Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Andrianomena, Sambatra"'
Autor:
Andrianomena, Sambatra, Hassan, Sultan
We present an approach that can be utilized in order to account for the covariate shift between two datasets of the same observable with different distributions, so as to improve the generalizability of a neural network model trained on in-distributi
Externí odkaz:
http://arxiv.org/abs/2411.10515
We demonstrate the use of deep network to learn the distribution of data from state-of-the-art hydrodynamic simulations of the CAMELS project. To this end, we train a generative adversarial network to generate images composed of three different chann
Externí odkaz:
http://arxiv.org/abs/2402.10997
Autor:
Andrianomena, Sambatra, Tang, Hongming
We propose to learn latent space representations of radio galaxies, and train a very deep variational autoencoder (\protect\Verb+VDVAE+) on RGZ DR1, an unlabeled dataset, to this end. We show that the encoded features can be leveraged for downstream
Externí odkaz:
http://arxiv.org/abs/2311.08331
Autor:
Hassan, Sultan, Andrianomena, Sambatra
Efficiently analyzing maps from upcoming large-scale surveys requires gaining direct access to a high-dimensional likelihood and generating large-scale fields with high fidelity, which both represent major challenges. Using CAMELS simulations, we emp
Externí odkaz:
http://arxiv.org/abs/2311.00833
Autor:
Andrianomena, Sambatra, Hassan, Sultan
We investigate the possibility of learning the representations of cosmological multifield dataset from the CAMELS project. We train a very deep variational encoder on images which comprise three channels, namely gas density (Mgas), neutral hydrogen d
Externí odkaz:
http://arxiv.org/abs/2311.00799
We build a bijective mapping between different physical fields from hydrodynamic CAMELS simulations. We train a CycleGAN on three different setups: translating dark matter to neutral hydrogen (Mcdm-HI), mapping between dark matter and magnetic fields
Externí odkaz:
http://arxiv.org/abs/2303.07473
We explore the possibility of using deep learning to generate multifield images from state-of-the-art hydrodynamic simulations of the CAMELS project. We use a generative adversarial network to generate images with three different channels that repres
Externí odkaz:
http://arxiv.org/abs/2211.05000
Autor:
Andrianomena, Sambatra, Hassan, Sultan
We investigate how the constraints on cosmological and astrophysical parameters ($\Omega_{\rm m}$, $\sigma_{8}$, $A_{\rm SN1}$, $A_{\rm SN2}$) vary when exploiting information from multiple fields in cosmology. We make use of a convolutional neural n
Externí odkaz:
http://arxiv.org/abs/2208.08927
Autor:
Andrianomena, Sambatra
In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the effect of clas
Externí odkaz:
http://arxiv.org/abs/2205.05765
Autor:
Hassan, Sultan, Villaescusa-Navarro, Francisco, Wandelt, Benjamin, Spergel, David N., Anglés-Alcázar, Daniel, Genel, Shy, Cranmer, Miles, Bryan, Greg L., Davé, Romeel, Somerville, Rachel S., Eickenberg, Michael, Narayanan, Desika, Ho, Shirley, Andrianomena, Sambatra
A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys. It is crucial to prepare for these surveys now and develop tools that can efficiently extract most information. We present HIFlow:
Externí odkaz:
http://arxiv.org/abs/2110.02983