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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]