Astro-rivelatore Gamma a Immagini Leggero (AGILE) is a space mission launched in 2007 to study X-ray and gamma-ray astronomy. The AGILE team developed real-time analysis pipelines to detect transient phenomena such as gamma-ray bursts (GRBs) and react to external science alerts received by other facilities. The AGILE anticoincidence system (ACS) comprises five panels surrounding the AGILE detectors to reject background-charged particles. It can also detect hard X-ray photons in the energy range 50–200 keV. The ACS data acquisition produces a time series for each panel. The time series are merged into a single multivariate time series (MTS). We present a new deep-learning model for the detection of GRBs in the ACS data using an anomaly detection technique. The model is implemented with a convolutional neural network autoencoder architecture trained in an unsupervised manner, using a data set of MTSs randomly extracted from the AGILE ACS data. The reconstruction error of the autoencoder is used as the anomaly score to classify the MTS. We calculated the associated p-value distribution, using more than 107 background-only MTSs, to define the statistical significance of the detections. We evaluate the trained model with a list of GRBs reported by the GRBWeb catalog. The results confirm the model’s capabilities to detect GRBs in the ACS data. We will implement this method in the AGILE real-time analysis pipeline.

https://iopscience.iop.org/article/10.3847/1538-4357/acba0a/pdf

A Deep-learning Anomaly-detection Method to Identify Gamma-Ray Bursts in the Ratemeters of the AGILE Anticoincidence System

Gamma-ray bursts were discovered by the Vela satellites in the late 1960s, but they were announced for the first time exactly 50 years ago, in 1973. The history of our understanding of gamma-ray bursts can be subdivided into several eras. We will highlight the main discoveries about GRBs, as well as the path toward the future that each GRB era could still indicate.

Gamma-Ray Bursts: 50 Years and Counting!

The AGILE real-time analysis software system to detect short-transient events in the multi-messenger era

The musical beat and style recognition have high application value in music information retrieval. However, the traditional methods mostly use a convolutional neural network (CNN) as the backbone and have poor performance. Accordingly, the present work chooses a recurrent neural network (RNN) in deep learning (DL) to identify musical beats and styles. The proposed model is applied to an interactive humanoid robot. First, DL-based musical beat and style recognition technologies are studied. On this basis, a note beat recognition method combining attention mechanism (AM) and independent RNN (IndRNN) [AM-IndRNN] is proposed. The AM-IndRNN can effectively avoid gradient vanishing and gradient exploding. Second, the audio music files are divided into multiple styles using the music signal's temporal features. A human dancing robot using a multimodal drive is constructed. Finally, the proposed method is tested. The results show that the proposed AM-IndRNN outperforms multiple parallel long short-term memory (LSTM) models and IndRNN in recognition accuracy (88.9%) and loss rate (0.0748). Therefore, the AM-optimized LSTM model has gained a higher recognition accuracy. The research results provide specific ideas for applying DL technology in musical beat and style recognition.

/pdf/recognition-of-musical-beat-and-style-and-applications-in-1f1n3gor.pdf

Recognition of musical beat and style and applications in interactive humanoid robot


 To investigate gamma-ray bursts (GRBs) in depth, it is crucial to develop an effective method for identifying GRBs accurately. Current criteria, e.g., onboard blind search, ground blind search, and target search, are limited by manually set thresholds and perhaps miss GRBs, especially for subthreshold events. We proposed a novel approach that utilizes convolutional neural networks (CNNs) to distinguish GRBs and non-GRBs directly. We structured three CNN models, plain-CNN, ResNet, and ResNet-CBAM, and endeavored to exercise fusing strategy models. Count maps of NaI detectors on board Fermi/Gamma-ray Burst Monitor were employed, as the input samples of data sets and models were implemented to evaluate their performance on different timescale data. The ResNet-CBAM model trained on the 64 ms data set achieves high accuracy overall, which includes residual and attention mechanism modules. The visualization methods of Grad-CAM and t-SNE explicitly displayed that the optimal model focuses on the key features of GRBs precisely. The model was applied to analyze 1 yr data, accurately identifying approximately 98% of GRBs listed in the Fermi burst catalog, eight out of nine subthreshold GRBs, and five GRBs triggered by other satellites, which demonstrated that the deep-learning methods could effectively distinguish GRBs from observational data. Besides, thousands of unknown candidates were retrieved and compared with the bursts of SGR J1935+2154, for instance, which exemplified the potential scientific value of these candidates indeed. Detailed studies on integrating our model into real-time analysis pipelines thus may improve their accuracy of inspection and provide valuable guidance for rapid follow-up observations of multiband telescopes.

Application of Deep-learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM Time-tagged Event Data

The follow-up of external science alerts received from Gamma-Ray Bursts (GRB) and Gravitational Waves (GW) detectors is one of the AGILE Team's current major activities. The AGILE team developed an automated real-time analysis pipeline to analyse AGILE Gamma-Ray Imaging Detector (GRID) data to detect possible counterparts in the energy range 0.1-10 GeV. This work presents a new approach for detecting GRBs using a Convolutional Neural Network (CNN) to classify the AGILE-GRID intensity maps improving the GRBs detection capability over the Li&Ma method, currently used by the AGILE team. The CNN is trained with large simulated datasets of intensity maps. The AGILE complex observing pattern due to the so-called 'spinning mode' is studied to prepare datasets to test and evaluate the CNN. A GRB emission model is defined from the Second Fermi-LAT GRB catalogue and convoluted with the AGILE observing pattern. Different p-value distributions are calculated evaluating with the CNN millions of background-only maps simulated varying the background level. The CNN is then used on real data to analyse the AGILE-GRID data archive, searching for GRB detections using the trigger time and position taken from the Swift-BAT, Fermi-GBM, and Fermi-LAT GRB catalogues. From these catalogues, the CNN detects 21 GRBs with a significance $\geq 3 \sigma$, while the Li&Ma method detects only two GRBs. The results shown in this work demonstrate that the CNN is more effective in detecting GRBs than the Li&Ma method in this context and can be implemented into the AGILE-GRID real-time analysis pipeline.

A Deep Learning Method for AGILE-GRID GRB Detection

A Deep Learning Method for AGILE-GRID Gamma-Ray Burst Detection

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A Deep Learning Method for AGILE-GRID GRB Detection

A Deep Learning Method for AGILE-GRID Gamma-Ray Burst Detection