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U. Sureshkumar

Publications and source records attributed to U. Sureshkumar.

6 recordsLinked to original sources

The MeerKAT Massive Distant Clusters Survey: a search for diffuse radio emission in 30 massive SZ-selected clusters at $z > 1$

We present the results of a search for diffuse radio emission in a uniformly selected sample of 30 of the most massive Sunyaev-Zel'dovich selected galaxy clusters at $z > 1$, providing the first statistical constraints on the evolution of cluster-scale diffuse emission beyond this redshift. We also analyse the scaling relations between radio power ($P_{1.4\,\mathrm{GHz}}$) and cluster mass ($M_{\rm 500c}$) in this high-redshift sample. It is well established that radio halos are primarily found in the most massive clusters, where turbulent energy from major mergers can re-accelerate relativistic electrons and amplify magnetic fields on megaparsec scales. Deep MeerKAT 1.28 GHz observations reveal diffuse radio halos in eight clusters (27$\%$), while the remaining 21 (70$\%$) show no emission; one additional cluster (3$\%$) was excluded from the radio analysis due to poor data quality. The halo detection rate in this high-redshift sample is lower than at intermediate redshift, but remains higher than the $\lesssim 10\%$ occurrence generally predicted by theoretical models at $z \gtrsim 1$. The detected radio halos scatter around the best-fitting $P_{1.4\,\mathrm{GHz}}$-$M_{\rm 500c}^{\rm {Unc}}$ relation derived for the MMDCS sample, whereas non-detections populate the lower envelope of the radio power-mass plane, similar to trends seen at lower redshift. No cluster-scale radio relics or mini-halos are identified. Our findings highlight MeerKAT's ability to probe non-thermal processes in the most distant clusters and the need for deeper, lower-frequency surveys to uncover faint diffuse emission and test the persistence of the $P_{1.4\,\mathrm{GHz}}$-$M_{\rm 500}$ relation across cosmic time.

astro-ph.GA

The MeerKAT Massive Distant Clusters Survey: detection of diffuse radio emission in galaxy clusters at $z > 1$

Diffuse, low surface-brightness radio emission in merging galaxy clusters provides insights into cosmic structure formation, the growth of magnetic fields, and turbulence. This paper reports a search for diffuse radio emission in a pilot sample of six high-redshift ($1.01 < z < 1.31$) galaxy clusters from the MeerKAT Massive Distant Cluster Survey (MMDCS). These six clusters are selected as the most massive $(M_{\rm 500c} = 6.7\,- 8.5 \times 10^{14}~\rm{M_{\odot}})$ systems based on their Sunyaev-Zel'dovich mass from the full MMDCS sample of 30 ACT DR5 clusters, and were observed first to explore the high-mass, high-redshift regime. Diffuse radio emission is confidently detected in four clusters and tentatively identified in two, with $k$-corrected radio powers scaled to 1.4 GHz ranging from $(0.46 \pm 0.16)$ to $(4.51 \pm 1.68) \times 10^{24}\, \mathrm{WHz^{-1}}$ and linear sizes between 0.47 and 1.08 Mpc. Combining $Chandra$ X-ray data with MeerKAT radio data, we find that 80$\%$ of clusters with X-ray observations exhibit disturbed morphologies indicative of mergers. These $z > 1$ galaxy clusters scatter around the established radio power-mass scaling relation observed at lower redshifts, supporting turbulent re-acceleration models in high-redshift mergers. However, their radio spectra are predicted to steepen ($\alpha < -1.5$) due to enhanced inverse Compton losses in the cosmic microwave background, rendering them under-luminous at 1.4 GHz and placing them below the correlation. Our results demonstrate that merger-driven turbulence can sustain radio halos even at $z > 1$ while highlighting MeerKAT's unique ability to probe non-thermal processes in the early universe.

astro-ph.GA

Do galaxy mergers prefer under-dense environments?

Galaxy mergers play a crucial role in galaxy evolution. However, the correlation between mergers and the local environment of galaxies is not fully understood. We aim to address the question of whether galaxy mergers prefer denser or less dense environments by quantifying the spatial clustering of mergers and non-mergers. We use two different indicators to classify mergers and non-mergers - classification based on a deep learning technique ($f$) and non-parametric measures of galaxy morphology, Gini-$M_{20}$ ($g$). We used a set of galaxy samples in the redshift range $0.1 < z < 0.15$ from the Galaxy and Mass Assembly (GAMA) survey with a stellar mass cut of $\log (M_{\star}/M_{\odot} ) > 9.5$. We measured and compared the two-point correlation function (2pCF) of mergers and non-mergers classified using the two merger indicators $f$ and $g$. We measured the marked correlation function (MCF), in which the galaxies are weighted by $f$ to probe the environmental dependence of galaxy mergers. We do not observe a statistically significant difference between the clustering strengths of mergers and non-mergers obtained using 2pCF. However, using the MCF measurements with $f$ as a mark, we observe an anti-correlation between the likelihood of a galaxy being a merger and its environment. Our results emphasise the advantage of MCF over 2pCF in probing the environmental correlations. Based on the MCF measurements, we conclude that the galaxy mergers prefer to occur in the under-dense environments on scales $> 50 \, h^{-1} \mathrm{kpc}$ of the large-scale structure (LSS). We attribute this observation to the high relative velocities of galaxies in the densest environments that prevent them from merging.

astro-ph.GA

Shedding Light on Low Surface Brightness Galaxies in Dark Energy Survey with Transformers

Low surface brightness galaxies (LSBGs) which are defined as galaxies that are fainter than the night sky, play a crucial role in understanding galaxy evolution and cosmological models. Upcoming large-scale surveys like Rubin Observatory Legacy Survey of Space and Time (LSST) and Euclid are expected to observe billions of astronomical objects. In this context, using semi-automatic methods to identify LSBGs would be a highly challenging and time-consuming process and demand automated or machine learning-based methods to overcome this challenge. We study the use of transformer models in separating LSBGs from artefacts in the data from the Dark Energy Survey (DES) data release 1. Using the transformer models, we then search for new LSBGs from the DES that the previous searches may have missed. Properties of the newly found LSBGs are investigated, along with an analysis of the properties of the total LSBG sample in DES. We identified 4,083 new LSBGs in DES, adding an additional $\sim17\% $ to the LSBGs already known in DES. This also increased the number density of LSBGs in DES to 5.5 deg$^{-2}$. We performed a clustering analysis of the LSBGs in DES using an angular two-point auto-correlation function and found that LSBGs cluster more strongly than their high surface brightness counterparts. We associated 1310 LSBGs with galaxy clusters and identified 317 among them as ultra-diffuse galaxies (UDGs). We found that these cluster LSBGs are getting bluer and larger in size towards the edge of the clusters when compared with those in the centre. Transformer models have the potential to be on par with convolutional neural networks as state-of-the-art algorithms in analysing astronomical data.

astro-ph.GA

Galaxy and Mass Assembly (GAMA): Mid-infrared properties as tracers of galaxy environment

We investigate how different mid-infrared (mid-IR) properties of galaxies trace the environment in which the galaxies are located. For this purpose, we first study the dependence of galaxy clustering on the absolute magnitude at 3.4 $\mu$m and redshift. Then, we look into the environmental dependence of mid-IR luminosities and the galaxy properties derived from these luminosities. We also explore how various infrared galaxy luminosity selections influence the galaxy clustering measurements. We use a set of W1 (3.4 $\mu$m) absolute magnitude ($M_\text{W1}$) selected samples from the Galaxy and Mass Assembly (GAMA) survey matched with mid-IR properties from the Wide-field Infrared Survey Explorer (WISE) in the redshift range $0.07 \leq z < 0.43$. We compute the galaxy two-point correlation function (2pCF) and compare the clustering lengths between subsamples binned in $M_\text{W1}$ and in redshift. We also measure the marked correlation functions (MCFs) using the luminosities in the WISE W1 to W4 (3.4 to 22 $\mu$m) bands as marks. Additionally, we compare the measurements of MCFs with different estimates of stellar mass and star formation rate used as marks. Finally, we check how different selections applied to the sample affect the clustering measurements. We show strong clustering dependence on the W1 absolute magnitude: galaxies brighter in the W1 band are more strongly clustered than their fainter counterparts. We also observe a lack of significant redshift dependence of clustering in the redshift range $0.07 \leq z < 0.43$. We show that although W1 and W2 bands are direct indicators of stellar mass, a galaxy sample selected based on W1 or W2 bands does not perfectly show the clustering behaviour of a stellar mass selected sample. Similar is the case with the proxy relation between W3 and W4 bands and star formation rate.

astro-ph.GA

Galaxy and Mass Assembly (GAMA): Tracing galaxy environment using the marked correlation function

We investigate how different galaxy properties - luminosities in u, g, r, J, K-bands, stellar mass, star formation rate and specific star formation rate trace the environment in the local universe. We also study the effect of survey flux limits on galaxy clustering measurements. We measure the two-point correlation function (2pCF) and marked correlation functions (MCFs) using the aforementioned properties as marks. We use nearly stellar-mass-complete galaxy sample in the redshift range 0.1 < z < 0.16 from the Galaxy And Mass Assembly (GAMA) survey with a flux limit of r < 19.8. Further, we impose a brighter flux limit of r < 17.8 to our sample and repeat the measurements to study how this affects galaxy clustering analysis. We compare our results to measurements from the Sloan Digital Sky Survey (SDSS) with flux limits of r < 17.8 and r < 16.8. We show that the stellar mass is the best tracer of galaxy environment, the K-band luminosity being a good substitute, although such a proxy sample misses close pairs of evolved, red galaxies. We also confirm that the u-band luminosity is a good, but not a perfect proxy of star formation rate in the context of galaxy clustering. We observe an effect of the survey flux limit on clustering studies - samples with a higher flux limit (smaller magnitude) miss some information about close pairs of starburst galaxies.

astro-ph.CO