SPECTRAL CLASSIFICATION OF GALAXIES AT 0.5 ⩽ z ⩽ 1 IN THE CDFS: THE ARTIFICIAL NEURAL NETWORK APPROACH by TEIMOORINIA, H

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Climbing the Cliffs: Classifying Young Stellar Objects in the Cosmic Cliffs JWST Data Using a Probabilistic Random Forest by Crompvoets, B. L., Di Francesco, J., Teimoorinia, H., Preibisch, T.

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The discrimination between star-forming and AGN galaxies in the absence of H α and [N ii]: a machine -learning approach by Teimoorinia, H, Keown, J

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Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps by Rahmani, S, Teimoorinia, H, Barmby, P

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The Great Observatories Origins Deep Survey: VLT/VIMOS spectroscopy in the GOODS-south field: Part II by BALESTRA, I, MAINIERI, V, FOSBURY, R. A. E, KUNTSCHNER, H, RETTURA, A, POPESSO, P, DICKINSON, M, NONINO, M, ROSATI, P, TEIMOORINIA, H, VANZELLA, E, CRISTIANI, S, CESARSKY, C

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Assessment of Astronomical Images Using Combined Machine-learning Models by Teimoorinia, H., Kavelaars, J. J., Gwyn, S. D. J., Durand, D., Rolston, K., Ouellette, A.

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Velocity curve analysis of the spectroscopic binary stars by the non-linear least squares by Karami, K, Teimoorinia, H

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The Great Observatories Origins Deep Survey by Balestra, I., Mainieri, V., Popesso, P., Dickinson, M., Nonino, M., Rosati, P., Teimoorinia, H., Vanzella, E., Cristiani, S., Cesarsky, C., Fosbury, R. A.E., Kuntschner, H., Rettura, A.

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Ly emitters in the GOODS-S field by Raiter, A, E Fosbury, RA, Teimoorinia, H

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Ly$\mathsf{\alpha}$ emitters in the GOODS-S field by Raiter, A., Fosbury, R. A. E., Teimoorinia, H.

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Ly$\mathsf{\alpha}$ emitters in the GOODS-S field: A powerful pure nebular SED with N IV] emission at z = 5.563 by Raiter, A., Fosbury, R. A. E., Teimoorinia, H.

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Climbing the Cliffs: Classifying YSOs in the Cosmic Cliffs JWST Data using a Probabilistic Random Forest by Crompvoets, B L, J Di Francesco, Teimoorinia, H, Preibisch, T

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Classifying galaxy spectra at 0.5<z<1 with self-organizing maps by Rahmani, S, Teimoorinia, H, Barmby, P

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LRP2020: Machine Learning Advantages in Canadian Astrophysics by Venn, K A, Fabbro, S, Liu, A, Hezaveh, Y, Perreault-Levasseur, L, Eadie, G, Ellison, S, Woo, J, Kavelaars, J J, Yi, K M, Hlozek, R, Bovy, J, Teimoorinia, H, Ravanbakhsh, S, Spencer, L

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Radial velocity-acceleration curve analysis of the spectroscopic binary stars by the nonlinear regression by Karami, K, Teimoorinia, H

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The Great Observatories Origins Deep Survey - VLT/VIMOS Spectroscopy in the GOODS-South Field: Part II by Balestra, I, Mainieri, V, Popesso, P, Dickinson, M, Nonino, M, Rosati, P, Teimoorinia, H, Vanzella, E, Cristiani, S, Cesarsky, C, Fosbury, R A E, Kuntschner, H, Rettura, A, the GOODS team

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Deep learning predictions of galaxy merger stage and the importance of observational realism by Bottrell, Connor, Hani, Maan H, Teimoorinia, Hossen, Ellison, Sara L, Moreno, Jorge, Torrey, Paul, Hayward, Christopher C, Thorp, Mallory, Simard, Luc, Hernquist, Lars

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Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG by Bickley, Robert W, Bottrell, Connor, Hani, Maan H, Ellison, Sara L, Teimoorinia, Hossen, Yi, Kwang Moo, Wilkinson, Scott, Gwyn, Stephen, Hudson, Michael J

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The combined and respective roles of imaging and stellar kinematics in identifying galaxy merger remnants by Bottrell, Connor, Hani, Maan H, Teimoorinia, Hossen, Patton, David R, Ellison, Sara L

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Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG by Bickley, Robert W, Connor Bottrell, Hani, Maan H, Ellison, Sara L, Hossen Teimoorinia, Yi, Kwang Moo, Wilkinson, Scott, Gwyn, Stephen, Hudson, Michael J

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