arXiv subjects

H. Teimoorinia

Publications and source records attributed to H. Teimoorinia.

6 recordsLinked to original sources

arXiv2025

Euclid: A machine-learning search for dual and lensed AGN at sub-arcsec separations

Cosmological models of hierarchical structure formation predict the existence of a widespread population of dual accreting supermassive black holes (SMBHs) on kpc-scale separations, corresponding to projected distances < 0".8 at redshifts higher than 0.5. However, close companions to known active galactic nuclei (AGN) or quasars (QSOs) can also be multiple images of the object itself, strongly lensed by a foreground galaxy, as well as foreground stars in a chance superposition. Thanks to its large sky coverage, sensitivity, and high spatial resolution, Euclid offers a unique opportunity to obtain a large, homogeneous sample of dual/lensed AGN candidates with sub-arcsec projected separations. Here we present a machine learning approach, in particular a Convolutional Neural Network (CNN), to identify close companions to known QSOs down to separations of $\sim\,$0".15 comparable to the Euclid VIS point spread function (PSF). We studied the effectiveness of the CNN in identifying dual AGN and demonstrated that it outperforms traditional techniques. Applying our CNN to a sample of $\sim\,$6000 QSOs from the Q1 Euclid data release, we find a fraction of about 0.25% dual AGN candidates with separation $\sim\,$0".4 (corresponding to $\sim$3 kpc at z=1). Estimating the foreground contamination from stellar objects, we find that most of the pair candidates with separation higher than 0".5 are likely contaminants, while below this limit, contamination is expected to be less than 20%. For objects at higher separation (>0".5, i.e. 4 kpc at z=1), we performed PSF subtraction and used colour-colour diagrams to constrain their nature. We present a first set of dual/lensed AGN candidates detected in the Q1 Euclid data, providing a starting point for the analysis of future data releases.

astro-ph.GA↗

arXiv2024

Climbing the Cliffs: Classifying YSOs in the Cosmic Cliffs JWST Data using a Probabilistic Random Forest

Among the first observations released to the public from the James Webb Space Telescope (JWST) was a section of the star-forming region NGC 3324 known colloquially as the "Cosmic Cliffs." We build a photometric catalog of the region and test the ability of using the Probabilistic Random Forest machine learning method to identify its Young Stellar Objects (YSOs). We find 450 candidate YSOs (cYSOs) out of 19~497 total objects within the field, 413 of which are cYSOs not found in previous works. These classifications are verified with several different metrics, including recall and precision. Using the obtained probabilities of objects being YSOs, we employ a Monte Carlo approach to determine the surface density of cYSOs in the Cosmic Cliffs, which we find to be largely coincident with column densities derived from Herschel data, up to a column density of 1.37 $\times$ 10$^{22}$ cm$^{-2}$. The newly determined number and spatial distribution of YSOs in the Cosmic Cliffs demonstrate that JWST is far more capable of detecting YSOs in dusty regions than Spitzer. Comparisons of the observed colors and brightness of faint cYSOs with those of pre-main-sequence models suggest JWST has detected a significant population of sub-stellar YSOs in the Cosmic Cliffs. The size of this population further suggests previous estimates of star formation efficiencies in molecular clouds have been systematically low.

astro-ph.SR↗

arXiv2019

LRP2020: Machine Learning Advantages in Canadian Astrophysics

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 powerful and accessible. As the field of ML enjoys a continuous stream of breakthroughs, its applications demonstrate the great potential of ML, ranging from achieving tens of millions of times increase in analysis speed (e.g., modeling of gravitational lenses or analysing spectroscopic surveys) to solutions of previously unsolved problems (e.g., foreground subtraction or efficient telescope operations). The number of astronomical publications that include ML has been steadily increasing since 2010. With the advent of extremely large datasets from a new generation of surveys in the 2020s, ML methods will become an indispensable tool in astrophysics. Canada is an unambiguous world leader in the development of the field of machine learning, attracting large investments and skilled researchers to its prestigious AI Research Institutions. This provides a unique opportunity for Canada to also be a world leader in the application of machine learning in the field of astrophysics, and foster the training of a new generation of highly skilled researchers.

astro-ph.IM↗

arXiv2018

Classifying galaxy spectra at 0.5<z<1 with self-organizing maps

The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 < z < 1 and the results compared to classifications performed using K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

astro-ph.GA↗

arXiv2011

Radial velocity-acceleration curve analysis of the spectroscopic binary stars by the nonlinear regression

We will introduce a new method to derive the orbital elements of the spectroscopic binary stars. Fitting the adopted radial velocity curves on the measured experimental data, the corresponding radial acceleration data are obtained. The orbital parameters are estimated by the nonlinear least squares of the radial velocity-acceleration curves. This method is implemented for the three double line spectroscopic binary systems RZ CAS, CC Cas and V1130 Tau. Numerical calculations show that the result are in good agreement with the those obtained by others using the method of Lehmann-Filhés.

astro-ph↗

arXiv2010

The Great Observatories Origins Deep Survey - VLT/VIMOS Spectroscopy in the GOODS-South Field: Part II

We present the full data set of the VIMOS spectroscopic campaign of the ESO/GOODS program in the CDFS, which complements the FORS2 ESO/GOODS spectroscopic campaign. The GOODS/VIMOS spectroscopic campaign is structured in two separate surveys using two different VIMOS grisms. The VIMOS Low Resolution Blue (LR-Blue) and Medium Resolution (MR) orange grisms have been used to cover different redshift ranges. The LR-Blue campaign is aimed at observing galaxies mainly at 1.8 3.5. The full GOODS/VIMOS spectroscopic campaign consists of 20 VIMOS masks. This release adds 8 new masks to the previous release (12 masks, Popesso et al. 2009). In total we obtained 5052 spectra, 3634 from the 10 LR-Blue masks and 1418 from the 10 MR masks. A significant fraction of the extracted spectra comes from serendipitously observed sources: ~21% in the LR-Blue and ~16% in the MR masks. We obtained 2242 redshifts in the LR-Blue campaign and 976 in the MR campaign for a total success rate of 62% and 69% respectively, which increases to 66% and 73% if only primary targets are considered. The typical redshift uncertainty is estimated to be ~0.0012 (~255 km/s) for the LR-Blue grism and ~0.00040 (~120 km/s) for the MR grism. By complementing our VIMOS spectroscopic catalog with all existing spectroscopic redshifts publicly available in the CDFS, we compiled a redshift master catalog with 7332 entries, which we used to investigate large scale structures out to z~3.7. We produced stacked spectra of LBGs in a few bins of equivalent width (EW) of the Ly-alpha and found evidence for a lack of bright LBGs with high EW of the Ly-alpha. Finally, we obtained new redshifts for 12 X-ray sources of the CDFS and extended-CDFS.

astro-ph.CO↗