Search

Broad searches for continuous gravitational wave signals rely on hierarchies of follow-up stages for candidates above a given significance threshold. An important step to simplify these follow-ups and reduce the computational cost is to bundle together in a single follow-up nearby candidates. This step is called clustering and we investigate carrying it out with a deep learning network. In our first paper [B. Beheshtipour and M. A. Papa, Phys. Rev. D 101, 064009 (2020)], we implemented a deep learning clustering network capable of correctly identifying clusters due to large signals. In this paper, a network is implemented that can detect clusters due to much fainter signals. These two networks are complementary and we show that a cascade of the two networks achieves an excellent detection efficiency across a wide range of signal strengths, with a false alarm rate comparable/lower than that of methods currently in use. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have gained importance in particle physics in the last few years. They have been shown to outperform traditional strategies in particle identification, tracking and energy reconstruction in the most modern high-energy physics experiments. The attractive feature of these techniques is their ability to model large dimensionality inputs and catch nontrivial correlations among the variables, which could be hidden or not easy to model. This paper focuses on the application of deep neural networks to the event reconstruction of the Limadou High-Energy Particle Detector on board the China Seismo-Electromagnetic Satellite. The core of the reconstruction chain is a set of fully connected neural networks that reconstructs the nature, the arrival direction and the kinetic energy of incoming electrons and protons, starting from the signals recorded in the detector. These networks are trained on a dedicated Monte Carlo simulation as representative as possible of real data. We describe the simulation, architecture and methodology adopted to design and train the networks, and finally report on the performance measured on simulated and flight data. [ABSTRACT FROM AUTHOR]

Accurate extractions of the detected gravitational wave (GW) signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the GWs. This however could be difficult in realistic scenarios where the signals detected by existing GW detectors could be contaminated with nonstationary and non-Gaussian noise. While the performance of existing waveform extraction methods are optimal, they are not fast enough for online application, which is important for multimessenger astronomy. In this paper, we demonstrate that a deep learning architecture consisting of convolutional neural network and bidirectional long short-term memory components can be used to extract binary black hole (BBH) GW waveforms from realistic noise in a few milliseconds. We have tested our network systematically on injected GW signals, with component masses uniformly distributed in the range of 10 to 80M⊙, on Gaussian noise and Laser Interferometer Gravitational Wave Observatory (LIGO) detector noise. We find that our model can extract GW waveforms with overlaps of more than 0.95 with pure numerical relativity templates for signals with signal-to-noise ratio greater than six and is also robust against interfering "glitches". We then apply our model to all ten detected BBH events from LIGO-Virgo's first (O1) and second (O2) observation runs, obtaining ≥ 0.97 overlaps for all ten extracted BBH waveforms with the corresponding pure templates. We discuss the implication of our result and its future applications to GW localization and mass estimation. [ABSTRACT FROM AUTHOR]

The precision anticipated from next-generation cosmic microwave background (CMB) surveys will create opportunities for characteristically new insights into cosmology. Secondary anisotropies of the CMB will have an increased importance in forthcoming surveys, due both to the cosmological information they encode and the role they play in obscuring our view of the primary fluctuations. Quadratic estimators have become the standard tools for reconstructing the fields that distort the primary CMB and produce secondary anisotropies. While successful for lensing reconstruction with current data, quadratic estimators will be suboptimal for the reconstruction of lensing and other effects at the expected sensitivity of the upcoming CMB surveys. In this paper we describe a convolutional neural network, ResUNet-CMB, that is capable of the simultaneous reconstruction of two sources of secondary CMB anisotropies, gravitational lensing and patchy reionization. We show that the ResUNet-CMB network significantly outperforms the quadratic estimator at low noise levels and is not subject to the lensing-induced bias on the patchy reionization reconstruction that would be present with a straightforward application of the quadratic estimator. [ABSTRACT FROM AUTHOR]

The gravitational waves emitted during the coalescence of binary black holes are an excellent probe to test the behavior of strong gravity. In this paper, we propose a new test called the merger-ringdown consistency test that focuses on probing the imprints of the dynamics in strong-gravity around the black-holes during the plunge-merger and ringdown phase. Furthermore, we present a scheme that allows us to efficiently combine information across multiple ringdown observations to perform a statistical null test of GR using the detected BH population. We present a proof-of-concept study for this test using simulated binary black hole ringdowns embedded in the next-generation ground-based detector noise. We demonstrate the feasibility of our test using a deep learning framework, setting a precedence for performing precision tests of gravity with neural networks. [ABSTRACT FROM AUTHOR]

The waveform templates of the matched filtering-based gravitational-wave search ought to cover wide range of parameters for the prosperous detection. Numerical relativity (NR) has been widely accepted as the most accurate method for modeling the waveforms. Still, it is well known that NR typically requires a tremendous amount of computational costs. In this paper, we demonstrate a proof-of-concept of a novel deterministic deep learning (DL) architecture that can generate gravitational waveforms from the merger and ringdown phases of the non-spinning binary black hole coalescence. Our model takes Ϭ(1) seconds for generating approximately 1500 waveforms with a 99.9% match on average to one of the state-of-the-art waveform approximants, the effective-one-body. We also perform matched filtering with the DL-waveforms and find that the waveforms can recover the event time of the injected gravitational-wave signals. [ABSTRACT FROM AUTHOR]

It is nontrivial to recognize phase transitions and track dynamics inside a stochastic process because of its intrinsic stochasticity. In this paper, we employ the deep learning method to classify the phase orders and predict the damping coefficient of fluctuating systems under Langevin description. As a concrete setup, we demonstrate this paradigm for the scalar condensation in QCD matter near the critical point, in which the order parameter of the chiral phase transition can be characterized in a 1+1-dimensional Langevin equation for the s field. In a supervised learning manner, convolutional neural networks accurately classify the first-order phase transition and crossover based on σ field configurations with fluctuations. Noise in the stochastic process does not significantly hinder the performance of the well-trained neural network for phase order recognition. For mixed dynamics with diverse dynamical parameters, we further devise and train the machine to predict the damping coefficients η in a broad range. The results show that it is robust to extract the dynamics from the bumpy field configurations. [ABSTRACT FROM AUTHOR]

The recent release of the second Gravitational-Wave Transient Catalog (GWTC-2) has increased significantly the number of known GW events, enabling unprecedented constraints on formation models of compact binaries. One pressing question is to understand the fraction of binaries originating from different formation channels, such as isolated field formation versus dynamical formation in dense stellar clusters. In this paper, we combine the cosmic binary population synthesis suite and the cmc code for globular cluster evolution to create a mixture model for black hole binary formation under both formation scenarios. For the first time, these code bodies are combined self-consistently, with cmc itself employing cosmic to track stellar evolution. We then use a deep-learning enhanced hierarchical Bayesian analysis to continuously sample over and constrain the common envelope efficiency α assumed in cosmic, the initial cluster virial radius r v adopted in cmc, and the intrinsic mixture fraction f between each channel. Under specific assumptions about other uncertain aspects of isolated binary and globular cluster evolution, we report the median and 90% confidence interval of three physical parameters for the intrinsic population (f,a, rv) = (0.20-0.18 +0.32, 2.26-1.84 +2.65, 2.71-1.17+0.83). This simultaneous constraint agrees with observed properties of globular clusters in the Milky Way and is an important first step in the pathway toward learning the astrophysics of compact binary formation through GW observations. [ABSTRACT FROM AUTHOR]

In recent years, convolutional neural network (CNN) and other deep learning models have been gradually introduced into the area of gravitational-wave (GW) data processing. Compared with the traditional matched-filtering techniques, CNN has significant advantages in efficiency in GW signal detection tasks. In addition, matched-filtering techniques are based on the template bank of the existing theoretical waveform, which makes it difficult to find GW signals beyond theoretical expectation. In this paper, based on the task of GW detection of binary black holes, we introduce the optimization techniques of deep learning, such as batch normalization and dropout, to CNN models. Detailed studies of model performance are carried out. Through this study, we recommend to use batch normalization and dropout techniques in CNN models in GW signal detection tasks. Furthermore, we investigate the generalization ability of CNN models on different parameter ranges of GW signals. We point out that CNN models are robust to the variation of the parameter range of the GW waveform. This is a major advantage of deep learning models over matched-filtering techniques. [ABSTRACT FROM AUTHOR]

In this paper, we seek to answer the question "given a rotating core collapse gravitational wave signal, can we determine its nuclear equation of state" To answer this question, we employ deep convolutional neural networks to learn visual and temporal patterns embedded within rotating core collapse gravitational wave (GW) signals in order to predict the nuclear equation of state (EOS). Using the 1824 rotating core collapse GW simulations by Richers et al. [Phys. Rev. D 95, 063019 (2017).], which have 18 different nuclear EOSs, we consider this to be a classic multiclass image classification and sequence classification problem. We attain up to 72% correct classifications in the test set, and if we consider the "top five" most probable labels, this increases to up to 97%, demonstrating that there is a moderate and measurable dependence of the rotating core collapse GW signal on the nuclear EOS. [ABSTRACT FROM AUTHOR]

The deep learning method has developed very fast as a tool for data analysis in recent years. Moreover, as a technique, it is quite promising as a way to analyze gravitational-wave detection data. Multiple works in the literature have already used deep learning to process simulated gravitational-wave data. In this paper, we apply deep learning to LIGO data. In order to improve the weak signal recognition, we design a new structure of the convolutional neural network (CNN). The key feature of our new CNN structure is the sensing layer. This layer mimics matched filtering but is different from the usual matched-filtering technique. Usually, the matched-filtering technique uses a full template bank to match the data. However, our sensing layer only uses tens of waveforms. Our new convolutional neural network admits comparable accuracy and efficiency of signal recognition compared to other deep learning works published in the literature. Based on our new CNN, we can clearly recognize the 11 confirmed gravitational-wave events included in O1 and O2. In addition, we find about 2000 gravitational-wave triggers in O1 data. [ABSTRACT FROM AUTHOR]

Deep learning algorithms, in particular neural networks, have been steadily gaining popularity among the gravitational wave community for the last few years. The reliability and accuracy of deep learning approaches in gravitational wave detection, parameter estimation, and glitch classification have already been proved and verified by several groups in recent years. In this paper, we report on the construction of the deep artificial neural network (ANN) to localize simulated gravitational wave signals in the sky with high accuracy. We have modeled the sky as a sphere and have considered cases in which the sphere is divided into 18, 50, 128, 1024, 2048, and 4096 sectors. The sky direction of the gravitational wave source is estimated by classifying the signal into one of these sectors based on its right ascension and declination values for each of these cases. To do this, we have injected simulated binary black hole gravitational wave signals of component masses sampled uniformly between 30 M⊙ and 80 M⊙ into Gaussian noise and used the whitened strain values to obtain the input features for training our ANN. We input features such as the delays in arrival times, phase differences, and amplitude ratios at each of the three detectors Hanford, Livingston, and Virgo, from the raw time-domain strain values as well as from analytical versions of these signals, obtained through Hilbert transformation. We show that our model is able to classify gravitational wave samples, not used in the training process, into their correct sectors with very high accuracy (greater than 90%) for coarse angular resolution using 18, 50, and 128 sectors. We also test our localization on test samples with injection parameters of the published LIGO binary black hole merger events GW150914, GW170818, and GW170823 for 1024, 2048, and 4096 sectors and compare the result with that from BAYESTAR and parameter estimation. In addition, we report that the time taken by our model to localize one gravitational wave signal is around 0.018 s on 14 Intel Xeon CPU cores. [ABSTRACT FROM AUTHOR]

Weak gravitational lensing is a powerful probe of the large-scale cosmic matter distribution. Wide-field galaxy surveys allow us to generate the so-called weak lensing maps, but actual observations suffer from noise due to imperfect measurement of galaxy shape distortions and to the limited number density of the source galaxies. In this paper, we explore a deep-learning approach to reduce the noise. We develop an image-to-image translation method with conditional adversarial networks (CANs), which learn efficient mapping from an input noisy weak lensing map to the underlying noise field. We train the CANs using 30000 image pairs obtained from 1000 ray-tracing simulations of weak gravitational lensing. We show that the trained CANs reproduce the true one-point probability distribution function (PDF) of the noiseless lensing map with a bias less than 1s on average, where s is the statistical error. We perform a Fisher analysis to make a forecast for cosmological parameter inference with the one-point lensing PDF. By our denoising method using CANs, the first derivative of the PDF with respect to the cosmic mean matter density and the amplitude of the primordial curvature perturbations becomes larger by ~50%. This allows us to improve the cosmological constraints by ~30%-40% using observational data from ongoing and upcoming galaxy imaging surveys. [ABSTRACT FROM AUTHOR]