[PDF][PDF] DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification.
A Ciprijanovic, D Kafkes… - Mach. Learn. Sci …, 2022 - research.manuscritpub.com
With increased adoption of supervised deep learning methods for work with cosmological
survey data, the assessment of data perturbation effects (that can naturally occur in the data …
survey data, the assessment of data perturbation effects (that can naturally occur in the data …
Extracting cosmological parameters from N-body simulations using machine learning techniques
A Lazanu - Journal of Cosmology and Astroparticle Physics, 2021 - iopscience.iop.org
We make use of snapshots taken from the Quijote suite of simulations, consisting of 2000
simulations where five cosmological parameters have been varied (Ω m, Ω b, h, ns and σ 8) …
simulations where five cosmological parameters have been varied (Ω m, Ω b, h, ns and σ 8) …
What to expect from dynamical modelling of cluster haloes–II. Investigating dynamical state indicators with Random Forest
We investigate the importance of various dynamical features in predicting the dynamical
state (ds) of galaxy clusters, based on the Random Forest (RF) machine-learning approach …
state (ds) of galaxy clusters, based on the Random Forest (RF) machine-learning approach …
Statistical data retrieval technique in astronomy computational physics
Computational astronomy is a very important branch in today's era, where physicists or
researchers can use computers to process statistics in astronomical physics. researchers …
researchers can use computers to process statistics in astronomical physics. researchers …
Machine learning and cosmology
Methods based on machine learning have recently made substantial inroads in many
corners of cosmology. Through this process, new computational tools, new perspectives on …
corners of cosmology. Through this process, new computational tools, new perspectives on …
DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification
A Ćiprijanović, D Kafkes, G Snyder… - Machine Learning …, 2022 - iopscience.iop.org
With increased adoption of supervised deep learning methods for work with cosmological
survey data, the assessment of data perturbation effects (that can naturally occur in the data …
survey data, the assessment of data perturbation effects (that can naturally occur in the data …
YOLO–CL: Galaxy cluster detection in the SDSS with deep machine learning
K Grishin, S Mei, S Ilić - Astronomy & Astrophysics, 2023 - aanda.org
Galaxy clusters are powerful probes for cosmological models. Next-generation, large-scale
optical and infrared surveys are poised to reach unprecedented depths and, thus, they …
optical and infrared surveys are poised to reach unprecedented depths and, thus, they …
Deep learning simulations of the microwave sky
We present 500 high-resolution, full-sky millimeter-wave deep learning (DL) simulations that
include lensed CMB maps and correlated foreground components. We find that these …
include lensed CMB maps and correlated foreground components. We find that these …
ComPACT: combined Atacama Cosmology Telescope+ Planck galaxy cluster catalogue
S Voskresenskaia, A Meshcheryakov… - Monthly Notices of the …, 2024 - academic.oup.com
Galaxy clusters are the most massive gravitationally bound systems consisting of dark
matter, hot baryonic gas, and stars. They play an important role in observational cosmology …
matter, hot baryonic gas, and stars. They play an important role in observational cosmology …
Classifying seismograms using the FastMap algorithm and support-vector machines
Neural networks and related deep learning methods are currently at the leading edge of
technologies used for classifying complex objects such as seismograms. However they …
technologies used for classifying complex objects such as seismograms. However they …