arXiv subjects

Patrick Sullivan

Publications and source records attributed to Patrick Sullivan.

4 recordsLinked to original sources

arXiv2024

The Arizona Molecular ISM Survey with the SMT: Survey Overview and Public Data Release

The CO(1-0) line has been carefully calibrated as a tracer of molecular gas mass. However, recent studies often favor higher J transitions of the CO molecule which are brighter and accessible for redshift ranges where CO(1-0) is not. These lines are not perfect analogues for CO(1-0), owing to their more stringent excitation conditions, and must be calibrated for use as molecular gas tracers. Here we introduce the Arizona Molecular ISM Survey with the SMT (AMISS), a multi-CO line survey of z~0 galaxies conducted to calibrate the CO(2-1) and CO(3-2) lines. The final survey includes CO(2-1) spectra of 176 galaxies and CO(3-2) spectra for a subset of 45. We supplement these with archival CO(1-0) spectra from xCOLD GASS for all sources and additional CO(1-0) observations with the Kitt Peak 12m Telescope. Targets were selected to be representative of the galaxy population in the stellar mass range of $10^9$ to $10^{11.5}$ M$_\odot$. Our project emphasized careful characterization of statistical and systematic uncertainties to enable studies of trends in CO line ratios. We show that optical and CO disk sizes are on average equal, for both the CO(1-0) and CO(2-1) line. We measure the distribution of CO line luminosity ratios, finding medians (16th-84th percentile) of 0.71 (0.51-0.96) for the CO(2-1)-to-CO(1-0) ratio, 0.39 (0.24-0.53) for the CO(3-2)-to-CO(1-0) ratio, and 0.53 (0.41-0.74) for the CO(3-2)-to-CO(2-1) ratio. A companion paper presents our study of CO(2-1)'s applicability as a molecular gas mass tracer and search for trends in the CO(2-1)-to-CO(1-0) ratio. Our catalog of CO line luminosities will be publicly available with the published version of this article.

astro-ph.GA↗

arXiv2022

COGEDAP: A COmprehensive GEnomic Data Analysis Platform

Non-sharable sensitive data collection and analysis in large-scale consortia for genomic research is complicated. Time consuming issues in installing software arise due to different operating systems, software dependencies and running the software. Therefore, easier, more standardized, automated protocols and platforms can be a solution to overcome these issues. We have developed one such solution for genomic data analysis using software container technologies. The platform, COGEDAP, consists of different software tools placed into Singularity containers with corresponding pipelines and instructions on how to perform genome-wide association studies (GWAS) and other genomic data analysis via corresponding tools. Using a provided helper script written in Python, users can obtain auto-generated scripts to conduct the desired analysis both on high-performance computing (HPC) systems and on personal computers. The analyses can be done by running these auto-generated scripts with the software containers. The helper script also performs minor re-formatting of the input/output data, so that the end user can work with a unified file format regardless of which genetic software is used for the analysis. COGEDAP is actively being used by users from different countries/projects to conduct their genomic data analyses. Thanks to this platform, users can easily run GWAS and other genomic analyses without spending much effort on software installation, data formats, and other technical requirements.

q-bio.GN↗

arXiv2019

Generating and Exploiting Probabilistic Monocular Depth Estimates

Beyond depth estimation from a single image, the monocular cue is useful in a broader range of depth inference applications and settings---such as when one can leverage other available depth cues for improved accuracy. Currently, different applications, with different inference tasks and combinations of depth cues, are solved via different specialized networks---trained separately for each application. Instead, we propose a versatile task-agnostic monocular model that outputs a probability distribution over scene depth given an input color image, as a sample approximation of outputs from a patch-wise conditional VAE. We show that this distributional output can be used to enable a variety of inference tasks in different settings, without needing to retrain for each application. Across a diverse set of applications (depth completion, user guided estimation, etc.), our common model yields results with high accuracy---comparable to or surpassing that of state-of-the-art methods dependent on application-specific networks.

cs.CV↗

arXiv2019

The Robust Kernel Association Test

Testing the association between SNP effects and a response is a common task. Such tests are often carried out through kernel machine methods based on least squares, such as the Sequence Kernel Association Test (SKAT). However, these least squares procedures assume a normally distributed response, which is often violated. Other robust procedures such as the Quantile Regression Kernel Machine (QRKM) restrict choice of loss function and only allow inference on conditional quantiles. We propose a general and robust kernel association test with flexible choice of loss function, no distributional assumptions, and has SKAT and QRKM as special cases. We evaluate our proposed robust association test (RobKAT) across various data distributions through simulation study. When errors are normally distributed, RobKAT controls type I error and shows comparable power to SKAT. In all other distributional settings investigated, our robust test has similar or greater power than SKAT. Finally, we apply our robust kernel association test on data from the CATIE clinical trial to detect associations between selected genes on chromosome 6, including the Major Histocompatibility Complex (MHC) region, and neurotrophic herpesvirus antibody levels in schizophrenia patients. RobKAT detected significant association with four SNP-sets (HST1H2BJ, MHC, POM12L2, and SLC17A1), three of which were undetected by SKAT.

stat.ME↗