Searching for Exoplanets Using Artificial Intelligence

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects which, unlike current methods uses a neural network. Neural networks, also called "deep learning" or "deep nets" are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time-series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.Comment: Accepted, 16 Pages, 14 Figures, https://github.com/pearsonkyle/Exoplanet-Artificial-Intelligenc

XO-2b: a hot Jupiter with a variable host star that potentially affects its measured transit depth

The transiting hot Jupiter XO-2b is an ideal target for multi-object photometry and spectroscopy as it has a relatively bright ($V$-mag = 11.25) K0V host star (XO-2N) and a large planet-to-star contrast ratio (R$_{p}$/R$_{s}\approx0.015$). It also has a nearby (31.21") binary stellar companion (XO-2S) of nearly the same brightness ($V$-mag = 11.20) and spectral type (G9V), allowing for the characterization and removal of shared systematic errors (e.g., airmass brightness variations). We have therefore conducted a multiyear (2012--2015) study of XO-2b with the University of Arizona's 61" (1.55~m) Kuiper Telescope and Mont4k CCD in the Bessel U and Harris B photometric passbands to measure its Rayleigh scattering slope to place upper limits on the pressure-dependent radius at, e.g., 10~bar. Such measurements are needed to constrain its derived molecular abundances from primary transit observations. We have also been monitoring XO-2N since the 2013--2014 winter season with Tennessee State University's Celestron-14 (0.36~m) automated imaging telescope to investigate stellar variability, which could affect XO-2b's transit depth. Our observations indicate that XO-2N is variable, potentially due to {cool star} spots, {with a peak-to-peak amplitude of $0.0049 \pm 0.0007$~R-mag and a period of $29.89 \pm 0.16$~days for the 2013--2014 observing season and a peak-to-peak amplitude of $0.0035 \pm 0.0007$~R-mag and $27.34 \pm 0.21$~day period for the 2014--2015 observing season. Because of} the likely influence of XO-2N's variability on the derivation of XO-2b's transit depth, we cannot bin multiple nights of data to decrease our uncertainties, preventing us from constraining its gas abundances. This study demonstrates that long-term monitoring programs of exoplanet host stars are crucial for understanding host star variability.Comment: published in ApJ, 9 pages, 11 figures, 3 tables; updated figures with more ground-based monitoring, added more citations to previous work

Determination of the Cosmic Distance Scale from Sunyaev-Zel'dovich Effect and Chandra X-ray Measurements of High Redshift Galaxy Clusters

We determine the distance to 38 clusters of galaxies in the redshift range 0.14 < z < 0.89 using X-ray data from Chandra and Sunyaev-Zeldovich Effect data from the Owens Valley Radio Observatory and the Berkeley-Illinois-Maryland Association interferometric arrays. The cluster plasma and dark matter distributions are analyzed using a hydrostatic equilibrium model that accounts for radial variations in density, temperature and abundance, and the statistical and systematic errors of this method are quantified. The analysis is performed via a Markov chain Monte Carlo technique that provides simultaneous estimation of all model parameters. We measure a Hubble constant of 76.9 +3.9-3.4 +10.0-8.0 km/s/Mpc (statistical followed by systematic uncertainty at 68% confidence) for an Omega_M=0.3, Omega_Lambda=0.7 cosmology. We also analyze the data using an isothermal beta model that does not invoke the hydrostatic equilibrium assumption, and find H_0=73.7 +4.6-3.8 +9.5-7.6 km/s/Mpc; to avoid effects from cool cores in clusters, we repeated this analysis excluding the central 100 kpc from the X-ray data, and find H_0=77.6 +4.8-4.3 +10.1-8.2 km/s/Mpc. The consistency between the models illustrates the relative insensitivity of SZE/X-ray determinations of H_0 to the details of the cluster model. Our determination of the Hubble parameter in the distant universe agrees with the recent measurement from the Hubble Space Telescope key project that probes the nearby universe.Comment: ApJ submitted (revised version

Rayleigh Scattering in the Atmosphere of the Warm Exo-Neptune GJ 3470b

GJ 3470b is a warm Neptune-size planet transiting an M dwarf star. Like the handful of other small exoplanets for which transmission spectroscopy has been obtained, GJ 3470b exhibits a flat spectrum in the near- and mid-infrared. Recently, a tentative detection of Rayleigh scattering in its atmosphere has been reported. This signal manifests itself as an observed increase of the planetary radius as a function of decreasing wavelength in the visible. We set out to verify this detection and observed several transits of this planet with the LCOGT network and the Kuiper telescope in four different bands (Sloan g, Sloan i, Harris B, and Harris V). Our analysis reveals a strong Rayleigh scattering slope, thus confirming previous results. This makes GJ 3470b the smallest known exoplanet with a detection of Rayleigh scattering. We find that the most plausible scenario is a hydrogen/helium-dominated atmosphere covered by clouds which obscure absorption features in the infrared and hazes which give rise to scattering in the visible. Our results demonstrate the feasibility of exoplanet atmospheric characterization from the ground, even with meter-class telescopes

X-ray and Sunyaev-Zel'dovich Effect Measurements of the Gas Mass Fraction in Galaxy Clusters

We present gas mass fractions of 38 massive galaxy clusters spanning redshifts from 0.14 to 0.89, derived from Chandra X-ray data and OVRO/BIMA interferometric Sunyaev-Zel'dovich Effect measurements. We use three models for the gas distribution: (1) an isothermal beta-model fit jointly to the X-ray data at radii beyond 100 kpc and to all of the SZE data,(2) a non-isothermal double beta-model fit jointly to all of the X-ray and SZE data, and (3) an isothermal beta-model fit only to the SZE spatial data. We show that the simple isothermal model well characterizes the intracluster medium (ICM) outside of the cluster core in clusters with a wide range of morphological properties. The X-ray and SZE determinations of mean gas mass fractions for the 100 kpc-cut isothermal beta-model are fgas(X-ray)=0.110 +0.003-0.003 +0.006-0.018 and fgas(SZE)=0.116 +0.005-0.005 +0.009-0.026, where uncertainties are statistical followed by systematic at 68% confidence. For the non-isothermal double beta-model, fgas(X-ray)=0.119 +0.003-0.003 +0.007-0.014 and fgas(SZE)=0.121 +0.005-0.005 +0.009-0.016. For the SZE-only model, fgas(SZE)=0.120 +0.009-0.009 +0.009-0.027. Our results indicate that the ratio of the gas mass fraction within r2500 to the cosmic baryon fraction is 0.68 +0.10-0.16 where the range includes statistical and systematic uncertainties. By assuming that cluster gas mass fractions are independent of redshift, we find that the results are in agreement with standard LambdaCDM cosmology and are inconsistent with a flat matter dominated universe.Comment: ApJ, submitted. 47 pages, 5 figures, 8 table

Investigating the physical properties of transiting hot Jupiters with the 1.5-m Kuiper Telescope

We present new photometric data of 11 hot Jupiter transiting exoplanets (CoRoT-12b, HAT-P-5b, HAT-P-12b, HAT-P-33b, HAT-P-37b, WASP-2b, WASP-24b, WASP-60b, WASP-80b, WASP-103b, XO-3b) in order to update their planetary parameters and to constrain information about their atmospheres. These observations of CoRoT-12b, HAT-P-37b and WASP-60b are the first follow-up data since their discovery. Additionally, the first near-UV transits of WASP-80b and WASP-103b are presented. We compare the results of our analysis with previous work to search for transit timing variations (TTVs) and a wavelength dependence in the transit depth. TTVs may be evidence of a third body in the system and variations in planetary radius with wavelength can help constrain the properties of the exoplanet's atmosphere. For WASP-103b and XO-3b, we find a possible variation in the transit depths that may be evidence of scattering in their atmospheres. The B-band transit depth of HAT-P-37b is found to be smaller than its near-IR transit depth and such a variation may indicate TiO/VO absorption. These variations are detected from 2-4.6$\sigma$, so follow-up observations are needed to confirm these results. Additionally, a flat spectrum across optical wavelengths is found for 5 of the planets (HAT-P-5b, HAT-P-12b, WASP-2b, WASP-24b, WASP-80b), suggestive that clouds may be present in their atmospheres. We calculate a refined orbital period and ephemeris for all the targets, which will help with future observations. No TTVs are seen in our analysis with the exception of WASP-80b and follow-up observations are needed to confirm this possible detection.Comment: 18 pages, 7 figures, 9 Tables. Light Curves available online. Accepted to MNRAS (2017 August 25

Using Photometrically-Derived Properties of Young Stars to Refine TESS's Transiting Young Planet Survey Completeness

The demographics of young exoplanets can shed light onto their formation and evolution processes. Exoplanet properties are derived from the properties of their host stars. As such, it is important to accurately characterize the host stars since any systematic biases in their derivation can negatively impact the derivation of planetary properties. Here, we present a uniform catalog of photometrically-derived stellar effective temperatures, luminosities, radii, and masses for 4,865 young (<1 Gyr) stars in 31 nearby clusters and moving groups within 200 pc. We compared our photometrically-derived properties to a subset of those derived from spectra, and found them to be in good agreement. We also investigated the effect of stellar properties on the detection efficiency of transiting short-period young planets with TESS as calculated in Fernandes et al. 2022, and found an overall increase in the detection efficiency when the new photometrically derived properties were taken into account. Most notably, there is a 1.5 times increase in the detection efficiencies for sub-Neptunes/Neptunes (1.8-6 Re) implying that, for our sample of young stars, better characterization of host star properties can lead to the recovery of more small transiting planets. Our homogeneously derived catalog of updated stellar properties, along with a larger unbiased stellar sample and more detections of young planets, will be a crucial input to the accurate estimation of the occurrence rates of young short-period planets.Comment: 16 pages, 5 Figures, 3 Tables. Revised and resubmitted to AJ after a favorable referee report. Co-First Author

Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.

Initial results from sequencing studies suggest that there are relatively few low-frequency (&lt;5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency-large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant-common phenotype associations-11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (&lt;5%), respectively, low frequency-large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P &lt; 1 × 10(-06) (false discovery rate ∼5%)] and one of eight biomarker associations at P &lt; 8 × 10(-10). Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect