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We propose a new method to estimate the photometric redshift of galaxies by using the full galaxy image in each measured band. This method draws from the latest techniques and advances in machine learning, in particular Deep Neural…
Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot…
We present our results from training and evaluating a convolutional neural network (CNN) to predict galaxy shapes from wide-field survey images of the first data release of the Dark Energy Survey (DES DR1). We use conventional shape…
In long-term deployments of sensor networks, monitoring the quality of gathered data is a critical issue. Over the time of deployment, sensors are exposed to harsh conditions, causing some of them to fail or to deliver less accurate data.…
This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban…
Benefited from the rapid and sustainable development of synthetic aperture radar (SAR) sensors, change detection from SAR images has received increasing attentions over the past few years. Existing unsupervised deep learning-based methods…
Simulated galaxy distributions are suitable for developing filament detection algorithms. However, samples of observed galaxies, being of limited size, cause difficulties that lead to a discontinuous distribution of filaments. We created a…
The distinction between stars and galaxies is a fundamental problem in the field of celestial classification. This issue has become challenging for these ongoing and upcoming digital surveys, which will produce terabytes and even petabytes…
Although supervised deep representation learning has attracted enormous attentions across areas of pattern recognition and computer vision, little progress has been made towards unsupervised deep representation learning for image…
We present results from a new modelling technique which can account for the observed optical/NIR - FIR/submm spectral energy distributions (SEDs) of normal star-forming galaxies in terms of a minimum number of essential parameters…
Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure by learning a basis that can efficiently express the…
We present a full-spectrum linear fitting method, SEW, for stellar population synthesis based on equivalent widths (EWs) to extract galaxy properties from observed spectra. This approach eliminates the need for prior assumptions about dust…
Our aim in this work is to answer, using simulated narrow-band photometry data, the following general question: What can we learn about galaxies from these new generation cosmological surveys? For instance, can we estimate stellar age and…
State-of-the-art spectral energy distribution (SED) analyses use a Bayesian framework to infer the physical properties of galaxies from observed photometry or spectra. They require sampling from a high-dimensional space of SED model…
We explore the ability of four different inverse population synthesis codes to recover the physical properties of galaxies from their spectra by SED fitting. Three codes, DynBaS, TGASPEX, and GASPEX, have been implemented by the authors and…
The spectral energy distribution (SED) of a galaxy represents the distribution of electromagnetic radiation emitted across all wavelengths, from radio waves to gamma rays. The galaxy SED is akin to its fingerprint, and serves as a…
The European Space Agency's Euclid mission will observe approximately 14,000 $\rm{deg}^{2}$ of the extragalactic sky and deliver high-quality imaging for many galaxies. The depth and high spatial resolution of the data will enable a…
In this paper, we employe a new statistical analysis technique, Ensemble Learning for Independent Component Analysis (EL-ICA), on the synthetic galaxy spectra from a newly released high resolution evolutionary model by Bruzual & Charlot. We…
Spectral population synthesis (PS) is a fundamental tool in extragalactic research that aims to decipher the assembly history of galaxies from their SED. However, until recently all PS codes were restricted to purely stellar fits,…
Understanding the star-formation properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditionally, stellar population synthesis models have been used to obtain best fit parameters…