The University of Wroclaw (Poland) is pleased to announce the international summer school "Spectroscopic data analysis with iSpec" (2nd edition). It will take place in Wroclaw, Poland between 2 to 5 September 2025 in Wrocław (Poland).
This school aims to provide participants with a solid foundation in the analysis of stellar spectra, with particular emphasis on deriving atmospheric parameters and chemical abundances. The sessions will be structured around iSpec, a comprehensive tool designed for the treatment and interpretation of stellar spectroscopic data. As part of the learning experience, the school will also incorporate and explore recent advances in artificial intelligence.
iSpec facilitates a wide range of spectroscopic tasks, including removing cosmic rays, continuum normalization, resolution degradation, radial velocity correction, identification of telluric lines, and resampling. It also enables the determination of fundamental stellar parameters—such as effective temperature, surface gravity, metallicity, microturbulence, macroturbulence, and rotational velocity—for A, F, G, K, and M-type stars. These parameters can be derived through two complementary approaches: the synthetic spectral fitting technique and the equivalent width method.
The software integrates MARCS and ATLAS model atmospheres. It supports several widely used radiative transfer codes, including SPECTRUM (R. O. Gray), Turbospectrum (Bertrand Plez), SME (Valenti and Piskunov), MOOG (Chris Sneden), and Synthe/WIDTH9 (Kurucz/ATLAS). While iSpec offers a well-suited graphical interface for introductory use and educational purposes, its full potential is realized through its Python-based implementation. This approach is recommended for advanced scientific analyses as it ensures reproducibility and access to an extended set of features and customization options.
During the school, participants will receive a series of introductory lectures on iSpec and engage in hands-on exercises focused on determining the atmospheric parameters of A, F, G, K, and M-type stars. These exercises will use publicly available observational data and pre-computed synthetic spectra. The lecturers include the leading developer of iSpec and experienced stellar spectroscopists who have extensively used this tool (among others) and have trained numerous PhD students and postdoctoral researchers.
I am glad to introduce the latest version of iSpec, available for download at the v2023.08.04 release page. This update brings some small new features, compatibility improvements, and bug fixes. Here’s a detailed breakdown of what's new:
Changes:
Compatibility Updates:
Bug Fixes:
Stay updated with the latest enhancements by downloading the new version!
We are happy to announce the online PhD School "Stellar Spectroscopy and Astrophysical parameterization", Sept. 21-23, 2021, in the framework of the EU COST action MW-Gaia.
The Gaia mission is revolutionizing Galactic astrophysics in many ways and, although this is not generally perceived, Gaia is also a formidable spectroscopy machine. However, the best results are obtained by combining Gaia abilities and ground based high resolution spectroscopy. Advantages and limitations must be understood for a better synergy.
The school will discuss the derivation of stellar parameters from spectroscopy and large survey pipelines using classical, high precision and data driven methods. Tutorial sessions are foreseen. The final lecture will be on publication skills. Space for participants to present their research is granted, either as poster or oral presentation.
All sessions will be on-line. Registration is free of charge, but participant number will be limited. Dead-line for registration and abstract submission: Sept. 5th, 2021
The main subjects and the speakers are:
More information at the web site:
https://indico.ict.inaf.it/event/1590/
Registration is now open!
iSpec now exclusively uses Python 3 (previous versions of Python were deprecated on January 1st 2020). The v2020.10.01 release does not remove or modify any of iSpec functionalities. It should be completely compatible with user written scripts as far as they are converted to Python 3.
In addition, most radiative transfer codes were updated, as well as the external Equivalent Width measurement tool (ARES), and the Gaia-ESO linelist version 6 was added. Regarding the input file, pre-computed grids were re-computed using the latest radiative transfer code versions, and line selections were re-done for a resolution of 47,000.
Summary:
Multiple codes are available to derive atmospheric parameters and individual chemical abundances from high-resolution spectra of AFGKM stars. Almost every spectroscopist has its own preferences regarding which code and method to use. But intrinsic differences between codes and methods lead to complex systematics that depend on multiple variables such as the selected spectral regions and the radiative transfer code used. I expanded iSpec, the popular open source spectroscopic tool, to support the most known radiative transfer codes and I assessed their similarities and biases when using multiple setups based on the equivalent width method and the synthetic spectral fitting technique (interpolating from a pre-computed grid of spectra or synthesizing with interpolated model atmospheres). This work shows that systematics on atmospheric parameter and abundances between most of the codes can be reduced when using the same method and a careful spectral feature selection is executed, but it may not be possible to ignore the remaining differences depending on what is the scientific case and the required precision. Regarding methods, equivalent width-based and spectrum fitting-analyses exhibit large differences that emerge due to their intrinsic differences, which is relevant given the popularity of these two methods. The results help us identify the key caveats of modern spectroscopy that any scientist should be aware of before trusting its own results or being tempted to combine atmospheric parameters and abundances from the literature.
Access to the article preprint and the ADS record.