Convolutional neural networks for the detection of the early inspiral of a gravitational-wave signal

peer reviewedGW170817 has led to the first example of multi-messenger astronomy with observations from gravitational wave interferometers and electromagnetic telescopes combined to characterise the source. However, detections of the early inspiral phase by the gravitational wave detectors would allow the observation of the earlier stages of the merger in the electromagnetic band, improving multi-messenger astronomy and giving access to new information. In this paper, we introduce a new machine-learning-based approach to produce early-warning alerts for an inspiraling binary neutron star system, based only on the early inspiral part of the signal. We give a proof of concept to show the possibility to use a combination of small convolutional neural networks trained on the whitened detector strain in the time domain to detect and classify early inspirals. Each of those is targeting a specific range of chirp masses dividing the binary neutron star category into three sub-classes: light, intermediate and heavy. In this work, we focus on one LIGO detector at design sensitivity and generate noise from the design power spectral density. We show that within this setup it is possible to produce an early alert up to 100 seconds before the merger for the best-case scenario. We also present some future upgrades that will enhance the detection capabilities of our convolutional neural networks. Finally, we also show that the current number of detections for a realistic binary neutron star population is comparable to that of matched filtering and that there is a high probability to detect GW170817- and GW190425-like events at design sensitivity

Transforming material relationships: 13th century revitalization of Cahokian religious-politics

The rise and eventual decline of Cahokia, the largest pre-Columbian city north of Mexico, reverberated deeply within the historical trajectories of the North American mid-continent and southeast. The 11th century emergence of this multi-ethnic, multi-vocal metropolis appears to have been deeply entangled within a social-religious movement that spread rapidly throughout the region. By A.D. 1100, however, that initial movement seems to have become highly politicized. This increased politicization occurred shortly before an outbreak of violence throughout the mid-continent around A.D. 1150. The transition from the 12th to the 13th century is marked by rapid large scale changes to spaces and objects that were part of the 12th century Cahokian religious-politics. Archaeological evidence from two thirteenth century villages in the uplands outside of Cahokia, the Olin and Copper sites, supports the supposition that these changes were intentional and targeted toward highly politicized Cahokian “elite” spaces and objects. At the same time, people maintained and/or re-integrated other practices, objects, and buildings reminiscent of the early Cahokian movement, with an increased emphasis on inclusivity. These changes suggest perhaps something akin to a revitalization movement – an intentional, material push for change – led to the return of certain religious practices, and production of their related objects, to the hands of local communities. Objects and spaces typically associated with warfare or violence, specifically fortifications, compounds, and imagery of warfare, appeared in conjunction with these changes. Given the timing and location of these materials of violence, they appear to be part of the 13th century revitalization movement in the American Bottom region. iii These two upland sites, Olin and Copper, demonstrate clearly different practices and regional relationships, indicating that people living at these sites were maintaining a certain amount of autonomy while participating within this revitalized Cahokian religious sphere. This decentralization of certain practices and material objects may have occurred at the expense of disentangling the social-political-religious relationships and obligations that may have tied these local communities to each other and to Cahokia. Furthermore, the material aspects of violence that appear during the 12th century to 13th century transformation form key elements of the so-called Southeastern Ceremonial Complex (SECC) that spread throughout the greater southeast

Convolutional neural network for gravitational-wave early alert: Going down in frequency

We present here the latest development of a machine-learning pipeline for pre-merger alerts from gravitational waves coming from binary neutron stars. This work starts from the convolutional neural networks introduced in our previous paper (PhysRevD.103.102003) that searched for three classes of early inspirals in simulated Gaussian noise colored with the design-sensitivity power-spectral density of LIGO. Our new network is able to search for any type of binary neutron stars, it can take into account all the detectors available, and it can see the events even earlier than the previous one. We study the performance of our method in three different types of noise: Gaussian O3 noise, real O3 noise, and predicted O4 noise. We show that our network performs almost as well in non-Gaussian noise as in Gaussian noise: our method is robust w.r.t. glitches and artifacts present in real noise. Although it would not have been able to trigger on the BNSs detected during O3 because their signal-to-noise ratio was too weak, we expect our network to find around 3 BNSs during O4 with a time before the merger between 3 and 88 s in advance.Comment: 11 pages, 10 figure

Convolutional neural network for gravitational-wave early alert: Going down in frequency

We present here the latest development of a machine-learning pipeline for premerger alerts from gravitational waves coming from binary neutron stars (BNSs). This work starts from the convolutional neural networks introduced in [Baltus et al., Phys. Rev. D 103, 102003 (2021)PRVDAQ2470-001010.1103/PhysRevD.103.102003] that searched for the early inspirals in simulated Gaussian noise colored with the design-sensitivity power-spectral density of LIGO. Our new network is able to search for any BNS with a chirp mass between 1 and 3 M⊙, it can take into account all the detectors available, and it can see the events even earlier than the previous one. We study the performance of our method in three different scenarios: colored Gaussian noise based on the O3 sensitivity, real O3 noise, colored Gaussian noise based on the predicted O4 sensitivity. We show that our network performs almost as well in non-Gaussian noise as in Gaussian noise: our method is robust with respect to glitches and artifacts present in real noise. Although it would not have been able to trigger on the BNSs detected during O3 because their signal-to-noise ratio was too weak, we expect our network to find around 3 BNSs during O4 with a time before the merger between 3 and 88 s in advance

Detecting the early inspiral of a gravitational-wave signal with convolutional neural networks

peer reviewedWe introduce a novel methodology for the operation of an early warning alert system for gravitational waves. It is based on short convolutional neural networks. We focus on compact binary coalescences, for light, intermediate and heavy binary-neutron-star systems. The signals are 1-dimensional time series $-$ the whitened time-strain $-$ injected in Gaussian noise built from the power-spectral density of the LIGO detectors at design sensitivity. We build short 1-dimensional convolutional neural networks to detect these types of events by training them on part of the early inspiral. We show that such networks are able to retrieve these signals from a small portion of the waveform

Convolutional neural networks for the detection of the early inspiral of a gravitational-wave signal

GW170817 has led to the first example of multimessenger astronomy with observations from gravitational wave interferometers and electromagnetic telescopes combined to characterize the source. However, detections of the early inspiral phase by the gravitational wave detectors would allow the observation of the earlier stages of the merger in the electromagnetic band, improving multimessenger astronomy and giving access to new information. In this paper, we introduce a new machine-learning-based approach to produce early-warning alerts for an inspiraling binary neutron star system, based only on the early inspiral part of the signal. We give a proof of concept to show the possibility to use a combination of small convolutional neural networks trained on the whitened detector strain in the time domain to detect and classify early inspirals. Each of those is targeting a specific range of chirp masses dividing the binary neutron star category into three subclasses: light, intermediate, and heavy. In this work, we focus on one LIGO detector at design sensitivity and generate noise from the design power spectral density. We show that within this setup it is possible to produce an early alert up to 100 seconds before the merger for the best-case scenario. We also present some future upgrades that will enhance the detection capabilities of our convolutional neural networks. Finally, we also show that the current number of detections for a realistic binary neutron star population is comparable to that of matched filtering and that there is a high probability to detect GW170817- and GW190425-like events at design sensitivity

Convolutional neural network for gravitational-wave early alert: Going down in frequency

We present here the latest development of a machine-learning pipeline for premerger alerts from gravitational waves coming from binary neutron stars (BNSs). This work starts from the convolutional neural networks introduced in [Baltus et al., Phys. Rev. D 103, 102003 (2021)PRVDAQ2470-001010.1103/PhysRevD.103.102003] that searched for the early inspirals in simulated Gaussian noise colored with the design-sensitivity power-spectral density of LIGO. Our new network is able to search for any BNS with a chirp mass between 1 and 3 M⊙, it can take into account all the detectors available, and it can see the events even earlier than the previous one. We study the performance of our method in three different scenarios: colored Gaussian noise based on the O3 sensitivity, real O3 noise, colored Gaussian noise based on the predicted O4 sensitivity. We show that our network performs almost as well in non-Gaussian noise as in Gaussian noise: our method is robust with respect to glitches and artifacts present in real noise. Although it would not have been able to trigger on the BNSs detected during O3 because their signal-to-noise ratio was too weak, we expect our network to find around 3 BNSs during O4 with a time before the merger between 3 and 88 s in advance

Convolutional neural networks for the detection of the early inspiral of a gravitational-wave signal

GW170817 has led to the first example of multimessenger astronomy with observations from gravitational wave interferometers and electromagnetic telescopes combined to characterize the source. However, detections of the early inspiral phase by the gravitational wave detectors would allow the observation of the earlier stages of the merger in the electromagnetic band, improving multimessenger astronomy and giving access to new information. In this paper, we introduce a new machine-learning-based approach to produce early-warning alerts for an inspiraling binary neutron star system, based only on the early inspiral part of the signal. We give a proof of concept to show the possibility to use a combination of small convolutional neural networks trained on the whitened detector strain in the time domain to detect and classify early inspirals. Each of those is targeting a specific range of chirp masses dividing the binary neutron star category into three subclasses: light, intermediate, and heavy. In this work, we focus on one LIGO detector at design sensitivity and generate noise from the design power spectral density. We show that within this setup it is possible to produce an early alert up to 100 seconds before the merger for the best-case scenario. We also present some future upgrades that will enhance the detection capabilities of our convolutional neural networks. Finally, we also show that the current number of detections for a realistic binary neutron star population is comparable to that of matched filtering and that there is a high probability to detect GW170817- and GW190425-like events at design sensitivity