AR-2019-2020

as Afρ = 680 ± 18 cm (r-sdss) and 887 ± 16 cm (g-sdss). The polarization map shows spatial varia- tions in polarization over the coma from about –3% near the nucleus to –8% at a cometocentric distance of about 150,000 km. Our simulations show that the dust particles are dominated (or covered) by ice and tholin-like organics. Spatial changes in the colour and polarization can be explained by particle fragmentation. This work has been done in collab- oration with Oleksandra Ivanova, Igor Lukyanyk, Ludmilla Kolokolova. Marek Husarik, Vera Rosen- bush, et al. Sudipta Das Dynamical system analysis for steep potentials In this work, we have performed the dynamical system analysis for steep(er) exponential potentials considering different values of the steepness index n . We have performed the analysis using centre manifold theory as well as by employing numerical method. We have shown that in most of the cases, the higher values of steepness index corresponds to an unstable solution. We have shown that with this steep(er) potentials, one cannot have a phase transition from dark matter to dark energy in the past. This work has been done in collaboration with Manisha Banerjee, and Namdam Roy. Abhirup Datta Detailed study of the ELAIS N1 field with the uGMRT - I. Characterizing the 325 MHz fore- ground for redshifted 21 cm observations In this work, we present initial results of newly upgraded Giant Metrewave Radio Telescope (uGMRT) observation of European Large-Area ISO Survey-North 1 (ELAISN1) at 325 MHz with 32 MHz bandwidth. Precise measurement of fluc- tuations in galactic and extragalactic foreground emission as a function of frequency as well as angu- lar scale is necessary for detecting redshifted 21 cm signal of neutral hydrogen from cosmic dawn, epoch of reionization (EoR) and post-reionization. Here, for the first time, we have statistically quantified the galactic and extragalactic foreground sources in the ELAIS-N1 field in the form of angular power spectrum using the newly developed tapered grid- ded estimator (TGE). We have calibrated the data with and without direction-dependent calibration techniques, and demonstrated the effectiveness of TGE against the direction-dependent effects by us- ing higher tapering of field of view (FoV). We have found that diffuse galactic synchrotron emission (DGSE) dominates the sky, after point source sub- traction, across the angular multipole range 1115 5083 and 1565 4754 for direction- dependent and -independent calibrated visibilities, respectively. The statistical fluctuations in DGSE has been quantified as a power law of the form C = A − β . The best-fitting values of ( A, β ) are (62 ± 6 mK 2 , 2 . 55 ± 0 . 3) and (48 ± 4 mK 2 , 2 . 28 ± 0 . 4) for the two different calibration approaches. For both the cases, the power-law index is consistent with the previous measurements of DGSE in other parts of sky. This work has been done in collabo- ration with Arnab Chakraborty, Samir Choudhuri, Nirupam Roy, Huib Intema, Madhurima Choud- hury, et al. Extracting the 21 cm global signal using articial neural networks The study of the cosmic dark ages, cosmic dawn, and epoch of reionization (EoR) using the all-sky averaged redshifted HI 21 cm signal, are some of the key science goals of most of the ongoing or upcom- ing experiments, for example, EDGES, SARAS, and the SKA. This signal can be detected by av- eraging over the entire sky, using a single radio telescope, in the form of a global signal as a func- tion of only redshifted HI 21 cm frequencies. One of the major challenges faced while detecting this signal is the dominating, bright foreground. The success of such detection lies in the accuracy of the foreground removal. The presence of instru- mental gain fluctuations, chromatic primary beam, radio frequency interference (RFI), and the Earths ionosphere corrupts any observation of radio signals from the Earth. Here, we propose the use of arti- ficial neural networks (ANNs) to extract the faint redshifted 21 cm global signal buried in a sea of bright galactic foregrounds and contaminated by different instrumental models. The most striking advantage of using ANNs is the fact that, when the corrupted signal is fed into a trained network, we can simultaneously extract the signal as well as foreground parameters very accurately. Our results show that ANNs can detect the global signal with 92 per cent accuracy even in cases of mock observa- tions, where the instrument has some residual time- varying gain across the spectrum. This study has been done in collaboration with Madurima Choud-