Peak Bagging Research Articles

Detecting stellar activity cycles in p-mode travel times

Context. The 11 yr solar cycle is known to affect the global modes of solar acoustic oscillations. In particular, p mode frequencies increase with solar activity. Aims. We propose a new method to detect the solar cycle from the p-mode autocorrelation function, and we validate this method using VIRGO/SPM photometric time series from solar cycles 23 and 24. Methods. The p-mode autocorrelation function shows multiple wavepackets separated by time lags of ∼123 min. Using a one-parameter fitting method (from local helioseismology), we measure the seismic travel times from each wavepacket up to skip number 40. Results. We find that the travel-time variations due to the solar cycle strongly depend on the skip number, with the strongest signature in odd skips from 17 to 31. Taking the noise covariance into account, the travel-time perturbations can be averaged over all skip numbers to enhance the signal-to-noise ratio. Conclusions. This method is robust to noise, simpler to implement than peak bagging in the frequency domain, and is promising for asteroseismology. We estimate that the activity cycle of a Sun-like star should be detectable with this new method in Kepler-like observations down to a visual magnitude of mK ∼ 11. However, for fainter stars, activity cycles are easier to detect in the photometric variability on rotational timescales.

Fast and Automated Peak Bagging with DIAMONDS (FAMED)

Stars of low and intermediate mass that exhibit oscillations may show tens of detectable oscillation modes each. Oscillation modes are a powerful tool to constrain the internal structure and rotational dynamics of the star, hence allowing one to obtain an accurate stellar age. The tens of thousands of solar-like oscillators that have been discovered thus far are representative of the large diversity of fundamental stellar properties and evolutionary stages available. Because of the wide range of oscillation features that can be recognized in such stars, it is particularly challenging to properly characterize the oscillation modes in detail, especially in light of large stellar samples. Overcoming this issue requires an automated approach, which has to be fast, reliable, and flexible at the same time. In addition, this approach should not only be capable of extracting the oscillation mode properties of frequency, linewidth, and amplitude from stars in different evolutionary stages, but also able to assign a correct mode identification for each of the modes extracted. Here we present the new freely available pipeline FAMED (Fast and AutoMated pEak bagging with DIAMONDS), which is capable of performing an automated and detailed asteroseismic analysis in stars ranging from the main sequence up to the core-helium-burning phase of stellar evolution. This, therefore, includes subgiant stars, stars evolving along the red giant branch (RGB), and stars likely evolving toward the early asymptotic giant branch. In this paper, we additionally show how FAMED can detect rotation from dipolar oscillation modes in main sequence, subgiant, low-luminosity RGB, and core-helium-burning stars.

The Progressive Political Foundations of Edward Abbey’s Environmental Advocacy

For several generations of environmentalists, Edward Abbey has held an iconic position as an advocate for wilderness preservation and environmental amelioration. In an article published in Telos, Tim Luke acknowledges Abbey’s lasting influence: “Much of contemporary American environmental thought, implicitly or explicitly, circles around the literary corpus of Edward Abbey.” An articulate spokesperson, Abbey entertained, challenged, and inspired in both his writings and actions. He embodies and gives voice to those who value wild ecosystems and habitats because his advocacy derived from experience: backpacking, river running, meditating, peak bagging, and hanging out—in actual places. His narratives continue to assert the intrinsic value of such places as well as the essential need we have as humans to experience environments minimally disturbed by the impacts of modern industrial development.1 Those inspired by similar experiences are moved to understanding and to action by Abbey’s books, most memorably the classic essays of Desert Solitaire and the picaresque novel The Monkey Wrench Gang.

The Rotational Shear Layer inside the Early Red-giant Star KIC 4448777

Abstract We present the asteroseismic study of the early red-giant star KIC 4448777, complementing and integrating a previous work, aimed at characterizing the dynamics of its interior by analyzing the overall set of data collected by the Kepler satellite during the four years of its first nominal mission. We adopted the Bayesian inference code diamonds for the peak bagging analysis and asteroseismic splitting inversion methods to derive the internal rotational profile of the star. The detection of new splittings of mixed modes, which are more concentrated in the very inner part of the helium core, allowed us to reconstruct the angular velocity profile deeper into the interior of the star and to disentangle the details better than in Paper I: the helium core rotates almost rigidly about 6 times faster than the convective envelope, while part of the hydrogen shell seems to rotate at a constant velocity about 1.15 times lower than the He core. In particular, we studied the internal shear layer between the fast-rotating radiative interior and the slow convective zone and we found that it lies partially inside the hydrogen shell above r ≃ 0.05R and extends across the core–envelope boundary. Finally, we theoretically explored the possibility for the future capabilty to sound the convective envelope in the red-giant stars and we concluded that the inversion of a set of splittings with only low-harmonic degree l ≤ 3, even supposing a very large number of modes, will not allow us to resolve the rotational profile of this region in detail.

Bayesian peak bagging analysis of 19 low-mass low-luminosity red giants observed with Kepler (Corrigendum)

Bayesian peak bagging analysis of 19 low-mass low-luminosity red giants observed with Kepler (Corrigendum)

Automated asteroseismic peak detections

Space observatories such as $\textit{Kepler}$ have provided data that can potentially revolutionise our understanding of stars. Through detailed asteroseismic analyses we are capable of determining fundamental stellar parameters and reveal the stellar internal structure with unprecedented accuracy. However, such detailed analyses, known as peak bagging, have so far been obtained for only a small percentage of the observed stars while most of the scientific potential of the available data remains unexplored. One of the major challenges in peak bagging is identifying how many solar-like oscillation modes are visible in a power density spectrum. Identification of oscillation modes is usually done by visual inspection which is time-consuming and has a degree of subjectivity. Here, we present a peak detection algorithm specially suited for the detection of solar-like oscillations. It reliably characterises the solar-like oscillations in a power density spectrum and estimates their parameters without human intervention. Furthermore, we provide a metric to characterise the false positive and false negative rates to provide further information about the reliability of a detected oscillation mode or the significance of a lack of detected oscillation modes. The algorithm presented here opens the possibility for detailed and automated peak bagging of the thousands of solar-like oscillators observed by $\textit{Kepler}$.

Formation history of open clusters constrained by detailed asteroseismology of red giant stars observed byKepler

Stars originate by the gravitational collapse of a turbulent molecular cloud of a diffuse medium, and are often observed to form clusters. Stellar clusters therefore play an important role in our understanding of star formation and of the dynamical processes at play. However, investigating the cluster formation is diffcult because the density of the molecular cloud undergoes a change of many orders of magnitude. Hierarchical-step approaches to decompose the problem into different stages are therefore required, as well as reliable assumptions on the initial conditions in the clouds. We report for the first time the use of the full potential of NASA Kepler asteroseismic observations coupled with 3D numerical simulations, to put strong constraints on the early formation stages of open clusters. Thanks to a Bayesian peak bagging analysis of about 50 red giant members of NGC 6791 and NGC 6819, the two most populated open clusters observed in the nominal Kepler mission, we derive a complete set of detailed oscillation mode properties for each star, with thousands of oscillation modes characterized. We therefore show how these asteroseismic properties lead us to a discovery about the rotation history of stellar clusters. Finally, our observational findings will be compared with hydrodynamical simulations for stellar cluster formation to constrain the physical processes of turbulence, rotation, and magnetic fields that are in action during the collapse of the progenitor cloud into a proto-cluster.

Oscillation frequencies for 35Keplersolar-type planet-hosting stars using Bayesian techniques and machine learning

\Kepler has revolutionised our understanding of both exoplanets and their host stars. Asteroseismology is a valuable tool in the characterisation of stars and \Kepler is an excellent observing facility to perform asteroseismology. Here we select a sample of 35 \Kepler solar-type stars which host transiting exoplanets (or planet candidates) with detected solar-like oscillations. Using available \Kepler short cadence data up to Quarter 16 we create power spectra optimised for asteroseismology of solar-type stars. We identify modes of oscillation and estimate mode frequencies by ``peak bagging'' using a Bayesian MCMC framework. In addition, we expand the methodology of quality assurance using a Bayesian unsupervised machine learning approach. We report the measured frequencies of the modes of oscillation for all 35 stars and frequency ratios commonly used in detailed asteroseismic modelling. Due to the high correlations associated with frequency ratios we report the covariance matrix of all frequencies measured and frequency ratios calculated. These frequencies, frequency ratios, and covariance matrices can be used to obtain tight constraint on the fundamental parameters of these planet-hosting stars.

Bayesian peak bagging analysis of 19 low-mass low-luminosity red giants observed withKepler

The currently available Kepler light curves contain an outstanding amount of information but a detailed analysis of the individual oscillation modes in the observed power spectra, also known as peak bagging, is computationally demanding and challenging to perform on a large number of targets. Our intent is to perform for the first time a peak bagging analysis on a sample of 19 low-mass low-luminosity red giants observed by Kepler for more than four years. This allows us to provide high-quality asteroseismic measurements that can be exploited for an intensive testing of the physics used in stellar structure models, stellar evolution and pulsation codes, as well as for refining existing asteroseismic scaling relations in the red giant branch regime. For this purpose, powerful and sophisticated analysis tools are needed. We exploit the Bayesian code Diamonds, using an efficient nested sampling Monte Carlo algorithm, to perform both a fast fitting of the individual oscillation modes and a peak detection test based on the Bayesian evidence. We find good agreement for the parameters estimated in the background fitting phase with those given in the literature. We extract and characterize a total of 1618 oscillation modes, providing the largest set of detailed asteroseismic mode measurements ever published. We report on the evidence of a change in regime observed in the relation between linewidths and effective temperatures of the stars occurring at the bottom of the RGB. We show the presence of a linewidth depression or plateau around $\nu_\mathrm{max}$ for all the red giants of the sample. Lastly, we show a good agreement between our measurements of maximum mode amplitudes and existing maximum amplitudes from global analyses provided in the literature, useful as empirical tools to improve and simplify the future peak bagging analysis on a larger sample of evolved stars.

Peak Bagging of red giant stars observed byKepler: first results with a new method based on Bayesian nested sampling

The peak bagging analysis, namely the fitting and identification of single oscillation modes in stars' power spectra, coupled to the very high-quality light curves of red giant stars observed by Kepler, can play a crucial role for studying stellar oscillations of different flavor with an unprecedented level of detail. A thorough study of stellar oscillations would thus allow for deeper testing of stellar structure models and new insights in stellar evolution theory. However, peak bagging inferences are in general very challenging problems due to the large number of observed oscillation modes, hence of free parameters that can be involved in the fitting models. Effciency and robustness in performing the analysis is what may be needed to proceed further. For this purpose, we developed a new code implementing the Nested Sampling Monte Carlo (NSMC) algorithm, a powerful statistical method well suited for Bayesian analyses of complex problems. In this talk we show the peak bagging of a sample of high signal-to-noise red giant stars by exploiting recent Kepler datasets and a new criterion for the detection of an oscillation mode based on the computation of the Bayesian evidence. Preliminary results for frequencies and lifetimes for single oscillation modes, together with acoustic glitches, are therefore presented.

DIAMONDS: a new Bayesian nested sampling tool

In the context of high-quality asteroseismic data provided by the NASA Kepler mission, we developed a new code, termed Diamonds (high-DImensional And multi-MOdal NesteD Sampling), for fast Bayesian parameter estimation and model comparison by means of the Nested Sampling Monte Carlo (NSMC) algorithm, an efficient and powerful method very suitable for high-dimensional problems (like the peak bagging analysis of solar-like oscillations) and multi-modal problems (i.e. problems that show multiple solutions). We applied the code to the peak bagging analysis of solar-like oscillations observed in a challenging F-type star. By means of Diamonds one is able to detect the different backgrounds in the power spectrum of the star (e.g. stellar granulation and faculae activity) and to understand whether one or two oscillation peaks can be identified or not. In addition, we demonstrate a novel approach to peak bagging based on multimodality, which is able to reduce significantly the number of free parameters involved in the peak bagging model. This novel approach is therefore of great interest for possible future automatization of the entire analysis technique.

DIAMONDS: A new Bayesian nested sampling tool

To exploit the full potential of Kepler light curves, sophisticated and robust analysis tools are now required more than ever. Characterizing single stars with an unprecedented level of accuracy and subsequently analyzing stellar populations in detail are fundamental to further constrain stellar structure and evolutionary models. We developed a new code, termed Diamonds, for Bayesian parameter estimation and model comparison by means of the nested sampling Monte Carlo (NSMC) algorithm, an efficient and powerful method very suitable for high-dimensional and multi-modal problems. A detailed description of the features implemented in the code is given with a focus on the novelties and differences with respect to other existing methods based on NSMC. Diamonds is then tested on the bright F8 V star KIC~9139163, a challenging target for peak-bagging analysis due to its large number of oscillation peaks observed, which are coupled to the blending that occurs between $\ell=2,0$ peaks, and the strong stellar background signal. We further strain the performance of the approach by adopting a 1147.5 days-long Kepler light curve. The Diamonds code is able to provide robust results for the peak-bagging analysis of KIC~9139163. We test the detection of different astrophysical backgrounds in the star and provide a criterion based on the Bayesian evidence for assessing the peak significance of the detected oscillations in detail. We present results for 59 individual oscillation frequencies, amplitudes and linewidths and provide a detailed comparison to the existing values in the literature. Lastly, we successfully demonstrate an innovative approach to peak bagging that exploits the capability of Diamonds to sample multi-modal distributions, which is of great potential for possible future automatization of the analysis technique.

YAOPBM—II: extension to higher degrees and to shorter time series

In 2005, I presented a new fitting methodology (Yet AnOther Peak Bagging Method -YAOPBM), derived for very-long time series (2088-day-long) and applied it to low degree modes, ι ≤ 25. That very-long time series was also sub-divided into shorter segments (728-day-long) that were each fitted over the same range of degrees, to estimate changes with solar activity levels.I present here the extension of this method in several ‘directions’: a) to substantially higher degrees (ι ≤ 125); b) to shorter time series (364- and 182-day-long); and c) to additional 728-day-long segments, covering now some 10 years of observations.I discuss issues with the fitting, namely the leakage matrix, and the f- and p1 mode at very low frequencies, and I present some of the characteristics of the observed temporal changes.

YAOPBM – yet another peak bagging method

AbstractI present and discuss the fitting methodology I developed for very‐long time series (2088‐day‐long). This new method was first used to fit low degree modes, 𝓁 ≤ 25. That time series was also sub‐divided in somewhat shorter segments (728‐daylong) and also fitted for these low degrees, in order to measure changes with the solar activity level.I have recently extended the fitting in several “directions”: 1) to substantially higher degrees (𝓁 ≤ 125), 2) to shorter time series (364‐ and 182‐day‐long), and, 3) to additional 728‐day‐long segments, covering now some 10 years of observations. I present and discuss issues related to this expansion, namely problems at low frequencies affecting the f and p1 modes, and the inadequacy of the leakage matrix at higher degrees. I also present some of the characteristics of the observed temporal changes in the resulting frequencies. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Peak bagging for peptide mass fingerprinting

Mass Spectrometry (MS)-based protein identification via peptide mass fingerprinting (PMF) is a key component in high-throughput proteome research. While PMF was the first commonly used protein identification method, provided higher throughput than the tandem MS-based method, its accuracy is lower than that of the tandem MS method. Thus, it is desirable to develop PMF-based algorithm with higher protein identification accuracy to facilitate proteome research. We propose a peak bagging method for single MS-based protein identification. It combines results from multiple PMF algorithms, where each PMF algorithm takes a random peak subset as input. Evaluation with a set of real MALDI-TOF MS spectra shows that the new peak bagging method provides consistent improvements over the single PMF algorithm.

Solar FLAG★ hare and hounds: on the extraction of rotational p-mode splittings from seismic, Sun-as-a-star data

We report on results from the first solar Fitting at Low-Angular degree Group (solar FLAG) hare-and-hounds exercise. The group is concerned with the development of methods for extracting the parameters of low-l solar p-mode data ('peak bagging'), collected by Sun-as-a-star observations. Accurate and precise estimation of the fundamental parameters of the p modes is a vital pre-requisite of all subsequent studies. Nine members of the FLAG (the 'hounds') fitted an artificial 3456-d data set. The data set was made by the 'hare' (WJC) to simulate full-disc Doppler velocity observations of the Sun. The rotational frequency splittings of the l = 1, 2 and 3 modes were the first parameter estimates chosen for scrutiny. Significant differences were uncovered at l = 2 and 3 between the fitted splittings of the hounds. Evidence is presented that suggests this unwanted bias had its origins in several effects. The most important came from the different way in which the hounds modelled the visibility ratio of the different rotationally split components. Our results suggest that accurate modelling of the ratios is vital to avoid the introduction of significant bias in the estimated splittings. This is of importance not only for studies of the Sun, but also of the solar analogues that will be targets for asteroseismic campaigns.

Higher than Everest: An Adventurer's Guide to the Solar System and Turn Right at Orion: Travels through the Cosmos

A little light reading is desirable now and then, especially over the holidays and/or during times of stress or periods of reflection. With this idea in mind, I reviewed two books that may be viewed as “science entertainment.” The first is Higher than Everest by Paul Hodge. Set some 200 years in the future, this book is structured as a guidebook for adventurers who are looking for thrills and spills at spectacular sites in the Solar System. Based on undergraduate‐level lectures in planetary science, the book has 20 chapters focusing on sites such as Olympus Mons and Valles Marineris, Mars; Copernicus Crater on the Moon; Maxwell Montes, Venus; Euboea Montes, Io; and geysers on Triton. With a “peak bagging” emphasis on exploring the highest (or lowest) features, recommendations are provided for siting base camps, including necessary equipment, recognizing environmental constraints, and mapping out traverses and itineraries. Along the way, participants are advised to keep an eye out for aspects of their surroundings that would help to resolve long‐standing “mysteries” regarding their origin. Parallels are drawn between extraterrestrial features and their terrestrial counterparts, and numerous photos are provided to reinforce the similarities among planetary surfaces in different environments.

Experiences in developing a tourism web site for hiking Arizona's highest summits and deepest canyons

Arizona's 20-20 Challenge is a web site for hikers, created for the Arizona Office of Tourism. The web site lists the 20 highest summit trails and the 20 deepest canyon trails of Arizona, and provides maps and information for each. The Challenge offers a southwestern twist on the increasingly popular concept of 'peak bagging', which is extended here for the first time to canyons. Developing the list proved to be more challenging than expected, in part because of the difficulty of determining what qualifies as a summit or canyon, whether the summit or canyon is independent of its neighbours and whether an open trail exists. Recreational, cultural and environmental factors were taken into account in addition to geo morphological criteria. The design and content of the web site is described and compared to other peak-bagging sites, which vary greatly in their emphases. Two such web sites, for Scotland and the Adirondacks, indicate that peak-baggers have completed at least 800,000 hikes in the two regions combined and that the activity is accelerating in popularity.