Virgo interferometer | EPFL Graph Search

The Virgo interferometer is a large Michelson interferometer designed to detect gravitational waves predicted by the general theory of relativity. It is located in Santo Stefano a Macerata, near the city of Pisa, Italy. The instrument's two arms are three kilometres long, hosting its mirrors and instrumentation inside an ultra-high vacuum. Virgo is hosted by the European Gravitational Observatory (EGO), a consortium founded by the French CNRS and Italian INFN. The Virgo Collaboration operates the detector and defines the strategy and policy for its use and upgrades. It is composed of around 850 members, representing 142 institutions in 16 different countries. Other interferometers similar to Virgo have the same goal of detecting gravitational waves, including the two LIGO interferometers in the United States (at the Hanford Site and in Livingston, Louisiana) and the Japanese interferometer KAGRA. Since 2007, Virgo and LIGO have agreed to share and jointly analyze the data recorded by their detectors and to jointly publish their results; this agreement now includes KAGRA since 2019. Because the interferometric detectors are not directional (they survey the whole sky) and are looking for signals which are weak and infrequent, simultaneous detection of a gravitational wave in multiple instruments is necessary to confirm the signal validity and to deduce the location of its source. The interferometer is named after the Virgo Cluster, a cluster of about 1,500 galaxies in the Virgo constellation, about 50 million light-years from Earth. Built as part of a franco-italian effort to detect the gravitational waves predicted by general relativity, it now plays a significant part in the global gravitational wave detector network, as gravitational wave astronomy takes its first steps. It has participated in a number of detections, and periodically undergoes improvements to enhance its sensitivity and increase its performance. The Virgo experiment is managed by the European Gravitational Observatory (EGO) consortium, created in December 2000 by the CNRS and INFN.

Challenges and opportunities of gravitational-wave searches at MHz to GHz frequencies

Daniel Garcia Figueroa, Valerie Fiona Domcke, Francisco Torrenti

The first direct measurement of gravitational waves by the LIGO and Virgo collaborations has opened up new avenues to explore our Universe. This white paper outlines the challenges and gains expected in gravitational-wave searches at frequencies above the LIGO/Virgo band, with a particular focus on Ultra High-Frequency Gravitational Waves (UHF-GWs), covering the MHz to GHz range. The absence of known astrophysical sources in this frequency range provides a unique opportunity to discover physics beyond the Standard Model operating both in the early and late Universe, and we highlight some of the most promising gravitational sources. We review several detector concepts that have been proposed to take up this challenge, and compare their expected sensitivity with the signal strength predicted in various models. This report is the summary of the workshop "Challenges and opportunities of high-frequency gravitational wave detection" held at ICTP Trieste, Italy in October 2019, that set up the stage for the recently launched Ultra-High-Frequency Gravitational Wave (UHF-GW) initiative.

SPRINGER INT PUBL AG2021

Low-dimensional data embedding for scalable astronomical imaging in the SKA telescope era

Vijay Kartik

Astronomy is one of the oldest sciences known to humanity. We have been studying celestial objects for millennia, and continue to peer deeper into space in our thirst for knowledge about our origins and the universe that surrounds us. Radio astronomy -- observing celestial objects at radio frequencies -- has helped push the boundaries on the kind of objects we can study. Indeed, some of the most important discoveries about the structure of our universe, like the cosmic microwave background, and entire classes of objects like quasars and pulsars, were made using radio astronomy. Radio interferometers are telescopes made of multiple antennas spread over a distance. Signals detected at different antennas are combined to provide images with much higher resolution and sensitivity than with a traditional single-dish radio telescope. The Square Kilometre Array (SKA) is one such radio interferometer, with plans to have antennas separated by as much as 3000,km. In its quest for ever-higher resolution and ever-wider coverage of the sky, the SKA heralds a data explosion, with an expected acquisition rate of 5,terabits per second. The high data rate fed into the pipeline can be handled with a two-pronged approach -- (i) scalable, parallel imaging algorithms that fully utilize the latest computing technologies like accelerators and distributed clusters, and (ii) dimensionality reduction methods that embed the high-dimensional telescope data to much smaller sizes without losing information and guaranteeing accurate recovery of the images, thereby enabling imaging methods to scale to big data sizes and alleviating heavy loads on pipeline buffers without compromising on the science goals of the SKA. In this thesis we propose fast and robust dimensionality reduction methods that embed data to very low sizes while preserving information present in the original data. These methods are presented in the context of compressed sensing theory and related signal recovery techniques. The effectiveness of the reduction methods is illustrated by coupling them with advanced convex optimization algorithms to solve a sparse recovery problem. Images thus reconstructed from extremely low-sized embedded data are shown to have quality comparable to those obtained from full data without any reduction. Comparisons with other standard `data compression' techniques in radio interferometry (like averaging) show a clear advantage in using our methods which provide higher quality images from much lower data sizes. We confirm these claims on both synthetic data simulating SKA data patterns as well as actual telescope data from a state-of-the-art radio interferometer. Additionally, imaging with reduced data is shown to have a lighter computational load -- smaller memory footprint owing to the size and faster iterative image recovery owing to the fast embedding. Extensions to the work presented in this thesis are already underway. We propose an `on-line' version of our reduction methods that works on blocks of data and thus can be applied on-the-fly on data as they are being acquired by telescopes in real-time. This is of immediate interest to the SKA where large buffers in the data acquisition pipeline are very expensive and thus undesirable. Some directions to be probed in the immediate future are in transient imaging, and imaging hyperspectral data to test computational load while in a high resolution, multi-frequency setting.

EPFL2018

Low-dimensional data embedding for scalable astronomical imaging in the SKA telescope era

Vijay Kartik

EPFL2018