اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدالاستنتاج على النظم الثنائية للأخصائي الأبيض باستخدام مجموعات البيانات الأولى لمشاريع تحديات داتا موك ليزا
Inference on white dwarf binary systems using the first round Mock LISA Data Challenges data sets
530
0
0.0
(
0
)
اسأل ChatGPT حول البحث
نحن نقدم تحليلا لمجموعات البيانات المصدر المحدد من الجولة الأولى من مهارات بيانات موك ليسا للأشبال البيضاء. أدينا برنامجا كاملا مؤلفا من وحدة معالجة موحدة معتمدة على الشبكة للكشف عن الإشارة، ووحدة معالجة ماركوف الشاملة التلقائية لتقييم الإشارة. نظرنا إلى أن الكشف عن الإشارة باستخدام طريقتنا الموحدة آمنة ودقيقة، وأنها تزداد بالدقة وتزيد مع المعلومات الإضافية على عوامل الإشارة من خلال طريقتنا ماركوف الشاملة. كما أظهرنا أن الطريقة الماركوف الشاملة قادرة على تحديد بدقة مستوى الضوضاء في نافذة التردد المعنية.
We report on the analysis of selected single source data sets from the first round of the Mock LISA Data Challenges (MLDC) for white dwarf binaries. We implemented an end-to-end pipeline consisting of a grid-based coherent pre-processing unit for signal detection, and an automatic Markov Chain Monte Carlo post-processing unit for signal evaluation. We demonstrate that signal detection with our coherent approach is secure and accurate, and is increased in accuracy and supplemented with additional information on the signal parameters by our Markov Chain Monte Carlo approach. We also demonstrate that the Markov Chain Monte Carlo routine is additionally able to determine accurately the noise level in the frequency window of interest.
قيم البحث
اقرأ أيضاً
The Mock LISA Data Challenges (MLDCs) have the dual purpose of fostering the development of LISA data analysis tools and capabilities, and demonstrating the technical readiness already achieved by the gravitational-wave community in distilling a rich
science payoff from the LISA data output. The first round of MLDCs has just been completed: nine data sets containing simulated gravitational wave signals produced either by galactic binaries or massive black hole binaries embedded in simulated LISA instrumental noise were released in June 2006 with deadline for submission of results at the beginning of December 2006. Ten groups have participated in this first round of challenges. Here we describe the challenges, summarise the results, and provide a first critical assessment of the entries.
We report on the performance of an end-to-end Bayesian analysis pipeline for detecting and characterizing galactic binary signals in simulated LISA data. Our principal analysis tool is the Blocked-Annealed Metropolis Hasting (BAM) algorithm, which ha
s been optimized to search for tens of thousands of overlapping signals across the LISA band. The BAM algorithm employs Bayesian model selection to determine the number of resolvable sources, and provides posterior distribution functions for all the model parameters. The BAM algorithm performed almost flawlessly on all the Round 1 Mock LISA Data Challenge data sets, including those with many highly overlapping sources. The only misses were later traced to a coding error that affected high frequency sources. In addition to the BAM algorithm we also successfully tested a Genetic Algorithm (GA), but only on data sets with isolated signals as the GA has yet to be optimized to handle large numbers of overlapping signals.
The Mock Data Challenges (MLDCs) have the dual purpose of fostering the development of LISA data-analysis tools and capabilities and of demonstrating the technical readiness already achieved by the gravitational-wave community in distilling a rich sc
ience payoff from the LISA data. The first round of MLDCs has just been completed and the second-round data sets are being released shortly after this workshop. The second-round data sets contain radiation from an entire Galactic population of stellar-mass binary systems, from massive--black-hole binaries, and from extreme--mass-ratio inspirals. These data sets are designed to capture much of the complexity that is expected in the actual LISA data, and should provide a fairly realistic setting to test advanced data-analysis techniques, and in particular the global aspect of the analysis. Here we describe the second round of MLDCs and provide details about its implementation.
The LISA International Science Team Working Group on Data Analysis (LIST-WG1B) is sponsoring several rounds of mock data challenges, with the purposeof fostering the development of LISA data analysis capabilities, and of demonstrating technical readi
ness for the maximum science exploitation of the LISA data. The first round of challenge data sets were released at the Sixth LISA Symposium. We briefly describe the objectives, structure, and time-line of this programme.
The F-statistic is an optimal detection statistic for continuous gravitational waves, i.e., long-duration (quasi-)monochromatic signals with slowly-varying intrinsic frequency. This method was originally developed in the context of ground-based detec
tors, but it is equally applicable to LISA where many signals fall into this class of signals. We report on the application of a LIGO/GEO F-statistic code to LISA data-analysis using the long-wavelength limit (LWL), and we present results of our search for white-dwarf binary signals in the first Mock LISA Data Challenge. Somewhat surprisingly, the LWL is found to be sufficient -- even at high frequencies -- for detection of signals and their accurate localization on the sky and in frequency, while a more accurate modelling of the TDI response only seems necessary to correctly estimate the four amplitude parameters.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
