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  1. ABSTRACT

    We report the detection of FRB20191107B with UTMOST radio telescope at a dispersion measure (DM) of 714.9 pc cm−3. The burst consists of three components, the brightest of which has an intrinsic width of only 11.3 μs and a scattering tail with an exponentially decaying time-scale of 21.4 μs measured at 835 MHz. We model the sensitivity of UTMOST and other major fast radio burst (FRB) surveys to such narrow events. We find that $\gt 60{{\ \rm per\, cent}}$ of FRBs like FRB20191107B are being missed, and that a significant population of very narrow FRBs probably exists and remains underrepresented in these surveys. The high DM and small scattering time-scale of FRB20191107B allows us to place an upper limit on the strength of turbulence in the intergalactic medium, quantified as scattering measure (SM), of SMIGM < 8.4 × 10−7 kpc m−20/3. Almost all UTMOST FRBs have full phase information due to real-time voltage capture, which provides us with the largest sample of coherently dedispersed single burst FRBs. Our 10.24 μs time resolution data yields accurately measured FRB scattering time-scales. We combine the UTMOST FRBs with 10 FRBs from the literature and find no obvious evidence for a DM-scattering relation, suggesting that IGM is not the dominant source of scatteringmore »in FRBs. We support the results of previous studies and identify the local environment of the source in the host galaxy as the most likely region that dominates the observed scattering of our FRBs.

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  2. Within months of birth, children develop meaningful expectations about the world around them. How much of this early knowledge can be explained through generic learning mechanisms applied to sensory data, and how much of it requires more substantive innate inductive biases? Addressing this fundamental question in its full generality is currently infeasible, but we can hope to make real progress in more narrowly defined domains, such as the development of high-level visual categories, thanks to improvements in data collecting technology and recent progress in deep learning. In this paper, our goal is precisely to achieve such progress by utilizing modern self-supervised deep learning methods and a recent longitudinal, egocentric video dataset recorded from the perspective of three young children (Sullivan et al., 2020). Our results demonstrate the emergence of powerful, high-level visual representations from developmentally realistic natural videos using generic self-supervised learning objectives.
  3. Abstract We present a broadband radio study of the transient jets ejected from the black hole candidate X-ray binary MAXI J1535–571, which underwent a prolonged outburst beginning on 2017 September 2. We monitored MAXI J1535–571 with the Murchison Widefield Array (MWA) at frequencies from 119 to 186 MHz over six epochs from 2017 September 20 to 2017 October 14. The source was quasi-simultaneously observed over the frequency range 0.84–19 GHz by UTMOST (the Upgraded Molonglo Observatory Synthesis Telescope) the Australian Square Kilometre Array Pathfinder (ASKAP), the Australia Telescope Compact Array (ATCA), and the Australian Long Baseline Array (LBA). Using the LBA observations from 2017 September 23, we measured the source size to be $34\pm1$ mas. During the brightest radio flare on 2017 September 21, the source was detected down to 119 MHz by the MWA, and the radio spectrum indicates a turnover between 250 and 500 MHz, which is most likely due to synchrotron self-absorption (SSA). By fitting the radio spectrum with a SSA model and using the LBA size measurement, we determined various physical parameters of the jet knot (identified in ATCA data), including the jet opening angle ( $\phi_{\rm op} = 4.5\pm1.2^{\circ}$ ) and the magnetic field strengthmore »( $B_{\rm s} = 104^{+80}_{-78}$ mG). Our fitted magnetic field strength agrees reasonably well with that inferred from the standard equipartition approach, suggesting the jet knot to be close to equipartition. Our study highlights the capabilities of the Australian suite of radio telescopes to jointly probe radio jets in black hole X-ray binaries via simultaneous observations over a broad frequency range, and with differing angular resolutions. This suite allows us to determine the physical properties of X-ray binary jets. Finally, our study emphasises the potential contributions that can be made by the low-frequency part of the Square Kilometre Array (SKA-Low) in the study of black hole X-ray binaries.« less
  4. In this paper, we consider a general distributed system with multiple agents who select and then implement actions in the system. The system has an operator with a centralized objective. The agents, on the other hand, are selfinterested and strategic in the sense that each agent optimizes its own individual objective. The operator aims to mitigate this misalignment by designing an incentive scheme for the agents. The problem is difficult due to the cost functions of the agents being coupled, the objective of the operator not being social welfare, and the operator having no direct control over actions being implemented by the agents. This problem has been studied in many fields, particularly in mechanism design and cost allocation. However, mechanism design typically assumes that the operator has knowledge of the cost functions of the agents and the actions being implemented by the operator. On the other hand, cost allocation classically assumes that agents do not anticipate the effect of their actions on the incentive that they obtain. We remove these assumptions and present an incentive rule for this setup by bridging the gap between mechanism design and classical cost allocation. We analyze whether the proposed design satisfies various desirable propertiesmore »such as social optimality, budget balance, participation constraint, and so on. We also analyze which of these properties can be satisfied if the assumptions of cost functions of the agents being private and the agents being anticipatory are relaxed.« less
  5. ABSTRACT XTE J1810−197 (J1810) was the first magnetar identified to emit radio pulses, and has been extensively studied during a radio-bright phase in 2003–2008. It is estimated to be relatively nearby compared to other Galactic magnetars, and provides a useful prototype for the physics of high magnetic fields, magnetar velocities, and the plausible connection to extragalactic fast radio bursts. Upon the rebrightening of the magnetar at radio wavelengths in late 2018, we resumed an astrometric campaign on J1810 with the Very Long Baseline Array, and sampled 14 new positions of J1810 over 1.3 yr. The phase calibration for the new observations was performed with two-phase calibrators that are quasi-colinear on the sky with J1810, enabling substantial improvement of the resultant astrometric precision. Combining our new observations with two archival observations from 2006, we have refined the proper motion and reference position of the magnetar and have measured its annual geometric parallax, the first such measurement for a magnetar. The parallax of 0.40 ± 0.05 mas corresponds to a most probable distance $2.5^{\, +0.4}_{\, -0.3}$ kpc for J1810. Our new astrometric results confirm an unremarkable transverse peculiar velocity of ≈200 $\rm km~s^{-1}$ for J1810, which is only at the average level among the pulsar population. The magnetar propermore »motion vector points back to the central region of a supernova remnant (SNR) at a compatible distance at ≈70 kyr ago, but a direct association is disfavoured by the estimated SNR age of ∼3 kyr.« less
  6. Abstract Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.
    Free, publicly-accessible full text available October 1, 2023
  7. Free, publicly-accessible full text available April 1, 2023