123 citations · 598 across the 13 of their papers we have counts for
6 papers · 1 filter
The PAU Survey: Uncovering the connection between intrinsic and observed galaxy properties using symbolic regression
Adarsh Kumar, Carlton M. Baugh, Suttikoon Koonkor +12
Estimating stellar masses for billions of galaxies in upcoming surveys requires methods that are both accurate and computationally efficient. We present a new approach using symbol…
The PAU Survey: Enhancing photometric redshift estimation using DEEPz
I. V. Daza-Perilla, M. Eriksen, D. Navarro-Gironés +20
We present photometric redshifts for 1 341 559 galaxies from the Physics of the Accelerating Universe Survey (PAUS) over 50.38 of sky to . Redshift e…
A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
S. Mucesh, W. G. Hartley, A. Palmese +72
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorit…
The PAU Survey: Photometric redshifts using transfer learning from simulations
M. Eriksen, A. Alarcon, L. Cabayol +15
In this paper we introduce the \textsc{Deepz} deep learning photometric redshift (photo-) code. As a test case, we apply the code to the PAU survey (PAUS) data in the COSMOS fie…
The PAU Survey: Early demonstration of photometric redshift performance in the COSMOS field
M. Eriksen, A. Alarcon, E. Gaztanaga +22
The PAU Survey (PAUS) is an innovative photometric survey with 40 narrow bands at the William Herschel Telescope (WHT). The narrow bands are spaced at 100Å intervals covering the r…
The STRong lensing Insights into the Dark Energy Survey (STRIDES) 2016 follow-up campaign. II. New quasar lenses from double component fitting
T. Anguita, P. L. Schechter, N. Kuropatkin +56
We report upon the follow up of 34 candidate lensed quasars found in the Dark Energy Survey using NTT-EFOSC, Magellan-IMACS, KECK-ESI and SOAR-SAMI. These candidates were selected…