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Register a new userA machine learning approach for GRB detection in AstroSat CZTI data
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We present a machine learning (ML) based method for automated detection of Gamma-Ray Burst (GRB) candidate events in the range 60 keV - 250 keV from the AstroSat Cadmium Zinc Telluride Imager data. We use density-based spatial clustering to detect excess power and carry out an unsupervised hierarchical clustering across all such events to identify the different light curves present in the data. This representation helps understand the instruments sensitivity to the various GRB populations and identify the major non-astrophysical noise artefacts present in the data. We use Dynamic Time Warping (DTW) to carry out template matching, which ensures the morphological similarity of the detected events with known typical GRB light curves. DTW alleviates the need for a dense template repository often required in matched filtering like searches. The use of a similarity metric facilitates outlier detection suitable for capturing previously unmodelled events. We briefly discuss the characteristics of 35 long GRB candidates detected using the pipeline and show that with minor modifications such as adaptive binning, the method is also sensitive to short GRB events. Augmenting the existing data analysis pipeline with such ML capabilities alleviates the need for extensive manual inspection, enabling quicker response to alerts received from other observatories such as the gravitational-wave detectors.
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We present spectro-polarimetric analysis of thisgrb using data from asat, fermi, and swift, to provide insights into the physical mechanisms of the prompt radiation and the jet geometry. Prompt emission from thisgrb was very bright (fluence $>10^{-4}$~ergs~cm$^{-2}$) and had a complex structure composed of the superimposition of several pulses. The energy spectra deviate from the typical Band function to show a low energy peak $sim 15$~keV --- which we interpret as a power-law with two breaks, with a synchrotron origin. Alternately, the prompt spectra can also be interpreted as Comptonized emission, or a blackbody combined with a Band function. Time-resolved analysis confirms the presence of the low energy component, while the peak energy is found to be confined in the range of 100--200~keV. Afterglow emission detected by fermi-LAT is typical of an external shock model, and we constrain the initial Lorentz factor using the peak time of the emission. swift-XRT measurements of the afterglow show an indication for a jet break, allowing us to constrain the jet opening angle to $>$ 6$degr$. Detection of a large number of Compton scattered events by asat-CZTI provides an opportunity to study hard X-ray polarization of the prompt emission. We find that the burst has high, time-variable polarization, with the emission {bf have higher polarization} at energies above the peak energy. We discuss all observations in the context of GRB models and polarization arising due to {bf due to physical or geometric effects:} synchrotron emission from multiple shocks with ordered or random magnetic fields, Poynting flux dominated jet undergoing abrupt magnetic dissipation, sub-photospheric dissipation, a jet consisting of fragmented fireballs, and the Comptonization model.
Cadmium-Zinc-Telluride Imager (CZTI) is one of the five payloads on-board recently launched Indian astronomy satellite AstroSat. CZTI is primarily designed for simultaneous hard X-ray imaging and spectroscopy of celestial X-ray sources. It employs the technique of coded mask imaging for measuring spectra in the energy range of 20 - 150 keV. It was the first scientific payload of AstroSat to be switched on after one week of the launch and was made operational during the subsequent week. Here we present preliminary results from the performance verification phase observations and discuss the in-orbit performance of CZTI.
AstroSat is Indias first space-based astronomical observatory, launched on September 28, 2015. One of the payloads aboard AstroSat is the Cadmium Zinc Telluride Imager (CZTI), operating at hard X-rays. CZTI employs a two-dimensional coded aperture mask for the purpose of imaging. In this paper, we discuss various image reconstruction algorithms adopted for the test and calibration of the imaging capability of CZTI and present results from CZTI on-ground as well as in-orbit image calibration.
The Cadmium Zinc Telluride Imager (CZTI) onboard AstroSat is designed for hard X-ray imaging and spectroscopy in the energy range of 20 - 100 keV. The CZT detectors are of 5 mm thickness and hence have good efficiency for Compton interactions beyond 100 keV. The polarisation analysis using CZTI relies on such Compton events and have been verified experimentally. The same Compton events can also be used to extend the spectroscopy up to 380 keV. Further, it has been observed that about 20% pixels of the CZTI detector plane have low gain, and they are excluded from the primary spectroscopy. If these pixels are included, then the spectroscopic capability of CZTI can be extended up to 500 keV and further up to 700 keV with a better gain calibration in the future. Here we explore the possibility of using the Compton events as well as the low gain pixels to extend the spectroscopic energy range of CZTI for ON-axis bright X-ray sources. We demonstrate this technique using Crab observations and explore its sensitivity.
The main goal of The Extreme Universe Space Observatory on a Super Pressure Balloon (EUSO-SPB1) was to observe from above extensive air showers caused by ultra-high energy cosmic rays. EUSO-SPB1 uses a fluorescence detector that observes the atmosphere in a nadir observation mode from a near space altitude. During the 12-day flight, an onboard first level trigger detected more than um{175000} candidate events. This paper presents an approach to recognize air showers in this dataset. The approach uses a feature extraction method to create a simpler representation of an event and then it uses established machine learning techniques to classify data into at least two classes - shower and noise. The machine learning models are trained on a set of air shower simulations put on top of the background observed during the flight and a set of events from the flight. We present the efficiency of the method on datasets of simulated events. The flight data events are also used in unsupervised learning methods to identify groups of events with similar features. The presented methods allow us to shorten the candidate events list and, thanks to the groups of similar events identified by the unsupervised methods, the classification of the triggered events is made simpler.
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