46 citations · 46 across the 1 of their papers we have counts for
7 papers
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…
Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects
B. Henghes, O. Lahav, D. W. Gerdes +56
In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data wh…
A statistical standard siren measurement of the Hubble constant from the LIGO/Virgo gravitational wave compact object merger GW190814 and Dark Energy Survey galaxies
A. Palmese, J. deVicente, M. E. S. Pereira +85
We present a measurement of the Hubble constant using the gravitational wave (GW) event GW190814, which resulted from the coalescence of a 23 black hole with a 2.6…
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…
Cosmological Constraints from Multiple Probes in the Dark Energy Survey
DES Collaboration, T. M. C. Abbott, A. Alarcon +166
The combination of multiple observational probes has long been advocated as a powerful technique to constrain cosmological parameters, in particular dark energy. The Dark Energy Su…
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…