Please see below  for a quick tour of my academic research in Astronomy. You can also find a list of my publications here.  

Most of my work involves studying large samples of quasar spectra using data from one of the largest spectroscopic databases in astronomy: The Sloan Digital Sky Survey. Using such large multi-dimensional samples, I have been able to explore statistical characteristics of quasars and uncover correlations among their properties.

Quasar Narrow Line Region & Optical Spectra

Quasar spectra show remarkably strong blue continua and broad emission lines with velocity widths of ≥1000s km/s. Such large velocity widths indicate that the emitting gas is located in the near vicinity of the supermassive black hole and are broadened under its gravitational influence.

In addition to the broad lines, a set of narrow forbidden lines is present in the optical spectra of quasars with velocity widths of ~500 km/s. The small velocity widths of those lines and the fact that they are forbidden in atomic physics suggest that the emitting gas has low density and is located far away from the central source (at distances of  ~kpc’s). One can think of the narrow-line region as the part of the quasar host galaxy that is being photo-ionized by radiation from the central source.

In Tammour et al. (2015), we look at the properties of those narrow-lines at different levels of continuum luminosity and the width of the broad Balmer lines (both can partly indicate the level of accretion) and at different redshifts.

 

Quasar winds Probed through UV Spectra

Quasar spectra show signatures of outflowing winds that can be seen through blueshifted emission lines (such as CIV 1550Å). In addition to the broad emission lines, quasar UV spectra also show blueshifted broad absorption in nearly 20% of optically selected samples. This absorption is often seen in high-ionization lines such as CIV 1550 Å as blueshifted troughs with velocity offsets of ~20,000 km/s. These broad-absorptio lines are seen at lines-of-sights passing through the winds.

 

Machine Learning – Unsupervised Clustering

Unsupervised clustering is a branch of Machine Learning that allows us to explore the properties of multidimensional data sets without previous assumptions about the classes (or labels) that each object belongs to. This method can be a powerful tool in finding structure within large samples in a high-dimensional space.

In Tammour et al. (2016) we utilize KMeans to look for structure in the multidimensional parameter space of quasar UV spectral properties using high and low ionization lines such as CIV and MgII.

Those slides from my talk at the Great Lakes Quasars Symposium provide a good summary of some of the research I did during my PhD studying quasar disk wind using clustering analysis.