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We seek to derive star formation rates (SFR) and stellar masses (M_star) in distant galaxies and to quantify the main uncertainties affecting their measurement. We explore the impact of the assumptions made in their derivation with standard…
In the current panorama of large surveys, the vast amount of data obtained with different methods, data types, formats, and stellar samples, is making an efficient use of the available information difficult. The Survey of Surveys is a…
One of the most important properties of a galaxy is the total stellar mass, or equivalently the stellar mass-to-light ratio (M/L). It is not directly observable, but can be estimated from stellar population synthesis. Currently, a galaxy's…
Convolutional Neural Networks (CNNs) are models that are utilized extensively for the hierarchical extraction of features. Vision transformers (ViTs), through the use of a self-attention mechanism, have recently achieved superior modeling…
We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…
A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are > $10^9$ photometrically cataloged sources, yet modern spectroscopic surveys are limited to ~few x $10^6$ targets. As we…
We present AspGap, a new approach to infer stellar labels from low-resolution Gaia XP spectra, including precise [$\alpha$/M] estimates for the first time. AspGap is a neural-network based regression model trained on APOGEE spectra. In the…
Estimating stellar masses for billions of galaxies in upcoming surveys requires methods that are both accurate and computationally efficient. We present a new approach using symbolic regression trained on a simulation to derive simple,…
The stellar mass-star formation rate ($\mathrm{M_*}$-$\mathrm{SFR}$) plane is a fundamental diagnostic for distinguishing galaxy populations. However, the evolutionary pathways of galaxies within this plane across cosmic time remain poorly…
Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years,…
A simple, fully connected neural network with a single hidden layer is used to estimate stellar masses for star-forming galaxies. The model is trained on broad-band photometry - from far-ultraviolet to mid-infrared wavelengths - generated…
Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…
Novel radio map estimation in optical wireless communications is proposed based on ML prediction rather than simulation techniques. ML training is performed on simulation and experimentally generated synthetic data and in both cases,…
We apply four statistical learning methods to a sample of $7941$ galaxies ($z<0.06$) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using $10$ features measured…
The Next Generation Space Telescope (NGST) will gather unprecedented spectro-photometric data on galaxies out to the highest redshifts. It is therefore crucial to identify the spectro-photometric diagnostics within reach of NGST, which will…
Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…
Deep learning-based bilateral grid processing has emerged as a promising solution for image enhancement, inherently encoding spatial and intensity information while enabling efficient full-resolution processing through slicing operations.…
With current and upcoming experiments such as WFIRST, Euclid and LSST, we can observe up to billions of galaxies. While such surveys cannot obtain spectra for all observed galaxies, they produce galaxy magnitudes in color filters. This data…
The evolution of the galaxy stellar mass--star formation rate relationship (M*-SFR) provides key constraints on the stellar mass assembly histories of galaxies. For star-forming galaxies, M*-SFR is observed to be fairly tight with a slope…
Morphology and structure of galaxies reflect their star formation and assembly histories. We use the framework of mutual information ($\mathrm{MI}$) to quantify interdependence among several structural variables and to rank them according…