144 citations · 976 across the 23 of their papers we have counts for
23 papers
Revisiting AGN Placement on the BPT Diagram: A Spectral Decomposition Approach
Hossen Teimoorinia, Sara Shishehchi, Finn Archinuk +5
Traditional single-fibre spectroscopy provides a single galaxy spectrum, forming the basis for crucial parameter estimation. However, its accuracy can be compromised by various sou…
Stellar Populations With Optical Spectra: Deep Learning vs. Popular Spectrum Fitting Codes
Joanna Woo, Dan Walters, Finn Archinuk +4
We compare the performance of several popular spectrum fitting codes (Firefly, starlight, pyPipe3D and pPXF), and a deep-learning convolutional neural network (StarNet), in recover…
Mitigating the Non-Linearities in a Pyramid Wavefront Sensor
Finn Archinuk, Rehan Hafeez, Sébastien Fabbro +2
For natural guide start adaptive optics (AO) systems, pyramid wavefront sensors (PWFSs) can provide significant increase in sensitivity over the traditional Shack-Hartmann, but at…
The combined and respective roles of imaging and stellar kinematics in identifying galaxy merger remnants
Connor Bottrell, Maan Hani, Hossen Teimoorinia +2
One of the central challenges to establishing the role of mergers in galaxy evolution is the selection of pure and complete merger samples in observations. In particular, while lar…
Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Hossen Teimoorinia, Finn Archinuk, Joanna Woo +2
Modern spectroscopic surveys of galaxies such as MaNGA consist of millions of diverse spectra covering different regions of thousands of galaxies. We propose and implement a deep u…
Forecasting Wavefront Corrections in an Adaptive Optics System
Rehan Hafeez, Finn Archinuk, Sébastien Fabbro +2
We use telemetry data from the Gemini North ALTAIR adaptive optics system to investigate how well the commands for wavefront correction (both Tip/Tilt and high-order turbulence) ca…
Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG
Robert W. Bickley, Connor Bottrell, Maan H. Hani +6
The Canada-France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ~5000 square degrees of the sky, representing a first-rate opportunity to identify…
An astronomical image content-based recommendation system using combined deep learning models in a fully unsupervised mode
Hossen Teimoorinia, Sara Shishehchi, Ahnaf Tazwar +4
We have developed a method that maps large astronomical images onto a two-dimensional map and clusters them. A combination of various state-of-the-art machine learning (ML) algorit…
A re-assessment of strong line metallicity conversions in the machine learning era
Hossen Teimoorinia, Mansoureh Jalilkhany, Jillian M. Scudder +2
Strong line metallicity calibrations are widely used to determine the gas phase metallicities of individual HII regions and entire galaxies. Over a decade ago, based on the Sloan D…
How do central and satellite galaxies quench? -- Insights from spatially resolved spectroscopy in the MaNGA survey
Asa F. L. Bluck, Roberto Maiolino, Joanna M. Piotrowska +7
We investigate how star formation quenching proceeds within central and satellite galaxies using spatially resolved spectroscopy from the SDSS-IV MaNGA DR15. We adopt a complete sa…
Assessment of astronomical images using combined machine learning models
Hossen Teimoorinia, J. J. Kavelaars, Stephen Gwyn +3
We present a two-component Machine Learning (ML) based approach for classifying astronomical images by data-quality via an examination of sources detected in the images and image p…
Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses
Hossen Teimoorinia, Robert D. Toyonaga, Sebastien Fabbro +1
Typically, binary classification lens-finding schemes are used to discriminate between lens candidates and non-lenses. However, these models often suffer from substantial false-pos…
The ALMaQUEST Survey: III. Scatter in the resolved star forming main sequence is primarily due to variations in star formation efficiency
Sara L. Ellison, Mallory D. Thorp, Lihwai Lin +6
Using a sample of 11,478 spaxels in 34 galaxies with molecular gas, star formation and stellar maps taken from the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we in…
Are galactic star formation and quenching governed by local, global or environmental phenomena?
Asa F. L. Bluck, Roberto Maiolino, Sebastian Sanchez +5
We present an analysis of star formation and quenching in the SDSS-IV MaNGA-DR15, utilising over 5 million spaxels from 3500 local galaxies. We estimate star formation rate s…
Deep learning predictions of galaxy merger stage and the importance of observational realism
Connor Bottrell, Maan H. Hani, Hossen Teimoorinia +7
Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be…
LRP2020: Machine Learning Advantages in Canadian Astrophysics
K. A. Venn, S. Fabbro, A Liu +12
The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing power and complex algorithms become more po…
Dwarfs or giants? Stellar metallicities and distances in the Canada-France-Imaging-Survey from multi-band photometry
Guillaume F. Thomas, Nicholaas Annau, Alan McConnachie +19
We present a new fully data-driven algorithm that uses photometric data from the Canada-France-Imaging-Survey (CFIS; ), Pan-STARRS 1 (PS1; ), and Gaia () to discriminat…
CLOVER: Convnet Line-fitting Of Velocities in Emission-line Regions
Jared Keown, James Di Francesco, Hossen Teimoorinia +2
When multiple star-forming gas structures overlap along the line-of-sight and emit optically thin emission at significantly different radial velocities, the emission can become non…
What shapes a galaxy? - Unraveling the role of mass, environment and star formation in forming galactic structure
Asa F. L. Bluck, Connor Bottrell, Hossen Teimoorinia +9
We investigate the dependence of galaxy structure on a variety of galactic and environmental parameters for ~500,000 galaxies at z<0.2, taken from the Sloan Digital Sky Survey data…
The discrimination between star-forming and AGN galaxies in the absence of Hα and [NII]: A machine learning approach
Hossen Teimoorinia, Jared Keown
In the absence of the two emission lines H and [NII] (6584Å) in a BPT diagram, we show that other spectral information is sufficiently informative to distinguish AGN galaxies fr…
Pattern recognition in the ALFALFA.70 and Sloan Digital Sky Surveys: A catalog of 500,000 HI gas fraction estimates based on artificial neural networks
Hossen Teimoorinia, Sara L. Ellison, David R. Patton
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 S…
The star formation rates of active galactic nuclei host galaxies
Sara L. Ellison, Hossen Teimoorinia, David J. Rosario +1
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…
An Artificial Neural Network Approach For Ranking Quenching Parameters In Central Galaxies
Hossen Teimoorinia, Asa F. L. Bluck, Sara L. Ellison
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 netw…