53 research outputs found
The relentless variability of Mrk 421 from the TeV to the radio
The origin of the gamma-ray emission of the blazar Mrk 421 is still a matter
of debate. We used 5.5 years of unbiased observing campaign data, obtained
using the FACT telescope and the Fermi LAT detector at TeV and GeV energies,
the longest and densest so far, together with contemporaneous multi-wavelength
observations, to characterise the variability of Mrk 421 and to constrain the
underlying physical mechanisms. We studied and correlated light curves obtained
by ten different instruments and found two significant results. The TeV and
X-ray light curves are very well correlated with a lag of <0.6 days. The GeV
and radio (15 Ghz band) light curves are widely and strongly correlated.
Variations of the GeV light curve lead those in the radio. Lepto-hadronic and
purely hadronic models in the frame of shock acceleration predict proton
acceleration or cooling timescales that are ruled out by the short variability
timescales and delays observed in Mrk 421. Instead the observations match the
predictions of leptonic models.Comment: 10 pages, 8 figures, 1 tabl
Fractional variability—a tool to study blazar variability
Active Galactic Nuclei emit radiation over the whole electromagnetic spectrum up to TeV energies. Blazars are one subtype with their jets pointing towards the observer. One of their typical features is extreme variability on timescales, from minutes to years. The fractional variability is an often used parameter for investigating the degree of variability of a light curve. Different detection methods and sensitivities of the instruments result in differently binned data and light curves with gaps. As they can influence the physics interpretation of the broadband variability, the effects of these differences on the fractional variability need to be studied. In this paper, we study the systematic effects of completeness in time coverage and the sampling rate. Using public data from instruments monitoring blazars in various energy ranges, we study the variability of the bright TeV blazars Mrk 421 and Mrk 501 over the electromagnetic spectrum, taking into account the systematic effects, and compare our findings with previous results. Especially in the TeV range, the fractional variability is higher than in previous studies, which can be explained by the much longer (seven years compared to few weeks) and more complete data sample
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
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
The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2
Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age 6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score 652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701
MAGIC and H.E.S.S. detect VHE gamma rays from the blazar OT081 for the first time: a deep multiwavelength study
https://pos.sissa.it/395/815/pdfPublished versio
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