We investigate the X-ray time lags of a recent ~630ks XMM-Newton observation of PG 1211+143. We find well-correlated variations across the XMM-Newton EPIC bandpass, with the first detection of a hard lag in this source with a mean time delay of up to
~3ks at the lowest frequencies. We find that the energy-dependence of the low-frequency hard lag scales approximately linearly with log(E) when averaged over all orbits, consistent with the propagating fluctuations model. However, we find that the low-frequency lag behaviour becomes more complex on timescales longer than a single orbit, suggestive of additional modes of variability. We also detect a high-frequency soft lag at ~10^{-4}Hz with the magnitude of the delay peaking at <0.8ks, consistent with previous observations, which we discuss in terms of small-scale reverberation.
Progress in astronomy comes from interpreting the signals encoded in the light received from distant objects: the distribution of light over the sky (images), over photon wavelength (spectrum), over polarization angle, and over time (usually called l
ight curves by astronomers). In the time domain we see transient events such as supernovae, gamma-ray bursts, and other powerful explosions; we see periodic phenomena such as the orbits of planets around nearby stars, radio pulsars, and pulsations of stars in nearby galaxies; and persistent aperiodic variations (`noise) from powerful systems like accreting black holes. I review just a few of the recent and future challenges in the burgeoning area of Time Domain Astrophysics, with particular attention to persistently variable sources, the recovery of reliable noise power spectra from sparsely sampled time series, higher-order properties of accreting black holes, and time delays and correlations in multivariate time series.
High frequency quasi-periodic oscillations (QPOs) from weakly magnetized neutron stars display rapid frequency variability and high coherence with quality factors up to at least 200 at frequencies around 850 Hz. Their parameters have been estimated s
o far from standard min(chi2) fitting techniques, after combining a large number of Power Density Spectra (PDS), as to have the powers normally distributed. Accounting for the statistical properties of PDS, we apply a maximum likelihood method to derive the QPO parameters in the non Gaussian regime. The method presented is general, easy to implement and can be applied to fitting individual PDS, several PDS simultaneously or their average, and is obviously not specific to the analysis of kHz QPO data. It applies to the analysis of any PDS optimized in frequency resolution and for low frequency variability or PDS containing features whose parameters vary on short timescales, as is the case for kHz QPOs. It is equivalent to the standard chi^2 minimization fitting when the number of PDS fitted is large. The accuracy, reliability and superiority of the method is demonstrated with simulations of synthetic PDS. We show that the maximum likelihood estimates of the QPO parameters are asymptotically unbiased, and have negligible bias when the QPO is reasonably well detected. By contrast, we show that the standard min(chi2) fitting method gives biased parameters with larger uncertainties. The maximum likelihood fitting method is applied to a subset of archival Rossi X-ray Timing Explorer (RXTE) data of the neutron star X-ray binary 4U1608-522. We show that the kHz QPO parameters can be measured on 8 second timescales and that the time evolution of the frequency is consistent with a random walk. This enables us to estimate the intrinsic quality factor of the QPO to be around 260, whereas previous analysis indicated a maximum value around 200 (abridged).
