In searching for continuous gravitational waves over very many (≈1017) templates, clustering is a powerful tool which increases the search sensitivity by identifying and bundling together candidates ...that are due to the same root cause. We implement a deep learning network that identifies clusters of signal candidates in the output of continuous gravitational wave searches and assess its performance. For loud signals, our network achieves a detection efficiency higher than 97% with a very low false alarm rate and maintains a reasonable detection efficiency for signals with lower amplitudes, i.e., at ≲ current upper limit values.
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.
Rapidly rotating neutron stars are promising sources of continuous gravitational wave radiation for the LIGO and Virgo interferometers. The majority of neutron stars in our galaxy have not been ...identified with electromagnetic observations. All-sky searches for isolated neutron stars offer the potential to detect gravitational waves from these unidentified sources. The parameter space of these blind all-sky searches, which also cover a large range of frequencies and frequency derivatives, presents a significant computational challenge. Different methods have been designed to perform these searches within acceptable computational limits. Here we describe the first benchmark in a project to compare the search methods currently available for the detection of unknown isolated neutron stars. The five methods compared here are individually referred to as the PowerFlux, sky Hough, frequency Hough, Einstein@Home, and time domain F-statistic methods. We employ a mock data challenge to compare the ability of each search method to recover signals simulated assuming a standard signal model. We find similar performance among the four quick-look search methods, while the more computationally intensive search method, Einstein@Home, achieves up to a factor of two higher sensitivity. We find that the absence of a second derivative frequency in the search parameter space does not degrade search sensitivity for signals with physically plausible second derivative frequencies. We also report on the parameter estimation accuracy of each search method, and the stability of the sensitivity in frequency and frequency derivative and in the presence of detector noise.
The inclusive hadroproduction of a Higgs boson and of a jet, featuring large transverse momenta and well separated in rapidity, is proposed as a novel probe channel for the manifestation of the ...Balitsky–Fadin–Kuraev–Lipatov (BFKL) dynamics. Using the standard BFKL approach, with partial inclusion of next-to-leading order effects, predictions are presented for azimuthal Higgs-jet correlations and other observables, to be possibly compared with experimental analyses at the LHC and with theoretical predictions obtained in different schemes.
All-sky surveys for isolated continuous gravitational waves present a significant data-analysis challenge. Semicoherent search methods are commonly used to efficiently perform the ...computationally-intensive task of searching for these weak signals in the noisy data of gravitational-wave detectors such as LIGO and Virgo. We present a new implementation of a semicoherent search method, weave, that for the first time makes full use of a parameter-space metric to generate banks of search templates at the correct resolution, combined with optimal lattices to minimize the required number of templates and hence the computational cost of the search. We describe the implementation of weave and associated design choices and characterize its behavior using semianalytic models.
Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis ...and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural netwoks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.
•A novel feature selection algorithm based on Bat Algorithm and Optimum-Path Forest.•A comparison against different transfer functions for agent’s positioning.•Several datasets have been employed to ...the experimental section.
Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness.
Abstract
We present the results of an all-sky search for continuous gravitational waves in the public LIGO O3 data. The search covers signal frequencies 20.0 Hz ≤
f
≤ 800.0 Hz and a spin-down range ...down to −2.6 × 10
−9
Hz s
−1
, motivated by detectability studies on synthetic populations of Galactic neutron stars. This search is the most sensitive all-sky search to date in this frequency/spin-down region. The initial search was performed using the first half of the public LIGO O3 data (O3a), utilizing graphical processing units provided in equal parts by the volunteers of the Einstein@Home computing project and by the ATLAS cluster. After a hierarchical follow-up in seven stages, 12 candidates remain. Six are discarded at the eighth stage, by using the remaining O3 LIGO data (O3b). The surviving six can be ascribed to continuous-wave fake signals present in the LIGO data for validation purposes. We recover these fake signals with very high accuracy with our last stage search, which coherently combines all O3 data. Based on our results, we set upper limits on the gravitational-wave amplitude
h
0
and translate these into upper limits on the neutron star ellipticity and on the
r
-mode amplitude. The most stringent upper limits are at 203 Hz, with
h
0
= 8.1 × 10
−26
at the 90% confidence level. Our results exclude isolated neutron stars rotating faster than 5 ms with ellipticities greater than
5
×
10
−
8
d
100
pc
within a distance
d
from Earth and
r
-mode amplitudes
α
≥
10
−
5
d
100
pc
for neutron stars spinning faster than 150 Hz.
Abstract
We conduct an all-sky search for continuous gravitational waves in the LIGO O2 data from the Hanford and Livingston detectors. We search for nearly monochromatic signals with frequency ...20.0 Hz ≤
f
≤ 585.15 Hz and spin-down
Hz s
−1
. We deploy the search on the Einstein@Home volunteer-computing project and follow-up the waveforms associated with the most significant results with eight further search stages, reaching the best sensitivity ever achieved by an all-sky survey up to 500 Hz. Six of the inspected waveforms pass all the stages but they are all associated with hardware injections, which are fake signals simulated at the LIGO detector for validation purposes. We recover all these fake signals with consistent parameters. No other waveform survives, so we find no evidence of a continuous gravitational wave signal at the detectability level of our search. We constrain the
h
0
amplitude of continuous gravitational waves at the detector as a function of the signal frequency, in half-Hz bins. The most constraining upper limit at 163.0 Hz is
h
0
= 1.3 × 10
−25
, at the 90% confidence level. Our results exclude neutron stars rotating faster than 5 ms with equatorial ellipticities larger than 10
−7
closer than 100 pc. These are deformations that neutron star crusts could easily support, according to some models.
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