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Register a new userDeep learning detection of transients
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Iftach Sadeh
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The next generation of observatories will facilitate the discovery of new types of astrophysical transients. The detection of such phenomena, whose characteristics are presently poorly constrained, will hinge on the ability to perform blind searches. We present a new algorithm for this purpose, based on deep learning. We incorporate two approaches, utilising anomaly detection and classification techniques. The first is model-independent, avoiding the use of background modelling and instrument simulations. The second method enables targeted searches, relying on generic spectral and temporal patterns as input. We compare our methodology with the existing approach to serendipitous detection of gamma-ray transients. The algorithm is shown to be more robust, especially for non-trivial spectral features. We use our framework to derive the detection prospects of low-luminosity gamma-ray bursts with the upcoming Cherenkov Telescope Array. Our method is an unbiased, completely data-driven approach for multiwavelength and multi-messenger transient detection.
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The next generation of observatories will facilitate the discovery of new types of astrophysical transients. The detection of such phenomena, whose characteristics are presently poorly constrained, will hinge on the ability to perform blind searches. We present a new algorithm for this purpose, based on deep learning. We incorporate two approaches, utilising anomaly detection and classification techniques. The first is model-independent, avoiding the use of background modelling and instrument simulations. The second method enables targeted searches, relying on generic spectral and temporal patterns as input. We compare our methodology with the existing approach to serendipitous detection of gamma-ray transients. We use our framework to derive the detection prospects of low-luminosity gamma-ray bursts with the upcoming Cherenkov Telescope Array. Our method is an unbiased, data-driven approach for multiwavelength and multi-messenger transient detection.
Ground-based $gamma$-ray observatories, such as the VERITAS array of imaging atmospheric Cherenkov telescopes, provide insight into very-high-energy (VHE, $mathrm{E}>100,mathrm{GeV}$) astrophysical transient events. Examples include the evaporation of primordial black holes, gamma-ray bursts and flaring blazars. Identifying such events with a serendipitous location and time of occurrence is difficult. Thus, employing a robust search method becomes crucial. An implementation of a transient detection method based on deep-learning techniques for VERITAS will be presented. This data-driven approach significantly reduces the dependency on the characterization of the instrument response and the modelling of the expected transient signal. The response of the instrument is affected by various factors, such as the elevation of the source and the night sky background. The study of these effects allows enhancing the deep learning method with additional parameters to infer their influences on the data. This improves the performance and stability for a wide range of observational conditions. We illustrate our method for an historic flare of the blazar BL Lac that was detected by VERITAS in October 2016. We find a promising performance for the detection of such a flare in timescales of minutes that compares well with the VERITAS standard analysis.
The primary challenge in the study of explosive astrophysical transients is their detection and characterisation using multiple messengers. For this purpose, we have developed a new data-driven discovery framework, based on deep learning. We demonstrate its use for searches involving neutrinos, optical supernovae, and gamma rays. We show that we can match or substantially improve upon the performance of state-of-the-art techniques, while significantly minimising the dependence on modelling and on instrument characterisation. Particularly, our approach is intended for near- and real-time analyses, which are essential for effective follow-up of detections. Our algorithm is designed to combine a range of instruments and types of input data, representing different messengers, physical regimes, and temporal scales. The methodology is optimised for agnostic searches of unexpected phenomena, and has the potential to substantially enhance their discovery prospects.
There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory. From the haystack of potential science targets, astronomers must allocate scarce resources to study a selection of needles in real time. Here we present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events using 1% of the PLAsTiCC dataset to train the autoencoder. Our unsupervised method uniquely works with unlabeled, real time, multivariate and aperiodic data. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. We find that our pipeline successfully ranks rarer classes of transients as more anomalous. Using simple cuts in anomaly score and uncertainty, we identify a pure (~95% pure) sample of rare transients (i.e., transients other than Type Ia, Type II and Type Ibc supernovae) including superluminous and pair-instability supernovae. Finally, our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow up studies in the era of the Rubin Observatory.
The follow-up of external science alerts received from Gamma-Ray Bursts (GRB) and Gravitational Waves (GW) detectors is one of the AGILE Teams 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.
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