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Detection and parameter inference of gravitational-wave signals from compact mergers rely on the comparison of the incoming detector strain data d(t) to waveform templates for the gravitational-wave strain h(t) that ultimately rely on the resolution of Einstein’s equations via numerical relativity simulations. These, however, commonly output a quantity known as the Newman-Penrose scalar ψ_{4}(t) which, under the Bondi gauge, is related to the gravitational-wave strain by ψ_{4}(t)=d^{2}h(t)/dt^{2}. Therefore, obtaining strain templates involves an integration process that introduces artifacts that need to be treated in a rather manual way. By taking second-order finite differences on the detector data and inferring the corresponding background noise distribution, we develop a framework to perform gravitational-wave data analysis directly using ψ_{4}(t) templates. We first demonstrate this formalism, and the impact of integration artifacts in strain templates, through the recovery of numerically simulated signals from head-on collisions of Proca stars injected in Advanced LIGO noise. Next, we reanalyze the event GW190521 under the hypothesis of a Proca-star merger, obtaining results equivalent to those previously published [Phys. Rev. Lett. 126, 081101 (2021)PRLTAO0031-900710.1103/PhysRevLett.126.081101], where we used the classical strain framework. We find, however, that integration errors would strongly impact our analysis if GW190521 was 4 times louder. Finally, we show that our framework fixes significant biases in the interpretation of the high-mass gravitational-wave trigger S200114f arising from the usage of strain templates. We remove the need to obtain strain waveforms from numerical relativity simulations, avoiding the associated systematic errors.

Purpose: The study is intended to perform an end-to-end test of the entire intraoperative process using cadaver heads. A simulation of tumor removal was performed, followed by irradiation of the bed and measurement of absorbed doses with radiochromic films. Materials and Methods: Low-energy X-ray intraoperative radiotherapy (IORT) was used for irradiation. A computed tomography study was performed at each site and the absorbed doses calculated by the treatment planning system, as well as absorbed doses with radiochromic films, were studied. Results: The absorbed doses in the organs at risk (OAR) were evaluated in each case, obtaining maximum doses within the tolerance limits. The absorbed doses in the target were verified and the deviations were

Purpose: This study aims to minimize monitor units (MUs) of intensity-modulated treatments in the Monaco treatment planning system while preserving plan quality by optimizing the “Minimum Segment Width” (MSW) and “Fluence Smoothing” parameters. Materials and Methods: We retrospectively analyzed 30 prostate, 30 gynecological, 15 breast cancer, 10 head and neck tumor, 11 radiosurgery, and 10 hypo-fractionated plans. Original prostate plans employed “Fluence Smoothing” = Off and were reoptimized with Low, Medium, and High settings. The remaining pathologies initially used MSW = 0.5 cm and were reoptimized with MSW = 1.0 cm. Plan quality, including total MU, delivery time, and dosimetric constraints, was statistically analyzed with a paired t-test. Results: Prostate plans exhibited the highest MU variation when changing “Fluence Smoothing” from Off to High (average ΔMU = −5.1%; P < 0.001). However, a High setting may increase overall MU when MSW = 0.5 cm. Gynecological plans changed substantially when MSW increased from 0.5 cm to 1.0 cm (average ΔMU = −29%; P < 0.001). Organs at risk sparing and planning target volumes remained within 1.2% differences. Replanning other pathologies with MSW = 1.0 cm affected breast and head and neck tumor plans (average ΔMU = −168.38, average Δt = −11.74 s, and average ΔMU = −256.56, average Δt = −15.05 s, respectively; all with P < 0.004). Radiosurgery and hypofractioned highly modulated plans did not yield statistically significant results. Conclusions: In breast, pelvis, head and neck, and prostate plans, starting with MSW = 1.0 cm optimally reduces MU and treatment time without compromising plan quality. MSW has a greater impact on MU than the “Fluence Smoothing” parameter. Plans with high modulation might present divergent behavior, requiring a case-specific analysis with MSW values higher than 0.5 cm.

We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation on noisy waveforms from binary-black-hole mergers. We build datasets of 100 000 elements for each of four different waveform models (or approximants) in order to test how approximant choice affects feature extraction. Our choices include SEOBNRv4P and IMRPhenomPv3 , which contain only the dominant quadrupole emission mode, alongside IMRPhenomPv3HM and NRHybSur3dq8 , which also account for high-order modes. Each dataset element is injected into detector noise corresponding to the third observing run of the LIGO-Virgo-KAGRA (LVK) collaboration. We identify the temporal convolutional network architecture as the overall best performer in terms of training and validation losses and absence of overfitting to data. Comparison of results between datasets shows that the choice of waveform approximant for the creation of a dataset conditions the feature extraction ability of a trained network. Hence, care should be taken when building a dataset for the training of neural networks, as certain approximants may result in better network convergence of evaluation metrics. However, this performance does not necessarily translate to data which is more faithful to numerical relativity simulations. We also apply this network on actual signals from LVK runs, finding that its feature-extracting performance can be effective on real data.