اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدA real-time Automated Glitch Detection Pipeline at Ooty Radio Telescope
68
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Glitches are the observational manifestations of superfluidity inside neutron stars. The aim of this paper is to describe an automated glitch detection pipeline, which can alert the observers on possible real-time detection of rotational glitches in pulsars. Post alert, the pulsars can be monitored at a higher cadence to measure the post-glitch recovery phase. Two algorithms namely, Median Absolute Deviation (MAD) and polynomial regression have been explored to detect glitches in real time. The pipeline has been optimized with the help of simulated timing residuals for both the algorithms. Based on the simulations, we conclude that the polynomial regression algorithm is significantly more effective for real time glitch detection. The pipeline has been tested on a few published glitches. This pipeline is presently implemented at the Ooty Radio Telescope. In the era of upcoming large telescopes like SKA, several hundreds of pulsars will be observed regularly and such a tool will be useful for both real-time detection as well as optimal utilization of observation time for such glitching pulsars.
قيم البحث
اقرأ أيضاً
Fast Radio Bursts (FRBs) are bright enigmatic radio pulses of roughly millisecond duration that come from extragalactic distances. As part of the MeerTRAP project, we use the MeerKAT telescope array in South Africa to search for and localise those bu
rsts to high precision in real-time. We aim to pinpoint FRBs to their host galaxies and, thereby, to understand how they are created. However, the transient nature of FRBs presents various challenges, e.g. in system design, raw compute power and real-time communication, where the real-time requirements are reasonably strict (a few tens of seconds). Rapid data processing is essential for us to be able to retain high-resolution data of the bursts, to localise them, and to minimise the delay for follow-up observations. We give a short overview of the data analysis pipeline, describe the challenges faced, and elaborate on our initial design and implementation of a real-time triggering infrastructure for FRBs at the MeerKAT telescope.
Dedicated surveys using different detection pipelines are being carried out at multiple observatories to find more Fast Radio Bursts (FRBs). Understanding the efficiency of detection algorithms and the survey completeness function is important to ena
ble unbiased estimation of the underlying FRB population properties. One method to achieve end-to-end testing of the system is by injecting mock FRBs in the live data-stream and searching for them blindly. Mock FRB injection is particularly effective for machine-learning-based classifiers, for which analytic characterisation is impractical. We describe a first-of-its-kind implementation of a real-time mock FRB injection system at the upgraded Molonglo Observatory Synthesis Telescope (UTMOST) and present our results for a set of 20,000 mock FRB injections. The injections have yielded clear insight into the detection efficiencies and have provided a survey completeness function for pulse width, fluence and DM. Mock FRBs are recovered with uniform efficiency over the full range of injected DMs, however the recovery fraction is found to be a strong function of the width and Signal-to-Noise (SNR). For low widths ($lesssim 20$ ms) and high SNR ($gtrsim$ 9) the recovery is highly effective with recovery fractions exceeding 90%. We find that the presence of radio frequency interference causes the recovered SNR values to be systematically lower by up to 20% compared to the injected values. We find that wider FRBs become increasingly hard to recover for the machine-learning-based classifier employed at UTMOST. We encourage other observatories to implement live injection set-ups for similar testing of their surveys.
We demonstrate a new technique for detecting radio transients based on interferometric closure quantities. The technique uses the bispectrum, the product of visibilities around a closed-loop of baselines of an interferometer. The bispectrum is calibr
ation independent, resistant to interference, and computationally efficient, so it can be built into correlators for real-time transient detection. Our technique could find celestial transients anywhere in the field of view and localize them to arcsecond precision. At the Karl G. Jansky Very Large Array (VLA), such a system would have a high survey speed and a 5-sigma sensitivity of 38 mJy on 10 ms timescales with 1 GHz of bandwidth. The ability to localize dispersed millisecond pulses to arcsecond precision in large volumes of interferometer data has several unique science applications. Localizing individual pulses from Galactic pulsars will help find X-ray counterparts that define their physical properties, while finding host galaxies of extragalactic transients will measure the electron density of the intergalactic medium with a single dispersed pulse. Exoplanets and active stars have distinct millisecond variability that can be used to identify them and probe their magnetospheres. We use millisecond time scale visibilities from the Allen Telescope Array (ATA) and VLA to show that the bispectrum can detect dispersed pulses and reject local interference. The computational and data efficiency of the bispectrum will help find transients on a range of time scales with next-generation radio interferometers.
The Geostationary Lightning Mapper (GLM) instrument onboard the GOES 16 and 17 satellites has been shown to be capable of detecting bolides (bright meteors) in Earths atmosphere. Due to its large, continuous field of view and immediate public data av
ailability, GLM provides a unique opportunity to detect a large variety of bolides, including those in the 0.1 to 3 m diameter range and complements current ground-based bolide detection systems, which are typically sensitive to smaller events. We present a machine learning-based bolide detection and light curve generation pipeline being developed at NASA Ames Research Center as part of NASAs Asteroid Threat Assessment Project (ATAP). The ultimate goal is to generate a large catalog of calibrated bolide lightcurves to provide an unprecedented data set which will be used to inform meteor entry models on how incoming bodies interact with the Earths atmosphere and to infer the pre-entry properties of the impacting bodies. The data set will also be useful for other asteroidal studies. This paper reports on the progress of the first part of this ultimate goal, namely, the automated bolide detection pipeline. Development of the training set, ML model training and iterative improvements in detection performance are presented. The pipeline runs in an automated fashion and bolide lightcurves along with other measured properties are promptly published on a NASA hosted publicly accessible website, https://neo-bolide.ndc.nasa.gov.
A major challenge in modern radio astronomy is dealing with the massive data volumes generated by wide-bandwidth receivers. Such massive data rates are often too great for a single device to cope, and so processing must be split across multiple devic
es working in parallel. These devices must work in unison to process incoming data in real time, reduce the data volume to a manageable size, and output a science-ready data product. The aim of this chapter is to give a broad overview of how digital systems for radio telescopes are commonly implemented, with a focus on real-time stream processing over multiple compute devices.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
