A Search for Analogs of KIC 8462852 (Boyajian’s Star): A Second List of Candidates

In data from the Kepler mission, the normal F3V star KIC 8462852 (Boyajian’s star) was observed to exhibit infrequent dips in brightness that have not been satisfactorily explained. A previous paper reported the first results of a search for other similar stars in a limited region of the sky around the Kepler field. This paper expands on that search to cover the entire sky between declinations of +22°and +68°. Fifteen new candidates with low rates of dipping, referred to as “slow dippers” in Paper I, have been identified. The dippers occupy a limited region of the HR diagram and an apparent clustering in space is found. This latter feature suggests that these stars are attractive targets for SETI searches.

Period Ratio Sculpting near Second-order Mean-motion Resonances

Second-order mean-motion resonances lead to an interesting phenomenon in the sculpting of the period-ratio distribution, due to their shape and width in period-ratio/eccentricity space. As the osculating periods librate in resonance, the time-averaged period ratio approaches the exact commensurability. The width of second-order resonances increases with increasing eccentricity, and thus more eccentric systems have a stronger peak at commensurability when averaged over sufficient time. The libration period is short enough that this time-averaging behavior is expected to appear on the timescale of the Kepler mission. Using N-body integrations of simulated planet pairs near the 5:3 and 3:1 mean-motion resonances, we investigate the eccentricity distribution consistent with the planet pairs observed by Kepler. This analysis, an approach independent from previous studies, shows no statistically significant peak at the 3:1 resonance and a small peak at the 5:3 resonance, placing an upper limit on the Rayleigh scale parameter, σ, of the eccentricity of the observed Kepler planets at σ = 0.245 (3:1) and σ = 0.095 (5:3) at 95% confidence, consistent with previous results from other methods.

How Complete Are Surveys for Nearby Transiting Hot Jupiters?

Hot Jupiters are a rare and interesting outcome of planet formation. Although more than 500 hot Jupiters (HJs) are known, most of them were discovered by a heterogeneous collection of surveys with selection biases that are difficult to quantify. Currently, our best knowledge of HJ demographics around FGK stars comes from the sample of ≈40 objects detected by the Kepler mission, which have a well-quantified selection function. Using the Kepler results, we simulate the characteristics of the population of nearby transiting HJs. A comparison between the known sample of nearby HJs and simulated magnitude-limited samples leads to four conclusions. (1) The known sample of HJs appears to be ≈75% complete for stars brighter than Gaia G ≤ 10.5, falling to ≲50% for G ≤ 12. (2) There are probably a few undiscovered HJs with host stars brighter than G ≈ 10 located within 10° of the Galactic plane. (3) The period and radius distributions of HJs may differ for F-type hosts (which dominate the nearby sample) and G-type hosts (which dominate the Kepler sample). (4) To obtain a magnitude-limited sample of HJs that is larger than the Kepler sample by an order of magnitude, the limiting magnitude should be approximately G ≈ 12.5. This magnitude limit is within the range for which NASA’s Transiting Exoplanet Survey Satellite can easily detect HJs, presenting the opportunity to greatly expand our knowledge of hot-Jupiter demographics.

CHROMOSPHERIC ACTIVITY NATURE OF KIC 6044064

This study presents results obtained from the data of KIC 6044064 (KOI 6652). KIC 6044064 was observed by the Kepler Mission for a total of 1384.254 days. 525 minima times were determined, 264 of which were primary minima and the rest were secondary minima. The OPEA model was derived and its parameters were obtained. On the secondary component, there are two different spot bands latitudinally outstretched, consisting of three spots located with a phase interval of 0.33. The average migration period was found to be 623.063±4.870 days (1.71±0.01 years) for the first spot group, while it was 1125.514±7.305 days (3.08±0.02 years) for the second group. The spectral types of the components seem to be G7V+K9V. Their masses and radii were determined to be 0.86Mʘ and 0.89Rʘ for the primary component and 0.54Mʘ and 0.62Rʘ for the secondary component.

Identifying Variable Stars from Kepler Data Using Machine Learning

Machine learning algorithms play an impressive role in modern technology and address automation problems in many fields as these techniques can be used to identify features with high sensitivity, which humans or other programming techniques aren’t capable of detecting. In addition, the growth of the availability of the data demands the need of faster, accurate, and more reliable automating methods of extracting information, reforming, and preprocessing, and analyzing them in the world of science. The development of machine learning techniques to automate complex manual programs is a time relevant research in astrophysics as it’s a field where, experts are dealing with large sets of data every day. In this study, an automated classification was built for 6 types of star classes Beta Cephei, Delta Scuti, Gamma Doradus, Red Giants, RR Lyrae and RV Tarui with widely varying properties, features extracted from training dataset of stellar light curves obtained from Kepler mission. The Random Forest classification model was used as the Machine Learning model and both periodic and non-periodic features extracted from light curves were used as the inputs to the model. Our implementation achieved an accuracy of 86.5%, an average precision level of 0.86, an average recall value of 0.87, and average F1-Score of 0.86 for the testing dataset obtained from the Kepler mission.

Microlensing survey combining Roman and Euclid Space Telescopes

<p>As the Kepler mission has done for hot exoplanets, the ESA Euclid and NASA Roman missions have the potential to create a breakthrough in our understanding of the demographics of cool exoplanets, including planets on very wide orbits, unbound, or "free-floating", planets (FFPs). Current ground-based microlensing observations have provided preliminary evidence for a potentially significant population of Super-Earth FFPs. Roman will dedicate part of its core survey program to the detection of cool exoplanets via microlensing, while Euclid may undertake a microlensing program as an ancillary science goal. We argue that simultaneous observations of short-duration microlensing events by Roman and Euclid will enable not just the verification of FFPs, but also a direct measurement of their masses, distances and transverse motions, via the detection of microlens parallax between Euclid and Roman. We use simulations of the joint-mission detection capabilities to show that parallax detections will be possible down to Earth-mass FFPs. The mass and phase-space measurements from a joint survey could thus provide strong clues to the primary mode of FFP formation.</p> <p>We also demonstrate that an early brief Euclid survey (∼5 h) of the Roman fields shortly after the Euclid launch would be also very valuable. It would allow the measurement of at least 10% of the events’  relative proper motions and 35% of the lens magnitudes very early on the life of the Roman Survey. We further discuss additional valuable science that will be facilitated by a joint Roman-Euclid microlensing campaign.</p>

Exoplanet Statistics and Theoretical Implications

In the past few years, significant advances have been made in understanding the distributions of exoplanet populations and the architecture of planetary systems. We review the recent progress of planet statistics, with a focus on the inner ≲1-AU region of the planetary system that has been fairly thoroughly surveyed by the Kepler mission. We also discuss the theoretical implications of these statistical results for planet formation and dynamical evolution. Expected final online publication date for the Annual Review of Astronomy and Astrophysics, Volume 59 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Fault Detection, Isolation and Prognosis of Aerospace Systems Using Adaptive Growing Recurrent Neural Networks

Due to the increase in complexity in aerospace systems, developing a diagnosis, prognosis and health monitoring (DPHM) framework is a challenge that must be considered to assure the safety of such systems. This thesis discusses this problem by proposing a novel growing neural network model to automate the process of DPHM for aerospace systems. The model optimizes the architecture of a recurrent neural network and was used to make Remaining Useful Lifetime (RUL) predictions for aircraft engines and detect failure for satellite attitude actuators (Reaction Wheels). It was tested on the CMAPSS and PHM08 aircraft engine datasets simulated by NASA, and it was able to make RUL predictions with root mean square errors as low as 14.31 engine cycles. Another application to test the proposed model was on the Kepler Spacecraft’s reaction wheels from which two have failed. The model detected the failure of the two failed reaction wheels by estimating a Health Index value which indicates the probability of failure of the reaction wheels using the residuals between the speed predictions made by the model and measured speed values. Failure was predicted using the model 105 days and 54 days before it occurred for reaction wheels two and four respectively. Prognostics were also applied on the Kepler Mission reaction wheels and RUL predictions were made with mean absolute errors ranging between 2-13 days depending on how close the reaction wheel is to failure. The proposed algorithm showed results in both applications that could regard it as a promising approach for DPHM models.

Fault Detection, Isolation and Prognosis of Aerospace Systems Using Adaptive Growing Recurrent Neural Networks

Due to the increase in complexity in aerospace systems, developing a diagnosis, prognosis and health monitoring (DPHM) framework is a challenge that must be considered to assure the safety of such systems. This thesis discusses this problem by proposing a novel growing neural network model to automate the process of DPHM for aerospace systems. The model optimizes the architecture of a recurrent neural network and was used to make Remaining Useful Lifetime (RUL) predictions for aircraft engines and detect failure for satellite attitude actuators (Reaction Wheels). It was tested on the CMAPSS and PHM08 aircraft engine datasets simulated by NASA, and it was able to make RUL predictions with root mean square errors as low as 14.31 engine cycles. Another application to test the proposed model was on the Kepler Spacecraft’s reaction wheels from which two have failed. The model detected the failure of the two failed reaction wheels by estimating a Health Index value which indicates the probability of failure of the reaction wheels using the residuals between the speed predictions made by the model and measured speed values. Failure was predicted using the model 105 days and 54 days before it occurred for reaction wheels two and four respectively. Prognostics were also applied on the Kepler Mission reaction wheels and RUL predictions were made with mean absolute errors ranging between 2-13 days depending on how close the reaction wheel is to failure. The proposed algorithm showed results in both applications that could regard it as a promising approach for DPHM models.