188 research outputs found
Matching matched filtering with deep networks in gravitational-wave astronomy
We report on the construction of a deep convolutional neural network that can
reproduce the sensitivity of a matched-filtering search for binary black hole
gravitational-wave signals. The standard method for the detection of well
modeled transient gravitational-wave signals is matched filtering. However, the
computational cost of such searches in low latency will grow dramatically as
the low frequency sensitivity of gravitational-wave detectors improves.
Convolutional neural networks provide a highly computationally efficient method
for signal identification in which the majority of calculations are performed
prior to data taking during a training process. We use only whitened time
series of measured gravitational-wave strain as an input, and we train and test
on simulated binary black hole signals in synthetic Gaussian noise
representative of Advanced LIGO sensitivity. We show that our network can
classify signal from noise with a performance that emulates that of match
filtering applied to the same datasets when considering the sensitivity defined
by Reciever-Operator characteristics.Comment: 5 pages, 3 figures, submitted to PR
Comparing short gamma-ray burst jet structure models
A structured gamma-ray burst (GRB) jet could explain the dimness of the prompt emission observed from GRB 170817A, but the exact form of this structure is still ambiguous. However, with the promise of future joint gravitational wave (GW) and GRB observations, we shall be able to examine populations of binary neutron star (BNS) mergers rather than on a case-by-case basis. We present an analysis that considers GW triggered BNS events both with and without short GRB counterparts assuming that events without a counterpart were observed off-axis. This allows for Bayes factors to be calculated to compare different jet structure models. We perform model comparison between a Gaussian and power-law apparent jet structure on simulated data to demonstrate that the correct model can be distinguished with a log Bayes factor of >5 after fewer than 100 events. Constraints on the apparent structure jet model parameters are also made. After 25(100) events the angular width of the core of a power-law jet structure can be constrained within a 90% credible interval of width ~9.1(4.4)°, and the outer beaming angle to be within ~19.9(8.5)°. Similarly, we show the width of a Gaussian jet structure to be constrained to ~2.8(1.6)°
Rosemary powder filtrate improves the oxidative stability and antioxidant properties of rapeseed oil : potential applications for domestic cooking
The authors would like to thank the Gen Foundation (London, UK) for providing funding to support this research.Peer reviewedPostprin
NNETFIX: An artificial neural network-based denoising engine for gravitational-wave signals
Instrumental and environmental transient noise bursts in gravitational-wave
detectors, or glitches, may impair astrophysical observations by adversely
affecting the sky localization and the parameter estimation of
gravitational-wave signals. Denoising of detector data is especially relevant
during low-latency operations because electromagnetic follow-up of candidate
detections requires accurate, rapid sky localization and inference of
astrophysical sources. NNETFIX is a machine learning-based algorithm designed
to remove glitches detected in coincidence with transient gravitational-wave
signals. NNETFIX uses artificial neural networks to estimate the portion of the
data lost due to the presence of the glitch, which allows the recalculation of
the sky localization of the astrophysical signal. The sky localization of the
denoised data may be significantly more accurate than the sky localization
obtained from the original data or by removing the portion of the data impacted
by the glitch. We test NNETFIX in simulated scenarios of binary black hole
coalescence signals and discuss the potential for its use in future low-latency
LIGO-Virgo-KAGRA searches. In the majority of cases for signals with a high
signal-to-noise ratio, we find that the overlap of the sky maps obtained with
the denoised data and the original data is better than the overlap of the sky
maps obtained with the original data and the data with the glitch removed.Comment: 26 pages, 10 figures, 10 table
Placental Growth Factor: A review of literature and future applications
Placental growth factor is an angiogenic protein, highly expressed during pregnancy, which correlates well with placental function. In this review, we highlight the origin, structure and function of Placental Growth Factor and its receptors. We discuss how their pro-angiogenic/anti-angiogenic synergism is critical for successful placentation and how their imbalance may be utilised as a diagnostic marker of disease or a potential therapeutic target for adverse pregnancy outcomes
Unpacking merger jets: a Bayesian analysis of GW170817, GW190425 and electromagnetic observations of short gamma-ray bursts
We present a novel fully Bayesian analysis to constrain short gamma-ray burst (sGRB) jet structures associated with cocoon, wide-angle, and simple top-hat jet models, as well as the binary neutron star (BNS) merger rate. These constraints are made given the distance and inclination information from GW170817, observed flux of GRB 170817A, observed rate of sGRBs detected by Swift, and the neutron star merger rate inferred from LIGO's first and second observing runs. A separate analysis is conducted where a fitted sGRB luminosity function is included to provide further constraints. The jet structure models are further constrained using the observation of GW190425, and we find that the assumption that it produced a GRB 170817–like sGRB which went undetected due to the jet geometry is consistent with previous observations. We find and quantify evidence for low-luminosity and wide-angle jet structuring in the sGRB population, independently from afterglow observations, with log Bayes factors of 0.45–0.55 for such models when compared to a classical top-hat jet. Slight evidence is found for a Gaussian jet structure model over all others when the fitted luminosity function is provided, producing log Bayes factors of 0.25–0.9 ± 0.05 when compared to the other models. However, without considering GW190425 or the fitted luminosity function, the evidence favors a cocoon-like model with log Bayes factors of 0.14 ± 0.05 over the Gaussian jet structure. We provide new constraints to the BNS merger rates of 1–1300 Gpc⁻³ yr⁻¹ or 2–680 Gpc⁻³ yr⁻¹ when a fitted luminosity function is assumed
Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder
The discovery of the optical counterpart, along with the gravitational waves
from GW170817, of the first binary neutron star merger, opened up a new era for
multi-messenger astrophysics. Combining the GW data with the optical
counterpart, also known as AT2017gfo, classified as a kilonova, has revealed
the nature of compact binary merging systems by extracting enriched information
about the total binary mass, the mass ratio, the system geometry, and the
equation of state. Even though the detection of kilonova brought about a
revolution in the domain of multi-messenger astronomy, since there has been
only one kilonova from a gravitational wave detected binary neutron star merger
event so far, this limits the exact understanding of the origin and propagation
of the kilonova. Here, we use a conditional variational autoencoder trained on
light curve data from two kilonova models having different temporal lengths,
and consequently, generate kilonova light curves rapidly based on physical
parameters of our choice with good accuracy. Once trained, the time scale for
light curve generation is of the order of a few milliseconds, thus speeding up
generating light curves by times compared to the simulation. The mean
squared error between the generated and original light curves is typically
with a maximum of for each set of considered physical parameter;
while having a maximum of error across the whole parameter space.
Hence, implementing this technique provides fast and reliably accurate results.Comment: 19 pages, 7 figures (3 additional figures in appendix), accepted to
Ap
A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties
In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten physical parameters are incorporated in the model. With such a complex model, likelihood evaluation for each Monte Carlo sampling point becomes a massive task and requires a significant amount of computational power. In this work, we perform quick parameter estimation on simulated GRB X-ray light curves using an interpolated physical GRB model. This is achieved by generating a grid of GRB afterglow light curves across the parameter space and replacing the likelihood with a simple interpolation function in the high-dimensional grid that stores all light curves. This framework, compared to the original method, leads to a ∼90× speedup per likelihood estimation. It will allow us to explore different jet models and enable fast model comparison in the future
Inclination estimates from off-axis GRB afterglow modelling
For gravitational wave (GW) detected neutron star mergers, one of the leading candidates for electromagnetic (EM) counterparts is the afterglow from an ultra-relativistic jet. Where this afterglow is observed, it will likely be viewed off-axis, such as the afterglow following GW170817/GRB 170817A. The temporal behaviour of an off-axis observed GRB afterglow can be used to reveal the lateral jet structure, and statistical model fits can put constraints on the various model free-parameters. Amongst these parameters is the inclination of the system to the line of sight. Along with the GW detection, the afterglow modelling provides the best constraint on the inclination to the line-of-sight and can improve the estimates of cosmological parameters, for example, the Hubble constant, from GW-EM events. However, modelling of the afterglow depends on the assumed jet structure and—often overlooked—the effects of lateral spreading. Here we show how the inclusion of lateral spreading in the afterglow models can affect the estimated inclination of GW-EM events
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