The Universe Online—A first generation data archive for the Hubble Space Telescope

1993 ◽  
Author(s):  
A. Minick Rushton
Keyword(s):  
First Generation ◽  
Hubble Space Telescope ◽  
Data Archive ◽  
Space Telescope ◽  
The Universe

scholarly journals The Evolution of Neutral Gas in the Universe

2006 ◽  
Vol 2 (S235) ◽  
pp. 440-440
Author(s):  
David Turnshek ◽  
Sandhya Rao ◽  
Eric Monier ◽  
Daniel Nestor ◽  
Anna Quider
Keyword(s):  
Absorption Line ◽  
Hubble Space Telescope ◽  
Reference List ◽  
Space Telescope ◽  
Neutral Gas ◽  
The Universe ◽  
Initial Results ◽  
Gas Component

AbstractWe give references to some of our work on the properties and evolution of the neutral gas component of the Universe (see reference list). The bulk of the observed neutral gas has been detected by identifying intervening damped Lyα (DLA) quasar absorption-line systems with N(H) ≥2 × 1020 atoms cm−2. We also present some initial results from a program to identify DLA absorbers near redshift z = 0.5 using Hubble Space Telescope ACS prism spectra (see Figure 1).

scholarly journals The Space Telescope Imaging Spectrograph

1989 ◽  
Vol 8 ◽  
pp. 445-448
Author(s):  
Bruce E Woodgate
Keyword(s):  
First Generation ◽  
Second Generation ◽  
Hubble Space Telescope ◽  
Space Telescope ◽  
Infra Red ◽  
Imaging Spectrograph

The Space Telescope Imaging Spectrograph (STIS) is a second generation instrument to be installed into the Hubble Space Telescope (HST) in-orbit 5–9 years after the first launch. Together with the infra-red instrument, it will provide a large increase in capability of the observatory, and be able to replace a first generation that had failed or degraded.

scholarly journals Probing the faintest galaxy population at the epoch of reionization with gravitational lensing

2019 ◽  
Vol 15 (S352) ◽  
pp. 26-26
Author(s):  
Hakim Atek
Keyword(s):  
Gravitational Lensing ◽  
Hubble Space Telescope ◽  
Space Telescope ◽  
Systematic Uncertainties ◽  
James Webb Space Telescope ◽  
Exciting Opportunity ◽  
Complete Dataset ◽  
Galaxy Populations ◽  
Galaxy Population ◽  
The Universe

AbstractUltra-deep observations of blank fields with the Hubble Space Telescope have made important inroads in characterizing galaxy populations at redshift z = 6 – 10. Gravitational lensing by massive galaxy clusters offers a new route to identify the faintest sources at the epoch of reionization. In particular, thanks to the Hubble Frontier Fields program, we robustly pushed the detection limit down to MAB = − 15 mag at z ∼ 6. I will present the latest results based on the complete dataset of the HFF clusters and parallel fields, and their implications on the ability of galaxies to reionize the Universe. I will also discuss the results of a comprehensive end-to-end modeling effort towards constraining the systematic uncertainties of the lens models, which are currently the last hurdle before extending the UV LF to fainter luminosities. Finally, I will discuss the great discoveries awaiting combination of such cosmic lenses with the upcoming James Webb Space Telescope and the exciting opportunity to probe the turnover of the UV LF, hence the limit of the star formation process at those early epochs.

scholarly journals Learning to Denoise Astronomical Images with U-nets

2020 ◽  
Author(s):  
Antonia Vojtekova ◽  
Maggie Lieu ◽  
Ivan Valtchanov ◽  
Bruno Altieri ◽  
Lyndsay Old ◽  
...  
Keyword(s):  
Information Gain ◽  
Hubble Space Telescope ◽  
Signal To Noise Ratio ◽  
Post Processing ◽  
Space Telescope ◽  
Noise Characteristics ◽  
Astronomical Imaging ◽  
Average Flux ◽  
Astronomical Images ◽  
The Universe

Abstract Astronomical images are essential for exploring and understanding the universe. Optical telescopes capable of deep observations, such as the Hubble Space Telescope, are heavily oversubscribed in the Astronomical Community. Images also often contain additive noise, which makes de-noising a mandatory step in post-processing the data before further data analysis. In order to maximise the efficiency and information gain in the post-processing of astronomical imaging, we turn to machine learning. We propose Astro U-net, a convolutional neural network for image de-noising and enhancement. For a proof-of-concept, we use Hubble space telescope images from WFC3 instrument UVIS with F555W and F606W filters. Our network is able to produce images with noise characteristics as if they are obtained with twice the exposure time, and with minimum bias or information loss. From these images, we are able to recover $95.9\%$ of stars with an average flux error of $2.26\%$. Furthermore the images have, on average, 1.63 times higher signal-to-noise ratio than the input noisy images, equivalent to the stacking of at least 3 input images, which means a significant reduction in the telescope time needed for future astronomical imaging campaigns.

Sign in / Sign up

Export Citation Format

Share Document