Template Bank Research Articles

Efficient Stochastic Template Bank Using Inner Product Inequalities

Gravitational wave searches are crucial for studying compact sources such as neutron stars and black holes. Many sensitive modeled searches use matched filtering to compare gravitational strain data to a set of waveform models known as template banks. We introduce a new stochastic placement method for constructing template banks, offering efficiency and flexibility to handle arbitrary parameter spaces, including orbital eccentricity, tidal deformability, and other extrinsic parameters. This method can be computationally limited by the ability to compare proposal templates with the accepted templates in the bank. To alleviate this computational load, we introduce the use of inner product inequalities to reduce the number of required comparisons. We also introduce a novel application of Gaussian Kernel Density Estimation to enhance waveform coverage in sparser regions. Our approach has been employed to search for eccentric binary neutron stars, low-mass neutron stars, primordial black holes, and supermassive black hole binaries. We demonstrate that our method produces self-consistent banks that recover the required minimum fraction of signals. For common parameter spaces, our method shows comparable computational performance and similar template bank sizes to geometric placement methods and stochastic methods, while easily extending to higher-dimensional problems. The time to run a search exceeds the time to generate the bank by a factor of O(105) for dedicated template banks, such as geometric, mass-only stochastic, and aligned spin cases, O(104) for eccentric and O(103) for the tidal deformable bank. With the advent of efficient template bank generation, the primary area for improvement is developing more efficient search methodologies.

New approach to template banks of gravitational waves with higher harmonics: Reducing matched-filtering cost by over an order of magnitude

New approach to template banks of gravitational waves with higher harmonics: Reducing matched-filtering cost by over an order of magnitude

Optimized search for a binary black hole merger population in LIGO-Virgo O3 data

Maximizing the number of detections in matched filter searches for compact binary coalescence (CBC) gravitational wave signals requires a model of the source population distribution. In previous searches using the framework, sensitivity to the population of binary black hole (BBH) mergers was improved by restricting the range of filter template mass ratios and use of a simple one-dimensional population model. However, this approach does not make use of our full knowledge of the population and cannot be extended to a full parameter space search. Here, we introduce a new ranking method, based on kernel density estimation with adaptive bandwidth, to accurately model the probability distributions of binary source parameters over a template bank, both for signals and for noise events. We demonstrate this ranking method by conducting a search over LIGO-Virgo O3 data for BBHs with unrestricted mass ratio, using a signal model derived from previous significant detected events. We achieve over 10% increase in sensitive volume for a simple power-law simulated signal population, compared to the previous BBH search. Correspondingly, with the new ranking, eight additional candidate events above an inverse false alarm rate threshold 0.5 yr are identified. Published by the American Physical Society 2024

Real-time signal detection for Cyclotron Radiation Emission Spectroscopy measurements using antenna arrays

Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for precision measurement of the energies of charged particles, which is being developed by the Project 8 Collaboration to measure the neutrino mass using tritium beta-decay spectroscopy. Project 8 seeks to use the CRES technique to measure the neutrino mass with a sensitivity of 40 meV, requiring a large supply of tritium atoms stored in a multi-cubic meter detector volume. Antenna arrays are one potential technology compatible with an experiment of this scale, but the capability of an antenna-based CRES experiment to measure the neutrino mass depends on the efficiency of the signal detection algorithms. In this paper, we develop efficiency models for three signal detection algorithms and compare them using simulations from a prototype antenna-based CRES experiment as a case-study. The algorithms include a power threshold, a matched filter template bank, and a neural network based machine learning approach, which are analyzed in terms of their average detection efficiency and relative computational cost. It is found that significant improvements in detection efficiency and, therefore, neutrino mass sensitivity are achievable, with only a moderate increase in computation cost, by utilizing either the matched filter or machine learning approach in place of a power threshold, which is the baseline signal detection algorithm used in previous CRES experiments by Project 8.

GravNet: A novel deep learning model with nonlinear filter for gravitational wave detection

Gravitational waves (GW) detected by LIGO, VIRGO, and upcoming facilities have ushered in a transformative era for astronomy and physics. However, these cosmic ripples present unique challenges. Most GW signals are not only weak but also fleeting, lasting mere seconds. This poses a significant hurdle to current search strategies. The prevalent matched filtering technique, while effective, demands an exhaustive search through a template bank, slowing down data processing.To overcome these limitations, machine learning, particularly Convolutional Neural Networks (CNNs), has emerged as a solution. Recent studies demonstrate that CNNs surpass traditional matched-filtering methods in detecting weak GW signals, extending beyond the training set parameters. Nonetheless, optimizing these deep learning models and assessing their robustness in GW signal detection remains essential. In this study, we explore various methods to enhance CNN models effectiveness using simulated data from three gravitational wave interferometers. Our investigation spans denoising techniques, CNN architectures, and pretrained AI models. Notably, the Constant-Q transform (CQT) outperforms the Fast Fourier transform in denoising raw gravitational signals. Furthermore, employing the pretrained model EfficientNet enhances GW detection efficiency. Our proposed CNN model, GravNet, combines CQT, EfficientNet, and an optimized CNN structure. GravNet achieves an impressive 76.5% accuracy and 0.85 AUC. This innovative approach offers valuable insights into harnessing deep learning models for more efficient and accurate gravitational wave detection and analysis.

Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA

Template bank for compact binary mergers in the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA

Adapting the PyCBC pipeline to find and infer the properties of gravitational waves from massive black hole binaries in LISA

The laser interferometer space antenna (LISA), due for launch in the mid 2030s, is expected to observe gravitational waves (GWs) from merging massive black hole binaries (MBHBs). These signals can last from days to months, depending on the masses of the black holes, and are expected to be observed with high signal to noise ratios (SNRs) out to high redshifts. We have adapted the PyCBC software package to enable a template bank search and inference of GWs from MBHBs. The pipeline is tested on the LISA data challenge’s Challenge 2a (‘Sangria’), which contains MBHBs and thousands of galactic binaries (GBs) in simulated instrumental LISA noise. Our search identifies all six MBHB signals with more than 92% of the optimal SNR. The subsequent parameter inference step recovers the masses and spins within their 90% confidence interval. Sky position parameters have eight high likelihood modes which are recovered but often our posteriors favour the incorrect sky mode. We observe that the addition of GBs biases the parameter recovery of masses and spins away from the injected values, reinforcing the need for a global fit pipeline which will simultaneously fit the parameters of the GB signals before estimating the parameters of MBHBs.

Banks of templates for directed and all-sky narrow-band searches of continuous gravitational waves from spinning neutron stars with several spindowns

We construct efficient banks of templates suitable for searches of continuous gravitational waves from isolated spinning neutron stars. We assume that the search algorithm is based on the time-domain maximum-likelihood -statistic and we consider narrow-band searches with several spindown parameters included. Our template banks are suitable for both all-sky and directed searches and they enable the usage of the fast Fourier transform in the computation of the -statistic as well as, in the case of all-sky searches, efficient resampling of data to barycentric time.

A search for Kuiper belt occultations using the Weizmann Fast Astronomical Survey Telescope

ABSTRACT Measuring the size distribution of small (kilometre-scale) Kuiper belt objects (KBOs) can help constrain models of Solar system formation and planetary migration. Such small, distant bodies are hard to detect with current or planned telescopes, but can be identified as sub-second occultations of background stars. We present the analysis of data from the Weizmann Fast Astronomical Survey Telescope, consisting of fast photometry of ∼106 star-hours at a frame rate of 10–25 Hz. Our pipeline utilizes a matched-filter approach with a large template bank, including red-noise treatment, and injection of simulated events for estimating the detection efficiency. The KBO radius at which our survey is 10 per cent (50 per cent) efficient is 1.1 (2.0) km. The data from 2020–2021 observing seasons were analysed and no occultations were identified. We discuss a sample of sub-second false-positive events, both occultation-like and flare-like, which are still not fully understood but could be instructive for future surveys looking for short-duration events. We use our null-detection result to set limits on the kilometre-scale KBO number density. Our individual radius bin limits are consistent with most previous works, with N(r > 1 km) ⪅ 106 deg−2 (95 per cent confidence limit). Our integrated (all size) limits, assuming a power law normalized to large (≈45 km) KBOs give a power-law index q < 3.93 (95 per cent confidence limit). Finally, our results are in tension with a recently reported KBO detection from the ground, at the p = 4 × 10−4 level.

Space-based gravitational wave signal detection and extraction with deep neural network

Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios.

INFORMATION-ENTROPY METHOD FOR DETECTING GRAVITATIONAL WAVE SIGNALS

The detection of gravitational waves came from a pair of merging black holes marked the beginning of the era of GW astronomy. Traditionally, to extract gravitational wave signals from experimental data, the scientific collaborations use the standard matched filtering technique. The matched filtering technique relies on the existing waveform templates, that makes it difficult to find gravitational wave signals that go beyond theoretical expectations. Moreover, the computational cost of matched filter is veryhigh, as it depends on the number of templates used. In this article, we propose a new information-entropy method for gravitational waves detection that does not require a theoretical bank of signal templates. To demonstrate the reliability of our method we conducted an analysis using simulated and real data. Through this study, we revealed that our measure of conditional information detects the gravitational wave signals and can be used along with the matched filtering method.

Detectability of subsolar mass neutron stars through a template bank search

We study the detectability of gravitational-wave signals from sub-solar mass binary neutron star systems by the current generation of ground-based gravitational-wave detectors. We find that finite size effects from large tidal deformabilities of the neutron stars and lower merger frequencies can significantly impact the sensitivity of the detectors to these sources. By simulating a matched-filter based search using injected binary neutron star signals with tidal deformabilities derived from physically motivated equations of state, we calculate the reduction in sensitivity of the detectors. We conclude that the loss in sensitive volume can be as high as $78.4 \%$ for an equal mass binary system of chirp mass $0.17 \, \textrm{M}_{\odot}$, in a search conducted using binary black hole template banks. We use this loss in sensitive volume, in combination with the results from the search for sub-solar mass binaries conducted on data collected by the LIGO-Virgo observatories during their first three observing runs, to obtain a conservative upper limit on the merger rate of sub-solar mass binary neutron stars. Since the discovery of a low-mass neutron star would provide new insight into formation mechanisms of neutron stars and further constrain the equation of state of dense nuclear matter, our result merits a dedicated search for sub-solar mass binary neutron star signals.

ECG data compression using the formation of QRS-complex segment bank and integer DCT-based plateau region processing

Electrocardiogram (ECG) data is essential for evaluating cardiac health conditions. Long-term ECG signal monitoring needs a considerable amount of memory for storage, impacting data bandwidth allocation. The ECG data compression is challenging in preserving significant features, using low computational resources, and achieving a higher compression ratio at lower reconstruction error. This paper proposes an innovative QRS-complex and plateau (Non-QRS) region segregation for QRS-complex subsegment bank formation and integer cosine transform-based ECG data compression. An adaptive QRS-complex subsegment template bank is formed by: (i) QRS and Non-QRS region segmentation; (ii) QRS region identification; (iii) QRS region alignment; (iv) Dc equalization and sub-template formation of the QRS region. A discrete integer cosine transform, quantization, and grouping with a significance map are used on the non-QRS segment of the ECG. Both the QRS and non-QRS regions are coded by lossless arithmetic encoding. Experiments are enumerated on the MIT BIH arrhythmia database, which validates the potential of the proposed approach by achieving an average value equal to 31.51, 0.84, and 28.57 for compression ratio, percentage-root-mean-square-difference, and quality-score, respectively. The average distortion measure for the proposed ECG compressor is less than 2%, which is within the clinically acceptable limit. The proposed method is also compared to existing state-of-the-art ECG compression techniques. It performs better in terms of compression ratio and preservation of ECG features. Additionally, this method typically requires less computational resources and is easy to implement, which is an advantage over other ECG compression methods.

Implementation of a new weave-based search pipeline for continuous gravitational waves from known binary systems

Scorpius X-1 (Sco X-1) has long been considered one of the most promising targets for detecting continuous gravitational waves with ground-based detectors. Observational searches for Sco X-1 have achieved substantial sensitivity improvements in recent years, to the point of starting to rule out emission at the torque-balance limit in the low-frequency range $\ensuremath{\sim}40--180\text{ }\text{ }\mathrm{Hz}$. In order to further enhance the detection probability, however, there is still much ground to cover for the full range of plausible signal frequencies $\ensuremath{\sim}20--1500\text{ }\text{ }\mathrm{Hz}$, as well as a wider range of uncertainties in binary orbital parameters. Motivated by this challenge, we have developed binaryweave, a new search pipeline for continuous waves from a neutron star in a known binary system such as Sco X-1. This pipeline employs a semicoherent StackSlide $\mathcal{F}$-statistic using efficient lattice-based metric template banks, which can cover wide ranges in frequency and unknown orbital parameters. We present a detailed timing model and extensive injection-and-recovery simulations that illustrate that the pipeline can achieve high detection sensitivities over a significant portion of the parameter space when assuming sufficiently large (but realistic) computing budgets. Our studies further underline the need for stricter constraints on the Sco X-1 orbital parameters from electromagnetic observations, in order to be able to push sensitivity below the torque-balance limit over the entire range of possible source parameters.

Optimizing the placement of numerical relativity simulations using a mismatch predicting neural network

Gravitational wave observations from merging compact objects are becoming commonplace, and as detectors improve and gravitational wave sources become more varied, it is increasingly important to have dense and expansive template banks of predicted gravitational waveforms. Since numerical relativity is the only way to fully solve the nonlinear merger regime of general relativity for comparably massed systems, numerical relativity simulations are critical for gravitational wave detection and analysis. These simulations are computationally expensive, with each simulation placing one point within the high dimensional parameter space of binary black hole coalescences. This makes it important to have a method of placing new simulations in ways that use our computational resources optimally while ensuring sufficient coverage of the parameter space. Accomplishing this requires predicting the impact of a new set of parameters before performing the simulation. To this effect, this paper introduces a neural network to predict the mismatch between the gravitational waves of two binary systems. Using this network, we then show how we can propose new numerical relativity simulations that will provide the most benefit. We also use the network to identify gaps in existing public catalogs and identify degeneracies in the binary black hole parameter space.

A reduction procedure and pipeline for the detection of trans-Neptunian objects using occultations

Abstract Kuiper belt objects smaller than a few kilometres are difficult to observe directly. They can be detected when they randomly occult a background star. Close to the ecliptic plane, each star is occulted once every tens of thousands of hours, and occultations typically last for less than a second. We present an algorithm, and companion pipeline, for detection of diffractive occultation events. Our approach includes: cleaning the data; an efficient and optimal matched filtering of the light curves with a template bank of diffractive occultations; treating the red-noise in the light curves; injection of simulated events for efficiency estimation; and applying data quality cuts. We discuss human vetting of the candidate events in a blinded way to reduce bias caused by the human-in-the-loop. We present Markov Chain Monte Carlo tools to estimate the parameters of candidate occultations, and test them on simulated events. This pipeline is used by the W-FAST. The methods discussed here can be applied to searches for other Trans-Neptunian objects, albeit with larger radii that correspond to a larger diffraction length scale.

An Implementation of Galactic White Dwarf Binary Data Analysis for MLDC-3.1

The space-borne gravitational wave detectors will observe a large population of double white dwarf binaries in the Milky Way. However, the search for double white dwarfs in the gravitational wave data will be time-consuming due to the large number of templates involved and antenna response calculation. In this paper, we implement an iterative combinatorial algorithm to search for double white dwarfs in MLDC-3.1 data. To quickly determine the rough parameters of the target sources, the following algorithms are adopted in a coarse search process: (1) using the downsampling method to reduce the number of original data points; (2) using the undersampling method to speed up the generation of a single waveform template; (3) using the stochastic template bank method to quickly construct the waveform template bank while achieving high coverage of the parameter space; (4) combining the FFT acceleration algorithm with the stochastic template bank to reduce the calculation time of a single template. A fine search process is applied to further determine the parameters of the signals based on the coarse search, for which we adopt the particle swarm optimization. Finally, we detect double white dwarf signals, validating the feasibility of our method.

Detection of Einstein telescope gravitational wave signals from binary black holes using deep learning

ABSTRACT The expected volume of data from the third-generation gravitational waves (GWs) Einstein Telescope (ET) detector would make traditional GWs search methods such as match filtering impractical. This is due to the large template bank required and the difficulties in waveforms modelling. In contrast, machine learning (ML) algorithms have shown a promising alternative for GWs data analysis, where ML can be used in developing semi-automatic and automatic tools for the detection and parameter estimation of GWs sources. Compared to second generation detectors, ET will have a wider accessible frequency band but also a lower noise. The ET will have a detection rate for Binary Black Holes (BBHs) and Binary Neutron Stars (BNSs) of the order of 105–106 and 7 × 104 yr−1, respectively. We explored the efficiency of using convolutional neural networks (CNNs) for the detection of BBHs’ mergers in synthetic noisy data that was generated according to ET’s parameters. Without performing data whitening or applying bandpass filtering, we trained four CNN networks with the state-of-the-art performance in computer vision, namely VGG, ResNet, and DenseNet. ResNet has significantly better performance, and was able to detect BBHs sources with SNR of 8 or higher with 98.5 per cent accuracy, and with 92.5 per cent, 85 per cent, 60 per cent, and 62 per cent accuracy for sources with SNR range of 7–8, 6–7, 5–6, and 4–5, respectively. ResNet, in qualitative evaluation, was able to detect a BBH’s merger at 60 Gpc with 4.3 SNR. It was also shown that CNN can be used efficiently for near-real time detection of BBHs.

Efficient gravitational wave template bank generation with differentiable waveforms

The most sensitive search pipelines for gravitational waves from compact binary mergers use matched filters to extract signals from the noisy data stream coming from gravitational wave detectors. Matched-filter searches require banks of template waveforms covering the physical parameter space of the binary system. Unfortunately, template bank construction can be a time-consuming task. Here we present a new method for efficiently generating template banks that utilizes automatic differentiation to calculate the parameter space metric. Principally, we demonstrate that automatic differentiation enables accurate computation of the metric for waveforms currently used in search pipelines, whilst being computationally cheap. Additionally, by combining random template placement and a Monte Carlo method for evaluating the fraction of the parameter space that is currently covered, we show that search-ready template banks for frequency-domain waveforms can be rapidly generated. Finally, we argue that differentiable waveforms offer a pathway to accelerating stochastic placement algorithms. We implement all our methods into an easy-to-use Python package based on the jax framework, diffbank, to allow the community to easily take advantage of differentiable waveforms for future searches.

Detecting gravitational waves from extreme mass ratio inspirals using convolutional neural networks

Extreme mass ratio inspirals (EMRIs) are among the most interesting gravitational wave (GW) sources for space-borne GW detectors. However, successful GW data analysis remains challenging due to many issues, ranging from the difficulty of modeling accurate waveforms, to the impractically large template bank required by the traditional matched filtering search method. In this work, we introduce a proof-of-principle approach for EMRI detection based on convolutional neural networks (CNNs). We demonstrate the performance with simulated EMRI signals buried in Gaussian noise. We show that over a wide range of physical parameters, the network is effective for EMRI systems with a signal-to-noise ratio larger than 50, and the performance is most strongly related to the signal-to-noise ratio. The method also shows good generalization ability towards different waveform models. Our study reveals the potential applicability of machine learning technology like CNNs towards more realistic EMRI data analysis.