Modern astronomy is inherently a multidisciplinary science. For example, electrical and mechanical engineers might help build the telescopes, mathematicians, statisticians and computer engineers might help analyse the data gathered by the telescopes, and astronomers, phsycists, chemists and biologists might use the reduced data to test various scientific hypotheses. Accordingly, our group is multidisciplinary. We have astronomers (Khan Asad), physicists (Arshad Momen), statisticians (Anandamayee Majumdar) and computer scientists (Ashraful Amin, Amin Ahsan Ali, AKM Mahbubur Rahman). We form an important part of the AGencyLab, a computational research group led by the CSE department of IUB. We have international collborations with astronomers and astrophysicists from multiple countries in Europe, Africa and America. And we work with two of the largest radio telescopes in the world: LOFAR and SKA.
We are working toward establishing the first observational astronomy and cosmology research group in Bangladesh. Currently, we are working under the umbrellas of five projects, as described below.
1. HI Intensity Mapping with LOFAR
The universe is almost 14 billion years old. Only during the last three decades, we have been able to dream about making a map of this immense span of space and time. We still know very little, but astronomers are looking for a signal of 21-cm wavelength coming from neutral Hydrogen (HI) which will bring a revolution in mapping the universe because the universe is mostly made of Hydrogen. A first glimpse of the signal has been obtained by the EDGES telescope, but we have not been able to detect it in the observations made by LOFAR, the largest radio telescope at low frequencies, because of the systematic errors of the telescope and the atmosphere of earth. We, at IUB, are analysing the direction-dependent errors of the telescope using Bayesian statistical methods to estimate their effect on the detection of the signal. Dr. Asad is an active member of the Dutch-led LOFAR-EoR project. For more details read Asad et al. 2015 and Mertens et al. 2020.
2. HI Intensity Mapping with SKA
SKA is a future telescope, but its precursor MeerKAT has already been built in South Africa. MeerKAT is also trying to detect the 21-cm signal. We are working toward creating a software package (based on HIDE&SEEK) that can simulate the 21-cm observations of MeerKAT. Using this simulation, we will be able to estimate the errors of the telescope more efficiently before analysing the observed data.
3. Minihalos in Galaxy Clusters
The 21-cm signal will help astronomers understand the evolution of the universe beginning from merely 20 million years after the big bang. But we can map our local part of the universe much more elaborately by directly observing the largest bound structures, the clusters of galaxies. Our third project aims at observing radio emission coming from the central mini-halos of massive cool-core galaxy clusters to better understand their origin and evolution. We have completely new observations of 5 galaxy clusters using the MeerKAT telescope of South Africa. This project is used to teach the fundamentals of radio astronomy to the undergraduate and graduate students of IUB.
4. Radio Astronomy Antennas
The fourth project (Radio Astronomy Antennas) is more technical in nature and it facilitates the aforementioned three projects. By making a model of the sensitivity of the radio telescopes toward different direction in the sky, we can facilitate the detection of the 21-cm signal and improve the quality of the galaxy cluster images. This, in turn, will improve our chances of extracting scientifically useful information from the big noisy datasets.
5. Galaxy Classification using Deep Learning
Our fifth and final project is about classifying radio galaxies using Deep Learning techniques. We are still on the first stages of this project. Currently we are collecting publicly available VLA images of radio galaxies from the FIRST survey and trying to classify them using our Deep Learning tools with the help of the existing tools. Eventually we will compare our technique with the existing classification techniques based on machine learning and publish our results.