A5092230903- Pulsars and Gravitational Waves Research
- Gamma-ray bursts and supernovae
- Seismology and Earthquake Studies
- Computational Physics and Python Applications
- Machine Learning and Algorithms
- Blind Source Separation Techniques
- Bayesian Methods and Mixture Models
- Geophysics and Gravity Measurements
- Model Reduction and Neural Networks
- Digital Media Forensic Detection
- Seismic Waves and Analysis
- Geophysical Methods and Applications
- Target Tracking and Data Fusion in Sensor Networks
- Solar and Space Plasma Dynamics
- Image and Signal Denoising Methods
- GNSS positioning and interference
- Cosmology and Gravitation Theories
- Radio Astronomy Observations and Technology
Carnegie Mellon University
2024-2025
University of Geneva
2024
IIT@MIT
2024
Abstract Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such have been traditionally used searches compact binary coalescences (CBCs), and employed all known detections so far. However, interesting science cases aside from mergers do not yet accurate enough modeling to make matched filtering possible, including core-collapse supernovae sources where stochasticity may be...
Abstract Low-latency noise regression algorithms are crucial for maximizing the science outcomes of LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in detectability, source localization pre-merger detectability signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate effectiveness DeepClean , a convolutional neural network architecture that uses witness sensors to estimate subtract non-linear non-stationary from strain data. Our...
The promise of multi-messenger astronomy relies on the rapid detection gravitational waves at very low latencies ($\mathcal{O}$(1\,s)) in order to maximize amount time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference a comparatively small computational footprint, making them an attractive family algorithms this context. However, integration these into gravitational-wave...
Abstract In the flourishing field of gravitational-wave astronomy, accurately inferring binary black hole merger formation channels is paramount. The Bayesian hierarchical model selection analysis offers a promising methodology (see, e.g., “One Channel to Rule Them All” ). However, recently, Cheng et al. highlighted critical caveat: observed absent in known models can bias branching fraction estimates. this research note, we introduce test detect missing such analyses. Our findings show...
Real-time noise regression algorithms are crucial for maximizing the science outcomes of LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in detectability, source localization pre-merger detectability signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate effectiveness \textit{DeepClean}, a convolutional neural network architecture that uses witness sensors to estimate subtract non-linear non-stationary from strain data. Our...
<title>Abstract</title> The promise of multi-messenger astronomy relies on the rapid detection gravitational waves at very low latencies (O(1s)) in order to maximize amount time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference a comparatively small computational footprint, making them an attractive family algorithms this context. However, integration these into gravitational-wave...
Abstract We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time for candidates detected by machine-learning compact binary coalescence search, Aframe. present details our algorithm and optimizations done related data-loading pre-processing accelerated hardware. train model black-hole (BBH) simulations real LIGO-Virgo detector noise. Our has ∼6 million trainable parameters...
We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time for candidates detected by machine-learning compact binary coalescence search, Aframe. present details our algorithm and optimizations done related data-loading pre-processing accelerated hardware. train model black-hole (BBH) simulations real LIGO-Virgo detector noise. Our has $\sim 6$ million trainable parameters with...
This paper presents the results of a Neural Network (NN)-based search for short-duration gravitational-wave transients in data from third observing run LIGO, Virgo, and KAGRA. The targets unmodeled with durations milliseconds to few seconds 30-1500 Hz frequency band, without assumptions about incoming signal direction, polarization, or morphology. Using Gravitational Wave Anomalous Knowledge (GWAK) method, three compact binary coalescences (CBCs) identified by existing pipelines are...
Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such have been traditionally used searches compact binary coalescences (CBCs), and employed all known detections so far. However, interesting science cases aside from mergers do not yet accurate enough modeling to make matched filtering possible, including core-collapse supernovae sources where stochasticity may be involved....
In the flourishing field of gravitational-wave astronomy, accurately inferring binary black hole merger formation channels is paramount. The Bayesian hierarchical model selection analysis offers a promising methodology (see, e.g., One Channel to Rule Them All, Zevin et al. 2021). However, recently, Cheng (2023) highlighted critical caveat: observed absent in known models can bias branching fraction estimates. this research note, we introduce test detect missing such analyses. Our findings...