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 given below:

Automated Spectral Classification of Two Spectral Libraries

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).

Group 1:   Akant Vats, Shrish Shrish, Ruchi Pandey  

          

Extragalactic Fishing Experiment in the MUSE Hubble deep field. Find high redshift galaxies not even seen by Hubble

Tutor: Roland Bacon (CRAL, Lyon)


Description:

Find and identify galaxies by exploring the deep datacube recently obtained by MUSE in the Hubble deep field area. We will make available to the participants the deepest spectroscopic observation ever made in the emblematic Hubble deep field.
First experiment: learn how to detect galaxies in datacubes using visualization tools (ds9, cubeviz). Search galaxies using (i) their continuum and/or (ii) emission lines.
Second experiment: using the results of advanced tools, explore the detected objects and identify galaxies using their spectral characteristics. Crossmatch detection with high resolution images from the Hubble space telescope. Build the final catalog.
Conclusion: perform statistics on the identify sample: i.e. distribution with redshift, magnitude. Compare results with the experts. Discuss disagreement.
Tools and material: a recent laptop (linux or mac-os) with 16 Gb of ram. A recent python 3.6 distribution installed. The latest version of ds9 and/or cubeviz. MPDAF (the MUSE python data analysis framework).

         Group 2:  Divya Pandey, Dimple Panchal, Anshuman Borgohain, Suraj Dhiwar

 

Identifying emission-line objects and understanding the detection limits

Tutor: Mohammad Akhlaghi (IAC, Tenerife)


Description:

The aim of this project is to identify emission-line only objects, detected in the deep MUSE HUDF field, see Bacon et al. (2017). Similar to that paper, the objects that don't have a Hubble Space Telescope (HST) broad-band imagining counterpart will be found (but with newer and much-improved versions of the software). Special emphasis will be put on the completeness and purity of the detection process to understand the limits of any later analysis done on them. The project will also be done in a reproducible manner. See the published reproduction pipeline of the analysis in Bacon et al. (2017).

Software requirement: GNU Astronomy Utilities (Gnuastro version 0.10), see  installation instructions)

 Group 3Deepak, Sheeraz Khandey, Manish Hiray, Amina Thekkoth     

     

      Stellar abundances using high-resolution spectra

Tutor:Sunetra Giridhar (IIA, Bengaluru)

Description:

It will involve spectroscopic reductions using IRAF. We will use wavelength calibrated, continuum normalized spectra to derive stellar atmospheric parameters and elemental abundances and learn about the evolutionary status of the star. Software requirement:  IRAF software developed by NOAO for spectroscopic data reduction (http://ast.noao.edu/data/software). MOOG software package for deriving stellar parameters and elemental abundances (https://www.as.utexas.edu/~chris/moog.html). Both this software run on Linux. MOOG requires SUPERMONGO for plotting.

  Group 4 Amar Aryan, Pranoti Panchbhai, Prabhakar Maya  

       

      Polarimetry Data Analysis

Tutor:A. N. Ramaprakash/S. Maharana (IUCAA, Pune)

         Description:

         In this project, the participants will analyze archival RoboPol(http://robopol.org/)linear polarimetry data and extract scientific information from different astrophysical systems. Semi-analyzed RoboPol data for stars/blazars will be provided from which the participants will compute Linear Stokes parameters, and correct it for instrumental errors and offsets. Error estimation and propagation at each step of the analysis process will be an important part of the exercise. Through the obtained Stokes parameters, the participants will then carry out one or more of the following science cases:

          1. Finding magnetic field morphology and dust grain properties in a patch of sky.
          2. Use high cadence linear polarization data of blazars to understand the evolution of jets.
          3. Find long term stability/variability of linear polarization for some stars. Software requirement: Standard python Softwares like Numpy, matplotlib

         Group 5Siddharth Maharana, P Jishnu, K Arvind

    Determination of stellar atmospheric parameters of globular cluster stars from MUSE observations

          Tutor: Philippe Prugniel (CRAL, Lyon)  

          Description:
 
           We will use the spectra of the globular cluster NGC 6397 stars extracted from MUSE cubes as described in
           Husser et al. (2016), in order to derive their atmospheric parameters and study the properties of the cluster.

           Software requirements: IDL (use your home institute or university license; IDL version 7 is sufficient) or GDL (the open-source alternative), and ULySS.

         Group 6Sonali Borah, M Raghu Prasad, Soumya Gupta 

 

Pattern speed of bars in disk galaxies using IFU data

Tutor: Kanak Saha (IUCAA, Pune)

Description:

More than 60% of disk galaxies including our Milky Way in the local universe host bars in their central region. Bars are one of the strongest non-axisymmetric patterns that are known to rotate with a fixed pattern speed. The goal of this project is to compute that pattern speed. The participant of this project will be provided with IFU data on a set of disk galaxies in FITS format. Appropriate formalism to carry out this analysis will be provided in the afternoon session of Day-I of IFAS5.

Softwares/coding: Ds9, Topcat or fv for FITS data visualization. Knowledge of Fortran or Python or IDL programming.

Group 7:  Suchira Sarkar, Kerdaris Kurbah, Soumavo Ghosh

 

 

PROJECT PRESENTATIONS on Saturday 24 August (15+5 mts)

Group 4: 1400-1420

Group 6: 1420-1440

Group 3: 1440-1500

Group 1: 1500-1520

Group 5: 1520-1540

Group 2: 1540-1600

Group 7: 1600-1620

Concluding Session (and Tea): 1620-1630

         

Online user: 1