Galaxy and QSO occupation distribution (GQOD) model
We developed a new framework to model the QSO distribution. Following the popular Halo Occupation distribution model (HOD), we propose to model the QSO occupation on galaxy population. Therefore we call it Galaxy and QSO occupation distribution (GQOD) model. Here you can find some code and data released with publications based on this model.
The GQOD model essentially try to model QSO on top of galaxy establishing the statistical connection between galaxies and super-massive black holes. It provides an alternate to HOD models for QSO and allow for a more flexible interpretation of halo mass distribution of observed QSO. It also provide an interesting way to derive mean properties of QSO host galaxies especially at high redshift where direct measurement of host properties is difficult due to the emission from QSO dominating (and wiping) out most of the details about the host galaxy themselves.
The first paper where we propose the GQOD framework and apply it to the extended Baryon Oscillation Spectroscopic Survey (eBOSS) final data between $z=0.7-1.1$ can be found on arXiv:2007.02612..
Understanding the links between the activity of supermassive black holes (SMBH) at the centres of galaxies and their host dark matter haloes is a key question in modern astrophysics.
The final data release of the SDSS-IV eBOSS provides the largest contemporary spectroscopic sample of galaxies and QSOs. Using this sample and covering the redshift interval $z=0.7-1.1$, we have measured the clustering properties of the eBOSS QSOs, Emission Line Galaxies (ELGs) and Luminous Red Galaxies (LRGs).
We have also measured the fraction of QSOs as a function of the overdensity defined by the galaxy population. Using these measurements, we investigate how QSOs populate and sample the galaxy population, and how the host dark-matter haloes of QSOs sample the underlying halo distribution. We find that the probability of a galaxy hosting a QSO is independent of the host dark matter halo mass of the galaxy. We also find that about 60\% of eBOSS QSOs are hosted by LRGs and about 20-40\% of QSOs are hosted by satellite galaxies. We find a slight preference for QSOs to populate satellite galaxies over central galaxies. This is connected to the host halo mass distribution of different types of galaxies. Based on our analysis, QSOs should be hosted by a very broad distribution of haloes, and their occurrence should be modulated only by the efficiency of galaxy formation processes.
Analysis steps along with codes for GQOD model
You can download the required code from here.
It requires following python libraries numpy, scipy, fitsio, pylab, time, collections, emcee, corrfunc.
All of this can be installed simply by typing pip install library, if you have pip installed.
Follow these steps once you have the code to perform the analysis:
More detailed description will be added to this webpage if there is interest.
Please contact firstname.lastname@example.org in case you have questions or something is not working as expected.
- Measurements from eBOSS data:
You can dowload the eBOSS catalogues from here and perform clustering measurements using this public code (some tutorial on clustering measurements are here). Alternatively we make our measurements publicly available here.
- Simulated galaxy catalogue:
We model QSO turn-on in galaxy catalogue. We used simulated galaxy catalogue from MTHOD model. The MTHOD model is described in arXiv:1910.05095. You can find instructions on how to create such catalogue or directly download them from this webpage. The MTHOD catalog we used is available here
- Likelihood Analysis for GQOD model:
Finally to perform likelood analysis and obtain parameter constraints you can use following code. The python notebook to make plots in the paper is also available.
|© Shadab Alam , email: salam AT roe.ac.uk
|| Last updated: September 13, 2021, 14:17