Click here to access my complete list of publications from the NASA/ADS database.
Recent first/second author publications:
- Huertas-Company et al. 2020, MNRAS, Stellar masses of giant clumps in CANDELS and simulated galaxies using machine learning- Huertas-Company et al. 2019, MNRAS, The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning
- Huertas-Company et al. 2018, ApJ, Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
- Tuccillo, Huertas-Company et al. 2018, MNRAS, 475, 894, Deep learning for galaxy surface brightness profile fitting
- Dominguez-Sanchez, Huertas-Company et al. 2018, MNRAS, 476, 3661, Improving galaxy morphologies for SDSS with Deep Learning
- Huertas-Company et al. 2016, MNRAS, 462, 4495, Mass assembly and morphological transformations since z ~3 from CANDELS
- Huertas-Company et al. 2015a, ApJ, 809, 95, The Morphologies of Massive Galaxies from z ~ 3—Witnessing the Two Channels of Bulge Growth
- Huertas-Company et al. 2015b, ApJS in press, A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
- Huertas-Company et al. 2014, MNRAS, Measuring galaxy morphology at z>1.
- Huertas-Company et al. 2013a, MNRAS, 428, 1715, The evolution of the mass-size relation for early-type galaxies from z ˜ 1 to the present: dependence on environment, mass range and detailed morphology
- Huertas-Company et al. 2013b, ApJ, 779, 29, No Evidence for a Dependence of the Mass-Size Relation of Early-type Galaxies on Environment in the Local Universe
Recent first/second author publications:
- Huertas-Company et al. 2020, MNRAS, Stellar masses of giant clumps in CANDELS and simulated galaxies using machine learning- Huertas-Company et al. 2019, MNRAS, The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning
- Huertas-Company et al. 2018, ApJ, Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
- Tuccillo, Huertas-Company et al. 2018, MNRAS, 475, 894, Deep learning for galaxy surface brightness profile fitting
- Dominguez-Sanchez, Huertas-Company et al. 2018, MNRAS, 476, 3661, Improving galaxy morphologies for SDSS with Deep Learning
- Huertas-Company et al. 2016, MNRAS, 462, 4495, Mass assembly and morphological transformations since z ~3 from CANDELS
- Huertas-Company et al. 2015a, ApJ, 809, 95, The Morphologies of Massive Galaxies from z ~ 3—Witnessing the Two Channels of Bulge Growth
- Huertas-Company et al. 2015b, ApJS in press, A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
- Huertas-Company et al. 2014, MNRAS, Measuring galaxy morphology at z>1.
- Huertas-Company et al. 2013a, MNRAS, 428, 1715, The evolution of the mass-size relation for early-type galaxies from z ˜ 1 to the present: dependence on environment, mass range and detailed morphology
- Huertas-Company et al. 2013b, ApJ, 779, 29, No Evidence for a Dependence of the Mass-Size Relation of Early-type Galaxies on Environment in the Local Universe