313 research outputs found
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 (-mag = 11.25) K0V
host star (XO-2N) and a large planet-to-star contrast ratio
(R/R). It also has a nearby (31.21") binary stellar
companion (XO-2S) of nearly the same brightness (-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 ~R-mag and a period of ~days for the 2013--2014
observing season and a peak-to-peak amplitude of ~R-mag and
~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
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
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
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, 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
Investigating the effectiveness of many-core network processors for high performance cyber protection systems. Part I, FY2011.
This report documents our first year efforts to address the use of many-core processors for high performance cyber protection. As the demands grow for higher bandwidth (beyond 1 Gbits/sec) on network connections, the need to provide faster and more efficient solution to cyber security grows. Fortunately, in recent years, the development of many-core network processors have seen increased interest. Prior working experiences with many-core processors have led us to investigate its effectiveness for cyber protection tools, with particular emphasis on high performance firewalls. Although advanced algorithms for smarter cyber protection of high-speed network traffic are being developed, these advanced analysis techniques require significantly more computational capabilities than static techniques. Moreover, many locations where cyber protections are deployed have limited power, space and cooling resources. This makes the use of traditionally large computing systems impractical for the front-end systems that process large network streams; hence, the drive for this study which could potentially yield a highly reconfigurable and rapidly scalable solution
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