSE data analysis.
We describe a new open source package for calculating properties of galaxy clusters, including NFW halo profiles with and without the effects of cluster miscentering. This pure-Python package, cluster-lensing, provides well-documented and easy-to-use classes and functions for calculating cluster scaling relations, including mass-richness and mass-concentration relations from the literature, as well as the surface mass density $Sigma(R)$ and differential surface mass density $DeltaSigma(R)$ profiles, probed by weak lensing magnification and shear. Galaxy cluster miscentering is especially a concern for stacked weak lensing shear studies of galaxy clusters, where offsets between the assumed and the true underlying matter distribution can lead to a significant bias in the mass estimates if not accounted for. This software has been developed and released in a public GitHub repository, and is licensed under the permissive MIT license. The cluster-lensing package is archived on Zenodo (Ford 2016). Full documentation, source code, and installation instructions are available at http://jesford.github.io/cluster-lensing/.
We present the first public version (v0.2) of the open-source and community-developed Python package, Astropy. This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as Flexible Image Transport System (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity
Advanced LIGO and Advanced Virgo could observe the first lensed gravitational waves in the coming years, while the future Einstein Telescope could observe hundreds of lensed events. Ground-based gravitational-wave detectors can resolve arrival time differences of the order of the inverse of the observed frequencies. As LIGO/Virgo frequency band spans from a few $rm Hz$ to a few $ rm kHz$, the typical time resolution of current interferometers is of the order of milliseconds. When microlenses are embedded in galaxies or galaxy clusters, lensing can become more prominent and result in observable time delays at LIGO/Virgo frequencies. Therefore, gravitational waves could offer an exciting alternative probe of microlensing. However, currently, only a few lensing configurations have been worked out in the context of gravitational-wave lensing. In this paper, we present lensingGW, a Python package designed to handle both strong and microlensing of compact binaries and the related gravitational-wave signals. This synergy paves the way for systematic parameter space investigations and the detection of arbitrary lens configurations and compact sources. We demonstrate the working mechanism of lensingGW and its use to study microlenses embedded in galaxies.
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