PypeIt is a Python package for semi-automated reduction of astronomical, spectroscopic data. Its algorithms build on decades-long development of previous data reduction pipelines by the developers (Bernstein, Burles, & Prochaska, 2015; Bochanski et al., 2009). The reduction procedure -- including a complete list of the input parameters and available functionality -- is provided as online documentation hosted by Read the Docs, which is regularly updated. (https://pypeit.readthedocs.io/en/latest/). Release v1.0.3 serves the following spectrographs: Gemini/GNIRS, Gemini/GMOS, Gemini/FLAMINGOS 2, Lick/Kast, Magellan/MagE, Magellan/Fire, MDM/OSMOS, Keck/DEIMOS (600ZD, 830G, 1200G), Keck/LRIS, Keck/MOSFIRE (J and Y gratings tested), Keck/NIRES, Keck/NIRSPEC (low-dispersion), LBT/Luci-I, Luci-II, LBT/MODS (beta), NOT/ALFOSC (grism4), VLT/X-Shooter (VIS, NIR), VLT/FORS2 (300I, 300V), WHT/ISIS.
DBSP_DRP is a python package that provides fully automated data reduction of data taken by the Double Spectrograph (DBSP) at the 200-inch Hale Telescope at Palomar Observatory (Oke & Gunn, 1982). The underlying data reduction functionality to extract 1D spectra, perform flux calibration and correction for atmospheric absorption, and coadd spectra together is provided by PypeIt (Prochaska et al., 2020). The new functionality that DBSP_DRP brings is in orchestrating the complex data reduction process by making smart decisions so that no user input is required after verifying the correctness of t…
We present the open source Python code BinaryStarSolver that solves for the orbital elements of a spectroscopic binary system. Given a time-series of radial velocity measurements, six orbital parameters are determined: the long-term mean, or systemic, radial velocity, the velocity amplitude, the argument of periastron, the eccentricity, the epoch of periastron, and the orbital period referred to by $\{{γ, K, ω, e, T_0, P}\}$ respectively. Also returned to the user is the projected length of the semi-major axis, $a_{1}\sin(i)$, and the mass function, $f(M)$. The determination of spectroscopic o…
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 u…
SpectraPy is an Astropy affiliated package for spectroscopic data reduction. It collects algorithms and methods for data reduction of astronomical spectra obtained by through-slits spectrographs. It has been created to fill the gap in Astropy between the already existing data handling libraries and those for spectra analysis. SpectraPy combines Astropy facilities with SAOImageDS9 features, providing a set of tools for spectra calibration and 2D extraction. It starts from raw frames, and using configuration files which describe the optical setup of the instrument, it automatically locates and e…
slicersim: A python package to simulate image slicer spectroscopic observations -- application to the Lazuli Spectrograph
Université Lyon 1, CNRS, IP2I Lyon（里昂第一大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Space Sciences Laboratory, University of California, Berkeley（加州大学伯克利分校空间科学实验室）；Astrophysics & Space Institute, Schmidt Sciences（施密特科学天体物理与空间研究所）；University of California, Berkeley（加州大学伯克利分校）
ScopeSim is a flexible multipurpose instrument data simulation framework built in Python. It enables both raw and reduced observation data to be simulated for a wide range of telescopes and instruments quickly and efficiently on a personal computer. The software is currently being used to generate simulated raw input data for developing the data reduction pipelines for the MICADO and METIS instruments at the ELT. The ScopeSim environment consists of three main packages which are responsible for providing on-sky target templates (ScopeSim_templates), the data to build the optical models of vari…
This tutorial is devoted to extending an existing catalogue with data taken elsewhere, either from CDS Vizier or Simbad database. As an example, we used the so-called 'Spectroscopic Survey of Stars in the Solar Neighborhood' (aka. S4N, Allende Prieto et al. 2004) in order to retrieve all objects with available data for the set of fundamental stellar parameters effective temperature, surface gravity and metallicity. Then for each object in this dataset we query Simbad database to retrieve the projected rotational velocity. This combines Vizier and Simbad queries made using Python astroquery mod…
SPAN: A cross-platform Python GUI software for optical and near-infrared spectral analysis
Universidad de Atacama（阿塔卡马大学）；Instituto de Astronomía y Ciencias Planetarias, Universidad de Atacama（阿塔卡马大学天文与行星科学研究所）；University of Naples Federico II（那不勒斯费德里科二世大学）；Department of Physics, University of Naples Federico II（那不勒斯费德里科二世大学物理系）；INAF – Osservatorio Astronomico d’Abruzzo（意大利国家天体物理研究所阿布鲁佐天文台）；Instituto de Astrofísica de Canarias（加那利群岛天体物理学研究所）；Universidad de La Laguna（拉古纳大学）；Departamento de Astrofísica, Universidad de La Laguna（拉古纳大学天体物理学系）
We present a convenient, all-in-one framework for the scientific analysis of fully reduced, (integral-field) spectroscopic data. The GIST pipeline (Galaxy IFU Spectroscopy Tool) is entirely written in Python3 and conducts all steps from the preparation of input data, over the scientific analysis to the production of publication-quality plots. In its basic setup, it extracts stellar kinematics, performs an emission-line analysis and derives stellar population properties from full spectral fitting as well as via the measurement of absorption line-strength indices by exploiting the well-known pPX…
pyspeckit is a toolkit and library for spectroscopic analysis in Python. We describe the pyspeckit package and highlight some of its capabilities, such as interactively fitting a model to data, akin to the historically widely-used splot function in IRAF. pyspeckit employs the Levenberg-Marquardt optimization method via the mpfit and lmfit implementations, and important assumptions regarding error estimation are described here. Wrappers to use pymc and emcee as optimizers are provided. A parallelized wrapper to fit lines in spectral cubes is included. As part of the astropy affiliated package e…
PyIRD: A Python-Based Data Reduction Pipeline for Subaru/IRD and REACH
Institute of Space and Astronautical Science, JAXA（宇宙航空研究开发机构 宇宙科学研究所）；Department of Astronomy, Graduate School of Science, The University of Tokyo（东京大学 理学研究科 天文学系）；Astrobiology Center（天体生物学中心）；SOKENDAI（日本综合研究院）；National Astronomical Observatory of Japan（日本国立天文台）；Department of Earth and Space Science, Osaka University（大阪大学 地球惑星科学科）
TelescopeML -- I. An End-to-End Python Package for Interpreting Telescope Datasets through Training Machine Learning Models, Generating Statistical Reports, and Visualizing Results
NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；Bay Area Environmental Research Institute（湾区环境研究所）；Universities Space Research Association（大学空间研究协会）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；University of California, Riverside（加州大学河滨分校）
Deep learning with artificial neural networks is increasingly gaining attention, because of its potential for data-driven astronomy. However, this methodology usually does not provide uncertainties and does not deal with incompleteness and noise in the training data. In this work, we design a neural network for high-resolution spectroscopic analysis using APOGEE data that mimics the methodology of standard spectroscopic analyses: stellar parameters are determined using the full wavelength range, but individual element abundances use censored portions of the spectrum. We train this network with…
Comparison of echelle spectra to synthetic models has become a computational statistics challenge, with over ten thousand individual spectral lines affecting a typical cool star echelle spectrum. Telluric artifacts, imperfect line lists, inexact continuum placement, and inflexible models frustrate the scientific promise of these information-rich datasets. Here we debut an interpretable machine-learning framework "blasé" that addresses these and other challenges. The semi-empirical approach can be viewed as "transfer learning" -- first pre-training models on noise-free precomputed synthetic spe…
PERRY: A Human-in-the-Loop GUI for Precision Alignment of MUSE Data
Australian Astronomical Optics, Faculty of Science and Engineering, Macquarie University（澳大利亚天文光学，麦考瑞大学科学与工程学院）；Astrophysics and Space Technologies Research Centre, Macquarie University（麦考瑞大学天体物理学与空间技术中心）；School of Physics, University of New South Wales（新南威尔士大学物理学院）
We present SIPGI, a spectroscopic pipeline to reduce optical/near-infrared data from slit-based spectrographs. SIPGI is a complete spectroscopic data reduction environment which retains the high level of flexibility and accuracy typical of the standard "by-hand" reduction methods but is characterized by a significantly higher level of efficiency. This is obtained by exploiting three main concepts: $i)$ the instrument model: at the core of the data reduction is an analytic description of the main calibration relations (e.g. spectra location and wavelength calibration) that can be easily checked…
IRIS (InfraRed Imaging Spectrograph) is the diffraction-limited first light instrument for the Thirty Meter Telescope (TMT) that consists of a near-infrared (0.84 to 2.4 $μ$m) imager and integral field spectrograph (IFS). The IFS makes use of a lenslet array and slicer for spatial sampling, which will be able to operate in 100's of different modes, including a combination of four plate scales from 4 milliarcseconds (mas) to 50 mas with a large range of filters and gratings. The imager will have a field of view of 34$\times$34 arcsec$^{2}$ with a plate scale of 4 mas with many selectable filter…
