Microlensing can be used to discover exoplanets of a wide range of masses with orbits beyond ∼1 au, and even free-floating planets. The Wide Field Infrared Survey Telescope (WFIRST) mission will use ...microlensing to discover approximately 1600 planets by monitoring ∼100 million stars to find ∼50,000 microlensing events. Modeling each microlensing event, especially the ones involving two or more lenses, is typically complicated and time consuming, and analyzing thousands of WFIRST microlensing events is possibly infeasible using current methods. Here, we present an algorithm that is able to rapidly evaluate thousands of simulated WFIRST binary-lens microlensing light curves, returning an estimate for the physical parameters of the lens systems. We find that this algorithm can recover projected separations between the planet and the star very well for low-mass-ratio events, and can also estimate mass ratios within an order of magnitude for events with wide and close caustic topologies.
Abstract
Light echoes (LEs) are the reflections of astrophysical transients off of interstellar dust. They are fascinating astronomical phenomena that enable studies of the scattering dust as well as ...of the original transients. LEs, however, are rare and extremely difficult to detect as they appear as faint, diffuse, time-evolving features. The detection of LEs still largely relies on human inspection of images, a method unfeasible in the era of large synoptic surveys. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will generate an unprecedented amount of astronomical imaging data at high spatial resolution, exquisite image quality, and over tens of thousands of square degrees of sky: an ideal survey for LEs. However, the Rubin data processing pipelines are optimized for the detection of point sources and will entirely miss LEs. Over the past several years, artificial intelligence (AI) object-detection frameworks have achieved and surpassed real-time, human-level performance. In this work, we leverage a data set from the Asteroid Terrestrial-impact Last Alert System telescope to test a popular AI object-detection framework, You Only Look Once, or YOLO, developed by the computer-vision community, to demonstrate the potential of AI for the detection of LEs in astronomical images. We find that an AI framework can reach human-level performance even with a size- and quality-limited data set. We explore and highlight challenges, including class imbalance and label incompleteness, and road map the work required to build an end-to-end pipeline for the automated detection and study of LEs in high-throughput astronomical surveys.
Abstract
Microlensing is a powerful tool for discovering cold exoplanets, and the Roman Space Telescope microlensing survey will discover over 1000 such planets. Rapid, automated classification of ...Roman’s microlensing events can be used to prioritize follow-up observations of the most interesting events. Machine learning is now often used for classification problems in astronomy, but the success of such algorithms can rely on the definition of appropriate features that capture essential elements of the observations that can map to parameters of interest. In this paper, we introduce tools that we have developed to capture features in simulated Roman light curves of different types of microlensing events, and we evaluate their effectiveness in classifying microlensing light curves. These features are quantified as parameters that can be used to decide the likelihood that a given light curve is due to a specific type of microlensing event. This method leaves us with a list of parameters that describe features like the smoothness of the peak, symmetry, the number of peaks, and the width and height of small deviations from the main peak. This will allow us to quickly analyze a set of microlensing light curves and later use the resulting parameters as input to machine learning algorithms to classify the events.
Microlensing can be used to discover exoplanets of a wide range of masses with orbits beyond ~ 1 AU, and even free-floating planets. The Roman space telescope will use microlensing to discover ...approximately 1600 planets by monitoring ~ 100 million stars to find ~ 50,000 microlensing events. Modeling each microlensing event, especially the ones involving two or more lenses, is typically complicated and time-consuming, and fully analyzing thousands of Roman microlensing events is possibly infeasible using current methods. The goal of this dissertation is to develop fast and efficient methods of analyzing and classifying high-cadence microlensing light curves. In the first part of this dissertation, we present an algorithm that is able to rapidly evaluate thousands of simulated Roman planetary binary-lens microlensing light curves, returning an estimate for the physical parameters of the lens systems. This algorithm recovers projected separations between the planet and the star very well for low-mass-ratio events. It can also estimate mass ratios within an order of magnitude for events with wide and close caustic topologies. Finding a fast approach towards characterizing planetary binary-lens microlensing light curves motivated us to develop fast and efficient tools to classify all types of microlensing light curves. In the second part of this dissertation, we present a package of tools we have developed for classifying single-lens, stellar binary-lens, and planetary binary-lens microlensing light curves, and also events that are strongly affected by the finite source effect. When the Roman telescope releases light curves of thousands of microlensing events it is important to detect the planetary systems quickly, so an algorithm is needed to quickly classify all microlensing signals and prioritize rapid follow-up observations. Our developed package includes a series of functional fits that captures features in simulated Roman light curves of different types of microlensing events. These features can be used to decide how likely a light curve contains a specific type of microlensing event. This method leaves us with a list of features like smoothness and symmetry of the peak, number of peaks, goodness of the fits, and width and height of the small deviations from the main peak. This will allow us to quickly analyze a set of microlensing light curves and later use the resulting parameters as input to machine learning algorithms to classify the events. In the last part of this dissertation, we introduce an algorithmic approach to use the list of features as input to machine learning classifiers, and we present the preliminary results of four classifiers trained on these features. We show that this approach is effective in classifying microlensing light curves quickly, and we discuss ways to improve the results.
Abstract
We present the discovery of KELT-24 b, a massive hot Jupiter orbiting a bright (
V
= 8.3 mag,
K
= 7.2 mag) young F-star with a period of 5.6 days. The host star, KELT-24 (HD 93148), has a
...T
eff
=
K, a mass of
M
*
=
M
⊙
, a radius of
R
*
= 1.506 ± 0.022
R
⊙
, and an age of
Gyr. Its planetary companion (KELT-24 b) has a radius of
R
P
= 1.272 ± 0.021
R
J
and a mass of
M
P
=
M
J
, and from Doppler tomographic observations, we find that the planet’s orbit is well-aligned to its host star’s projected spin axis (
). The young age estimated for KELT-24 suggests that it only recently started to evolve from the zero-age main sequence. KELT-24 is the brightest star known to host a transiting giant planet with a period between 5 and 10 days. Although the circularization timescale is much longer than the age of the system, we do not detect a large eccentricity or significant misalignment that is expected from dynamical migration. The brightness of its host star and its moderate surface gravity make KELT-24b an intriguing target for detailed atmospheric characterization through spectroscopic emission measurements since it would bridge the current literature results that have primarily focused on lower mass hot Jupiters and a few brown dwarfs.
We present the discovery of KELT-24 b, a massive hot Jupiter orbiting a bright (V=8.3 mag, K=7.2 mag) young F-star with a period of 5.6 days. The host star, KELT-24 (HD 93148), has a ...Teff=-+65094950K, a mass of M*=+1.4600.0590.055Me, a radius of R*=1.506±0.022Re, and an age of +0.780.420.61Gyr. Its planetary companion (KELT-24 b) has a radius of RP=1.272±0.021RJ and a mass of MP=-+5.180.220.21MJ, and from Doppler tomographic observations, we find that the planet’s orbit is well aligned to its host star’s projected spin axis (l=-+2.63.65.1). The young age estimated for KELT-24 suggests that it only recently started to evolve from the zero-age main sequence. KELT-24 is the brightest star known to host a transiting giant planet with a period between 5 and 10 days. Although the circularization timescale is much longer than the age of the system, we do not detect a large eccentricity or significant misalignment that is expected from dynamical migration. The brightness of its host star and its moderate surface gravity make KELT-24b an intriguing target for detailed atmospheric characterization through spectroscopic emission measurements since it would bridge the current literature results that have primarily focused on lower mass hot Jupiters and a few brown dwarfs.
While the spectroscopic classification scheme for Stripped envelope supernovae (SESNe) is clear, and we know that they originate from massive stars that lost some or all their envelopes of Hydrogen ...and Helium, the photometric evolution of classes within this family is not fully characterized. Photometric surveys, like the Vera C. Rubin Legacy Survey of Space and Time, will discover tens of thousands of transients each night and spectroscopic follow-up will be limited, prompting the need for photometric classification and inference based solely on photometry. We have generated 54 data-driven photometric templates for SESNe of subtypes IIb, Ib, Ic, Ic-bl, and Ibn in U/u, B, g, V, R/r, I/i, J, H, Ks, and Swift w2, m2, w1 bands using Gaussian Processes and a multi-survey dataset composed of all well-sampled open-access light curves (165 SESNe, 29531 data points) from the Open Supernova Catalog. We use our new templates to assess the photometric diversity of SESNe by comparing final per-band subtype templates with each other and with individual, unusual and prototypical SESNe. We find that SNe Ibns and Ic-bl exhibit a distinctly faster rise and decline compared to other subtypes. We also evaluate the behavior of SESNe in the PLAsTiCC and ELAsTiCC simulations of LSST light curves highlighting differences that can bias photometric classification models trained on the simulated light curves. Finally, we investigate in detail the behavior of fast-evolving SESNe (including SNe Ibn) and the implications of the frequently observed presence of two peaks in their light curves.
Microlensing can be used to discover exoplanets of a wide range of masses with orbits beyond ~ 1 AU, and even free-floating planets. The WFIRST mission will use microlensing to discover approximately ...1600 planets by monitoring ~100 million stars to find ~50000 microlensing events. Modelling each microlensing event, especially the ones involving two or more lenses, is typically complicated and time-consuming, and analyzing thousands of WFIRST microlensing events is possibly infeasible using current methods. Here, we present an algorithm that is able to rapidly evaluate thousands of simulated WFIRST binary-lens microlensing light curves, returning an estimate for the physical parameters of the lens systems. We find that this algorithm can recover projected separations between the planet and the star very well for low-mass-ratio events, and can also estimate mass ratios within an order of magnitude for events with wide and close caustic topologies.
Nancy Grace Roman Space Telescope will revolutionize our understanding of the Galactic Bulge with its Galactic Bulge Time Domain survey. At the same time, Rubin Observatories's Legacy Survey of Space ...and Time (LSST) will monitor billions of stars in the Milky Way. The proposed Roman survey of the Galactic Plane, with its NIR passbands and exquisite spacial resolution, promises groundbreaking insights for a wide range of time-domain galactic astrophysics. In this white paper, we describe the scientific returns possible from the combination of the Roman Galactic Plane Survey with the data from LSST.
PDF
