Search for: All records

ABSTRACT Neutron star mergers produce a substantial amount of fast-moving ejecta, expanding outwardly for years after the merger. The interaction of these ejecta with the surrounding medium may produce a weak isotropic radio remnant, detectable in relatively nearby events. We use late-time radio observations of short duration gamma-ray bursts (sGRBs) to constrain this model. Two samples of events were studied: four sGRBs that are possibly in the local (<200 Mpc) Universe were selected to constrain the remnant non-thermal emission from the sub-relativistic ejecta, whereas 17 sGRBs at cosmological distances were used to constrain the presence of a proto-magnetar central engine, possibly re-energizing the merger ejecta. We consider the case of GRB 170817A/GW170817 and find that in this case the early radio emission may be quenched by the jet blast-wave. In all cases, for ejecta mass range of $${M}_{\rm {ej}}\lesssim 10^{-2}\, (5\times 10^{-2})\, \mathrm{M}_\odot$$, we can rule out very energetic merger ejecta $${E}_{\rm {ej}}\gtrsim 5\times 10^{52}\, (10^{53})\, \rm erg$$, thus excluding the presence of a powerful magnetar as a merger remnant. 

In-memory key-value stores that use kernel-bypass networking serve millions of operations per second per machine with microseconds of latency. They are fast in part because they are simple, but their simple interfaces force applications to move data across the network. This is inefficient for operations that aggregate over large amounts of data, and it causes delays when traversing complex data structures. Ideally, applications could push small functions to storage to avoid round trips and data movement; however, pushing code to these fast systems is challenging. Any extra complexity for interpreting or isolating code cuts into their latency and throughput benefits. We present Splinter, a low-latency key-value store that clients extend by pushing code to it. Splinter is designed for modern multi-tenant data centers; it allows mutually distrusting tenants to write their own fine-grained extensions and push them to the store at runtime. The core of Splinter’s design relies on type- and memory-safe extension code to avoid conventional hardware isolation costs. This still allows for bare-metal execution, avoids data copying across trust boundaries, and makes granular storage functions that perform less than a microsecond of compute practical. Our measurements show that Splinter can process 3.5 million remote extension invocations per second with a median round-trip latency of less than 9 μs at densities of more than 1,000 tenants per server. We provide an implementation of Facebook’s TAO as an 800 line extension that, when pushed to a Splinter server, improves performance by 400 Kop/s to perform 3.2 Mop/s over online graph data with 30 μs remote access times. 

The performance of compute hardware varies: software run repeatedly on the same server (or a different server with supposedly identical parts) can produce performance results that differ with each execution. This variation has important effects on the reproducibility of systems research and ability to quantitatively compare the performance of different systems. It also has implications for commercial computing, where agreements are often made conditioned on meeting specific performance targets. Over a period of 10 months, we conducted a large-scale study capturing nearly 900,000 data points from 835 servers. We examine this data from two perspectives: that of a service provider wishing to offer a consistent environment, and that of a systems researcher who must understand how variability impacts experimental results. From this examination, we draw a number of lessons about the types and magnitudes of performance variability and the effects on confidence in experiment results. We also create a statistical model that can be used to understand how representative an individual server is of the general population. The full dataset and our analysis tools are publicly available, and we have built a system to interactively explore the data and make recommendations for experiment parameters based on statistical analysis of historical data. 

The ALICE Collaboration reports measurements of the large relative transverse momentum ( $k_T$) component of jet substructure in $pp$and Pb-Pb collisions at center-of-mass energy per nucleon pair $\sqrt{s_{\mathrm{NN}}}=5.02\mathrm{TeV}$. Enhancement in the yield of such large- $k_{\mathrm{T}}$emissions in head-on Pb-Pb collisions is predicted to arise from partonic scattering with quasiparticles of the quark-gluon plasma. The analysis utilizes charged-particle jets reconstructed by the anti- $k_{\mathrm{T}}$algorithm with resolution parameter $R=0.2$in the transverse-momentum interval $60<p_{\mathrm{T},\mathrm{ch},\mathrm{jet}}<80\mathrm{GeV}/c$. The soft drop and dynamical grooming algorithms are used to identify high transverse momentum splittings in the jet shower. Comparison of measurements in Pb-Pb and $pp$collisions shows medium-induced narrowing, corresponding to yield suppression of high- $k_T$splittings, in contrast to the expectation of yield enhancement due to quasiparticle scattering. The measurements are compared to theoretical model calculations incorporating jet modification due to jet-medium interactions (“jet quenching”), both with and without quasiparticle scattering effects. These measurements provide new insight into the underlying mechanisms and theoretical modeling of jet quenching. 

Abstract This paper presents a study of the inclusive forward J/ψyield as a function of forward charged-particle multiplicity in pp collisions at$$ \sqrt{s} $$ $\sqrt{s}$= 13 TeV using data collected by the ALICE experiment at the CERN LHC. The results are presented in terms of relativeJ/ψyields and relative charged-particle multiplicities with respect to these quantities obtained in inelastic collisions having at least one charged particle in the pseudorapidity range |η|<1. The J/ψmesons are reconstructed via their decay intoμ⁺μ⁻pairs in the forward rapidity region (2.5< y <4). The relative multiplicity is estimated in the forward pseudorapidity range which overlaps with the J/ψrapidity region. The results show a steeper-than-linear increase of the J/ψyields versus the multiplicity. They are compared with previous measurements and theoretical model calculations. 

Abstract Event-by-event fluctuations of the event-wise mean transverse momentum,$$\langle p_{\textrm{T}}\rangle $$ $⟨p_{\text{T}}⟩$, of charged particles produced in proton–proton (pp) collisions at$$\sqrt{s}$$ $\sqrt{s}$= 5.02 TeV, Xe–Xe collisions at$$\sqrt{s_{\textrm{NN}}}$$ $\sqrt{s_{\text{NN}}}$= 5.44 TeV, and Pb–Pb collisions at$$\sqrt{s_{\textrm{NN}}}$$ $\sqrt{s_{\text{NN}}}$= 5.02 TeV are studied using the ALICE detector based on the integral correlator$$\langle \!\langle \Delta p_\textrm{T}\Delta p_\textrm{T}\rangle \!\rangle $$ $⟨⟨\Delta p_{\text{T}}\Delta p_{\text{T}}⟩⟩$. The correlator strength is found to decrease monotonically with increasing produced charged-particle multiplicity measured at midrapidity in all three systems. In Xe–Xe and Pb–Pb collisions, the multiplicity dependence of the correlator deviates significantly from a simple power-law scaling as well as from the predictions of the HIJING and AMPT models. The observed deviation from power-law scaling is expected from transverse radial flow in semicentral to central Xe–Xe and Pb–Pb collisions. In pp collisions, the correlation strength is also studied by classifying the events based on the transverse spherocity,$$S_0$$ $S_0$, of the particle production at midrapidity, used as a proxy for the presence of a pronounced back-to-back jet topology. Low-spherocity (jetty) events feature a larger correlation strength than those with high spherocity (isotropic). The strength and multiplicity dependence of jetty and isotropic events are well reproduced by calculations with the PYTHIA 8 and EPOS LHC models. 

A<sc>bstract</sc> We report on the measurement of inclusive, non-prompt, and prompt J/ψ-hadron correlations by the ALICE Collaboration at the CERN Large Hadron Collider in pp collisions at a center-of-mass energy of 13 TeV. The correlations are studied at midrapidity (|y| <0.9) in the transverse momentum rangesp_T<40 GeV/cfor the J/ψand 0.15< p_T<10 GeV/cand |η|<0.9 for the associated hadrons. The measurement is based on minimum bias and high multiplicity data samples corresponding to integrated luminosities ofL_int= 34 nb⁻¹andL_int= 6.9 pb⁻¹, respectively. In addition, two more data samples are employed, requiring, on top of the minimum bias condition, a threshold on the tower energy ofE= 4 and 9 GeV in the ALICE electromagnetic calorimeters, which correspond to integrated luminosities ofL_int= 0.9 pb⁻¹andL_int= 8.4 pb⁻¹, respectively. The azimuthally integrated near and away side yields of associated charged hadrons per J/ψtrigger are presented as a function of the J/ψand associated hadron transverse momentum. The measurements are discussed in comparison to PYTHIA calculations. 

Given the highly empirical nature of research in cloud computing, networked systems, and related fields, testbeds play an important role in the research ecosystem. In this paper, we cover one such facility, CloudLab, which supports systems research by providing raw access to programmable hardware, enabling research at large scales, and creating as hared platform for repeatable research.We present our experiences designing CloudLab and operating it for four years, serving nearly 4,000 users who have run over 79,000 experiments on 2,250 servers, switches, and other pieces of datacenter equipment. From this experience,we draw lessons organized around two themes. The first set comes from analysis of data regarding the use of CloudLab:how users interact with it, what they use it for, and the implications for facility design and operation. Our second set of lessons comes from looking at the ways that algorithms used“under the hood,” such as resource allocation, have important—and sometimes unexpected—effects on user experience and behavior. These lessons can be of value to the designers and operators of IaaS facilities in general, systems testbeds in particular, and users who have a stake in understanding how these systems are built.