Investigating the impact of optical selection effects on observed rest frame prompt GRB properties

Measuring gamma-ray burst (GRB) properties in their rest-frame is crucial to understand the physics at work in gamma-ray bursts. This can only be done for GRBs with known redshift. Since redshifts are usually measured from the optical spectrum of the afterglow, correlations between prompt and afterglow emissions may introduce biases in the distribution of rest-frame properties of the prompt emission. Our analysis is based on a sample of 90 GRBs with good optical follow-up and well measured prompt emission. 76 of them have a measure of redshift and 14 have no redshift. We estimate their optical brightness with their R magnitude measured two hours after the trigger and compare the rest frame prompt properties of different classes of GRB afterglow brightness. We find that the optical brightness of GRBs in our sample is mainly driven by their intrinsic afterglow luminosity. We show that GRBs with low and high afterglow optical fluxes have similar Epi , Eiso , Liso , indicating that the rest-frame distributions computed from GRBs with a redshift are not significantly distorted by optical selection effects. However we found that the rest frame T90 distribution is not immune to optical selection effect, which favor the selection of GRBs with longer durations. Finally, we note that GRBs in the upper part of the Epi-Eiso plane have fainter optical afterglows and we show that optical selection effects strongly favor the detection of GRBs with bright afterglows located close or below the best-fit Epi-Eiso relation, whose redshift is easily measurable.Comment: 41 pages, 10 figures, 7 tables. arXiv admin note: substantial text overlap with arXiv:1503.0276

Finite-scale singularity in the renormalization group flow of a reaction-diffusion system

International audienceWe study the nonequilibrium critical behavior of the pair contact process with diffusion (PCPD) by means of nonperturbative functional renormalization group techniques. We show that usual perturbation theory fails because the effective potential develops a nonanalyticity at a finite length scale: Perturbatively forbidden terms are dynamically generated and the flow can be continued once they are taken into account. Our results suggest that the critical behavior of PCPD can be either in the directed percolation or in a new (conjugated) universality class

A jet model for the fast IR variability of the black hole X-ray binary GX 339-4

Using the simultaneous Infra-Red (IR) and X-ray light curves obtained by Kalamkar et al., we perform a Fourier analysis of the IR/X-ray timing correlations of the black hole X-ray binary (BHB) GX 339-4. The resulting IR vs X-ray Fourier coherence and lag spectra are similar to those obtained in previous studies of GX 339-4 using optical light curves. In particular, above 1 Hz, the lag spectrum features an approximately constant IR lag of about 100 ms. We model simultaneously the radio to IR Spectral Energy Distribution (SED), the IR Power Spectral Density (PSD), and the coherence and lag spectra using the jet internal shock model ISHEM assuming that the fluctuations of the jet Lorentz factor are driven by the accretion flow. It turns out that most of the spectral and timing features, including the 100-ms lag, are remarkably well-reproduced by this model. The 100-ms time-scale is then associated with the travel time from the accretion flow to the IR emitting zone. Our exploration of the parameter space favours a jet which is at most mildly relativistic (¯ < 3), and a linear and positive relation between the jet Lorentz factor and X-ray light curve i.e. (t) − 1∝LX(t). The presence of a strong Low-Frequency Quasi-Periodic Oscillation (LFQPO) in the IR light curve could be caused by jet precession driven by Lense–Thirring precession of the jet-emitting accretion flow. Our simulations confirm that this mechanism can produce an IR LFQPO similar to that observed in GX 339-4

TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of &gt;50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022

CAGIRE: a wide-field NIR imager for the COLIBRI 1.3 meter robotic telescope

The use of high energy transients such as Gamma Ray Bursts (GRBs) as probes of the distant universe relies on the close collaboration between space and ground facilities. In this context, the Sino-French mission SVOM has been designed to combine a space and a ground segment and to make the most of their synergy. On the ground, the 1.3 meter robotic telescope COLIBRI, jointly developed by France and Mexico, will quickly point the sources detected by the space hard X-ray imager ECLAIRs, in order to detect and localise their visible/NIR counterpart and alert large telescopes in minutes. COLIBRI is equipped with two visible cameras, called DDRAGO-blue and DDRAGO-red, and an infrared camera, called CAGIRE, designed for the study of high redshift GRBs candidates. Being a low-noise NIR camera mounted at the focus of an alt-azimutal robotic telescope imposes specific requirements on CAGIRE. We describe here the main characteristics of the camera: its optical, mechanical and electronics architecture, the ALFA detector, and the operation of the camera on the telescope. The instrument description is completed by three sections presenting the calibration strategy, an image simulator incorporating known detector effects, and the automatic reduction software for the ramps acquired by the detector. This paper aims at providing an overview of the instrument before its installation on the telescope.Comment: Accepted by Experimental Astronom

Architecture and performance of the KM3NeT front-end firmware

The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches