Related papers: Deep Learning nearby galaxy peculiar velocities
The measurement of exoplanet masses using the radial velocity (RV) technique is currently limited by stellar activity, which introduces quasiperiodic variability signals that must be modeled and removed to enhance the sensitivity of the RV…
Determining the dynamical mass profiles of dispersion-supported galaxies is particularly challenging due to projection effects and the unknown shape of their velocity anisotropy profile. Our goal is to develop a machine learning algorithm…
The luminosity functions of galaxies and quasars provide invaluable information about galaxy and quasar formation. Estimating the luminosity function from magnitude limited samples is relatively straightforward, provided that the distances…
Cosmologists aim to model the evolution of initially low amplitude Gaussian density fluctuations into the highly non-linear "cosmic web" of galaxies and clusters. They aim to compare simulations of this structure formation process with…
We present preliminary results of applying a neighbour search code to a large sample of galaxies drawn from the Sloan Digitial Sky Survey (SDSS). We draw our sample of target galaxies from the spectroscopic catalogue of the SDSS, which has…
We present a method for automatic detection and classification of galaxies which includes a novel data-augmentation procedure to make trained models more robust against the data taken from different instruments and contrast-stretching…
We present a maximum probability approach to reconstructing spatial maps of the peculiar velocity field at redshifts $z\sim0.1$, where the velocities have been measured from distance indicators (DI) such as $D_n-\sigma$ relations or…
Image-generating machine learning models are typically trained with loss functions based on distance in the image space. This often leads to over-smoothed results. We propose a class of loss functions, which we call deep perceptual…
Galaxy cluster peculiar velocities can be inferred from high-sensitivity, high-resolution multiple-frequency observations in the 30 to 400 GHz range. While galaxy cluster counts and power spectra are sensitive to the growth factor, peculiar…
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images…
Quantifying image distortions caused by strong gravitational lensing and estimating the corresponding matter distribution in lensing galaxies has been primarily performed by maximum likelihood modeling of observations. This is typically a…
Predicting novel views of a scene from real-world images has always been a challenging task. In this work, we propose a deep convolutional neural network (CNN) which learns to predict novel views of a scene from given collection of images.…
A deep neural network (NN) is used to simultaneously detect laser beams in images and measure their center coordinates, radii and angular orientations. A dataset of images containing simulated laser beams and a dataset of images with…
I review selected current observations of distant galaxies and our interpretation of the fragile (and occasionally contradictory) data. Galaxies at the ``contemporary limit'' of technology and redshift (z~6) are difficult to locate in the…
Place recognition is one of the most challenging problems in computer vision, and has become a key part in mobile robotics and autonomous driving applications for performing loop closure in visual SLAM systems. Moreover, the difficulty of…
Wide field images taken in several photometric bands allow simultaneous measurement of redshifts for thousands of galaxies. A variety of algorithms to make this measurement have appeared in the last few years, the majority of which can be…
Maps of cosmic structure produced by galaxy surveys are one of the key tools for answering fundamental questions about the Universe. Accurate theoretical predictions for these quantities are needed to maximize the scientific return of these…
The possibility to constrain cosmological parameters from galaxy surveys using field-level machine learning methods that bypass traditional summary statistics analyses, depends crucially on our ability to generate simulated training sets.…
Cosmological galaxy formation simulations are still limited by their spatial/mass resolution and cannot model from first principles some of the processes, like star formation, that are key in driving galaxy evolution. As a consequence they…
The DENIS survey is currently imaging the entire southern sky in the I, J, and K wavebands. The current star/galaxy separation algorithm is presented and the galaxy counts are nearly perfectly Euclidean. 95% complete and reliable galaxy…