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Register a new userAutoRegressive Planet Search: Feasibility Study for Irregular Time Series
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Sensitive signal processing methods are needed to detect transiting planets from ground-based photometric surveys. Caceres et al. (2019) show that the AutoRegressive Planet Search (ARPS) method --- a combination of autoregressive integrated moving average (ARIMA) parametric modeling, a new Transit Comb Filter (TCF) periodogram, and machine learning classification --- is effective when applied to evenly spaced light curves from space-based missions. We investigate here whether ARIMA and TCF will be effective for ground-based survey light curves that are often sparsely sampled with high noise levels from atmospheric and instrumental conditions. The ARPS procedure is applied to selected light curves with strong planetary signals from the Kepler mission that have been altered to simulate the conditions of ground-based exoplanet surveys. Typical irregular cadence patterns are used from the HATSouth survey. We also evaluate recovery of known planets from HATSouth. Simulations test transit signal recovery as a function of cadence pattern and duration, stellar magnitude, planet orbital period and transit depth. Detection rates improve for shorter periods and deeper transits. The study predicts that the ARPS methodology will detect planets with $gtrsim 0.1$% transit depth and periods $lesssim 40$ days in HATSouth stars brighter than $sim$15 mag. ARPS methodology is therefore promising for planet discovery from ground-based exoplanet surveys with sufficiently dense cadence patterns.
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The detection of periodic signals from transiting exoplanets is often impeded by extraneous aperiodic photometric variability, either intrinsic to the star or arising from the measurement process. Frequently, these variations are autocorrelated wherein later flux values are correlated with previous ones. In this work, we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) and related statistical models that treat a wide variety of stochastic processes, as well as nonstationarity, to improve detection of new planetary transits. Providing a time series is evenly spaced or can be placed on an evenly spaced grid with missing values, these low-dimensional parametric models can prove very effective. We introduce a planet-search algorithm to detect periodic transits in the residuals after the application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), is closely related to the traditional Box-fitting Least Squares and provides an analogous periodogram. Finally, if a previously identified or simulated sample of planets is available, selected scalar features from different stages of the analysis -- the original light curves, ARIMA fits, TCF periodograms, and folded light curves -- can be collectively used with a multivariate classifier to identify promising candidates while efficiently rejecting false alarms. We use Random Forests for this task, in conjunction with Receiver Operating Characteristic (ROC) curves, to define discovery criteria for new, high fidelity planetary candidates. The ARPS methodology can be applied to both evenly spaced satellite light curves and densely cadenced ground-based photometric surveys.
The 4-year light curves of 156,717 stars observed with NASAs Kepler mission are analyzed using the AutoRegressive Planet Search (ARPS) methodology described by Caceres et al. (2019). The three stages of processing are: maximum likelihood ARIMA modeling of the light curves to reduce stellar brightness variations; constructing the Transit Comb Filter periodogram to identify transit-like periodic dips in the ARIMA residuals; Random Forest classification trained on Kepler Team confirmed planets using several dozen features from the analysis. Orbital periods between 0.2 and 100 days are examined. The result is a recovery of 76% of confirmed planets, 97% when period and transit depth constraints are added. The classifier is then applied to the full Kepler dataset; 1,004 previously noticed and 97 new stars have light curve criteria consistent with the confirmed planets, after subjective vetting removes clear False Alarms and False Positive cases. The 97 Kepler ARPS Candidate Transits mostly have periods $P<10$ days; many are UltraShort Period hot planets with radii $<1$% of the host star. Extensive tabular and graphical output from the ARPS time series analysis is provided to assist in other research relating to the Kepler sample.
Celestial objects exhibit a wide range of variability in brightness at different wavebands. Surprisingly, the most common methods for characterizing time series in statistics -- parametric autoregressive modeling -- is rarely used to interpret astronomical light curves. We review standard ARMA, ARIMA and ARFIMA (autoregressive moving average fractionally integrated) models that treat short-memory autocorrelation, long-memory $1/f^alpha$ `red noise, and nonstationary trends. Though designed for evenly spaced time series, moderately irregular cadences can be treated as evenly-spaced time series with missing data. Fitting algorithms are efficient and software implementations are widely available. We apply ARIMA models to light curves of four variable stars, discussing their effectiveness for different temporal characteristics. A variety of extensions to ARIMA are outlined, with emphasis on recently developed continuous-time models like CARMA and CARFIMA designed for irregularly spaced time series. Strengths and weakness of ARIMA-type modeling for astronomical data analysis and astrophysical insights are reviewed.
As the NASA Transiting Exoplanet Survey Satellite (TESS) fulfills its primary mission it is executing an unprecedented all-sky survey with the potential to discover distant planets in our own solar system, as well as hundreds of Transneptunian Objects (TNOs) and Centaurs. We demonstrate that shift-and-stack techniques can be used to efficiently search the Full-Frame Image (FFI) data from the TESS mission and survey the entire sky for outer Solar System objects down to $sim22^{nd}$ magnitude.
HD 15082 (WASP-33) is the hottest and fastest rotating star known to harbor a transiting extrasolar planet (WASP-33b). The lack of high precision radial velocity (RV) data stresses the need for precise light curve analysis and gathering further RV data. By using available photometric and RV data, we perform a blend analysis, compute more accurate system parameters, confine the planetary mass and attempt to cast light on the observed transit anomalies. We combine the original HATNet observations and various followup data to jointly analyze the signal content and extract the transit component and use our RV data to aid the global parameter determination. The blend analysis of the combination of multicolor light curves yields the first independent confirmation of the planetary nature of WASP-33b. We clearly identify three frequency components in the 15-21 1/day regime with amplitudes 7-5 mmag. These frequencies correspond to the delta Scuti-type pulsation of the host star. None of these pulsation frequencies or their low-order linear combinations are in close resonance with the orbital frequency. We show that these pulsation components explain some but not all of the observed transit anomalies. The grand-averaged transit light curve shows that there is a ~1.5 mmag brightening shortly after the planet passes the mid-transit phase. Although the duration and amplitude of this brightening varies, it is visible even through the direct inspections of the individual transit events (some 40-60% of the followup light curves show this phenomenon). We suggest that the most likely explanation of this feature is the presence of a well-populated spot belt which is highly inclined to the orbital plane. This geometry is consistent with the inference from the spectroscopic anomalies. Finally, we constrain the planetary mass to M_p=3.27+/-0.73 M_J by using our RV data collected by the TRES spectrograph.
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