123 citations · 598 across the 13 of their papers we have counts for
5 papers · 1 filter
The Dark Energy Survey Supernova Program: Modelling selection efficiency and observed core collapse supernova contamination
M. Vincenzi, M. Sullivan, O. Graur +79
The analysis of current and future cosmological surveys of type Ia supernovae (SNe Ia) at high-redshift depends on the accurate photometric classification of the SN events detected…
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…