Related papers: Toward Complete Merger Identification at Cosmic No…
We apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask R-CNN image processing framework, a…
Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and…
Machine learning, and eventually true artificial intelligence techniques, are extremely important advancements in astrophysics and astronomy. We explore the application of deep learning using neural networks in order to automate the…
We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for…
In deep, ground-based imaging, about 15%-30% of object detections are expected to correspond to two or more true objects - these are called ``unrecognized blends''. We use Machine Learning algorithms to detect unrecognized blends in deep…
The science behind galaxy interaction and mergers has a fundamental role and gives us an insight into galaxy formation and its evolution. Fluctuating angular momentum is responsible for extraordinary events like polar rings, tidal tails,…
The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, were cross-matched by Bilicki et al. (2016) (B16) to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were therein separated from…
We measure the merger fraction of massive galaxies using the UltraVISTA/COSMOS $Ks$-band selected catalog, complemented with the deeper, higher resolution 3DHST+CANDELS catalog selected in the HST/WFC3 $H$-band, presenting the largest…
Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it's impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of…
Photometric redshifts (photo-$z$'s) are crucial for the cosmology, galaxy evolution, and transient science drivers of next-generation imaging facilities like the Euclid Mission, the Rubin Observatory, and the Nancy Grace Roman Space…
We present BlendHunter, a proof-of-concept for a deep transfer learning based approach for the automated and robust identification of blended sources in galaxy survey data. We take the VGG-16 network with pre-trained convolutional layers…
Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently.…
Aims. This work aims to reproduce the time before or after a merger event of merging galaxies from the IllustrisTNG cosmological simulation using machine learning. Methods. Images of merging galaxies were created in the u, g, r, and i bands…
We estimate the evolution of the galaxy-galaxy merger fraction for $M_\star>10^{10.5}M_\odot$ galaxies over $0.25<z<1$ in the $\sim$18.6 deg$^2$ deep CLAUDS+HSC-SSP surveys. We do this by training a Random Forest Classifier to identify…
Galaxy cluster-scale strong gravitational lensing systems are rare yet valuable tools for investigating the properties of dark matter and dark energy, as well as providing the opportunity to study the distant universe at flux levels and…
We present a novel graph-based machine learning classifier for identifying the dark matter cosmic web environments of galaxies. Large galaxy surveys offer comprehensive statistical views of how galaxy properties are shaped by large-scale…
Non-parametric morphology statistics have been used for decades to classify galaxies into morphological types and identify mergers in an automated way. In this work, we assess how reliably we can identify galaxy post-mergers with…
Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions…
We demonstrate the potential of Deep Learning methods for measurements of cosmological parameters from density fields, focusing on the extraction of non-Gaussian information. We consider weak lensing mass maps as our dataset. We aim for our…
Hydrodynamical simulations play a fundamental role in modern cosmological research, serving as a crucial bridge between theoretical predictions and observational data. However, due to their computational intensity, these simulations are…