Related papers: Accurately Estimating Redshifts from CSST Slitless…
The Chinese Space Station Optical Survey (CSS-OS) is a planned full sky survey operated by the Chinese Space Station Telescope (CSST). It can simultaneously perform the photometric imaging and spectroscopic slitless surveys, and will probe…
We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
The Baryon Acoustic Oscillation (BAO) feature in the power spectrum of galaxies provides a standard ruler to probe the accelerated expansion of the Universe. The current surveys covering a comoving volume sufficient to unveil the BAO scale…
The scientific value of the next generation of large continuum surveys would be greatly increased if the redshifts of the newly detected sources could be rapidly and reliably estimated. Given the observational expense of obtaining…
The installation of the Space Telescope Imaging Spectrograph (STIS) on the Hubble Space Telescope (HST) allows for the first time two-dimensional optical and ultraviolet slitless spectroscopy of faint objects from space. The STIS Parallel…
Context: New spectroscopic surveys will increase the number of astronomical objects requiring characterization by over tenfold.. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine…
The Chinese Space Station Survey Telescope (CSST) is a next-generation Stage-IV facility renowned for its wide field of view, high image quality, and multi-band observational capabilities. Among the five instruments onboard the CSST, the…
The 2-m aperture Chinese Space Station Telescope (CSST), which observes at wavelengths ranging from 255 to 1000 nm, is expected to start science operations in 2024. An ultra-deep field observation program covering approximately 10 square…
We present the first detection of the baryon acoustic oscillations (BAO) signal obtained using unblinded data collected during the initial two months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI).…
With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of millions of galaxies, spectra classification will become overwhelming for standard analysis techniques. To prepare for this challenge, we…
We investigate the impact of photometric signal-to-noise (S/N) on the precision of photometric redshifts in multi-band imaging surveys, using both simulations and real data. We simulate the optical 4-band (BVRz) Deep Lens Survey (DLS,…
We present a supervised neural network approach to the determination of photometric redshifts. The method was tuned to match the characteristics of the Sloan Digital Sky Survey and it exploits the spectroscopic redshifts provided by this…
Photometric redshift estimation plays a crucial role in modern cosmological surveys for studying the universe's large-scale structures and the evolution of galaxies. Deep learning has emerged as a powerful method to produce accurate…
We describe the redmonster automated redshift measurement and spectral classification software designed for the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digital Sky Survey IV (SDSS-IV). We describe the…
Accurate photometric redshift estimation is critical for observational cosmology, especially in large-scale surveys where spectroscopic measurements are impractical. Traditional approaches include template fitting and machine learning, each…
We introduce the Spectroscopy Pre-trained Transformer (SpecPT), a transformer-based model designed to analyze spectroscopic data, with applications in spectrum reconstruction and redshift measurement. Using the Early Data Release (EDR) of…
Deep Learning models have been increasingly exploited in astrophysical studies, yet such data-driven algorithms are prone to producing biased outputs detrimental for subsequent analyses. In this work, we investigate two major forms of…
The multiplexing capability of slitless spectroscopy is a powerful asset in creating large spectroscopic datasets, but issues such as spectral confusion make the interpretation of the data challenging. Here we present a new method to search…
Accurate photometric redshift (photo-$z$) estimation requires support from multi-band observational data. However, in the actual process of astronomical observations and data processing, some sources may have missing observational data in…
X-ray Photoelectron Spectroscopy (XPS) is a crucial technique for material surface analysis, yet interpreting its spectra is often challenging for both human analysts and automated methods due to the prevalence of variable spectral shifts…