We present ARC2 (Astrophysically Robust Correction 2), an open-source Python-based systematics-correction pipeline to correct for the Kepler prime mission long cadence light curves. The ARC2 pipeline identifies and corrects any isolated discontinuities in the light curves, then removes trends common to many light curves. These trends are modelled using the publicly available co-trending basis vectors, within an (approximate) Bayesian framework with `shrinkage' priors to minimise the risk of over-fitting and the injection of any additional noise into the corrected light curves, while keeping any astrophysical signals intact. We show that the ARC2 pipeline's performance matches that of the standard Kepler PDC-MAP data products using standard noise metrics, and demonstrate its ability to preserve astrophysical signals using injection tests with simulated stellar rotation and planetary transit signals. Although it is not identical, the ARC2 pipeline can thus be used as an open source alternative to PDC-MAP, whenever the ability to model the impact of the systematics removal process on other kinds of signal is important.
Abstract We demonstrate a direct mapping of max k -SAT problems (and weighted max k -SAT) to a Chimera graph, which is the non-planar hardware graph of the devices built by D-Wave Systems Inc. We further show that this mapping can be used to map a similar class of maximum satisfiability problems where the clauses are replaced by parity checks over potentially large numbers of bits. The latter is of specific interest for applications in decoding for communication. We discuss an example in which the decoding of a turbo code, which has been demonstrated to perform near the Shannon limit, can be mapped to a Chimera graph. The weighted max k -SAT problem is the most general class of satisfiability problems, so our result effectively demonstrates how any satisfiability problem may be directly mapped to a Chimera graph. Our methods faithfully reproduce the low energy spectrum of the target problems, so therefore may also be used for maximum entropy inference.
In this paper, we present a framework for assessing the effect of non-stationary Gaussian noise and radio frequency interference (RFI) on the signal to noise ratio, the number of false positives detected per true positive and the sensitivity of standard pulsar search pipelines. The results highlight the necessity to develop algorithms that are able to identify and remove non-stationary variations from the data before RFI excision and searching is performed in order to limit false positive detections. The results also show that the spectrum whitening algorithms currently employed, severely affect the efficiency of pulsar search pipelines by reducing their sensitivity to long period pulsars.
Thermoregulation of the thorax allows endothermic insects to achieve power outputs during flight that are among the highest in the animal kingdom. Flying endothermic insects, including the honeybee Apis mellifera , are believed to thermoregulate almost exclusively by varying heat loss. Here it is shown that a rise in air temperature from 20° to 40°C causes large decreases in metabolic heat production and wing-beat frequency in honeybees during hovering, agitated, or loaded flight. Thus, variation in heat production may be the primary mechanism for achieving thermal stability in flying honeybees, and this mechanism may occur commonly in endothermic insects.
To date, the radial velocity (RV) method has been one of the most productive techniques for detecting and confirming extrasolar planetary candidates. Unfortunately, stellar activity can induce RV variations which can drown out or even mimic planetary signals – and it is notoriously difficult to model and thus mitigate the effects of these activity-induced nuisance signals. This is expected to be a major obstacle to using next-generation spectrographs to detect lower mass planets, planets with longer periods, and planets around more active stars. Enter Gaussian processes (GPs) which, we note, have a number of attractive features that make them very well suited to disentangling stellar activity signals from planetary signals. We present here a GP framework we developed to model RV time series jointly with ancillary activity indicators (e.g. bisector velocity spans, line widths, chromospheric activity indices), allowing the activity component of RV time series to be constrained and disentangled from e.g. planetary components. We discuss the mathematical details of our GP framework, and present results illustrating its encouraging performance on both synthetic and real RV data sets, including the publicly available Alpha Centauri B data set.
One of the tenets of the radio pulsar observational picture is that the integrated pulse profiles are constant with time. This assumption underpins much of the fantastic science made possible via pulsar timing. Over the past few years, however, this assumption has come under question with a number of pulsars showing pulse shape changes on a range of timescales. Here, we show the dramatic appearance of a bright component in the pulse profile of PSR J0738-4042 (B0736-40). The component arises on the leading edge of the profile. It was not present in 2004 but strongly present in 2006 and all observations thereafter. A subsequent search through the literature shows the additional component varies in flux density over timescales of decades. We show that the polarization properties of the transient component are consistent with the picture of competing orthogonal polarization modes. Faced with the general problem of identifying and characterising average profile changes, we outline and apply a statistical technique based on a Hidden Markov Model. The value of this technique is established through simulations, and is shown to work successfully in the case of low signal-to-noise profiles.
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