activity
20182022
most citedAre galactic star formation and quenching governed by local, global or environmental phenomena?

138 citations · 543 across the 8 of their papers we have counts for

collaborators

15 papers

astro-ph.GA202233 cited

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…

astro-ph.GA202183 cited

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…

astro-ph.IM2021

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…

astro-ph.GA202123 cited

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…

astro-ph.GA2020

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…

astro-ph.IM2020

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…