School description > Research projects

The students will be separated in groups of four to five, and assigned to a research project under the supervision of an expert tutor. A total of 50 hours will be available to work on the projects.

The projects will be carried-on using the students own laptops (and access to remote servers when necessary), that shall ideally be running a Linux-like environment. Prescriptions concerning the software packages to be pre-installed will be communicated before the school.

The list of projects is below:

1. Stellar Spectral Classification and Interpolation

Tutors: Ranjan Gupta (IUCAA, Pune) & Harinder P Singh (University of Delhi, New Delhi)
Description: The Project will involve Artificial Neural Network (ANN) based classification tools for two dimensional classification (Spectral Type and Luminosity) of stellar spectral libraries. The 1D spectra (ascii files) will be provided to the project group.
Software requirements: The participants shall keep Python/Matlab based open source ANN classification tools ready for performing the project on their laptops (preferably on LINUX or MAC; use the Matlab license of your home institute or university).

2. Chemical composition of neutral gas in a distant galaxy. 

Tutor: Jens-Kristian Krogager (CRAL, Lyon, France)

We will study the chemical composition of the neutral gas in a distant galaxy at redshift 1.7 using absorption lines from various metal species (carbon, silicon, zinc, iron etc). Some specific absorption lines from neutral carbon atoms provide additional information about the physical conditions, like density and temperature, so we can estimate these as well. However, there are still uncertainties about the theoretical strength of these lines. One aim is to constrain the line strength ratios of these lines, for which very high spectral resolution is required. For this purpose, we will be working on data from the VLT/ESPRESSO spectrograph and comparing to previous VLT/UVES data of the same object at lower spectral resolution.  

Software requirements: Python, VoigtFit (python package), and/or other scripting language for preparing plots or analysing results.

3. Determination of stellar atmospheric parameters of globular cluster stars from MUSE observation

Tutor: Philippe Prugniel (CRAL, Lyon, France) 

Description: We will derive the atmospheric parameters of stars from the globular cluster NGC6397, using spectra extracted from MUSE cubes as described in Husser et al. (2016). We will compare with the resuls obtained by the former paper, and by Jain et al. (2020). MUSE started the era of crowded-fields spectroscopy, where the whole field of a star cluster can be observed at once, to be then decomposed into individual spectra. In this way, the complete population can be observed, down to some flux limit. A star cluster is an ideal place to check the stellar evolution models, and to benchmark the different approaches foreseen to evaluate the chemical composition and physical parameters of stars.
Software requirements:FERRE (a F90 code), and whatever scripting language, like Python, GDL or IDL (for figures and to prepare the data)

4. Modelling a Lyman continuum leaker at z~1 from AstroSat

Tutor: Kanak Saha (IUCAA, Pune)
Description: Lyman continuum (LyC) leakers are the objects from which photons with wavelength shorter than 912 angstrom are able to escape their host. What fration of these photons escape the host remains unclear. Not only that, depending on the redshift (in particular from high z), many of these photons get absorbed by the intervening intergaactic medium (IGM) on their way to Earth. However, the second problem is comparatively less severe for low-z galaxies. Analog of these low-z galaxies might have played a crucial role during the Cosmic reionization of our universe. The primary goal of this project is to model a relatively low-z galaxy at z~1 and estimate the escape fraction of the LyC photons.       

Data: Far and Near-Ultra-Violet imaging data (unpublished) from AUDF will be provided. Archival data from HST, VLT and Spitzer (downloadable). Spectroscopic data from MUSE and HST grism.

Objectives: Construct a spectral energy distribution and modelling using PCIGALE/BPASS. Derive physical parameters: stellar mass, Z, dust, escape fraction.

Tools/Software: Good knowledge of photometry: PSF, daophot, aperture photometry.  Python with astropy, numpy, scipy. Install Pcigale with anaconda env. Candidates with knowledge of spectral analysis will be a plus.