Multi-Wavelength Analysis Of Star Formation In Galaxy Clusters

Galaxy clusters are one of the most massive structures in the Universe, consisting of hundreds to thousands of galaxies bound together by gravity. They are important laboratories for the study of the formation and evolution of galaxies over the age of the Universe. The high-density cluster environment affects the physical and morphological properties of cluster galaxies. The main goal of this dissertation is to study the effect of the cluster environment on galaxy evolution using the star-formation rate (SFR) of cluster galaxies. Multi-wavelength data at ultraviolet, u-band, and infrared wavelengths for a sample of 74 low-redshift (0.022 \u3c z \u3c 0.184 ) galaxy clusters were used for the analysis of this study. WIYN 0.9m+HDI telescope/detector at the Kitt Peak National Observatory was used to obtain u-band measurements of 14 galaxy clusters. This dataset was supplemented by 18 clusters from the study of Barkhouse et al. (2007), 10 clusters from Omizzolo et al. (2014), 13 clusters from Rude et al. (2020), and 19 clusters from Valentinuzzi et al. (2011). Archival data from the GALaxy Evolution EXplorer (GALEX) and the Wide-field Infrared Survey Explorer (WISE) satellites were used for ultraviolet and IR analysis, respectively. Redshifts of galaxies were obtained from SDSS spectroscopy data and were used to select cluster galaxies. The dispersion of the background-corrected red-sequence was used to separate cluster galaxies based on color into red and blue systems. A dynamical radius, r200, was calculated for each cluster using the cluster velocity dispersion, and used as a normalization factor to compare cluster characteristics. For each passband, the radial dependence (0.0 \u3c r/r200 \u3c 1.0) of the SFR was measured for all cluster galaxies. Evidence for the quenching of star formation towards the cluster center was found for both red and blue galaxies, with the blue galaxy SFR decreasing more than for the red galaxies. The cluster galaxy sample was divided into giant (high-mass) and dwarf (low-mass) galaxies using their absolute r-band magnitude. It was found that dwarfs are more susceptible to environmental effects compared to giant systems. These results are consistent for all multi-wavelength data used in this study. While ram pressure stripping plays a more important role in quenching star formation towards the cluster core, other mechanisms, such as galaxy harassment and starvation, were found to be more effective in the cluster outskirts

Mapping Star Formation from the Core to the Outskirts of Galaxy Clusters

We propose for time to complete our u- and r-band imaging program of 30 low-redshift (z ≤ 0.03) galaxy clusters using the CTIO Blanco 4m+DECam telescope/detector combination. These data will allow us to probe star formation from the cluster core to the infall region, and complete the acquisition of observations for the Ph.D. dissertation of Gihan Gamage (University of North Dakota). The deep u- and r-band data will allow us to explore relative changes in the luminosity function, dwarf-to-giant ratio, blue fraction, and galaxy morphological type as a function of cluster-centric radius for a statistically significant sample of 30 clusters. The large field-of-view of the telescope+detector will permit us to not only map star formation out to the infall region, but also to probe dwarf galaxies using a reasonable exposure time due to the low redshift of our target sample. The comparison of u- and r-band observations will provide the necessary leverage to look for enhancements/quenching of star formation as galaxies fall into the cluster environment from the low density field region

U-band Measurement of Star Formation in Cluster Galaxies

We propose to obtain deep U-band observations of 14 low-redshift (z ≤ 0.06) galaxy clusters using the WIYN 0.9m+HDI telescope/detector to complete our survey to probe star formation of galaxies in high-density environments. These observations, combined with previously obtained data of 11 clusters observed using the same telescope+detector, will give us a statistically significant sample for the Ph.D. dissertation of co-I Gihan Gamage. Clusters are selected from 57 clusters in which we have obtained deep B- and R-band data using the KPNO 0.9m+MOSA. U-band data will allow us to explore relative changes in the luminosity function for the U- and R-band as a function of cluster-centric radius. The large field-of-view of the telescope+detector will permit us to map out the spatial distribution of star forming galaxies from the core region to the outskirts. Comparing U-band observations with our R-band data will provide the necessary leverage to look for enhancements/quenching of star formation as galaxies fall into the cluster. These observations allow us to probe ~ 2 mag fainter than SDSS

Globular cluster population of the HST frontier fields galaxy J07173724+3744224

We present the first measurement of the globular cluster population surrounding the elliptical galaxy J07173724+3744224 (z=0.1546). This galaxy is located in the foreground in the field-of-view of the Hubble Space Telescope (HST) Frontier Fields observations of galaxy cluster MACS J0717.5+3745 (z=0.5458). Based on deep HST ACS F435W, F606W, and F814W images, we find a total globular cluster population of N_tot = 3441 +/- 1416. Applying the appropriate extinction correction and filter transformation from ACS F814W to the Johnson V-band, we determine that the host galaxy has an absolute magnitude of M_V = -22.2. The specific frequency was found to be S_N = 4.5 +/- 1.8. The radial profile of the globular cluster system was best fit using a powerlaw of the form $\sigma\sim R^{-0.6}$, with the globular cluster population found to be more extended than the halo light of the host galaxy ($\sigma_{halo}\sim R^{-1.7}$). The F435W-F814W colour distribution suggests a bimodal population, with red globular clusters 1-3x more abundant than blue clusters. These results are consistent with the host elliptical galaxy J07173724+3744224 having formed its red metal-rich GCs in situ, with the blue metal-poor globular clusters accreted from low-mass galaxies.Comment: 21 pages, 14 figures, 2 tables, revised following peer-review, accepted for publication in MNRA

Star formation in low-redshift cluster dwarf galaxies

Evolution of galaxies in dense environments can be affected by close encounters with neighbouring galaxies and interactions with the intracluster medium. Dwarf galaxies (dGs) are important as their low mass makes them more susceptible to these effects than giant systems. Combined luminosity functions (LFs) in the r and u band of 15 galaxy clusters were constructed using archival data from the Canada–France–Hawaii Telescope. LFs were measured as a function of clustercentric radius from stacked cluster data. Marginal evidence was found for an increase in the faint-end slope of the u-band LF relative to the r-band with increasing clustercentric radius. The dwarf-to-giant ratio (DGR) was found to increase toward the cluster outskirts, with the u-band DGR increasing faster with clustercentric radius compared to the r-band. The dG blue fraction was found to be ∼2 times larger than the giant galaxy blue fraction over all clustercentric distance (∼5σ level). The central concentration (C) was used as a proxy to distinguish nucleated versus non-nucleated dGs. The ratio of high-C to low-C dGs was found to be ∼2 times greater in the inner cluster region compared to the outskirts (2.8σ level). The faint-end slope of the r-band LF for the cluster outskirts (0.6 ≤ r/r200 \u3c 1.0) is steeper than the Sloan Digital Sky Survey field LF, while the u-band LF is marginally steeper at the 2.5σ level. Decrease in the faint-end slope of the r- and u-band cluster LFs towards the cluster centre is consistent with quenching of star formation via ram pressure stripping and galaxy–galaxy interactions

A Crowdsourced Gameplay for Whole-Genome Assembly via Short Reads

Next-generation sequencing has revolutionized the field of genomics by producing accurate, rapid and cost-effective genome analysis with the use of high throughput sequencing technologies. This has intensified the need for accurate and performance efficient genome assemblers to assemble a large set of short reads produced by next-generation sequencing technology. Genome assembly is an NP-hard problem that is computationally challenging. Therefore, the current methods that rely on heuristic and approximation algorithms to assemble genomes prevent them from arriving at the most accurate solution. This paper presents a novel approach by gamifying whole-genome shotgun assembly from next-generation sequencing data; we present "Geno", a human-computing game designed with the aim of improving the accuracy of whole-genome shotgun assembly. We evaluate the feasibility of crowdsourcing the problem of whole-genome shotgun assembly by breaking the problem into small subtasks. The evaluation results, for single-cell Escherichia coli K-12 substr. MG1655 with a read length of 25 bp that produced 144,867 game instances of mean 25 sequences per instance at 40x coverage indicate the feasibility of sub-tasking the problem of genome assembly to be solved using crowdsourcing

Phylogenetic Tree Construction Using K-Mer Forest- Based Distance Calculation

Phylogenetics is one of the dominant data engineering research disciplines based on biological information. More particularly here, we consider raw DNA sequences and do comparative analysis in order to come up with important conclusions. When representing evolutionary relationships among different organisms in a concise manner, the phylogenetic tree helps significantly. When constructing phylogenetic trees, the elementary step is to calculate the genetic distance among species. Alignment-based sequencing and alignment-free sequencing are the two main distance computation methods that are used to find genetic relatedness of different species. In this paper we propose a novel alignment-free, pairwise, distance calculation method based on k-mers and a state of art machine learning-based phylogenetic tree construction mechanism. With the proposed approach we can convert longer DNA sequences into compendious k-mer forests which gear up the efficiency of comparison. Later we construct the phylogenetic tree based on calculated distances with the help of an algorithm build upon k-medoid clustering, which guaranteed significant efficiency and accuracy compared to traditional phylogenetic tree construction methods

Phylogenetic Tree Construction Using K-Mer Forest- Based Distance Calculation

Phylogenetics is one of the dominant data engineering research disciplines based on biological information. More particularly here, we consider raw DNA sequences and do comparative analysis in order to come up with important conclusions. When representing evolutionary relationships among different organisms in a concise manner, the phylogenetic tree helps significantly. When constructing phylogenetic trees, the elementary step is to calculate the genetic distance among species. Alignment-based sequencing and alignment-free sequencing are the two main distance computation methods that are used to find genetic relatedness of different species. In this paper we propose a novel alignment-free, pairwise, distance calculation method based on k-mers and a state of art machine learning-based phylogenetic tree construction mechanism. With the proposed approach we can convert longer DNA sequences into compendious k-mer forests which gear up the efficiency of comparison. Later we construct the phylogenetic tree based on calculated distances with the help of an algorithm build upon k-medoid clustering, which guaranteed significant efficiency and accuracy compared to traditional phylogenetic tree construction methods