The infrared/X-ray correlation of GX 339-4: Probing hard X-ray emission in accreting black holes

GX 339-4 has been one of the key sources for unravelling the accretion ejection coupling in accreting stellar mass black holes. After a long period of quiescence between 1999 and 2002, GX 339-4 underwent a series of 4 outbursts that have been intensively observed by many ground based observatories [radio, infrared(IR), optical] and satellites (X-rays). Here, we present results of these broad-band observational campaigns, focusing on the optical-IR (OIR)/X-ray flux correlations over the four outbursts. We found tight OIR/X-ray correlations over four decades with the presence of a break in the IR/X-ray correlation in the hard state. This correlation is the same for all four outbursts. This can be interpreted in a consistent way by considering a synchrotron self-Compton origin of the X-rays in which the break frequency varies between the optically thick and thin regime of the jet spectrum. We also highlight the similarities and differences between optical/X-ray and IR/X-ray correlations which suggest a jet origin of the near-IR emission in the hard state while the optical is more likely dominated by the blackbody emission of the accretion disc in both hard and soft state. However we find a non negligible contribution of 40 per cent of the jet emission in the V-band during the hard state. We finally concentrate on a soft-to-hard state transition during the decay of the 2004 outburst by comparing the radio, IR, optical and hard X-rays light curves. It appears that unusual delays between the peak of emission in the different energy domains may provide some important constraints on jet formation scenario.Comment: Accepted for publication in MNRAS, 12 pages, 8 figure

Global optical/infrared - X-ray correlations in X-ray binaries: quantifying disc and jet contributions

The optical/near-infrared (OIR) region of the spectra of low-mass X-ray binaries appears to lie at the intersection of a variety of different emission processes. In this paper we present quasi-simultaneous OIR - X-ray observations of 33 XBs in an attempt to estimate the contributions of various emission processes in these sources, as a function of X-ray state and luminosity. A global correlation is found between OIR and X-ray luminosity for low-mass black hole candidate XBs (BHXBs) in the hard X-ray state, of the form L_OIR is proportional to Lx^0.6. This correlation holds over 8 orders of magnitude in Lx and includes data from BHXBs in quiescence and at large distances (LMC and M31). A similar correlation is found in low-mass neutron star XBs (NSXBs) in the hard state. For BHXBs in the soft state, all the near-infrared (NIR) and some of the optical emission is suppressed below the correlation, a behaviour indicative of the jet switching off/on in transition to/from the soft state. We compare these relations to theoretical models of a number of emission processes. We find that X-ray reprocessing in the disc and emission from the jets both predict a slope close to 0.6 for BHXBs, and both contribute to the OIR in BHXBs in the hard state, the jets producing ~90 percent of the NIR emission at high luminosities. X-ray reprocessing dominates the OIR in NSXBs in the hard state, with possible contributions from the jets (only at high luminosity) and the viscously heated disc. We also show that the optically thick jet spectrum of BHXBs extends to near the K-band. (abridged)Comment: Accepted for publication in MNRAS; 19 pages, 7 figure

The loss function of sensorimotor learning

Motor learning can be defined as changing performance so as to optimize some function of the task, such as accuracy. The measure of accuracy that is optimized is called a loss function and specifies how the CNS rates the relative success or cost of a particular movement outcome. Models of pointing in sensorimotor control and learning usually assume a quadratic loss function in which the mean squared error is minimized. Here we develop a technique for measuring the loss associated with errors. Subjects were required to perform a task while we experimentally controlled the skewness of the distribution of errors they experienced. Based on the change in the subjects' average performance, we infer the loss function. We show that people use a loss function in which the cost increases approximately quadratically with error for small errors and significantly less than quadratically for large errors. The system is thus robust to outliers. This suggests that models of sensorimotor control and learning that have assumed minimizing squared error are a good approximation but tend to penalize large errors excessively

Economic decision-making compared with an equivalent motor task

There is considerable evidence that human economic decision-making deviates from the predictions of expected utility theory (EUT) and that human performance conforms to EUT in many perceptual and motor decision tasks. It is possible that these results reflect a real difference in decision-making in the 2 domains but it is also possible that the observed discrepancy simply reflects typical differences in experimental design. We developed a motor task that is mathematically equivalent to choosing between lotteries and used it to compare how the same subject chose between classical economic lotteries and the same lotteries presented in equivalent motor form. In experiment 1, we found that subjects are more risk seeking in deciding between motor lotteries. In experiment 2, we used cumulative prospect theory to model choice and separately estimated the probability weighting functions and the value functions for each subject carrying out each task. We found no patterned differences in how subjects represented outcome value in the motor and the classical tasks. However, the probability weighting functions for motor and classical tasks were markedly and significantly different. Those for the classical task showed a typical tendency to overweight small probabilities and underweight large probabilities, and those for the motor task showed the opposite pattern of probability distortion. This outcome also accounts for the increased risk-seeking observed in the motor tasks of experiment 1. We conclude that the same subject distorts probability, but not value, differently in making identical decisions in motor and classical form

A Bayesian model predicts the response of axons to molecular gradients

Axon guidance by molecular gradients plays a crucial role in wiring up the nervous system. However, the mechanisms axons use to detect gradients are largely unknown. We first develop a Bayesian “ideal observer” analysis of gradient detection by axons, based on the hypothesis that a principal constraint on gradient detection is intrinsic receptor binding noise. Second, from this model, we derive an equation predicting how the degree of response of an axon to a gradient should vary with gradient steepness and absolute concentration. Third, we confirm this prediction quantitatively by performing the first systematic experimental analysis of how axonal response varies with both these quantities. These experiments demonstrate a degree of sensitivity much higher than previously reported for any chemotacting system. Together, these results reveal both the quantitative constraints that must be satisfied for effective axonal guidance and the computational principles that may be used by the underlying signal transduction pathways, and allow predictions for the degree of response of axons to gradients in a wide variety of in vivo and in vitro settings