Abstract
We present a novel machine-learning approach for detecting faint point sources in high-contrast adaptive optics (AO) imaging data sets. The most widely used algorithms for primary ...subtraction aim to decouple bright stellar speckle noise from planetary signatures by subtracting an approximation of the temporally evolving stellar noise from each frame in an imaging sequence. Our approach aims to improve the stellar noise approximation and increase the planet detection sensitivity by leveraging deep learning in a novel direct imaging post-processing algorithm. We show that a convolutional autoencoder neural network, trained on an extensive reference library of real imaging sequences, accurately reconstructs the stellar speckle noise at the location of a potential planet signal. This tool is used in a post-processing algorithm we call Direct Exoplanet Detection with Convolutional Image Reconstruction, or
ConStruct
. The reliability and sensitivity of
ConStruct
are assessed using real Keck/NIRC2 angular differential imaging data sets. Of the 30 unique point sources we examine,
ConStruct
yields a higher signal-to-noise ratio than traditional principal component analysis-based processing for 67% of the cases and improves the relative contrast by up to a factor of 2.6. This work demonstrates the value and potential of deep learning to take advantage of a diverse reference library of point-spread function realizations to improve direct imaging post-processing.
ConStruct
and its future improvements may be particularly useful as tools for post-processing high-contrast images from JWST and extreme AO instruments, both for the current generation and those being designed for the upcoming 30 m class telescopes.
We analyzed coastal sediments of the Santa Barbara Basin, California, for historical changes in microplastic deposition using a box core that spanned 1834-2009. The sediment was visually sorted for ...plastic, and a subset was confirmed as plastic polymers via FTIR (Fourier transform infrared) spectroscopy. After correcting for contamination introduced during sample processing, we found an exponential increase in plastic deposition from 1945 to 2009 with a doubling time of 15 years. This increase correlated closely with worldwide plastic production and southern California coastal population increases over the same period. Increased plastic loading in sediments has unknown consequences for deposit-feeding benthic organisms. This increase in plastic deposition in the post-World War II years can be used as a geological proxy for the Great Acceleration of the Anthropocene in the sedimentary record.
Filters relying on the Gaussian approximation typically incorporate the measurement linearly, i.e., the value of the measurement is premultiplied by a matrix-valued gain in the state update. ...Nonlinear filters that relax the Gaussian assumption, on the other hand, typically approximate the distribution of the state with a finite sum of point masses or Gaussian distributions. In this work, the distribution of the state is approximated by a polynomial transformation of a Gaussian distribution, allowing for all moments, central and raw, to be rapidly computed in a closed form. Knowledge of the higher order moments is then employed to perform a polynomial measurement update, i.e., the value of the measurement enters the update function as a polynomial of arbitrary order. A filter employing a Gaussian approximation with linear update is, therefore, a special case of the proposed algorithm when both the order of the series and the order of the update are set to one: it reduces to the extended Kalman filter. At the cost of more computations, the new methodology guarantees performance better than the linear/Gaussian approach for nonlinear systems. This work employs monomial basis functions and Taylor series, developed in the differential algebra framework, but it is readily extendable to an orthogonal polynomial basis.
This article presents a flexible modeling framework for multitarget tracking based on the theory of outer probability measures. The notion of labeled uncertain finite set is introduced and utilized ...as the basis to derive a possibilistic analog of the \delta-generalized labeled multi-Bernoulli (\delta-GLMB) filter, in which the uncertainty in the multitarget system is represented by possibility functions instead of probability distributions. The proposed method inherits the capability of the standard probabilistic \delta-GLMB filter to yield joint state, number, and trajectory estimates of multiple appearing and disappearing targets. Beyond that, it is capable to account for epistemic uncertainty due to ignorance or partial knowledge regarding the multitarget system, e.g., the absence of complete information on dynamical model parameters (e.g., probability of detection, birth) and initial number and state of newborn targets. The features of the developed filter are demonstrated using two simulated scenarios.
The initiation of tracks for newly discovered objects presents unique challenges in space situational awareness. Recent work explores the use of admissible regions to initialize filters for space ...object tracking; however, these methods require follow-on measurements to refine the initial admissible region solution. This paper presents an approach to the joint search and track problem, designed to allow a single sensor to build and maintain a catalog of objects without requiring an a priori estimate.
To reduce computation time while limiting loss in accuracy when propagating an orbit state probability density function, this work seeks to develop an adaptive approach to multi-fidelity uncertainty ...propagation for applications in astrodynamics. Using the method of stochastic collocation, a set of particles produced via a low-fidelity solver defines a basis used in the surrogate over the space of propagated states. This basis allows for identifying a subset of important samples that are re-propagated using a high-fidelity propagator, which defines a correction for the original basis. This approach reduces computation time for propagating a particle ensemble or a Gaussian mixture model via the unscented transform. This paper demonstrates the efficacy of this method for several Earth-orbit test cases, and provides a means for merging general and special perturbation theories to produce a posterior probability density function more statistically consistent with a precise estimated state.
•Multi-fidelity approach reduces runtime for orbit-state uncertainty propagation.•Combines low- and high-fidelity models for orbit state propagation.•Apply a high-fidelity correction to low-fidelity samples via stochastic collocation.•Demonstrate with particle and Gaussian mixture approaches to uncertainty propagation.
Abstract
Benchmark brown dwarf companions with well-determined ages and model-independent masses are powerful tools to test substellar evolutionary models and probe the formation of giant planets and ...brown dwarfs. Here, we report the independent discovery of HIP 21152 B, the first imaged brown dwarf companion in the Hyades, and conduct a comprehensive orbital and atmospheric characterization of the system. HIP 21152 was targeted in an ongoing high-contrast imaging campaign of stars exhibiting proper-motion changes between Hipparcos and Gaia, and was also recently identified by Bonavita et al. (2022) and Kuzuhara et al. (2022). Our Keck/NIRC2 and SCExAO/CHARIS imaging of HIP 21152 revealed a comoving companion at a separation of 0.″37 (16 au). We perform a joint orbit fit of all available relative astrometry and radial velocities together with the Hipparcos-Gaia proper motions, yielding a dynamical mass of
24
−
4
+
6
M
Jup
, which is 1–2
σ
lower than evolutionary model predictions. Hybrid grids that include the evolution of cloud properties best reproduce the dynamical mass. We also identify a comoving wide-separation (1837″ or 7.9 × 10
4
au) early-L dwarf with an inferred mass near the hydrogen-burning limit. Finally, we analyze the spectra and photometry of HIP 21152 B using the Saumon & Marley (2008) atmospheric models and a suite of retrievals. The best-fit grid-based models have
f
sed
= 2, indicating the presence of clouds,
T
eff
= 1400 K, and
log
g
=
4.5
dex
. These results are consistent with the object’s spectral type of T0 ± 1. As the first benchmark brown dwarf companion in the Hyades, HIP 21152 B joins the small but growing number of substellar companions with well-determined ages and dynamical masses.
Background:
Patients with adolescent idiopathic scoliosis (AIS) often report chronic back pain; however, there is inadequate research on psychological factors associated with pain in this patient ...population. Pain catastrophizing, a psychological factor that describes a pattern of negative thoughts and feelings about pain, has been associated with poorer responses to medical treatment for pain. The purpose of this study was to report the prevalence of pain catastrophizing in the AIS population and assess its relationship with preoperative and postoperative self-reported outcomes.
Methods:
In this prospective cohort study of consecutive patients undergoing posterior spinal fusion (PSF) for AIS, patients experiencing clinically relevant pain catastrophizing, defined as a Pain Catastrophizing Scale for Children (PCS) score in the 75th percentile or higher, were compared with patients with normal PCS scores. Preoperative and 2-year postoperative Scoliosis Research Society Society Questionnaire-30 (SRS-30) scores were correlated with the preoperative PCS score.
Results:
One hundred and eighty-nine patients underwent PSF for AIS, and 20 (10.6%) were considered to be experiencing pain catastrophizing. Despite comparable demographic and radiographic variables, pain catastrophizing was associated with significantly lower preoperative scores than were found in the normal-PCS group in all SRS-30 domains, including pain (2.98 versus 3.95; p < 0.001), appearance (2.98 versus 3.48; p < 0.001), activity (3.51 versus 4.06; p < 0.001), mental health (3.12 versus 4.01; p < 0.001), and total score (3.18 versus 3.84; p < 0.001), except satisfaction (3.72 versus 3.69; p > 0.999). At 2 years, the pain catastrophizing group experienced significant improvement from their preoperative scores in most SRS-30 domains, including a large clinically relevant improvement in pain (from 2.98 preoperatively to 3.84 postoperatively; p < 0.001) and the total score (from 3.18 to 3.85; p < 0.001), but continued to have lower scores than the normal-PCS group for pain (3.84 versus 4.22; p = 0.028) and the total score (3.85 versus 4.15; p = 0.038). Receiver operating characteristic (ROC) curve analysis indicated that an SRS-30 pain score of <3.5 has good sensitivity for predicting pain catastrophizing (PCS ≥75th percentile).
Conclusions:
In this cohort, patients with AIS who exhibited pain catastrophizing experienced significant improvement in self-reported health 2 years after PSF. However, they did not have the same levels of self-reported health as the normal-PCS group. Pain catastrophizing may be identifiable by lower preoperative SRS-30 pain scores.
Level of Evidence:
Prognostic
Level II
. See Instructions for Authors for a complete description of levels of evidence.
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