arXiv subjects

Hossen Teimoorinia

Publications and source records attributed to Hossen Teimoorinia.

23 records · Page 2Linked to original sources

arXiv2018

The discrimination between star-forming and AGN galaxies in the absence of Hα and [NII]: A machine learning approach

In the absence of the two emission lines H$α$ and [NII] (6584Å) in a BPT diagram, we show that other spectral information is sufficiently informative to distinguish AGN galaxies from star-forming galaxies. We use pattern recognition methods and a sample of galaxy spectra from the Sloan Digital Sky Survey (SDSS) to show that, in this survey, the flux and equivalent width of [OIII] (5007Å) and H$β$, along with the 4000Å-break, can be used to classify galaxies in a BPT diagram. This method provides a higher accuracy of predictions than those which use stellar mass and [OIII]/H$β$. First, we use BPT diagrams and various physical parameters to re-classify the galaxies. Next, using confusion matrices, we determine the `correctly' predicted classes as well as confused cases. In this way, we investigate the effect of each parameter in the confusion matrices and rank the physical parameters used in the discrimination of the different classes. We show that in this survey, for example, $\rm{g - r}$ colour can provide the same accuracy as galaxy stellar mass to predict whether or not a galaxy hosts an AGN. Finally, with the same information, we also rank the parameters involved in the discrimination of Seyfert and LINER galaxies.

astro-ph.GA↗

arXiv2016

Pattern recognition in the ALFALFA.70 and Sloan Digital Sky Surveys: A catalog of $\sim$ 500,000 HI gas fraction estimates based on artificial neural networks

The application of artificial neural networks (ANNs) for the estimation of HI gas mass fraction (\fgas) is investigated, based on a sample of 13,674 galaxies in the Sloan Digital Sky Survey (SDSS) with HI detections or upper limits from the Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA). We show that, for an example set of fixed input parameters ($g-r$ colour and $i$-band surface brightness), a multidimensional quadratic model yields \fgas\ scaling relations with a smaller scatter (0.22 dex) than traditional linear fits (0.32 dex), demonstrating that non-linear methods can lead to an improved performance over traditional approaches. A more extensive ANN analysis is performed using 15 galaxy parameters that capture variation in stellar mass, internal structure, environment and star formation. Of the 15 parameters investigated, we find that $g-r$ colour, followed by stellar mass surface density, bulge fraction and specific star formation rate have the best connection with \fgas. By combining two control parameters, that indicate how well a given galaxy in SDSS is represented by the ALFALFA training set (\pr) and the scatter in the training procedure (\sigf), we develop a strategy for quantifying which SDSS galaxies our ANN can be adequately applied to, and the associated errors in the \fgas\ estimation. In contrast to previous works, our \fgas\ estimation has no systematic trend with galactic parameters such as M$_{\star}$, $g-r$ and SFR. We present a catalog of \fgas\ estimates for more than half a million galaxies in the SDSS, of which $\sim$ 150,000 galaxies have a secure selection parameter with average scatter in the \fgas\ estimation of 0.22 dex.

astro-ph.GA↗

arXiv2016

The impact of galactic properties and environment on the quenching of central and satellite galaxies: A comparison between SDSS, Illustris and L-Galaxies

We quantify the impact that a variety of galactic and environmental properties have on the quenching of star formation. We collate a sample of $\sim$ 400,000 central and $\sim$ 100,000 satellite galaxies from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Specifically, we consider central velocity dispersion ($σ_{c}$), stellar, halo, bulge and disk mass, local density, bulge-to-total ratio, group-centric distance and galaxy-halo mass ratio. We develop and apply a new statistical technique to quantify the impact on the quenched fraction ($f_{\rm Quench}$) of varying one parameter, while keeping the remaining parameters fixed. For centrals, we find that the $f_{\rm Quench} - σ_{c}$ relationship is tighter and steeper than for any other variable considered. We compare to the Illustris hydrodynamical simulation and the Munich semi-analytic model (L-Galaxies), finding that our results for centrals are qualitatively consistent with their predictions for quenching via radio-mode AGN feedback, hinting at the viability of this process in explaining our observational trends. However, we also find evidence that quenching in L-Galaxies is too efficient and quenching in Illustris is not efficient enough, compared to observations. For satellites, we find strong evidence that environment affects their quenched fraction at fixed central velocity dispersion, particularly at lower masses. At higher masses, satellites behave identically to centrals in their quenching. Of the environmental parameters considered, local density affects the quenched fraction of satellites the most at fixed central velocity dispersion.

astro-ph.GA↗

arXiv2016

The star formation rates of active galactic nuclei host galaxies

Using artificial neural network (ANN) predictions of total infra-red luminosities (LIR), we compare the host galaxy star formation rates (SFRs) of ~21,000 optically selected active galactic nuclei (AGN), 466 low excitation radio galaxies (LERGs) and 721 mid-IR selected AGN. SFR offsets (Delta SFR) relative to a sample of star-forming `main sequence' galaxies (matched in M*, z and local environment) are computed for the AGN hosts. Optically selected AGN exhibit a wide range of Delta SFR, with a distribution skewed to low SFRs and a median Delta SFR = -0.06 dex. The LERGs have SFRs that are shifted to even lower values with a median Delta SFR = -0.5 dex. In contrast, mid-IR selected AGN have, on average, SFRs enhanced by a factor ~1.5. We interpret the different distributions of Delta SFR amongst the different AGN classes in the context of the relative contribution of triggering by galaxy mergers. Whereas the LERGs are predominantly fuelled through low accretion rate secular processes which are not accompanied by enhancements in SFR, mergers, which can simultaneously boost SFRs, most frequently lead to powerful, obscured AGN.

astro-ph.GA↗

arXiv2016

An Artificial Neural Network Approach For Ranking Quenching Parameters In Central Galaxies

We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a population of over 400,000 central galaxies taken from the Sloan Digital Sky Survey Data Release 7. We utilise a variety of physical galaxy properties for training the pattern recognition algorithm to recognise star forming and passive systems, for a `training set' of $\sim$100,000 galaxies. We then apply the ANN model to a `verification set' of $\sim$100,000 different galaxies, randomly chosen from the remaining sample. The success rate of each parameter singly, and in conjunction with other parameters, is taken as an indication of how important the parameters are to the process(es) of central galaxy quenching. We find that central velocity dispersion, bulge mass and B/T are excellent predictors of the passive state of the system, indicating that properties related to the central mass of the galaxy are most closely linked to the cessation of star formation. Larger scale galaxy properties (total or disk stellar masses), or those linked to environment (halo masses or $δ_5$) perform significantly less well. Our results are plausibly explained by AGN feedback driving the quenching of central galaxies, although we discuss other possibilities as well.

astro-ph.GA↗