An Automated Approach to the Study and Classification of Colliding and Interacting Galaxies

Colliding galaxies are perhaps the greatest events changing and evolving our Universe. Consequently, the need for an understanding of how that interaction originated is very important. This thesis presents a framework in which the study of these events can be conducted in a timely and efficient manner. A genetic algorithm coupled with an initial conditions generator, a physics engine and an analysis package performs an automated search to visually match an unknown galactic interaction with a known event, thus providing the starting conditions that created such an interaction

Measuring the Scatter in the Cluster Optical Richness-Mass Relation with Machine Learning

The distribution of massive clusters of galaxies depends strongly on the total cosmic mass density, the mass variance, and the dark energy equation of state. As such, measures of galaxy clusters can provide constraints on these parameters and even test models of gravity, but only if observations of clusters can lead to accurate estimates of their total masses. Here, we carry out a study to investigate the ability of a blind spectroscopic survey to recover accurate galaxy cluster masses through their line-of- sight velocity dispersions (LOSVD) using probability based and machine learning methods. We focus on the Hobby Eberly Telescope Dark Energy Experiment (HETDEX), which will employ new Visible Integral-Field Replicable Unit Spectrographs (VIRUS), over 420 degree2 on the sky with a 1/4.5 fill factor. VIRUS covers the blue/optical portion of the spectrum (3500 - 5500 Å), allowing surveys to measure redshifts for a large sample of galaxies out to z < 0.5 based on their absorption or emission (e.g., [O II], Mg II, Ne V) features. We use a detailed mock galaxy catalog from a semi-analytic model to simulate surveys observed with VIRUS, including: (1) Survey, a blind, HETDEX-like survey with an incomplete but uniform spectroscopic selection function; and (2) Targeted, a survey which targets clusters directly, obtaining spectra of all galaxies in a VIRUS-sized field. For both surveys, we include realistic uncertainties from galaxy magnitude and line-flux limits. We benchmark both surveys against spectroscopic observations with \perfect" knowledge of galaxy line-of-sight velocities. With Survey observations, we can recover cluster masses to ~ 0.1 dex which can be further improved to < 0.1 dex with Targeted observations. This level of cluster mass recovery provides important measurements of the intrinsic scatter in the optical richness-cluster mass relation, and enables constraints on the key cosmological parameter, σ8, to < 20%. As a demonstration of the methods developed previously, we present a pilot survey with integral field spectroscopy of ten galaxy clusters optically selected from the Sloan Digital Sky Survey's DR8 at z = 0.2 – 0.3. Eight of the clusters are rich (λ > 60) systems with total inferred masses (1.58 -17.37) ×1014 Mʘ (M200c), and two are poor (λ < 15) systems with inferred total masses ~ 0.5 × 1014 Mʘ (M200c). We use the Mitchell Spectrograph, (formerly the VIRUS-P spectrograph, a prototype of the HETDEX VIRUS instrument) located on the McDonald Observatory 2.7m telescope, to measure spectroscopic redshifts and line-of-sight velocities of the galaxies in and around each cluster, determine cluster membership and derive LOSVDs. We test both a LOSVD-cluster mass scaling relation and a machine learning based approach to infer total cluster mass. After comparing the cluster mass estimates to the literature, we use these independent cluster mass measurements to estimate the absolute cluster mass scale, and intrinsic scatter in the optical richness-mass relationship. We measure the intrinsic scatter in richness at fixed cluster mass to be σMǀλ = 0.27 ± 0.07 dex in excellent agreement with previous estimates of σMǀλ ~ 0.2 – 0.3 dex. We discuss the importance of the data used to train the machine learning methods and suggest various strategies to import the accuracy of the bias (offset) and scatter in the optical richness-cluster mass relation. This demonstrates the power of blind spectroscopic surveys such as HETDEX to provide robust cluster mass estimates which can aid in the determination of cosmological parameters and help to calibrate the observable-mass relation for future photometric large area-sky surveys

The Role of Bulge Formation in the Homogenization of Stellar Populations at z∼2z\sim2 as revealed by Internal Color Dispersion in CANDELS

We use data from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey to study how the spatial variation in the stellar populations of galaxies relate to the formation of galaxies at $1.5 < z < 3.5$. We use the Internal Color Dispersion (ICD), measured between the rest-frame UV and optical bands, which is sensitive to age (and dust attenuation) variations in stellar populations. The ICD shows a relation with the stellar masses and morphologies of the galaxies. Galaxies with the largest variation in their stellar populations as evidenced by high ICD have disk-dominated morphologies (with S\'{e}rsic indexes $< 2$) and stellar masses between $10 < \mathrm{Log~M/ M_\odot}< 11$. There is a marked decrease in the ICD as the stellar mass and/or the S\'ersic index increases. By studying the relations between the ICD and other galaxy properties including sizes, total colors, star-formation rate, and dust attenuation, we conclude that the largest variations in stellar populations occur in galaxies where the light from newly, high star-forming clumps contrasts older stellar disk populations. This phase reaches a peak for galaxies only with a specific stellar mass range, $10 < \mathrm{Log~M/ M_\odot} < 11$, and prior to the formation of a substantial bulge/spheroid. In contrast, galaxies at higher or lower stellar masses, and/or higher S\'{e}rsic index ($n > 2$) show reduced ICD values, implying a greater homogeneity of their stellar populations. This indicates that if a galaxy is to have both a quiescent bulge along with a star forming disk, typical of Hubble Sequence galaxies, this is most common for stellar masses $10 < \mathrm{Log~M/M_\odot} < 11$ and when the bulge component remains relatively small ($n<2$).Comment: 15 pages, 14 figure

The Role of Bulge Formation in the Homogenization of Stellar Populations at \u3cem\u3eZ\u3c/em\u3e ~ 2 as Revealed by Internal Color Dispersion in CANDELS

We use data from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey to study how the spatial variation in the stellar populations of galaxies relates to the formation of galaxies at 1.5 \u3c z \u3c 3.5. We use the internal color dispersion (ICD), measured between the rest-frame UV and optical bands, which is sensitive to age (and dust attenuation) variations in stellar populations. The ICD shows a relation with the stellar masses and morphologies of the galaxies. Galaxies with the largest variation in their stellar populations as evidenced by high ICD have disk-dominated morphologies (with Sérsic indexes M/M⊙) \u3c 11. There is a marked decrease in the ICD as the stellar mass and/or the Sérsic index increases. By studying the relations between the ICD and other galaxy properties including size, total color, star formation rate, and dust attenuation, we conclude that the largest variations in stellar populations occur in galaxies where the light from newly, high star-forming clumps contrasts older stellar disk populations. This phase reaches a peak for galaxies only with a specific stellar mass range, 10 \u3c log(M/M⊙) \u3c 11, and prior to the formation of a substantial bulge/spheroid. In contrast, galaxies at higher or lower stellar masses and/or higher Sérsic index (n \u3e 2) show reduced ICD values, implying a greater homogeneity of their stellar populations. This indicates that if a galaxy is to have a quiescent bulge along with a star-forming disk, typical of Hubble sequence galaxies, this is most common for stellar masses 10 \u3c log(M/M⊙) \u3c 11 and when the bulge component remains relatively small (n \u3e 2)

Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder.

A mechanistic understanding of the pathology of psychiatric disorders has been hampered by extensive heterogeneity in biology, symptoms, and behavior within diagnostic categories that are defined subjectively. We investigated whether leveraging individual differences in information-processing impairments in patients with post-traumatic stress disorder (PTSD) could reveal phenotypes within the disorder. We found that a subgroup of patients with PTSD from two independent cohorts displayed both aberrant functional connectivity within the ventral attention network (VAN) as revealed by functional magnetic resonance imaging (fMRI) neuroimaging and impaired verbal memory on a word list learning task. This combined phenotype was not associated with differences in symptoms or comorbidities, but nonetheless could be used to predict a poor response to psychotherapy, the best-validated treatment for PTSD. Using concurrent focal noninvasive transcranial magnetic stimulation and electroencephalography, we then identified alterations in neural signal flow in the VAN that were evoked by direct stimulation of that network. These alterations were associated with individual differences in functional fMRI connectivity within the VAN. Our findings define specific neurobiological mechanisms in a subgroup of patients with PTSD that could contribute to the poor response to psychotherapy.PEV was supported by the Medical Research Council (grant no. MR/K020706/1) and is a Fellow of MQ: Transforming Mental Health (MQF17_24)

Convergent genetic and expression data implicate immunity in Alzheimer's disease

Background Late–onset Alzheimer's disease (AD) is heritable with 20 genes showing genome wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease we extended these genetic data in a pathway analysis. Methods The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain. Results ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (p = 3.27×10-12 after multiple testing correction for pathways), regulation of endocytosis (p = 1.31×10-11), cholesterol transport (p = 2.96 × 10-9) and proteasome-ubiquitin activity (p = 1.34×10-6). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected p 0.002 – 0.05). Conclusions The immune response, regulation of endocytosis, cholesterol transport and protein ubiquitination represent prime targets for AD therapeutics