Research Spotlight: Recent Gravitational-Wave Glitch Studies

We’re learning more about glitches in gravitational wave detectors all the time. The most recent LIGO–Virgo–KAGRA Collaboration meeting (12–16 September 2022) in Cardiff featured the latest progress on our detectors and data analysis. Many research projects were presented as posters: several were looking at topics related to glitches, and many used results from the Gravity Spy project for some part of their work! We’ve reached out to authors of these posters to spotlight their work in this blog post. Below you’ll find brief summaries about these research projects, and a glimpse into some of the science that contributing to Gravity Spy can enable.


Investigations of Increased Detector Noise due to Trains at LIGO Livingston – Jane Glanzer (LSU)

Scattered light is one of several types of noise sources present in the LIGO detectors. Specifically, Fast Scattering (known as Crown in Gravity Spy) is a type of scattering that occurs with increased ground motion in the 1-6 Hz (anthropogenic) and 0.1–0.3 Hz (microseism) band. Its structure takes the form of small arches present in time frequency space.

Scattering happens when light from the main laser path is scattered by a mirror reflected by another surface. A fraction of this scattered light can rejoin the main path, and introduce noise back into the main gravitational wave data channel. Environmental seismic disturbances contribute to the production of scattered light and limited detector sensitivity.

A scattered light glitch occurring in the LIGO Livingston detector.

Trains near the LIGO Livingston detector are one of the main causes for increased seismic motion. Through the use of the linear regression tool LASSO, we searched for narrow band seismic frequencies to determine possible detector couplings responsible for these fast scattering glitches. This is done by looking for correlations between increases in ground motion and the calibrated strain data. The results find that the most common seismic frequencies that correlate with increases in detector noise are 0.6–0.8 Hz, 1.7–1.9 Hz, 1.8–2.0 Hz, and 2.3–2.5 Hz. In the Livingston detector, the arm cavity baffles and cryobaffles have resonances such that they may be the culprits for some of the Fast Scattering seen in O3 due to the train motion.


ArchEnemy: Subtracting scattered light artefacts from gravitational-wave data – Arthur Tolley (Portsmouth)

While the LVK have observed ~90 gravitational-wave signals to date, searching for these signals in the gravitational-wave strain data isn’t easy! This is made more complicated by the presence of random bursts of noise, known as glitches, in the data.

A very common glitch seen in the third observing run is caused by the scattering of light within the gravitational-wave detectors, colloquially called Scattered Light. I have been developing the ArchEnemy pipeline which can find these glitches and remove them from gravitational-wave data. We model scattered light glitches and produce a template bank of scattered light templates to matched filter with gravitational-wave data, allowing us to identify these glitches in a manner similar to how we search for gravitational-wave signals. Following the identification of the best fitting template for each scattered light glitch in the data, we can clean the gravitational-wave data by subtracting them. Cleaning gravitational-wave data can uncover previously obscured gravitational-wave events and will ultimately improve the sensitivity of the detectors.

Scattered light glitches in gravitational-wave data with the best fitting scattered light glitch templates identified by the ArchEnemy search pipeline (red overlays).

A random forest classifier to distinguish between chirps and glitches – Neev Shah (UBC)

Glitches can often hinder the search for gravitational waves by showing up as false candidates. We develop a new statistical distinguisher that can distinguish between different types of glitches and chirps. Assuming all events as real signals (glitches are not!), we project them onto a gravitational-wave model to infer the (posterior) probability distributions of their astrophysical parameters. We extract particular features from these posterior distributions and use a tool called Random Forests for the purpose of classification. Random forests can identify spatial clusterings in the parameter space that can help separate different classes of glitches and gravitational waves.

Examples of the glitch classes we work with and are commonly encountered in the LIGO data, and an actual event, GW190521 that visually looks more like a glitch than a chirp.

We find that for specific astrophysical parameters like the mass and spins of the binaries, the posteriors for glitches are starkly different from those of simulated gravitational waves, and the random forest can identify them to separate different glitch families and gravitational waves. We train our model using hundreds of simulated gravitational waves and thousands of glitches from 5 glitch families (Blips, Tomtes, Koi Fish, Fast Scattering and Scattered Light) that were pre-classified by Gravity Spy! We find that our method can be quite useful for the purpose of classification as it has an overall accuracy of about 97% and a Chirp recall of about 90%. This tool might help in future observing runs when there would be a lot of candidate events, and a large number of glitches among them.


GSpyNetTree: Improving Gravity Spy classifications toward O4 – Sofía Álvarez-López (UBC)

When detecting gravitational waves, removing glitches is one of our most significant challenges. Gravity Spy has helped us classify many of these glitches, proving very helpful for LIGO detector characterization. However, as one of LIGO’s core missions is the detection of gravitational waves, it is worth focusing on their search. In this sense, we discovered that Gravity Spy has the potential to be more than a glitch classifier: a gravitational wave vs glitch classifier!

A simulated gravitational wave (white box) and a Tomte glitch (red box) occur within 0.011 s from one another.

Nevertheless, we had to restructure Gravity Spy to do so. Moreover, in the context of the 4th LIGO–Virgo–KAGRA observing run, new challenges arise in gravitational-wave classification, as detectors are expected to be more sensitive. The possible appearance of new glitches, and the likely occurrence of overlapping glitches and GWs (as shown in the image above), suggest the need for a new model for GW classification. We studied how Gravity Spy responded to these challenges and developed GSpyNetTree, the Gravity Spy Convolutional Neural Network Decision Tree: a gravitational wave vs glitch classifier that aims to tackle these challenges.


Towards unified modelling of astrophysical and background populations – Jack Heinzel (MIT)

One of the challenges of LIGO analysis is identifying which signals are astrophysical in origin and which signals are environmental noise. Recent progress has been made in identifying common signatures of Gravity Spy-identified environmental noise, which are unlikely to occur for real astrophysical gravitational waves.

In particular, when environmental noise is analyzed as a coalescence of a compact binary, the (nonphysical) parameters corresponding to the signal are so different from parameters we usually observe in astrophysical signals that we can confidently separate the noise population from the astrophysical population. We can then analyze the properties of events which are more ambiguous in origin, simultaneously estimating the probability of astrophysical vs terrestrial origin. The astrophysical information is then folded in to constrain the population of astrophysical events.


That’s all from us. Once again, a big thanks for all the classifying you’ve all done so far: as you can see, we’re chipping away at the glitch problem but there’s still plenty of work to be done!

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