AR-2019-2020

using the VLA at 1.6 and 6.0 GHz. The third data release of the sample, namely, the B configuration 1.6 GHz sample and spectral index maps between 1.6 and 6.0 GHz with a matched resolution of ∼ 3 arcsec have been presented. The authors examine the possible presence of low-luminosity active galactic nuclei (AGNs) in the sample, as well as some in-disk structure. New features can be seen in the spectral index maps that are masked in the total intensity emission, including hidden spiral arms in NGC 3448 and two previously unknown radio lobes on either side of the nucleus of NGC 3628. The AGN detection rate, using only radio criteria, is ∼ 55%. D.J. Saikia and collaborators present results of HI absorption experiment done using the GMRT towards 27 low- and intermediate-luminosity radio active galactic nuclei (AGNs), classified as either low excitation radio galaxies (LERGs) or high excitation radio galaxies (HERGs) and with WISE colour W 2[4 . 6 μm ] − W 3[12 μm ] > 2 . They report HI absorption detection towards seven radio AGNs, six of which are new. Combined with other sources from the literature, they show that compact radio AGNs with WISE colour W 2 − W 3 > 2 have higher detection rates compared to those with W 2 − W 3 < 2 . HI absorption detection rate is shown to be higher for HERGs compared to LERGs, mainly due to a larger fraction of HERGs being gas and dust rich with a younger stellar population and thus with W 2 − W 3 > 2 compared to LERGs. The detection rates are similar for similar WISE colours W 2 − W 3 > 2, implying detection of HI gas may not necessarily mean high excitation mode AGN. From an analysis of the kinematics, they find that LERGs show a significantly wider range in the shift of centroid velocities of the absorbing gas than HERGs possibly due to differences in jet-interstellar medium interaction. Gravitational Waves Optimal optical search strategy for finding transient in large sky error region under realistic constraints Javed Rana , Shreya Anand, and Sukanta Bose developed this new method in multi-messenger astronomy. In order to identify the rapidly-fading, optical transient counterparts of gravitational wave (GW) sources, an efficient follow-up strategy is required. Since most ground-based optical observatories aimed at following-up, GW sources have a telescope with a small field-of-view (FOV) as compared to the GW sky error region, they focus on a search strategy that involves dividing the GW patch into tiles of the same area as the telescope FOV to strategically image the entire patch. They presented an improvement over the past optimal telescope-scheduling algorithms by combining the tiling and galaxy-targeted search strategies, and factoring the effects of the source air-mass and telescope slew, along with setting constraints, into the scheduling algorithm in order to increase the chances of identifying the GW counterpart. Using the observatory site of the GROWTH-India telescope as an example, they generated 100s of skymaps to test the performance of their algorithms. Their results indicate that slew-optimization can reduce the cumulative slew angle in the observing schedule by 100s of degrees, saving several minutes of observing time without the loss of tiles and probability. Further, they demonstrated that as compared to the greedy algorithm, the airmass- weighted algorithm could acquire up to 20% more probability and 30 sq. degree more in areal coverage for skymaps of all sizes and configurations. Regularised map-making of a stochastic gravitational wave background Estimating a map of the SGWB sky from noisy data is a challenging problem. This is becoming progressively relevant as the number of detectors are increasing and the existing ones are becoming more and more sensitive, and a detection of the isotropic background map happen in a few years time. Since the matrix that couples the true anisotropic sky to the data is ill conditioned, deconvolution of the observed map becomes more reliable when known features about the expected signal is incorporated in the process. S. Panda, Shweta Bhagawat, J. Suresh and Sanjit Mitra introduced a widely used procedure to incorporate these “priors” through a Bayesian regularization scheme in this context. They demonstrate that regularization significantly enhances the quality of reconstruction,