Reconstructing the mid-infrared spectra of galaxies using ultraviolet to submillimeter photometry and Deep Generative Networks

The mid-infrared spectra of galaxies are rich in features such as the Polycyclic Aromatic Hydrocarbon (PAH) and silicate dust features which give valuable information about the physics of galaxies and their evolution. For example they can provide information about the relative contribution of star formation and accretion from a supermassive black hole to the power output of galaxies. However, the mid-infrared spectra are currently available for a very small fraction of galaxies that have been detected in deep multi-wavelength surveys of the sky. In this paper we explore whether Deep Generative Network methods can be used to reconstruct mid-infrared spectra in the 5-35{\mu}m range using the limited multi-wavelength photometry in ~20 bands from the ultraviolet to the submillimeter which is typically available in extragalactic surveys. For this purpose we use simulated spectra computed with a combination of radiative transfer models for starbursts, active galactic nucleus (AGN) tori and host galaxies. We find that our method using Deep Generative Networks, namely Generative Adversarial Networks and Generative Latent Optimization models, can efficiently produce high quality reconstructions of mid-infrared spectra in ~70% of the cases.Comment: Published in Astronomy and Computing (Volume 47, April 2024, 100823

Graph Theoretical Analysis of local ultraluminous infrared galaxies and quasars

We present a methodological framework for studying galaxy evolution by utilizing Graph Theory and network analysis tools. We study the evolutionary processes of local ultraluminous infrared galaxies (ULIRGs) and quasars and the underlying physical processes, such as star formation and active galactic nucleus (AGN) activity, through the application of Graph Theoretical analysis tools. We extract, process and analyse mid-infrared spectra of local (z < 0.4) ULIRGs and quasars between 5-38 microns through internally developed Python routines, in order to generate similarity graphs, with the nodes representing ULIRGs being grouped together based on the similarity of their spectra. Additionally, we extract and compare physical features from the mid-IR spectra, such as the polycyclic aromatic hydrocarbons (PAHs) emission and silicate depth absorption features, as indicators of the presence of star-forming regions and obscuring dust, in order to understand the underlying physical mechanisms of each evolutionary stage of ULIRGs. Our analysis identifies five groups of local ULIRGs based on their mid-IR spectra, which is quite consistent with the well established fork classification diagram by providing a higher level classification. We demonstrate how graph clustering algorithms and network analysis tools can be utilized as unsupervised learning techniques for revealing direct or indirect relations between various galaxy properties and evolutionary stages, which provides an alternative methodology to previous works for classification in galaxy evolution. Additionally, our methodology compares the output of several graph clustering algorithms in order to demonstrate the best-performing Graph Theoretical tools for studying galaxy evolution.Comment: Accepted for publication in Astronomy and Computin

A genome-wide pleiotropy scan for prostate cancer risk

Background No single-nucleotide polymorphisms (SNPs) specific for aggressive prostate cancer have been identified in genome-wide association studies (GWAS). Objective To test if SNPs associated with other traits may also affect the risk of aggressive prostate cancer. Design, setting, and participants SNPs implicated in any phenotype other than prostate cancer (p ≤ 10-7) were identified through the catalog of published GWAS and tested in 2891 aggressive prostate cancer cases and 4592 controls from the Breast and Prostate Cancer Cohort Consortium (BPC3). The 40 most significant SNPs were followed up in 4872 aggressive prostate cancer cases and 24 534 controls from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Outcome measurements and statistical analysis Odds ratios (ORs) and 95% confidence intervals (CIs) for aggressive prostate cancer were estimated. Results and limitations A total of 4666 SNPs were evaluated by the BPC3. Two signals were seen in regions already reported for prostate cancer risk. rs7014346 at 8q24.21 was marginally associated with aggressive prostate cancer in the BPC3 trial (p = 1.6 × 10-6), whereas after meta-analysis by PRACTICAL the summary OR was 1.21 (95% CI 1.16-1.27; p = 3.22 × 10-18). rs9900242 at 17q24.3 was also marginally associated with aggressive disease in the meta-analysis (OR 0.90, 95% CI 0.86-0.94; p = 2.5 × 10-6). Neither of these SNPs remained statistically significant when conditioning on correlated known prostate cancer SNPs. The meta-analysis by BPC3 and PRACTICAL identified a third promising signal, marked by rs16844874 at 2q34, independent of known prostate cancer loci (OR 1.12, 95% CI 1.06-1.19; p = 4.67 × 10;bsupesup it has been shown that SNPs correlated with this signal affect glycine concentrations. The main limitation is the heterogeneity in the definition of aggressive prostate cancer between BPC3 and PRACTICAL. Conclusions We did not identify new SNPs for aggressive prostate cancer. However, rs16844874 may provide preliminary genetic evidence on the role of the glycine pathway in prostate cancer etiology. Patient summary We evaluated whether genetic variants associated with several traits are linked to the risk of aggressive prostate cancer. No new such variants were identified