Using multi-wavelength data, from UV-optical-near-mid IR, for ∼6000 galaxies in the local Universe, we study the dependence of star formation on the morphological Ttypes for massive galaxies (log M * /M ≥ 10). We find that, early-type spirals (Sa-Sbc) and S0s predominate in the green valley, which is a transition zone between the star forming and quenched regions. Within the early-type spirals, as we move from Sa to Sbc spirals the fraction of green valley and quenched galaxies decreases, indicating the important role of the bulge in the quenching of galaxies. The fraction of early-type spirals decreases as we enter the green valley from the blue cloud, which coincides with the increase in the fraction of S0s. This points towards the morphological transformation of early-type spiral galaxies into S0s which can happen due to environmental effects such as ram-pressure stripping, galaxy harassment, or tidal interactions. We also find a second population of S0s which are actively star-forming and are present in all environments. Since morphological T-type, specific star formation rate (sSFR), and environmental density are all correlated with each other, we compute the partial correlation coefficient for each pair of parameters while keeping the third parameter as a control variable. We find that morphology most strongly correlates with sSFR, independent of the environment, while the other two correlations (morphology-density and sSFR-environment) are weaker. Thus, we conclude that, for massive galaxies in the local Universe, the physical processes that shape their morphology are also the ones that determine their star-forming state.

Here we report the discovery with the Giant Metrewave Radio Telescope of an extremely large (∼115 kpc in diameter) Hi ring off-centered from a massive quenched galaxy, AGC 203001. This ring does not have any bright extended optical counterpart, unlike several other known ring galaxies. Our deep g, r, and i optical imaging of the Hi ring, using the MegaCam instrument on the Canada-France-Hawaii Telescope, however, shows several regions with faint optical emission at a surface brightness level of ∼28 mag/arcsec 2 . Such an extended Hi structure is very rare with only one other case known so far -the Leo ring. Conventionally, off-centered rings have been explained by a collision with an "intruder" galaxy leading to expanding density waves of gas and stars in the form of a ring. However, in such a scenario the impact also leads to large amounts of star formation in the ring which is not observed in the ring presented in this paper. We discuss possible scenarios for the formation of such Hi dominated rings.

We have carried out a systematic search for outlying Hα emitters in the entire data release 14 of the Sloan Digital Sky Survey (SDSS) IV Mapping Nearby Galaxies at APO (MaNGA) survey. We have discovered six outlying Hα emitters with no bright underlying optical continuum emission in the imaging data release 5 from the Dark Energy Camera Legacy Survey (DECaLS) and data release 6 of the Mayall z-band Legacy Survey (MzLS) + Beijing-Arizona Sky Survey (BASS). They also show a velocity field which is different from that of the host galaxy. These outlying Hα emitters all have extended structure in the Hα image. Their emission line ratios show that they are photoionised due to an active galactic nucleus (AGN) or a mixture of both an AGN and star formation. Some of them are very likely to be fainter counterparts of Hanny's Voorwerp like objects.

Understanding the star-formation properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditionally, stellar population synthesis models have been used to obtain best fit parameters that characterise star formation in galaxies. As multiband flux measurements become available for thousands of galaxies, an alternative approach to characterising star formation using machine learning becomes feasible. In this work, we present the use of deep learning techniques to predict three important star formation properties -stellar mass, star formation rate and dust luminosity. We characterise the performance of our deep learning models through comparisons with outputs from a standard stellar population synthesis code.Deep learning is inspired by the synaptic connections of

We investigate the origin of rare star-formation in an otherwise red-and-dead population of S0 galaxies using spatially resolved spectroscopy. Our sample consists of 120 low redshift (z < 0.1) star-forming S0 (SF-S0) galaxies from the SDSS-IV MaNGA DR15. We have selected this sample after a visual inspection of deep images from the DESI Legacy Imaging Surveys DR9 and the Subaru/HSC-SSP survey PDR3, to remove contamination from spiral galaxies. We also construct two control samples of star-forming spirals (SF-Sps) and quenched S0s (Q-S0s) to explore their evolutionary link with the star-forming S0s. To study star-formation at resolved scales, we use dust-corrected Hα luminosity and stellar density (Σ⋆) maps to construct radial profiles of star-formation rate (SFR) surface density (ΣSFR) and specific SFR (sSFR). Examining these radial profiles, we find that star-formation in SF-S0s is centrally dominated as opposed to disc dominated star-formation in spirals. We also compared various global (size-mass relation, bulge-to-total luminosity ratio) and local (central stellar velocity dispersion) properties of SF-S0s to those of the control sample galaxies. We find that SF-S0s are structurally similar to the quenched S0s and are different from star-forming spirals. We infer that SF-S0s are unlikely to be fading spirals. Inspecting stellar and gas velocity maps, we find that more than $50{{\ \rm per\ cent}}$ of the SF-S0 sample shows signs of recent galaxy interactions such as kinematic misalignment, counter-rotation, and unsettled kinematics. Based on these results, we conclude that in our sample of SF-S0s, star-formation has been rejuvenated, with minor mergers likely to be a major driver.

One of the major science goals of square kilometre array (SKA) is to understand the role played by atomic hydrogen (Hi) gas in the evolution of galaxies throughout cosmic time. The hyperfine transition line of the hydrogen atom at 21-cm is one of the best tools to detect and study the properties of Hi gas associated with galaxies. In this paper, we review our current understanding of Hi gas and its relationship with galaxies through observations of the 21-cm line both in emission and absorption. In addition, we provide an overview of the Hi science that will be possible with SKA and its precursors and pathfinders, i.e., Hi 21-cm emission and absorption studies of galaxies from nearby to high redshifts that will trace various processes governing galaxy evolution.

We present a deep learning model to predict the r-band bulge-to-total luminosity ratio (B/T) of nearby galaxies using their multi-band JPEG images alone. Our Convolutional Neural Network (CNN) based regression model is trained on a large sample of galaxies with reliable decomposition into the bulge and disk components. The existing approaches to estimate the B/T ratio use galaxy light-profile modelling to find the best fit. This method is computationally expensive, prohibitively so for large samples of galaxies, and requires a significant amount of human intervention. Machine learning models have the potential to overcome these shortcomings. In our CNN model, for a test set of 20000 galaxies, 85.7 per cent of the predicted B/T values have absolute error (AE) less than 0.1. We see further improvement to 87.5 per cent if, while testing, we only consider brighter galaxies (with r-band apparent magnitude < 17) with no bright neighbours. Our model estimates the B/T ratio for the 20000 test galaxies in less than a minute. This is a significant improvement in inference time from the conventional fitting pipelines, which manage around 2-3 estimates per minute. Thus, the proposed machine learning approach could potentially save a tremendous amount of time, effort and computational resources while predicting B/T reliably, particularly in the era of next-generation sky surveys such as the Legacy Survey of Space and Time (LSST) and the Euclid sky survey which will produce extremely large samples of galaxies.

We present a radio continuum study of a population of extremely young and starburst galaxies, termed as blueberries at ∼ 1 GHz using the upgraded Giant Metrewave Radio Telescope (uGMRT). We find that their radio-based star formation rate (SFR) is suppressed by a factor of ∼ 3.4 compared to the SFR based on optical emission lines. This might be due to (i) the young ages of these galaxies as a result of which a stable equilibrium via feedback from supernovae has not yet been established (ii) escape of cosmic ray electrons via diffusion or galactic scale outflows. The estimated non-thermal fraction in these galaxies has a median value of ∼ 0.49, which is relatively lower than that in normal star-forming galaxies at such low frequencies. Their inferred equipartition magnetic field has a median value of 27 µG, which is higher than those in more evolved systems like spiral galaxies. Such high magnetic fields suggest that small-scale dynamo rather than large-scale dynamo mechanisms might be playing a major role in amplifying magnetic fields in these galaxies.