261 research outputs found
AI-driven spatio-temporal engine for finding gravitationally lensed supernovae
We present a spatio-temporal AI framework that concurrently exploits both the
spatial and time-variable features of gravitationally lensed supernovae in
optical images to ultimately aid in the discovery of such exotic transients in
wide-field surveys. Our spatio-temporal engine is designed using recurrent
convolutional layers, while drawing from recent advances in variational
inference to quantify approximate Bayesian uncertainties via a confidence
score. Using simulated Young Supernova Experiment (YSE) images as a showcase,
we find that the use of time-series images yields a substantial gain of nearly
20 per cent in classification accuracy over single-epoch observations, with a
preliminary application to mock observations from the Legacy Survey of Space
and Time (LSST) yielding around 99 per cent accuracy. Our innovative deep
learning machinery adds an extra dimension in the search for gravitationally
lensed supernovae from current and future astrophysical transient surveys.Comment: 6+8 pages, 10 figures, 2 tables. For submission to a peer-reviewed
journal. Comments welcom
Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
We demonstrate the application of a convolutional neural network to the
gravitational wave signals from core collapse supernovae. Using simulated time
series of gravitational wave detectors, we show that based on the explosion
mechanisms, a convolutional neural network can be used to detect and classify
the gravitational wave signals buried in noise. For the waveforms used in the
training of the convolutional neural network, our results suggest that a
network of advanced LIGO, advanced VIRGO and KAGRA, or a network of LIGO A+,
advanced VIRGO and KAGRA is likely to detect a magnetorotational core collapse
supernovae within the Large and Small Magellanic Clouds, or a Galactic event if
the explosion mechanism is the neutrino-driven mechanism. By testing the
convolutional neural network with waveforms not used for training, we show that
the true alarm probabilities are 52% and 83% at 60 kpc for waveforms R3E1AC and
R4E1FC L. For waveforms s20 and SFHx at 10 kpc, the true alarm probabilities
are 70% and 93% respectively. All at false alarm probability equal to 10%
Background Rejection in Atmospheric Cherenkov Telescopes using Recurrent Convolutional Neural Networks
In this work, we present a new, high performance algorithm for background
rejection in imaging atmospheric Cherenkov telescopes. We build on the already
popular machine-learning techniques used in gamma-ray astronomy by the
application of the latest techniques in machine learning, namely recurrent and
convolutional neural networks, to the background rejection problem. Use of
these machine-learning techniques addresses some of the key challenges
encountered in the currently implemented algorithms and helps to significantly
increase the background rejection performance at all energies.
We apply these machine learning techniques to the H.E.S.S. telescope array,
first testing their performance on simulated data and then applying the
analysis to two well known gamma-ray sources. With real observational data we
find significantly improved performance over the current standard methods, with
a 20-25\% reduction in the background rate when applying the recurrent neural
network analysis. Importantly, we also find that the convolutional neural
network results are strongly dependent on the sky brightness in the source
region which has important implications for the future implementation of this
method in Cherenkov telescope analysis.Comment: 11 pages, 7 figures. To be submitted to The European Physical Journal
Search for Gravitational Waves from Core Collapse Supernovae in Ligo\u27s Observation Runs Using a Network of Detectors
Core-Collapse Supernova (CCSN) is one of the most anticipated sources of Gravitational Waves (GW) in the fourth observation run (O4) of LIGO and other network of GW detectors. A very low rate of galactic CCSN, coupled with the fact that the CCSN waveforms are unmodeled, make detection of these signals extremely challenging. Mukherjee et. al. have developed a new burst search pipeline, the Multi-Layer Signal Enhancement with cWB and CNN or MuLaSEcC, that integrates a non-parametric signal estimation and Machine Learning. MuLaSEcC operates on GW data from a network of detectors and enhances the detection probability while reducing the false alarm significantly. The aim of this research is to analyze the detection probability of CCSN during O4 and how well the signals may be reconstructed for parameter estimation. CCSN waveforms are generated in supercomputers by the implementation of complex physics. The CCSN GW waveforms used in this analysis correspond to various explosion scenarios. These are Powell and Muller s18, Scheidegger R3E1AC_L, Ott 2013_s27_fheat1d00, Mezzacappa 2020_c15_3D, Morozova 2018_M13_SFHo_multipole, Andresen 2019 s15fr, Kuroda 2016_TM1, Kuroda 2017 s11.2 and Richers 2017 A300w0_50_HSDD2. The study has demonstrated improved result in terms of reduction in the false alarm rate and broadband reconstruction of the detected signals. Efficiency of the pipeline as a function of distance has been seen to be sensitive up to the galactic range. Receiver operating characteristics have been generated to demonstrate the performance of the pipeline in comparison to other standard operating pipelines within the GW community
Image-based deep learning for classification of noise transients in gravitational wave detectors
The detection of gravitational waves has inaugurated the era of gravitational
astronomy and opened new avenues for the multimessenger study of cosmic
sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo
interferometers will probe a much larger volume of space and expand the
capability of discovering new gravitational wave emitters. The characterization
of these detectors is a primary task in order to recognize the main sources of
noise and optimize the sensitivity of interferometers. Glitches are transient
noise events that can impact the data quality of the interferometers and their
classification is an important task for detector characterization. Deep
learning techniques are a promising tool for the recognition and classification
of glitches. We present a classification pipeline that exploits convolutional
neural networks to classify glitches starting from their time-frequency
evolution represented as images. We evaluated the classification accuracy on
simulated glitches, showing that the proposed algorithm can automatically
classify glitches on very fast timescales and with high accuracy, thus
providing a promising tool for online detector characterization.Comment: 25 pages, 8 figures, accepted for publication in Classical and
Quantum Gravit
Study of efficient methods of detection and reconstruction of gravitational waves from nonrotating 3D general relativistic core collapse supernovae explosion using multilayer signal estimation method
In the post-detection era of gravitational wave (GW) astronomy, core collapse supernovae (CCSN) are one of the most interesting potential sources of signals arriving at the Advanced LIGO detectors. Mukherjee et al. have developed and implemented a new method to search for GW signals from the CCSN search based on a multistage, high accuracy spectral estimation to effectively achieve higher detection signal to noise ratio (SNR). The study has been further enhanced by incorporation of a convolutional neural network (CNN) to significantly reduce false alarm rates (FAR). The combined pipeline is termed multilayer signal estimation (MuLaSE) that works in an integrative manner with the coherent wave burst (cWB) pipeline. In order to compare the performance of this new search pipeline, termed “MuLaSECC”, with the cWB, an extensive analysis has been performed with two families of core collapse supernova waveforms corresponding to two different three dimensional (3D) general relativistic CCSN explosion models, viz. Kuroda 2017 and the Ott 2013. The performance of this pipeline has been characterized through receiver operating characteristics (ROC) and the reconstruction of the detected signals. The MuLaSECC is found to have higher efficiency in low false alarm range, a higher detection probability of weak signals and an improved reconstruction, especially in the lower frequency domain
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