Lord of the Rings: A Kinematic Distance to Circinus X-1 from a Giant X-Ray Light Echo

Circinus X-1 exhibited a bright X-ray flare in late 2013. Follow-up observations with Chandra and XMM-Newton from 40 to 80 days after the flare reveal a bright X-ray light echo in the form of four well-defined rings with radii from 5 to 13 arcminutes, growing in radius with time. The large fluence of the flare and the large column density of interstellar dust towards Circinus X-1 make this the largest and brightest set of rings from an X-ray light echo observed to date. By deconvolving the radial intensity profile of the echo with the MAXI X-ray lightcurve of the flare we reconstruct the dust distribution towards Circinus X-1 into four distinct dust concentrations. By comparing the peak in scattering intensity with the peak intensity in CO maps of molecular clouds from the Mopra Southern Galactic Plane CO Survey we identify the two innermost rings with clouds at radial velocity ~ -74 km/s and ~ -81 km/s, respectively. We identify a prominent band of foreground photoelectric absorption with a lane of CO gas at ~ -32 km/s. From the association of the rings with individual CO clouds we determine the kinematic distance to Circinus X-1 to be $D_{Cir X-1} = 9.4^{+0.8}_{-1.0}$ kpc. This distance rules out earlier claims of a distance around 4 kpc, implies that Circinus X-1 is a frequent super-Eddington source, and places a lower limit of $\Gamma \gtrsim 22$ on the Lorentz factor and an upper limit of $\theta_{jet} \lesssim 3^{\circ}$ on the jet viewing angle.Comment: 20 pages, 21 figures, Astrophysical Journal, in prin

Natural variability is essential to learning new faces

We learn new faces throughout life, for example in everyday settings like watching TV. Recent research has shown that image variability is key to this ability: if we learn a new face over highly variable images, we are better able to recognize that person in novel pictures. Here we asked people to watch TV shows they had not seen before, and then tested their ability to recognize the actors. Some participants watched TV shows in the conventional manner, whereas others watched them upside down or contrast-reversed. Image variability is equivalent across these conditions, and yet we observed that viewers were unable to learn the faces upside down or contrast-reversed - even when tested in the same format as learning. We conclude that variability is a necessary, but not sufficient, condition for face learning. Instead, mechanisms underlying this process are tuned to extract useful information from variability falling within a critical range that corresponds to natural, everyday variation

Robust social categorization emerges from learning the identities of very few faces

Viewers are highly accurate at recognizing sex and race from faces-though it remains unclear how this is achieved. Recognition of familiar faces is also highly accurate across a very large range of viewing conditions, despite the difficulty of the problem. Here we show that computation of sex and race can emerge incidentally from a system designed to compute identity. We emphasize the role of multiple encounters with a small number of people, which we take to underlie human face learning. We use highly variable everyday 'ambient' images of a few people to train a Linear Discriminant Analysis (LDA) model on identity. The resulting model has human-like properties, including a facility to cohere previously unseen ambient images of familiar (trained) people-an ability which breaks down for the faces of unknown (untrained) people. The first dimension created by the identity-trained LDA classifies both familiar and unfamiliar faces by sex, and the second dimension classifies faces by race- even though neither of these categories was explicitly coded at learning. By varying the numbers and types of face identities on which a further series of LDA models were trained, we show that this incidental learning of sex and race reflects covariation between these social categories and face identity, and that a remarkably small number of identities need be learnt before such incidental dimensions emerge. The task of learning to recognize familiar faces is sufficient to create certain salient social categories

Understanding facial impressions between and within identities

A paradoxical finding from recent studies of face perception is that observers are error-prone and inconsistent when judging the identity of unfamiliar faces, but nevertheless reasonably consistent when judging traits. Our aim is to understand this difference. Using everyday ambient images of faces, we show that visual image statistics can predict observers' consensual impressions of trustworthiness, attractiveness and dominance, which represent key dimensions of evaluation in leading theoretical accounts of trait judgement. In Study 1, image statistics derived from ambient images of multiple face identities were able to account for 51% of the variance in consensual impressions of entirely novel ambient images. Shape properties were more effective predictors than surface properties, but a combination of both achieved the best results. In Study 2 and Study 3, statistics derived from multiple images of a particular face achieved the best generalisation to new images of that face, but there was nonetheless significant generalisation between images of the faces of different individuals. Hence, whereas idiosyncratic variability across different images of the same face is sufficient to cause substantial problems in judging the identities of unfamiliar faces, there are consistencies between faces which are sufficient to support (to some extent) consensual trait judgements. Furthermore, much of this consistency can be captured in simple operational models based on image statistics

Hybrid Ion-Detector/Data-Acquisition System for a TOF-MS

A modified ion-detector/data-acquisition system has been devised to increase the dynamic range of a time-of-flight mass spectrometer (TOF-MS) that, previously, included a microchannel-plate detector and a data-acquisition system based on counting pulses and time-tagging them by use of a time-to-digital converter (TDC). The dynamic range of the TOF-MS was limited by saturation of the microchannel plate detector, which can handle no more than a few million counts per second. The modified system includes (1) a combined microchannel plate/discrete ion multiplier and (2) a hybrid data-acquisition system that simultaneously performs analog current or voltage measurements and multianode single-ion-pulse-counting time-of-flight measurements to extend the dynamic range of a TDC into the regime in which a mass peak comprises multiple ions arriving simultaneously at the detector. The multianode data are used to determine, in real time, whether the detector is saturated. When saturation is detected, the data-acquisition system selectively enables circuitry that simultaneously determines the ion-peak intensity by measuring the time profile of the analog current or voltage detector-output signal

Understanding face familiarity

It has been known for many years that identifying familiar faces is much easier than identifying unfamiliar faces, and that this familiar face advantage persists across a range of tasks. However, attempts to understand face familiarity have mostly used a binary contrast between ‘familiar’ and ‘unfamiliar’ faces, with no attempt to incorporate the vast range of familiarity we all experience. From family members to casual acquaintances and from personal to media exposure, familiarity is a more complex categorisation than is usually acknowledged. Here we model levels of familiarity using a generic statistical analysis (PCA combined with LDA) computed over some four thousand naturally occurring images that include a large variation in the numbers of images for each known person. Using a strong test of performance with entirely novel, untrained everyday images, we show that such a model can simulate widely documented effects of familiarity in face recognition and face matching, and offers a natural account of the internal feature advantage for familiar faces. Furthermore, as with human viewers, the benefits of familiarity seem to accrue from being able to extract consistent information across different photos of the same face. We argue that face familiarity is best understood as reflecting increasingly robust statistical descriptions of idiosyncratic within-person variability. Understanding how faces become familiar appears to rely on both bottom-up statistical image descriptions (modelled here with PCA), and top-down processes that cohere superficially different images of the same person (modelled here with LDA)

Enthalpy of Solvation Correlations for Gaseous Solutes Dissolved in Chloroform and 1,2-dichloroethane Based on the Abraham Model

This article discusses enthalpy of solvation correlations for gaseous solutes dissolved in chloroform and 1,2-dichloroethane based on the Abraham model