اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدThe simulation framework of the timing-based localization for future all-sky gamma-ray observations with a fleet of Cubesats
75
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The timing-based localization, which utilize the triangulation principle with the different arrival time of gamma-ray photons, with a fleet of Cubesats is a unique and powerful solution for the future all-sky gamma-ray observation, which is a key for identification of the electromagnetic counterpart of the gravitational wave sources. The Cubesats Applied for MEasuring and Localising Transients (CAMELOT) mission is now being promoted by the Hungarian and Japanese collaboration with a basic concept of the nine Cubesats constellations in low earth orbit. The simulation framework for estimation of the localization capability has been developed including orbital parameters, an algorithm to estimate the expected observed profile of gamma-ray photons, finding the peak of the cross-correlation function, and a statistical method to find a best-fit position and its uncertainty. It is revealed that a degree-scale localization uncertainty can be achieved by the CAMELOT mission concept for bright short gamma-ray bursts, which could be covered by future large field of view ground-based telescopes. The new approach utilizing machine-learning approach is also investigated to make the procedure automated for the future large scale constellations. The trained neural network with 10$^6$ simulated light curves generated by the artificial short burst templates successfully predicts the time-delay of the real light curve and achieves a comparable performance to the cross-correlation algorithm with full automated procedures.
قيم البحث
اقرأ أيضاً
A sensitive survey of the MeV gamma-ray sky is needed to understand important astrophysical problems such as gamma-ray bursts in the early universe, progenitors of Type Ia supernovae, and the nature of dark matter. However, the study has not progress
ed remarkably since the limited survey by COMPTEL onboard CGRO in the 1990s. Tanimori et al. have developed a Compton camera that tracks the trajectory of each recoil electron in addition to the information obtained by the conventional Compton cameras, leading to superior imaging. This Electron Tracking Compton Camera (ETCC) facilitates accurate reconstruction of the incoming direction of each MeV photon from a wide sky at ~degree angular resolution and with minimized particle background using trajectory information. The latest ETCC model, SMILE-2+, made successful astronomical observations during a day balloon flight in 2018 April and detected diffuse continuum and 511 keV annihilation line emission from the Galactic Center region at a high significance in ~2.5 hours. We believe that MeV observations from space with upgraded ETCCs will dramatically improve our knowledge of the MeV universe. We advocate for a space-based all-sky survey mission with multiple ETCCs onboard and detail its expected benefits.
Because many of our X-ray telescopes are optimized towards observing faint sources, observations of bright sources like X-ray binaries in outburst are often affected by instrumental biases. These effects include dead time and photon pile-up, which ca
n dramatically change the statistical inference of physical parameters from these observations. While dead time is difficult to take into account in a statistically consistent manner, simulating dead time-affected data is often straightforward. This structure makes the issue of inferring physical properties from dead time-affected observations fall into a class of problems common across many scientific disciplines. There is a growing number of methods to address them under the names of Approximate Bayesian Computation (ABC) or Simulation-Based Inference (SBI), aided by new developments in density estimation and statistical machine learning. In this paper, we introduce SBI as a principled way to infer variability properties from dead time-affected light curves. We use Sequential Neural Posterior Estimation to estimate the posterior probability for variability properties. We show that this method can recover variability parameters on simulated data even when dead time is variable, and present results of an application of this approach to NuSTAR observations of the galactic black hole X-ray binary GRS 1915+105.
The All-sky Medium Energy Gamma-ray Observatory (AMEGO) is a probe class mission concept that will provide essential contributions to multimessenger astrophysics in the late 2020s and beyond. AMEGO combines high sensitivity in the 200 keV to 10 GeV e
nergy range with a wide field of view, good spectral resolution, and polarization sensitivity. Therefore, AMEGO is key in the study of multimessenger astrophysical objects that have unique signatures in the gamma-ray regime, such as neutron star mergers, supernovae, and flaring active galactic nuclei. The order-of-magnitude improvement compared to previous MeV missions also enables discoveries of a wide range of phenomena whose energy output peaks in the relatively unexplored medium-energy gamma-ray band.
The Fermi Gamma-ray Burst Monitor (GBM) has detected over 1400 Gamma-Ray Bursts (GRBs) since it began science operations in July, 2008. We use a subset of over 300 GRBs localized by instruments such as Swift, the Fermi Large Area Telescope, INTEGRAL,
and MAXI, or through triangulations from the InterPlanetary Network (IPN), to analyze the accuracy of GBM GRB localizations. We find that the reported statistical uncertainties on GBM localizations, which can be as small as 1 degree, underestimate the distance of the GBM positions to the true GRB locations and we attribute this to systematic uncertainties. The distribution of systematic uncertainties is well represented (68% confidence level) by a 3.7 degree Gaussian with a non-Gaussian tail that contains about 10% of GBM-detected GRBs and extends to approximately 14 degrees. A more complex model suggests that there is a dependence of the systematic uncertainty on the position of the GRB in spacecraft coordinates, with GRBs in the quadrants on the Y-axis better localized than those on the X-axis.
GRBAlpha is a 1U CubeSat mission with an expected launch date in the first half of 2021. It carries a 75 x 75 x 5 mm CsI(Tl) scintillator, read out by a dual-channel multi-pixel photon counter (MPPC) setup, to detect gamma-ray bursts (GRBs). The GRB
detector is an in-orbit demonstration for the detector system on the Cubesats Applied for MEasuring and LOcalising Transients (CAMELOT) mission. While GRBAlpha provides 1/8th of the expected effective area of CAMELOT, the comparison of the observed light curves with other existing GRB monitoring satellites will allow us to validate the core idea of CAMELOT, i.e. the feasibility of timing-based localization.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
