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Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of…
The availability of large, public, multi-modal astronomical datasets presents an opportunity to execute novel research that straddles the line between science of AI and science of astronomy. Photometric redshift estimation is a…
We explore the accuracy of the clustering-based redshift inference within the MICE2 simulation. This method uses the spatial clustering of galaxies between a spectroscopic reference sample and an unknown sample. The goal of this study is to…
Estimating redshifts from broadband photometry is often limited by how accurately we can map the colors of galaxies to an underlying spectral template. Current techniques utilize spectrophotometric samples of galaxies or spectra derived…
We present a new approach, kernel regression, to determine photometric redshifts for 399,929 galaxies in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS). In our case, kernel regression is a weighted average of spectral…
Photometric redshifts of galaxies obtained by multi-wavelength data are widely used in photometric surveys because of its high efficiency. Although various methods have been developed, template fitting is still adopted as one of the most…
A method of obtaining approximate redshifts and spectral types of galaxies using a photometric system of six broad-bandpass filters is developed. The technique utilizes a smallest maximum difference approach rather than a least-squares…
Luminosity functions and their integrated luminosity densities are presented for the 6dF Galaxy Survey (6dFGS). This ongoing survey ultimately aims to measure around 150,000 redshifts and 15,000 peculiar velocities over almost the entire…
We present SHEEP, a new machine learning approach to the classic problem of astronomical source classification, which combines the outputs from the XGBoost, LightGBM, and CatBoost learning algorithms to create stronger classifiers. A novel…
We present a method, PhotoWeb, for estimating photometric redshifts of individual galaxies, and their equivalent distance, with megaparsec and even sub-megaparsec accuracy using the Cosmic Web as a constraint over photo-z estimates.…
Estimating redshift is a central task in astrophysics, but its measurement is costly and time-consuming. In addition, current image-based methods are often validated on homogeneous datasets. The development and comparison of networks able…
Determining the distribution of redshifts of galaxies observed by wide-field photometric experiments like the Dark Energy Survey is an essential component to mapping the matter density field with gravitational lensing. In this work we…
Machine learning (ML) is a standard approach for estimating the redshifts of galaxies when only photometric information is available. ML photo-z solutions have traditionally ignored the morphological information available in galaxy images…
We have investigated and applied machine-learning algorithms for Infrared Colour Selection of Galactic Wolf-Rayet (WR) candidates. Objects taken from the GLIMPSE catalogue of the infrared objects in the Galactic plane can be classified into…
Deep near-infrared imaging surveys allow us to select and study distant galaxies in the rest-frame optical, and have transformed our understanding of the early Universe. As the vast majority of K- or IRAC-selected galaxies is too faint for…
We derive a simple empirical photometric redshift estimator using a training set of galaxies with multiband photometry and measured redshifts in the Hubble Deep Field (HDF). This estimator is model-independent; it does not use spectral…
Gamma-Ray Bursts (GRBs), due to their high luminosities are detected up to redshift 10, and thus have the potential to be vital cosmological probes of early processes in the universe. Fulfilling this potential requires a large sample of…
In this paper we present photometric redshifts for 2.7 million galaxies in the XMM-LSS and COSMOS fields, both with rich optical and near-infrared data from VISTA and HyperSuprimeCam. Both template fitting (using galaxy and Active Galactic…
We use multi-band optical and near-infrared photometric observations of galaxies in the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (CANDELS) to predict photometric redshifts using artificial neural networks. The…
Data-driven approaches play a crucial role in space computing, and our paper focuses on analyzing data to learn more about celestial objects. Photometric redshift, a measure of the shift of light towards the red part of the spectrum, helps…