اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدImproving the Data Quality of Advanced LIGO Based on Early Engineering Run Results
59
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) detectors have completed their initial upgrade phase and will enter the first observing run in late 2015, with detector sensitivity expected to improve in future runs. Through the combined efforts of on-site commissioners and the Detector Characterization group of the LIGO Scientific Collaboration, interferometer performance, in terms of data quality, at both LIGO observatories has vastly improved from the start of commissioning efforts to present. Advanced LIGO has already surpassed Enhanced LIGO in sensitivity, and the rate of noise transients, which would negatively impact astrophysical searches, has improved. Here we give details of some of the work which has taken place to better the quality of the LIGO data ahead of the first observing run.
قيم البحث
اقرأ أيضاً
Cosmic strings are topological defects which can be formed in GUT-scale phase transitions in the early universe. They are also predicted to form in the context of string theory. The main mechanism for a network of Nambu-Goto cosmic strings to lose en
ergy is through the production of loops and the subsequent emission of gravitational waves, thus offering an experimental signature for the existence of cosmic strings. Here we report on the analysis conducted to specifically search for gravitational-wave bursts from cosmic string loops in the data of Advanced LIGO 2015-2016 observing run (O1). No evidence of such signals was found in the data, and as a result we set upper limits on the cosmic string parameters for three recent loop distribution models. In this paper, we initially derive constraints on the string tension $Gmu$ and the intercommutation probability, using not only the burst analysis performed on the O1 data set, but also results from the previously published LIGO stochastic O1 analysis, pulsar timing arrays, cosmic microwave background and Big-Bang nucleosynthesis experiments. We show that these data sets are complementary in that they probe gravitational waves produced by cosmic string loops during very different epochs. Finally, we show that the data sets exclude large parts of the parameter space of the three loop distribution models we consider.
The first observing run of Advanced LIGO spanned 4 months, from September 12, 2015 to January 19, 2016, during which gravitational waves were directly detected from two binary black hole systems, namely GW150914 and GW151226. Confident detection of g
ravitational waves requires an understanding of instrumental transients and artifacts that can reduce the sensitivity of a search. Studies of the quality of the detector data yield insights into the cause of instrumental artifacts and data quality vetoes specific to a search are produced to mitigate the effects of problematic data. In this paper, the systematic removal of noisy data from analysis time is shown to improve the sensitivity of searches for compact binary coalescences. The output of the PyCBC pipeline, which is a python-based code package used to search for gravitational wave signals from compact binary coalescences, is used as a metric for improvement. GW150914 was a loud enough signal that removing noisy data did not improve its significance. However, the removal of data with excess noise decreased the false alarm rate of GW151226 by more than two orders of magnitude, from 1 in 770 years to less than 1 in 186000 years.
We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 data set. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks and, for the first
time, kink-kink collisions.cA template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension, $Gmu$, as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models.cAdditionally, we develop and test a third model which interpolates between these two models. Our results improve upon the previous LIGO-Virgo constraints on $Gmu$ by one to two orders of magnitude depending on the model which is tested. In particular, for one loop distribution model, we set the most competitive constraints to date, $Gmulesssim 4times 10^{-15}$.
Advanced LIGO and Advanced Virgo are actively monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their firs
t and second observing runs. The main data products are the gravitational-wave strain arrays, released as time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software.
Accurate parameter estimation is key to maximizing the scientific impact of gravitational-wave astronomy. Parameters of a binary merger are typically estimated using Bayesian inference. It is necessary to make several assumptions when doing so, one o
f which is that the the detectors output stationary Gaussian noise. We test the validity of these assumptions by performing percentile-percentile tests in both simulated Gaussian noise and real detector data in the first observing run of Advanced LIGO (O1). We add simulated signals to 512s of data centered on each of the three events detected in O1 -- GW150914, GW151012, and GW151226 -- and check that the recovered credible intervals match statistical expectations. We find that we are able to recover unbiased parameter estimates in the real detector data, indicating that the assumption of Gaussian noise does not adversely effect parameter estimates. However, we also find that both the parallel-tempered sampler emcee_pt and the nested sampler dynesty struggle to produced unbiased parameter estimates for GW151226-like signals, even in simulated Gaussian noise. The emcee_pt sampler does produce unbiased estimates for GW150914-like signals. This highlights the importance of performing percentile-percentile tests in different targeted areas of parameter space.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
