Related papers: Generating transient noise artifacts in gravitatio…
Deep generative models parametrised by neural networks have recently started to provide accurate results in modelling natural images. In particular, generative adversarial networks provide an unsupervised solution to this problem. In this…
Adversarial perturbations can pose a serious threat for deploying machine learning systems. Recent works have shown existence of image-agnostic perturbations that can fool classifiers over most natural images. Existing methods present…
Images synthesized by powerful generative adversarial network (GAN) based methods have drawn moral and privacy concerns. Although image forensic models have reached great performance in detecting fake images from real ones, these models can…
Non-Gaussian noise transients in interferometric gravitational-wave detectors increase the background in searches for short-duration and un-modelled signals. We describe a method for vetoing noise transients by ranking the statistical…
Random noise arising from physical processes is an inherent characteristic of measurements and a limiting factor for most signal processing and data analysis tasks. Given the recent interest in generative adversarial networks (GANs) for…
We introduce the use of deep learning ensembles for real-time, gravitational wave detection of spinning binary black hole mergers. This analysis consists of training independent neural networks that simultaneously process strain data from…
Advanced LIGO and Advanced Virgo are expected to make the first direct detections of gravitational waves (GW) in the next several years. Possible types of GW emission include short-duration bursts, signals from the coalescence of compact…
Excess noise from scattered light poses a persistent challenge in the analysis of data from gravitational wave detectors such as LIGO. We integrate a physically motivated model for the behavior of these "glitches" into a standard Bayesian…
The output of gravitational-wave interferometers, such as LIGO and Virgo, can be highly non-stationary. Broadband detector noise can affect the detector sensitivity on the order of tens of seconds. Gravitational-wave transient searches,…
Searching the data of gravitational-wave detectors for signals from compact binary mergers is a computationally demanding task. Recently, machine learning algorithms have been proposed to address current and future challenges. However, the…
We present a new ${\it{gating}}$ method to remove non-Gaussian noise transients in gravitational wave data. The method does not rely on any a-priori knowledge on the amplitude or duration of the transient events. In light of the character…
Deep neural networks have been shown to perform well in many classical machine learning problems, especially in image classification tasks. However, researchers have found that neural networks can be easily fooled, and they are surprisingly…
Unphysical templates for inspiral binary merger signals have emerged as an effective way to veto signals (rule out false positives) identified as glitches. These templates help reduce the parameters needed to distinguish glitches from real…
Convolutional Neural Networks (CNNs) have demonstrated potential for the real-time analysis of data from gravitational-wave detector networks for the specific case of signals from coalescing compact-object binaries such as black-hole…
The Omicron software is a tool developed to perform a multi-resolution time-frequency analysis of data from gravitational-wave detectors: the LIGO, Virgo, and KAGRA detectors. Omicron generates spectrograms from whitened data streams,…
Low-latency gravitational-wave alerts provide the greater multi-messenger community with information about the candidate events detected by the International Gravitational-Wave Network (IGWN). Prompt release of data products such as the sky…
The spoofing attack is critical to bypass physical-layer signal authentication. This paper presents a deep learning-based spoofing attack to generate synthetic wireless signals that cannot be statistically distinguished from intended…
Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test…
As LIGO and Virgo are upgraded, improving calibration systems to keep pace with the anticipated signal-to-noise enhancements will be challenging. We explore here a calibration method that uses astronomical signals, namely inspiral signals…
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. It was recently shown that given a dataset and…