No Time for Dead Time: Use the Fourier Amplitude Differences to Normalize Dead-time-affected Periodograms

Dead time affects many of the instruments used in X-ray astronomy, by producing a strong distortion in power density spectra. This can make it difficult to model the aperiodic variability of the source or look for quasi-periodic oscillations. Whereas in some instruments a simple a priori correction for dead-time-affected power spectra is possible, this is not the case for others such as NuSTAR, where the dead time is non-constant and long (~2.5 ms). Bachetti et al. 2015 suggested the cospectrum obtained from light curves of independent detectors within the same instrument as a possible way out, but this solution has always only been a partial one: the measured rms was still affected by dead time, because the width of the power distribution of the cospectrum was modulated by dead time in a frequency-dependent way. In this Letter we suggest a new, powerful method to normalize cospectra and, with some caveats, even power density spectra. Our approach uses the difference of the Fourier amplitudes from two independent detectors to characterize and filter out the effect of dead time. This method is crucially important for the accurate modelling of periodograms derived from instruments affected by dead time on board current missions like NuSTAR and ASTROSAT, but also future missions such as IXPEComment: 8 pages, 5 figures, Published on ApJL on 2018 January 3

Intermittency and Lifetime of the 625 Hz QPO in the 2004 Hyperflare from the Magnetar SGR 1806-20 as evidence for magnetic coupling between the crust and the core

Quasi-periodic oscillations (QPOs) detected in the 2004 giant flare from SGR 1806-20 are often interpreted as global magneto-elastic oscillations of the neutron star. There is, however, a large discrepancy between theoretical models, which predict that the highest frequency oscillations should die out rapidly, and the observations, which suggested that the highest-frequency signals persisted for ~100s in X-ray data from two different spacecraft. This discrepancy is particularly important for the high-frequency QPO at ~625 Hz. However, previous analyses did not systematically test whether the signal could also be there in much shorter data segments, more consistent with the theoretical predictions. Here, we test for the presence of the high-frequency QPO at 625 Hz in data from both the Rossi X-ray Timing Explorer (RXTE) and the Ramaty High Energy Solar Spectroscopic Imager (RHESSI) systematically both in individual rotational cycles of the neutron star, as well as averaged over multiple successive rotational cycles at the same phase. We find that the QPO in the RXTE data is consistent with being only present in a single cycle, for a short duration of ~0.5s, whereas the RHESSI data are as consistent with a short-lived signal that appears and disappears as with a long-lived QPO. Taken together, this data provides evidence for strong magnetic interaction between the crust and the core.Comment: Accepted for publication in ApJ. The data and simulations are available at http://figshare.com/articles/SGR_1806_20_Giant_Flare_Data_and_Simulations/1126082 , the code can be downloaded from https://github.com/dhuppenkothen/giantflare-paper , some documentation is under http://nbviewer.ipython.org/github/dhuppenkothen/giantflare-paper/blob/master/documents/giantflare-analysis.ipyn

Fourier Domain

The changes in brightness of an astronomical source as a function of time are key probes into that source's physics. Periodic and quasi-periodic signals are indicators of fundamental time (and length) scales in the system, while stochastic processes help uncover the nature of turbulent accretion processes. A key method of studying time variability is through Fourier methods, the decomposition of the signal into sine waves, which yields a representation of the data in frequency space. With the extension into \textit{spectral timing} the methods built on the Fourier transform can not only help us characterize (quasi-)periodicities and stochastic processes, but also uncover the complex relationships between time, photon energy and flux in order to help build better models of accretion processes and other high-energy dynamical physics. In this Chapter, we provide a broad, but practical overview of the most important relevant methods.Comment: 50 pages, 13 figures. This Chapter will appear in the Section "Timing Analysis" of the "Handbook of X-ray and Gamma-ray Astrophysics" (Editors in chief: C. Bambi and A. Santangelo

Hack Weeks as a model for Data Science Education and Collaboration

Across almost all scientific disciplines, the instruments that record our experimental data and the methods required for storage and data analysis are rapidly increasing in complexity. This gives rise to the need for scientific communities to adapt on shorter time scales than traditional university curricula allow for, and therefore requires new modes of knowledge transfer. The universal applicability of data science tools to a broad range of problems has generated new opportunities to foster exchange of ideas and computational workflows across disciplines. In recent years, hack weeks have emerged as an effective tool for fostering these exchanges by providing training in modern data analysis workflows. While there are variations in hack week implementation, all events consist of a common core of three components: tutorials in state-of-the-art methodology, peer-learning and project work in a collaborative environment. In this paper, we present the concept of a hack week in the larger context of scientific meetings and point out similarities and differences to traditional conferences. We motivate the need for such an event and present in detail its strengths and challenges. We find that hack weeks are successful at cultivating collaboration and the exchange of knowledge. Participants self-report that these events help them both in their day-to-day research as well as their careers. Based on our results, we conclude that hack weeks present an effective, easy-to-implement, fairly low-cost tool to positively impact data analysis literacy in academic disciplines, foster collaboration and cultivate best practices.Comment: 15 pages, 2 figures, submitted to PNAS, all relevant code available at https://github.com/uwescience/HackWeek-Writeu

NuSTAR Hard X-ray View of Low-luminosity Active Galactic Nuclei: High-energy Cutoff and Truncated Thin Disk

We report the analysis of simultaneous XMM-Newton+NuSTAR observations of two low-luminosity Active Galactic Nuclei (LLAGN), NGC 3998 and NGC 4579. We do not detect any significant variability in either source over the ~3 day length of the NuSTAR observations. The broad-band 0.5-60 keV spectrum of NGC 3998 is best fit with a cutoff power-law, while the one for NGC 4579 is best fit with a combination of a hot thermal plasma model, a power-law, and a blend of Gaussians to fit an Fe complex observed between 6 and 7 keV. Our main spectral results are the following: (1) neither source shows any reflection hump with a $3\sigma$ reflection fraction upper-limits $R<0.3$ and $R<0.18$ for NGC 3998 and NGC 4579, respectively; (2) the 6-7 keV line complex in NGC 4579 could either be fit with a narrow Fe K line at 6.4 keV and a moderately broad Fe XXV line, or 3 relatively narrow lines, which includes contribution from Fe XXVI; (3) NGC 4579 flux is 60% brighter than previously detected with XMM-Newton, accompanied by a hardening in the spectrum; (4) we measure a cutoff energy $E_{\rm cut}=107_{-18}^{+27}$ keV in NGC 3998, which represents the lowest and best constrained high-energy cutoff ever measured for an LLAGN; (5) NGC 3998 spectrum is consistent with a Comptonization model with either a sphere ($\tau\approx3\pm1$) or slab ($\tau\approx1.2\pm0.6$) geometry, corresponding to plasma temperatures between 20 and 150 keV. We discuss these results in the context of hard X-ray emission from bright AGN, other LLAGN, and hot accretion flow models.Comment: 14 pages, 11 figures, 4 tables, accepted for publication in Ap

Mapping the X-ray variability of GRS1915+105 with machine learning

Black hole X-ray binary systems (BHBs) contain a close companion star accreting onto a stellar-mass black hole. A typical BHB undergoes transient outbursts during which it exhibits a sequence of long-lived spectral states, each of which is relatively stable. GRS 1915+105 is a unique BHB that exhibits an unequaled number and variety of distinct variability patterns in X-rays. Many of these patterns contain unusual behaviour not seen in other sources. These variability patterns have been sorted into different classes based on count rate and color characteristics by Belloni et al (2000). In order to remove human decision-making from the pattern-recognition process, we employ an unsupervised machine learning algorithm called an auto-encoder to learn what classifications are naturally distinct by allowing the algorithm to cluster observations. We focus on observations taken by the Rossi X-ray Timing Explorer's Proportional Counter Array. We find that the auto-encoder closely groups observations together that are classified as similar under the Belloni et al (2000) system, but that there is reasonable grounds for defining each class as made up of components from 3 groups of distinct behaviour.Comment: 17 pages, 27 figures. For associated projection code to view the interactive 3D plot, see https://github.com/bjricketts/grs1915-auto-encode

Extending the Zn2Z^2_n and HH statistics to generic pulsed profiles

The search for astronomical pulsed signals within noisy data, in the radio band, is usually performed through an initial Fourier analysis to find "candidate" frequencies and then refined through the folding of the time series using trial frequencies close to the candidate. In order to establish the significance of the pulsed profiles found at these trial frequencies, pulsed profiles are evaluated with a chi-squared test, to establish how much they depart from a null hypothesis where the signal is consistent with a flat distribution of noisy measurements. In high-energy astronomy, the chi-squared statistic has widely been replaced by the $Z^2_n$ statistic and the H-test as they are more sensitive to extra information such as the harmonic content of the pulsed profile. The $Z^2_n$ statistic and H-test were originally developed for the use with "event data", composed of arrival times of single photons, leaving it unclear how these methods could be used in radio astronomy. In this paper, we present a version of the $Z^2_n$ statistic and H-test for pulse profiles with Gaussian uncertainties, appropriate for radio or even optical pulse profiles. We show how these statistical indicators provide better sensitivity to low-significance pulsar candidates with respect to the usual chi-squared method, and a straightforward way to discriminate between pulse profile shapes. Moreover, they provide an additional tool for Radio Frequency Interference (RFI) rejection.Comment: 15 pages, 5 figure