A new method to study energy-dependent arrival delays on photons from astrophysical sources

Correlations between the arrival time and the energy of photons emitted in outbursts of astrophysical objects are predicted in quantum and classical gravity scenarios and can appear as well as a result of complex acceleration mechanisms responsible for the photon emission at the source. This paper presents a robust method to study such correlations that overcomes some limitations encountered in previous analysis, and is based on a Likelihood function built from the physical picture assumed for the emission, propagation and detection of the photons. The results of the application of this method to a flare of Markarian 501 observed by the MAGIC telescope are presented. The method is also applied to a simulated dataset based on the flare of PKS 2155-304 recorded by the H.E.S.S. observatory to proof its applicability to complex photon arrival time distributions.Comment: 18 pages, 7 figure

Roaming Real-Time Applications - Mobility Services in IPv6 Networks

Emerging mobility standards within the next generation Internet Protocol, IPv6, promise to continuously operate devices roaming between IP networks. Associated with the paradigm of ubiquitous computing and communication, network technology is on the spot to deliver voice and videoconferencing as a standard internet solution. However, current roaming procedures are too slow, to remain seamless for real-time applications. Multicast mobility still waits for a convincing design. This paper investigates the temporal behaviour of mobile IPv6 with dedicated focus on topological impacts. Extending the hierarchical mobile IPv6 approach we suggest protocol improvements for a continuous handover, which may serve bidirectional multicast communication, as well. Along this line a multicast mobility concept is introduced as a service for clients and sources, as they are of dedicated importance in multipoint conferencing applications. The mechanisms introduced do not rely on assumptions of any specific multicast routing protocol in use.Comment: 15 pages, 5 figure

Superluminal neutrinos in long baseline experiments and SN1987a

Precise tests of Lorentz invariance in neutrinos can be performed using long baseline experiments such as MINOS and OPERA or neutrinos from astrophysical sources. The MINOS collaboration reported a measurement of the muonic neutrino velocities that hints to super-luminal propagation, very recently confirmed at 6 sigma by OPERA. We consider a general parametrisation which goes beyond the usual linear or quadratic violation considered in quantum-gravitational models. We also propose a toy model showing why Lorentz violation can be specific to the neutrino sector and give rise to a generic energy behaviour E^alpha, where alpha is not necessarily an integer number. Supernova bounds and the preferred MINOS and OPERA regions show a tension, due to the absence of shape distortion in the neutrino bunch in the far detector of MINOS. The energy independence of the effect has also been pointed out by the OPERA results.Comment: 22 pages, 7 figures; comment on Cherenkov emission added, version matching JHEP published pape

Cosmology with the lights off: Standard sirens in the Einstein Telescope era

We explore the prospects for constraining cosmology using gravitational-wave (GW) observations of neutron-star binaries by the proposed Einstein Telescope (ET), exploiting the narrowness of the neutron-star mass function. Double neutron-star (DNS) binaries are expected to be one of the first sources detected after "first-light" of Advanced LIGO and are expected to be detected at a rate of a few tens per year in the advanced era. However the proposed ET could catalog tens of thousands per year. Combining the measured source redshift distributions with GW-network distance determinations will permit not only the precision measurement of background cosmological parameters, but will provide an insight into the astrophysical properties of these DNS systems. Of particular interest will be to probe the distribution of delay times between DNS-binary creation and subsequent merger, as well as the evolution of the star-formation rate density within ET's detection horizon. Keeping H_0, \Omega_{m,0} and \Omega_{\Lambda,0} fixed and investigating the precision with which the dark-energy equation-of-state parameters could be recovered, we found that with 10^5 detected DNS binaries we could constrain these parameters to an accuracy similar to forecasted constraints from future CMB+BAO+SNIa measurements. Furthermore, modeling the merger delay-time distribution as a power-law, and the star-formation rate (SFR) density as a parametrized version of the Porciani and Madau SF2 model, we find that the associated astrophysical parameters are constrained to within ~ 10%. All parameter precisions scaled as 1/sqrt(N), where N is the number of cataloged detections. We also investigated how precisions varied with the intrinsic underlying properties of the Universe and with the distance reach of the network (which may be affected by the low-frequency cutoff of the detector).Comment: 24 pages, 11 figures, 6 tables. Minor changes to reflect published version. References updated and correcte

Online Visual Robot Tracking and Identification using Deep LSTM Networks

Collaborative robots working on a common task are necessary for many applications. One of the challenges for achieving collaboration in a team of robots is mutual tracking and identification. We present a novel pipeline for online visionbased detection, tracking and identification of robots with a known and identical appearance. Our method runs in realtime on the limited hardware of the observer robot. Unlike previous works addressing robot tracking and identification, we use a data-driven approach based on recurrent neural networks to learn relations between sequential inputs and outputs. We formulate the data association problem as multiple classification problems. A deep LSTM network was trained on a simulated dataset and fine-tuned on small set of real data. Experiments on two challenging datasets, one synthetic and one real, which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar