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Yifan Zhou

Publications and source records attributed to Yifan Zhou.

At least 181 records · Page 10Linked to original sources
arXiv2023

The Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy (SCALES): driving science cases and expected outcomes

The Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy (SCALES) is a $2-5~μ$m, high-contrast integral field spectrograph (IFS) currently being built for Keck Observatory. With both low ($R\lesssim250$) and medium ($R\sim3500-7000$) spectral resolution IFS modes, SCALES will detect and characterize significantly colder exoplanets than those accessible with near-infrared ($\sim1-2~μ$m) high-contrast spectrographs. This will lead to new progress in exoplanet atmospheric studies, including detailed characterization of benchmark systems that will advance the state of the art of atmospheric modeling. SCALES' unique modes, while designed specifically for direct exoplanet characterization, will enable a broader range of novel (exo)planetary observations as well as galactic and extragalactic studies. Here we present the science cases that drive the design of SCALES. We describe an end-to-end instrument simulator that we use to track requirements, and show simulations of expected science yields for each driving science case. We conclude with a discussion of preparations for early science when the instrument sees first light in $\sim2025$.

astro-ph.IM↗
arXiv2023

Physics-Aware Neural Dynamic Equivalence of Power Systems

This letter devises Neural Dynamic Equivalence (NeuDyE), which explores physics-aware machine learning and neural-ordinary-differential-equations (ODE-Net) to discover a dynamic equivalence of external power grids while preserving its dynamic behaviors after disturbances. The contributions are threefold: (1) an ODE-Net-enabled NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems; (2) a physics-informed NeuDyE learning method (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE without an additional verification module; (3) a physics-guided NeuDyE (PG-NeuDyE) to enhance the method's applicability even in the absence of analytical physics models. Extensive case studies in the NPCC system validate the efficacy of NeuDyE, and, in particular, its capability under various contingencies.

eess.SY↗
arXiv2023

Physics-Informed Induction Machine Modelling

This rapid communication devises a Neural Induction Machine (NeuIM) model, which pilots the use of physics-informed machine learning to enable AI-based electromagnetic transient simulations. The contributions are threefold: (1) a formation of NeuIM to represent the induction machine in phase domain; (2) a physics-informed neural network capable of capturing fast and slow IM dynamics even in the absence of data; and (3) a data-physics-integrated hybrid NeuIM approach which is adaptive to various levels of data availability. Extensive case studies validate the efficacy of NeuIM and in particular, its advantage over purely data-driven approaches.

cs.LG↗
arXiv2023

Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation

Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.

cs.CV↗
arXiv2023

UV-Optical Emission of AB Aur b is Consistent with Scattered Stellar Light

The proposed protoplanet AB Aur b is a spatially concentrated emission source imaged in the mm-wavelength disk gap of the Herbig Ae/Be star AB Aur. Its near-infrared spectrum and absence of strong polarized light have been interpreted as evidence supporting the protoplanet interpretation. However, the complex scattered light structures in the AB Aur disk pose challenges in resolving the emission source and interpreting the true nature of AB Aur b. We present new images of the AB Aur system obtained using the Hubble Space Telescope Wide Field Camera 3 in the ultraviolet (UV) and optical bands. AB Aur b and the known disk spirals are recovered in the F336W, F410M, and F645N bands. The spectral energy distribution of AB Aur b shows absorption in the Balmer jump, mimicking those of early-type stars. By comparing the colors of AB Aur b to those of the host star, the disk spirals, and predictions from scattered light and self-luminous models, we find that the emission from AB Aur b is inconsistent with planetary photospheric or accretion shock models. Instead, it is consistent with those measured in the circumstellar disks that trace scattered light. We conclude that the UV and visible emission from AB Aur b does not necessitate the presence of a protoplanet. We synthesize observational constraints on AB Aur b and discuss inconsistent interpretations of AB Aur b among different datasets. Considering the significance of the AB Aur b discovery, we advocate for further observational evidence to verify its planetary nature.

astro-ph.EP↗
arXiv2023

MagAO-X and HST high-contrast imaging of the AS209 disk at H$α$

The detection of emission lines associated with accretion processes is a direct method for studying how and where gas giant planets form, how young planets interact with their natal protoplanetary disk and how volatile delivery to their atmosphere takes place. H$α$ ($λ=0.656\,μ$m) is expected to be the strongest accretion line observable from the ground with adaptive optics systems, and is therefore the target of specific high-contrast imaging campaigns. We present MagAO-X and HST data obtained to search for H$α$ emission from the previously detected protoplanet candidate orbiting AS209, identified through ALMA observations. No signal was detected at the location of the candidate, and we provide limits on its accretion. Our data would have detected an H$α$ emission with $F_\mathrm{Hα}>2.5\pm0.3 \times10^{-16}$ erg s$^{-1}$ cm$^{-2}$, a factor 6.5 lower than the HST flux measured for PDS70b (Zhou et al., 2021). The flux limit indicates that if the protoplanet is currently accreting it is likely that local extinction from circumstellar and circumplanetary material strongly attenuates its emission at optical wavelengths. In addition, the data reveal the first image of the jet north of the star as expected from previous detections of forbidden lines. Finally, this work demonstrates that current ground-based observations with extreme adaptive optics systems can be more sensitive than space-based observations, paving the way to the hunt for small planets in reflected light with extremely large telescopes.

astro-ph.EP↗
arXiv2023

Enhancing State Estimation in Robots: A Data-Driven Approach with Differentiable Ensemble Kalman Filters

This paper introduces a novel state estimation framework for robots using differentiable ensemble Kalman filters (DEnKF). DEnKF is a reformulation of the traditional ensemble Kalman filter that employs stochastic neural networks to model the process noise implicitly. Our work is an extension of previous research on differentiable filters, which has provided a strong foundation for our modular and end-to-end differentiable framework. This framework enables each component of the system to function independently, leading to improved flexibility and versatility in implementation. Through a series of experiments, we demonstrate the flexibility of this model across a diverse set of real-world tracking tasks, including visual odometry and robot manipulation. Moreover, we show that our model effectively handles noisy observations, is robust in the absence of observations, and outperforms state-of-the-art differentiable filters in terms of error metrics. Specifically, we observe a significant improvement of at least 59% in translational error when using DEnKF with noisy observations. Our results underscore the potential of DEnKF in advancing state estimation for robotics. Code for DEnKF is available at https://github.com/ir-lab/DEnKF

cs.RO↗
arXiv2023

Projecting Robot Intentions Through Visual Cues: Static vs. Dynamic Signaling

Augmented and mixed-reality techniques harbor a great potential for improving human-robot collaboration. Visual signals and cues may be projected to a human partner in order to explicitly communicate robot intentions and goals. However, it is unclear what type of signals support such a process and whether signals can be combined without adding additional cognitive stress to the partner. This paper focuses on identifying the effective types of visual signals and quantify their impact through empirical evaluations. In particular, the study compares static and dynamic visual signals within a collaborative object sorting task and assesses their ability to shape human behavior. Furthermore, an information-theoretic analysis is performed to numerically quantify the degree of information transfer between visual signals and human behavior. The results of a human subject experiment show that there are significant advantages to combining multiple visual signals within a single task, i.e., increased task efficiency and reduced cognitive load.

cs.RO↗
arXiv2023

Neuro-Dynamic State Estimation for Networked Microgrids

We devise neuro-dynamic state estimation (Neuro-DSE), a learning-based dynamic state estimation (DSE) algorithm for networked microgrids (NMs) under unknown subsystems. Our contributions include: 1) a data-driven Neuro-DSE algorithm for NMs DSE with partially unidentified dynamic models, which incorporates the neural-ordinary-differential-equations (ODE-Net) into Kalman filters; 2) a self-refining Neuro-DSE algorithm (Neuro-DSE+) which enables data-driven DSE under limited and noisy measurements by establishing an automatic filtering, augmenting and correcting framework; 3) a Neuro-KalmanNet-DSE algorithm which further integrates KalmanNet with Neuro-DSE to relieve the model mismatch of both neural- and physics-based dynamic models; and 4) an augmented Neuro-DSE for joint estimation of NMs states and unknown parameters (e.g., inertia). Extensive case studies demonstrate the efficacy of Neuro-DSE and its variants under different noise levels, control modes, power sources, observabilities and model knowledge, respectively.

eess.SY↗
arXiv2023

Astrometric Accelerations as Dynamical Beacons: A Giant Planet Imaged Inside the Debris Disk of the Young Star AF Lep

We present the direct imaging discovery of a giant planet orbiting the young star AF Lep, a 1.2 $M_{\odot}$ member of the 24 $\pm$ 3 Myr $β$ Pic moving group. AF Lep was observed as part of our ongoing high-contrast imaging program targeting stars with astrometric accelerations between Hipparcos and Gaia that indicate the presence of substellar companions. Keck/NIRC2 observations in $L'$ with the Vector Vortex Coronagraph reveal a point source, AF Lep b, at ${\approx}340$ mas which exhibits orbital motion at the 6-$σ$ level over the course of 13 months. A joint orbit fit yields precise constraints on the planet's dynamical mass of 3.2$^{+0.7}_{-0.6}$ $M_\mathrm{Jup}$, semi-major axis of $8.4^{+1.1}_{-1.3}$ au, and eccentricity of $0.24^{+0.27}_{-0.15}$. AF Lep hosts a debris disk located at $\sim$50 au, but it is unlikely to be sculpted by AF Lep b, implying there may be additional planets in the system at wider separations. The stellar inclination ($i_* = 54^{+11}_{-9} {}^\circ$) and orbital inclination ($i_o = 50^{+9}_{-12} {}^\circ$) are in good agreement, which is consistent with the system having spin-orbit alignment. AF Lep b is the lowest-mass imaged planet with a dynamical mass measurement and highlights the promise of using astrometric accelerations as a tool to find and characterize long-period planets.

astro-ph.EP↗
arXiv2023

The JWST Early Release Science Program for Direct Observations of Exoplanetary Systems I: High Contrast Imaging of the Exoplanet HIP 65426 b from 2-16 $μ$m

We present JWST Early Release Science (ERS) coronagraphic observations of the super-Jupiter exoplanet, HIP 65426 b, with the Near-Infrared Camera (NIRCam) from 2-5 $μ$m, and with the Mid-Infrared Instrument (MIRI) from 11-16 $μ$m. At a separation of $\sim$0.82" (86$^{+116}_{-31}$ au), HIP 65426 b is clearly detected in all seven of our observational filters, representing the first images of an exoplanet to be obtained by JWST, and the first ever direct detection of an exoplanet beyond 5 $μ$m. These observations demonstrate that JWST is exceeding its nominal predicted performance by up to a factor of 10, depending on separation and subtraction method, with measured 5$σ$ contrast limits of $\sim$1$\times10^{-5}$ and $\sim$2$\times10^{-4}$ at 1" for NIRCam at 4.4 $μ$m and MIRI at 11.3 $μ$m, respectively. These contrast limits provide sensitivity to sub-Jupiter companions with masses as low as 0.3$M_\mathrm{Jup}$ beyond separations of $\sim$100 au. Together with existing ground-based near-infrared data, the JWST photometry are well fit by a BT-SETTL atmospheric model from 1-16 $μ$m, and span $\sim$97% of HIP 65426 b's luminous range. Independent of the choice of model atmosphere we measure an empirical bolometric luminosity that is tightly constrained between $\mathrm{log}\!\left(L_\mathrm{bol}/L_{\odot}\right)$=-4.31 to $-$4.14, which in turn provides a robust mass constraint of 7.1$\pm$1.2 $M_\mathrm{Jup}$. In totality, these observations confirm that JWST presents a powerful and exciting opportunity to characterise the population of exoplanets amenable to high-contrast imaging in greater detail.

astro-ph.EP↗
arXiv2023

Hotter Than Expected: HST/WFC3 Phase-resolved Spectroscopy of a Rare Irradiated Brown Dwarf with Strong Internal Heat Flux

With infrared flux contrasts larger than typically seen in hot Jupiter, tidally-locked white dwarf-brown dwarf binaries offer a superior opportunity to investigate atmospheric processes in irradiated atmospheres. NLTT5306 is such a system, with a M$_{BD}$ = 52 $\pm$ 3 M$_{Jup}$ brown dwarf orbiting a T$_{eff}$ = 7756 $\pm$ 35 K white dwarf with an ultra-short period of $\sim$102 min. We present HST/WFC3 spectroscopic phase curves of NLTT5306, consisting of 47 spectra from 1.1 to 1.7 microns with an average S/N=65 per wavelength. We extracted the phase-resolved spectra of the brown dwarf NLTT5306B, finding a small (<100 K) day/night temperature difference ($\sim$5 percent of the average day-side temperature). Our best-fit model phase curves revealed a complex wavelength-dependence on amplitudes and relative phase offsets, suggesting longitudinal-vertical atmospheric structure. The night-side spectrum was well-fit by a cloudy, non-irradiated atmospheric model while the day-side was best-matched by a cloudy, weakly irradiated model. Additionally, we created a simple radiative energy redistribution model of the atmosphere and found evidence for efficient day-to-night heat redistribution and a moderately high Bond albedo. We also discovered an internal heat flux much higher than expected given the published system age, leading to an age reassessment that resulted in NLTT5306B most likely being much younger. We find that NLTT5306B is the only known significantly irradiated brown dwarf where the global temperature structure is not dominated by external irradiation, but rather its own internal heat. Our study provides an essential insight into the drivers of global circulation and day-to-night heat transport as a function of irradiation, rotation rate, and internal heat.

astro-ph.EP↗
arXiv2023

The JWST Early Release Science Program for Direct Observations of Exoplanetary Systems: Best Practices for Data Collection in Cycle 2 and Beyond

We present a set of recommended best practices for JWST data collection for members of the community focussed on the direct imaging and spectroscopy of exoplanetary systems. These findings and recommendations are based on the early analysis of the JWST Early Release Science Program 1386, "High-Contrast Imaging of Exoplanets and Exoplanetary Systems with JWST." Our goal is for this information to be useful for observers in preparation of JWST proposals for Cycle 2 and beyond. In addition to compiling a set of best practices from our ERS program, in a few cases we also draw on the expertise gained within the instrument commissioning programs, as well as include a handful of data processing best practices. We anticipate that this document will be regularly updated and resubmitted to arXiv.org to ensure that we have distributed our knowledge of best-practices for data collection as widely and efficiently as possible.

astro-ph.IM↗
arXiv2023

Rotation Periods, Inclinations, and Obliquities of Cool Stars Hosting Directly Imaged Substellar Companions: Spin-Orbit Misalignments are Common

The orientation between a star's spin axis and a planet's orbital plane provides valuable information about the system's formation and dynamical history. For non-transiting planets at wide separations, true stellar obliquities are challenging to measure, but lower limits on spin-orbit orientations can be determined from the difference between the inclination of the star's rotational axis and the companion's orbital plane ($Δi$). We present results of a uniform analysis of rotation periods, stellar inclinations, and obliquities of cool stars (SpT $\gtrsim$ F5) hosting directly imaged planets and brown dwarf companions. As part of this effort, we have acquired new $v \sin i_*$ values for 22 host stars with the high-resolution Tull spectrograph at the Harlan J. Smith telescope. Altogether our sample contains 62 host stars with rotation periods, most of which are newly measured using light curves from the Transiting Exoplanet Survey Satellite. Among these, 53 stars have inclinations determined from projected rotational and equatorial velocities, and 21 stars predominantly hosting brown dwarfs have constraints on $Δi$. Eleven of these (52$^{+10}_{-11}$% of the sample) are likely misaligned, while the remaining ten host stars are consistent with spin-orbit alignment. As an ensemble, the minimum obliquity distribution between 10-250 AU is more consistent with a mixture of isotropic and aligned systems than either extreme scenario alone--pointing to direct cloud collapse, formation within disks bearing primordial alignments and misalignments, or architectures processed by dynamical evolution. This contrasts with stars hosting directly imaged planets, which show a preference for low obliquities. These results reinforce an emerging distinction between the orbits of long-period brown dwarfs and giant planets in terms of their stellar obliquities and orbital eccentricities.

astro-ph.EP↗
arXiv2022

Imitation Learning based Auto-Correction of Extrinsic Parameters for A Mixed-Reality Setup

In this paper, we discuss an imitation learning based method for reducing the calibration error for a mixed reality system consisting of a vision sensor and a projector. Unlike a head mounted display, in this setup, augmented information is available to a human subject via the projection of a scene into the real world. Inherently, the camera and projector need to be calibrated as a stereo setup to project accurate information in 3D space. Previous calibration processes require multiple recording and parameter tuning steps to achieve the desired calibration, which is usually time consuming process. In order to avoid such tedious calibration, we train a CNN model to iteratively correct the extrinsic offset given a QR code and a projected pattern. We discuss the overall system setup, data collection for training, and results of the auto-correction model.

cs.RO↗
arXiv2022

Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation

Language-conditioned policies allow robots to interpret and execute human instructions. Learning such policies requires a substantial investment with regards to time and compute resources. Still, the resulting controllers are highly device-specific and cannot easily be transferred to a robot with different morphology, capability, appearance or dynamics. In this paper, we propose a sample-efficient approach for training language-conditioned manipulation policies that allows for rapid transfer across different types of robots. By introducing a novel method, namely Hierarchical Modularity, and adopting supervised attention across multiple sub-modules, we bridge the divide between modular and end-to-end learning and enable the reuse of functional building blocks. In both simulated and real world robot manipulation experiments, we demonstrate that our method outperforms the current state-of-the-art methods and can transfer policies across 4 different robots in a sample-efficient manner. Finally, we show that the functionality of learned sub-modules is maintained beyond the training process and can be used to introspect the robot decision-making process. Code is available at https://github.com/ir-lab/ModAttn.

cs.RO↗
arXiv2022

PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding

Optical flow estimation is a basic task in self-driving and robotics systems, which enables to temporally interpret traffic scenes. Autonomous vehicles clearly benefit from the ultra-wide Field of View (FoV) offered by 360° panoramic sensors. However, due to the unique imaging process of panoramic cameras, models designed for pinhole images do not directly generalize satisfactorily to 360° panoramic images. In this paper, we put forward a novel network framework--PanoFlow, to learn optical flow for panoramic images. To overcome the distortions introduced by equirectangular projection in panoramic transformation, we design a Flow Distortion Augmentation (FDA) method, which contains radial flow distortion (FDA-R) or equirectangular flow distortion (FDA-E). We further look into the definition and properties of cyclic optical flow for panoramic videos, and hereby propose a Cyclic Flow Estimation (CFE) method by leveraging the cyclicity of spherical images to infer 360° optical flow and converting large displacement to relatively small displacement. PanoFlow is applicable to any existing flow estimation method and benefits from the progress of narrow-FoV flow estimation. In addition, we create and release a synthetic panoramic dataset FlowScape based on CARLA to facilitate training and quantitative analysis. PanoFlow achieves state-of-the-art performance on the public OmniFlowNet and the established FlowScape benchmarks. Our proposed approach reduces the End-Point-Error (EPE) on FlowScape by 27.3%. On OmniFlowNet, PanoFlow achieves a 55.5% error reduction from the best published result. We also qualitatively validate our method via a collection vehicle and a public real-world OmniPhotos dataset, indicating strong potential and robustness for real-world navigation applications. Code and dataset are publicly available at https://github.com/MasterHow/PanoFlow.

cs.CV↗
arXiv2022

Noise-Resilient Quantum Power Flow

Quantum power flow (QPF) provides inspiring directions for tackling power flow's computational burdens leveraging quantum computing. However, existing QPF methods are mainly based on noise-sensitive quantum algorithms, whose practical utilization is significantly hindered by the limited capability of today's noisy-intermediate-scale quantum (NISQ) devices. This paper devises a NISQ-QPF algorithm, which enables power flow calculation on noisy quantum computers. The main contributions include: (1) a variational quantum circuit (VQC)-based AC power flow formulation, which enables QPF using short-depth quantum circuits; (2) noise-resilient QPF solvers based on the variational quantum linear solver (VQLS) and modified fast decoupled power flow; (3) a practical NISQ-QPF framework for implementable and reliable power flow analysis on noisy quantum machines. Promising case studies validate the effectiveness and accuracy of NISQ-QPF on IBM's real, noisy quantum devices.

quant-ph↗