No Arabic abstract
Nova SMC 2016 has been the most luminous nova known in the direction of the Magellanic Clouds. It turned into a very luminous supersoft X-ray source between day 16 and 28 after the optical maximum. We observed it with Chandra, the HRC-S camera and the Low Energy Transmission Grating (LETG) on 2016 November and 2017 January (days 39 and 88 after optical maximum), and with XMM-Newton on 2016 December (day 75). We detected the compact white dwarf (WD) spectrum as a luminous supersoft X-ray continuum with deep absorption features of carbon, nitrogen, magnesium, calcium, probably argon and sulfur on day 39, and oxygen, nitrogen and carbon on days 75 and 88. The spectral features attributed to the WD atmosphere are all blue-shifted, by about 1800 km/s on day 39 and up to 2100 km/s in the following observations. Spectral lines attributed to low ionization potential transitions in the interstellar medium are also observed. Assuming the distance of the Small Magellanic Cloud, the bolometric luminosity exceeded Eddington level for at least three months. A preliminary analysis with atmospheric models indicates effective temperature around 700,000 K on day 39, peaking at the later dates in the 850,000-900,000 K range, as expected for a 1.25 m(sol) WD. We suggest a possible classification as an oxygen-neon WD, but more precise modeling is needed to accurately determine the abundances. The X-ray light curves show large, aperiodic ux variability, not associated with spectral variability. We detected red noise, but did not find periodic or quasi-periodic modulations.
Magnetic fields and stellar spots can alter the equivalent widths of absorption lines in stellar spectra, varying during the activity cycle. This also influences the information that we derive through spectroscopic analysis. In this study we analyse high-resolution spectra of 211 Sun-like stars observed at different phases of their activity cycles, in order to investigate how stellar activity affects the spectroscopic determination of stellar parameters and chemical abundances. We observe that equivalent widths of lines can increase as a function of the activity index log R$^prime_{rm HK}$ during the stellar cycle, which also produces an artificial growth of the stellar microturbulence and a decrease in effective temperature and metallicity. This effect is visible for stars with activity indexes log R$^prime_{rm HK}$$geq$$-$5.0 (i.e., younger than 4-5 Gyr) and it is more significant at higher activity levels. These results have fundamental implications on several topics in astrophysics that are discussed in the paper, including stellar nucleosynthesis, chemical tagging, the study of Galactic chemical evolution, chemically anomalous stars, the structure of the Milky Way disk, stellar formation rates, photoevaporation of circumstellar disks, and planet hunting.
Densely time sampled multi-frequency flux measurements of the extreme BL Lac object S5 0716+714 over the past three years allow us to study its broad-band variability, and the detailed underlying physics, with emphasis on the location and size of the emitting regions and the evolution with time. We study the characteristics of some prominent mm-/gamma-ray flares in the context of the shock-in-jet model and investigate the location of the high energy emission region. The rapid rise and decay of the radio flares is in agreement with the formation of a shock and its evolution, if a geometrical variation is included in addition to intrinsic variations of the source. We find evidence for a correlation between flux variations at gamma-ray and radio frequencies. A two month time-delay between gamma-ray and radio flares indicates a non-cospatial origin of gamma-rays and radio flux variations in S5 0716+714.
The learning rate is an information-theoretical quantity for bipartite Markov chains describing two coupled subsystems. It is defined as the rate at which transitions in the downstream subsystem tend to increase the mutual information between the two subsystems, and is bounded by the dissipation arising from these transitions. Its physical interpretation, however, is unclear, although it has been used as a metric for the sensing performance of the downstream subsystem. In this paper, we explore the behaviour of the learning rate for a number of simple model systems, establishing when and how its behaviour is distinct from the instantaneous mutual information between subsystems. In the simplest case, the two are almost equivalent. In more complex steady-state systems, the mutual information and the learning rate behave qualitatively distinctly, with the learning rate clearly now reflecting the rate at which the downstream system must update its information in response to changes in the upstream system. It is not clear whether this quantity is the most natural measure for sensor performance, and, indeed, we provide an example in which optimising the learning rate over a region of parameter space of the downstream system yields an apparently sub-optimal sensor.
The LIGO/Virgo gravitational-wave (GW) interferometers have to-date detected ten merging black hole (BH) binaries, some with masses considerably larger than had been anticipated. Stellar-mass BH binaries at the high end of the observed mass range (with chirp mass ${cal M} gtrsim 25 M_{odot}$) should be detectable by a space-based GW observatory years before those binaries become visible to ground-based GW detectors. This white paper discusses some of the synergies that result when the same binaries are observed by instruments in space and on the ground. We consider intermediate-mass black hole binaries (with total mass $M sim 10^2 -10^4 M_{odot}$) as well as stellar-mass black hole binaries. We illustrate how combining space-based and ground-based data sets can break degeneracies and thereby improve our understanding of the binarys physical parameters. While early work focused on how space-based observatories can forecast precisely when some mergers will be observed on the ground, the reverse is also important: ground-based detections will allow us to dig deeper into archived, space-based data to confidently identify black hole inspirals whose signal-to-noise ratios were originally sub-threshold, increasing the number of binaries observed in both bands by a factor of $sim 4 - 7$.
We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting, which shows consistent gains across all tasks.