arXiv subjects

Minghan Chen

Publications and source records attributed to Minghan Chen.

At least 19 recordsLinked to original sources

arXiv2026

cuSOAP: a GPU-accelerated Generator of Smooth Overlap of Atomic Positions Descriptor

The Smooth Overlap of Atomic Positions (SOAP) descriptor is one of the most widely adopted representations of atomic environments in molecular machine learning, but the cost of evaluating it and its derivatives remains a principal bottleneck of SOAP-based interatomic potentials, particularly at the large radial and angular basis sizes demanded by complex, multi-species condensed-phase environments. We present cuSOAP, a GPU-accelerated generator of atom-wise SOAP vectors and their analytic derivatives. Built on PyTorch, cuSOAP evaluates closed-form expressions or quadratures for the projection coefficients and their Cartesian gradients for Gaussian-type-orbital and polynomial radial bases, through fused CUDA and Triton kernels that eliminate the multi-gigabyte intermediates of a naive tensor formulation. The package is a drop-in replacement for the CPU-based reference DScribe, reproducing its constructor signature, feature ordering, and output to within ${\sim}10^{-6}$, and accepts structures directly as Atomistic Simulation Environment (ASE) Atoms objects. On an NVIDIA Grace--Blackwell (GB200) node, a single Blackwell GPU generates the full descriptor-plus-Jacobian workload for a 1000-molecule water cluster up to two orders of magnitude faster than DScribe, i.e., from $13\times$ to $227\times$ across the entire $(n_{\max}, l_{\max})$ hyperparameter map, with the speedup growing with the angular band limit $l_{\max}$. Descriptor-only generation scales as $t \propto n^{1.34}$, close to linear, up to a million-atom water cluster, which is processed in 19.5s on one GPU and, through a shared-memory multiprocessing driver, in 5.7s on the four GPUs of the node, demonstrating $\sim$90% parallel efficiency with no sign of saturation as devices are added. These results bring on-the-fly SOAP evaluation for large-scale condensed-phase simulation within reach.

physics.chem-ph↗

arXiv2026

Discovery of a Low-Mass Companion to the Accelerating Star HIP 53005 with Strongly Conflicting Mass Estimates

We present the discovery of a low-mass companion located at $\rho$ $\sim$ 0\farcs{}85 ($r_{\rm proj} \approx 62~au$) from the early-type 1.2 Gyr-old star HIP 53005 using direct imaging data from the Subaru and Keck Telescopes and astrometry from the Hipparcos-Gaia Catalog of Accelerations. The companion, HIP 53005 C, is a component of a multiple system also including a $\approx$ 12\farcs{}4-separation M dwarf companion inducing a negligible proper motion acceleration. HIP~53005 C's position on color-magnitude diagrams, the fit of its spectral energy distribution to atmosphere models, and its location on an empirical mass-magnitude diagram all suggest that it lies at the M/L transition and near the hydrogen-burning limit ($\sim80~M_{\rm Jup}$). However, our orbital fitting combining direct-imaging relative astrometry with proper motion acceleration favors a much higher dynamical mass of $\sim185\ M_{\rm Jup}$. An additional unseen, more closely-orbiting companion below the detection limit (at $\rho\lesssim0\farcs2$)) may explain this discrepancy. Alternatively, HIP~53005C could be a low-mass binary like Gliese~229Bab, making this system an intriguing laboratory for studying multiple star formation.

astro-ph.SR↗

arXiv2026

Tau-BNO: Brain Neural Operator for Tau Transport Model

Mechanistic modeling provides a biophysically grounded framework for studying the spread of pathological tau protein in tauopathies like Alzheimer's disease. Existing approaches typically model tau propagation as a diffusive process on the brain's structural connectome, reproducing macroscopic patterns but neglecting microscale cellular transport and reaction mechanisms. The Network Transport Model (NTM) was introduced to fill this gap, explaining how region-level progression of tau emerges from microscale biophysical processes. However, the NTM faces a common challenge for complex models defined by large systems of partial differential equations: the inability to perform parameter inference and mechanistic discovery due to high computational burden and slow model simulations. To overcome this barrier, we propose Tau-BNO, a Brain Neural Operator surrogate framework for rapidly approximating NTM dynamics that captures both intra-regional reaction kinetics and inter-regional network transport. Tau-BNO combines a function operator that encodes kinetic parameters with a query operator that preserves initial state information, while approximating anisotropic transport through a spectral kernel that retains directionality. Empirical evaluations demonstrate high predictive accuracy ($R^2\approx$ 0.98) across diverse biophysical regimes and an 89\% performance improvement over state-of-the-art sequence models like Transformers and Mamba, which lack inherent structural priors. By reducing simulation time from hours to seconds, we show that the surrogate model is capable of producing new insights and generating new hypotheses. This framework is readily extensible to a broader class of connectome-based biophysical models, showcasing the transformative value of deep learning surrogates to accelerate analysis of large-scale, computationally intensive dynamical systems.

cs.CE↗

arXiv2025

Catastrophic Forgetting in Kolmogorov-Arnold Networks

Catastrophic forgetting is a longstanding challenge in continual learning, where models lose knowledge from earlier tasks when learning new ones. While various mitigation strategies have been proposed for Multi-Layer Perceptrons (MLPs), recent architectural advances like Kolmogorov-Arnold Networks (KANs) have been suggested to offer intrinsic resistance to forgetting by leveraging localized spline-based activations. However, the practical behavior of KANs under continual learning remains unclear, and their limitations are not well understood. To address this, we present a comprehensive study of catastrophic forgetting in KANs and develop a theoretical framework that links forgetting to activation support overlap and intrinsic data dimension. We validate these analyses through systematic experiments on synthetic and vision tasks, measuring forgetting dynamics under varying model configurations and data complexity. Further, we introduce KAN-LoRA, a novel adapter design for parameter-efficient continual fine-tuning of language models, and evaluate its effectiveness in knowledge editing tasks. Our findings reveal that while KANs exhibit promising retention in low-dimensional algorithmic settings, they remain vulnerable to forgetting in high-dimensional domains such as image classification and language modeling. These results advance the understanding of KANs' strengths and limitations, offering practical insights for continual learning system design.

cs.LG↗

arXiv2025

SEED-GRPO: Semantic Entropy Enhanced GRPO for Uncertainty-Aware Policy Optimization

Large language models (LLMs) exhibit varying levels of confidence across input prompts (questions): some lead to consistent, semantically similar answers, while others yield diverse or contradictory outputs. This variation reflects LLM's uncertainty about the input prompt, a signal of how confidently the model understands a given problem. However, vanilla Group Relative Policy Optimization (GRPO) treats all prompts equally during policy updates, ignoring this important information about the model's knowledge boundaries. To address this limitation, we propose SEED-GRPO (Semantic Entropy EnhanceD GRPO), which explicitly measures LLMs' uncertainty of the input prompts semantic entropy. Semantic entropy measures the diversity of meaning in multiple generated answers given a prompt and uses this to modulate the magnitude of policy updates. This uncertainty-aware training mechanism enables dynamic adjustment of policy update magnitudes based on question uncertainty. It allows more conservative updates on high-uncertainty questions while maintaining the original learning signal on confident ones. Experimental results on five mathematical reasoning benchmarks (AIME24 56.7, AMC 68.7, MATH 83.4, Minerva 34.2, and OlympiadBench 48.0) demonstrate that SEED-GRPO achieves new state-of-the-art performance in average accuracy, validating the effectiveness of uncertainty-aware policy optimization.

cs.AI↗

arXiv2025

Fourier-enhanced Neural Networks For Systems Biology Applications

In the field of systems biology, differential equations are commonly used to model biological systems, but solving them for large-scale and complex systems can be computationally expensive. Recently, the integration of machine learning and mathematical modeling has offered new opportunities for scientific discoveries in biology and health. The emerging physics-informed neural network (PINN) has been proposed as a solution to this problem. However, PINN can be computationally expensive and unreliable for complex biological systems. To address these issues, we propose the Fourier-enhanced Neural Networks for systems biology (SB-FNN). SB-FNN uses an embedded Fourier neural network with an adaptive activation function and a cyclic penalty function to optimize the prediction of biological dynamics, particularly for biological systems that exhibit oscillatory patterns. Experimental results demonstrate that SB-FNN achieves better performance and is more efficient than PINN for handling complex biological models. Experimental results on cellular and population models demonstrate that SB-FNN outperforms PINN in both accuracy and efficiency, making it a promising alternative approach for handling complex biological models. The proposed method achieved better performance on six biological models and is expected to replace PINN as the most advanced method in systems biology.

cs.LG↗

arXiv2024

Linear-Scaling Potential-Free Data-Driven Molecular Dynamics for Arbitrary-Sized Water Clusters $(\text{H}_2\text{O})_n$

Conventional molecular dynamics (MD) simulation approaches, such as $\textit{ab initio}$ MD (AIMD) and empirical force field MD (EFFMD), face significant trade-offs between physical accuracy and computational efficiency. This work presents a linear-scaling potential-free data-driven molecular dynamics (PDMD) framework for predicting system energy and atomic forces of arbitrary-sized water clusters $(\text{H}_2\text{O})_n$. Specifically, PDMD employs a Gaussian-based atomic geometry descriptor to generate high-dimensional, atomistic footprints, then leverages ChemGNN, a graph neural network model that adaptively learns the atomic chemical environments without requiring $\textit{a priori}$ knowledge. Through an iterative self-consistent training approach, the converged PDMD achieves a mean absolute error of 1.39 meV/atom for energy, outperforming other state-of-the-art models such as DeepMD, MACE, NequIP, and SevenNet by at least 2.6x in accuracy with the same dataset. As a result, the linear-scaling PDMD can reproduce the AIMD properties of water clusters at orders-of-magnitude lower computational cost, as illustrated by simulations of systems consisting of thousands or more molecules. These results demonstrate that the proposed PDMD offers multiphase predictive power and enables ultra-fast, general-purpose MD simulations while retaining AIMD-level accuracy. This accuracy is achieved by efficiently capturing many-body potentials that are critical in numerous polyatomic systems but are often missing in EFFMD. Moreover, we have constructed an $\textit{ab initio}$ dataset with over 300,000 $(\text{H}_2\text{O})_n$ structures, standardized in a unified PyTorch Geometric framework, to support scalable evaluation of artificial intelligence methods for molecular dynamics.

cond-mat.dis-nn↗

arXiv2024

SCExAO/CHARIS Spectroscopic Characterization of Cloudy L/T Transition Companion Brown Dwarf HIP 93398 B

Brown dwarfs with measured dynamical masses and spectra from direct imaging are benchmarks that anchor substellar atmosphere cooling and evolution models. We present Subaru SCExAO/CHARIS infrared spectroscopy of HIP 93398 B, a brown dwarf companion recently discovered by Li et al. 2023 as part of an informed survey using the Hipparcos-Gaia Catalog of Accelerations. This object was previously classified as a T6 dwarf based on its luminosity, with its independently-derived age and dynamical mass in tension with existing models of brown dwarf evolution. Spectral typing via empirical standard spectra, temperatures derived by fitting substellar atmosphere models, and J-H, J-K and H-L' colors all suggest that this object has a substantially higher temperature and luminosity, consistent with classification as a late-L dwarf near the L/T transition (T = 1200$^{+140}_{-119}$ K) with moderate to thick clouds possibly present in its atmosphere. When compared with the latest generation of evolution models that account for clouds with our revised luminosity and temperature for the object, the tension between the model-independent mass/age and model predictions is resolved.

astro-ph.SR↗

arXiv2024

SCExAO/CHARIS Near-Infrared Scattered-Light Imaging and Integral Field Spectropolarimetry of the AB Aurigae Protoplanetary System

We analyze near-infrared integral field spectropolarimetry of the AB Aurigae protoplanetary disk and protoplanet (AB Aur b), obtained with SCExAO/CHARIS in 22 wavelength channels covering the J, H, and K passbands ($\lambda_{\rm o}$ = 1.1--2.4 $\mu m$) over angular separations of $\rho$ $\approx$ 0.13" to 1.1" ($\sim$20--175 au). Our images resolve spiral structures in the disk in each CHARIS channel. At the longest wavelengths, the data may reveal an extension of the western spiral seen in previous polarimetric data at $\rho$ $<$ 0.3" out to larger distances clockwise from the protoplanet AB Aur b, coincident with the ALMA-detected $CO$ gas spiral. While AB Aur b is detectable in complementary total intensity data, it is a non-detection in polarized light at $\lambda$ $>$ 1.3 $\mu $m. While the observed disk color is extremely red across $JHK$, the disk has a blue intrinsic scattering color consistent with small dust grains. The disk's polarization spectrum is redder than AB Aur b's total intensity spectrum. The polarization fraction peaks at $\sim$ 0.6 along the major disk axis. Radiative transfer modeling of the CHARIS data shows that small, porous dust grains with a porosity of $p$ = 0.6--0.8 better reproduce the scattered-light appearance of the disk than more compact spheres ($p$ = 0.3), especially the polarization fraction. This work demonstrates the utility of integral field spectropolarimetry to characterize structures in protoplanetary disks and elucidate the properties of the disks' dust.

astro-ph.EP↗

arXiv2024

Hierarchical Gradient-Based Genetic Sampling for Accurate Prediction of Biological Oscillations

Biological oscillations are periodic changes in various signaling processes crucial for the proper functioning of living organisms. These oscillations are modeled by ordinary differential equations, with coefficient variations leading to diverse periodic behaviors, typically measured by oscillatory frequencies. This paper explores sampling techniques for neural networks to model the relationship between system coefficients and oscillatory frequency. However, the scarcity of oscillations in the vast coefficient space results in many samples exhibiting non-periodic behaviors, and small coefficient changes near oscillation boundaries can significantly alter oscillatory properties. This leads to non-oscillatory bias and boundary sensitivity, making accurate predictions difficult. While existing importance and uncertainty sampling approaches partially mitigate these challenges, they either fail to resolve the sensitivity problem or result in redundant sampling. To address these limitations, we propose the Hierarchical Gradient-based Genetic Sampling (HGGS) framework, which improves the accuracy of neural network predictions for biological oscillations. The first layer, Gradient-based Filtering, extracts sensitive oscillation boundaries and removes redundant non-oscillatory samples, creating a balanced coarse dataset. The second layer, Multigrid Genetic Sampling, utilizes residual information to refine these boundaries and explore new high-residual regions, increasing data diversity for model training. Experimental results demonstrate that HGGS outperforms seven comparative sampling methods across four biological systems, highlighting its effectiveness in enhancing sampling and prediction accuracy.

cs.LG↗

arXiv2024

Hydra-SGG: Hybrid Relation Assignment for One-stage Scene Graph Generation

DETR introduces a simplified one-stage framework for scene graph generation (SGG) but faces challenges of sparse supervision and false negative samples. The former occurs because each image typically contains fewer than 10 relation annotations, while DETR-based SGG models employ over 100 relation queries. Each ground truth relation is assigned to only one query during training. The latter arises when one ground truth relation may have multiple queries with similar matching scores, leading to suboptimally matched queries being treated as negative samples. To address these, we propose Hydra-SGG, a one-stage SGG method featuring a Hybrid Relation Assignment. This approach combines a One-to-One Relation Assignment with an IoU-based One-to-Many Relation Assignment, increasing positive training samples and mitigating sparse supervision. In addition, we empirically demonstrate that removing self-attention between relation queries leads to duplicate predictions, which actually benefits the proposed One-to-Many Relation Assignment. With this insight, we introduce Hydra Branch, an auxiliary decoder without self-attention layers, to further enhance One-to-Many Relation Assignment by promoting different queries to make the same relation prediction. Hydra-SGG achieves state-of-the-art performance on multiple datasets, including VG150 (16.0 mR@50), Open Images V6 (50.1 weighted score), and GQA (12.7 mR@50).

cs.CV↗

arXiv2024

Multiband polarimetric imaging of HD 34700 with SCExAO/CHARIS

We present Subaru/SCExAO + CHARIS broadband (JHK) integral field spectroscopy of HD 34700 A in polarized light. CHARIS has the unique ability to obtain polarized integral field images at 22 wavelength channels in broadband, as the incoming light is first split into different polarization states before passing though the lenslet array. We recover the transition disk around HD 34700 A in multiband polarized light in our data. We combine our polarized intensity data with previous total intensity data to examine the scattering profiles, scattering phase functions and polarized fraction of the disk at multiple wavelengths. We also carry out 3D Monte Carlo radiative transfer simulations of the disk using MCFOST, and make qualitative comparisons between our models and data to constrain dust grain properties. We find that in addition to micron-sized dust grains, a population of sub-micron grains is needed to match the surface brightness in polarized light and polarized fraction. This could indicate the existence of a population of small grains in the disk, or it could be caused by Mie theory simulations using additional small grains to compensate for sub-micron structures of real dust aggregates. We find models that match the polarized fraction of the data but the models do not apply strong constraints on the dust grain type or compositions. We find no models that can match all observed properties of the disk. More detailed modeling using realistic dust aggregates with irregular surfaces and complex structures is required to further constrain the dust properties.

astro-ph.SR↗

arXiv2024

UAHOI: Uncertainty-aware Robust Interaction Learning for HOI Detection

This paper focuses on Human-Object Interaction (HOI) detection, addressing the challenge of identifying and understanding the interactions between humans and objects within a given image or video frame. Spearheaded by Detection Transformer (DETR), recent developments lead to significant improvements by replacing traditional region proposals by a set of learnable queries. However, despite the powerful representation capabilities provided by Transformers, existing Human-Object Interaction (HOI) detection methods still yield low confidence levels when dealing with complex interactions and are prone to overlooking interactive actions. To address these issues, we propose a novel approach \textsc{UAHOI}, Uncertainty-aware Robust Human-Object Interaction Learning that explicitly estimates prediction uncertainty during the training process to refine both detection and interaction predictions. Our model not only predicts the HOI triplets but also quantifies the uncertainty of these predictions. Specifically, we model this uncertainty through the variance of predictions and incorporate it into the optimization objective, allowing the model to adaptively adjust its confidence threshold based on prediction variance. This integration helps in mitigating the adverse effects of incorrect or ambiguous predictions that are common in traditional methods without any hand-designed components, serving as an automatic confidence threshold. Our method is flexible to existing HOI detection methods and demonstrates improved accuracy. We evaluate \textsc{UAHOI} on two standard benchmarks in the field: V-COCO and HICO-DET, which represent challenging scenarios for HOI detection. Through extensive experiments, we demonstrate that \textsc{UAHOI} achieves significant improvements over existing state-of-the-art methods, enhancing both the accuracy and robustness of HOI detection.

cs.CV↗

arXiv2024

The Keck-HGCA Pilot Survey II: Direct Imaging Discovery of HD 63754 B, a ~20 au Massive Companion Near the Hydrogen Burning Limit

We present the joint astrometric and direct imaging discovery, mass measurement, and orbital analysis of HD 63754 B (HIP 38216 B), a companion near the stellar-substellar boundary orbiting ~20 AU from its Sun-like host. HD 63754 was observed in our ongoing high-contrast imaging survey targeting stars with significant proper-motion accelerations between Hipparcos and Gaia consistent with wide-separation substellar companions. We utilized archival HIRES and HARPS radial velocity (RV) data, together with the host star's astrometric acceleration extracted from the Hipparcos-Gaia Catalog of Accelerations (HGCA), to predict the location of the candidate companion around HD 63754 A. We subsequently imaged HD 63754 B at its predicted location using the Near Infrared Camera 2 (NIRC2) in the $L'$ band at the W. M. Keck Observatory. We then jointly modeled the orbit of HD 63754 B with RVs, Hipparcos-Gaia accelerations, and our new relative astrometry, measuring a dynamical mass of ${81.9}_{-5.8}^{+6.4} M_{jup}$, an eccentricity of ${0.260}_{-0.059}^{+0.065}$, and a nearly face-on inclination of $174.81_{-0.50}^{+0.48}$ degrees. For HD 63754 B, we obtain an L' band absolute magnitude of $L' = 11.39\pm0.06$ mag, from which we infer a bolometric luminosity of $log(L_{bol}/L_{\odot})= -4.55 \pm0.08$ dex using a comparison sample of L and T dwarfs with measured luminosities. Although uncertainties linger in age and dynamical mass estimates, our analysis points toward HD 63754 B's identity as a brown dwarf on the L/T transition rather than a low-mass star, indicated by its inferred bolometric luminosity and model-estimated effective temperature. Future RV, spectroscopic, and astrometric data such as those from JWST and Gaia DR4 will clarify HD 63754 B's mass, and enable spectral typing and atmospheric characterization.

astro-ph.EP↗

arXiv2024

Subaru/CHARIS High-Resolution Mode Spectroscopy of the Brown Dwarf Companion HD 33632 Ab

Brown dwarfs (BD) are model degenerate in age and mass. High-contrast imaging and spectroscopy of BD companions to host stars where the mass and age can be independently constrained by dynamics and stellar age indicators respectively provide valuable tests of BD evolution models. In this paper, we present a new epoch of Subaru/CHARIS H- and K-band observations of one such previously discovered system, HD 33632 Ab. We reanalyze the mass and orbit using our new epoch of extracted relative astrometry, and fit extracted spectra to the newest generation of equilibrium, disequilibrium, and cloudy spectral and evolution models for BDs. No spectral model perfectly agrees with evolutionary tracks and the derived mass and age, instead favoring a somewhat younger BD than the host star's inferred age. This tension can potentially be resolved using atmosphere and evolution models that consider both clouds and disequilibrium chemistry simultaneously, or by additional future spectra at higher resolution or in other band passes. Photometric measurements alone remain consistent with the luminosity predicted by evolutionary tracks. Our work highlights the importance of considering complexities like clouds, disequilibrium chemistry, and composition when comparing spectral models to evolutionary tracks.

astro-ph.EP↗

arXiv2024

Direct-imaging Discovery of a Substellar Companion Orbiting the Accelerating Variable Star, HIP 39017

We present the direct-imaging discovery of a substellar companion (a massive planet or low-mass brown dwarf) to the young, $\gamma$ Doradus-type variable star, HIP 39017 (HD 65526). The companion's SCExAO/CHARIS JHK ($1.1-2.4\mu$m) spectrum and Keck/NIRC2 L$^{\prime}$ photometry indicate that it is an L/T transition object. A comparison of the JHK+L$^{\prime}$ spectrum to several atmospheric model grids finds a significantly better fit to cloudy models than cloudless models. Orbit modeling with relative astrometry and precision stellar astrometry from Hipparcos and Gaia yields a semi-major axis of $23.8^{+8.7}_{-6.1}$ au, a dynamical companion mass of $30^{+31}_{-12}$~M$_J$, and a mass ratio of $\sim$1.9\%, properties most consistent with low-mass brown dwarfs. However, its mass estimated from luminosity models is a lower $\sim$13.8 $M_{\rm J}$ due to an estimated young age ($\lesssim$ 115 Myr); using a weighted posterior distribution informed by conservative mass constraints from luminosity evolutionary models yields a lower dynamical mass of $23.6_{-7.4}^{+9.1}$~M$_J$ and a mass ratio of $\sim$1.4\%. Analysis of the host star's multi-frequency $\gamma$ Dor-type pulsations, astrometric monitoring of HIP 39017b, and Gaia Data Release 4 astrometry of the star will clarify the system age and better constrain the mass and orbit of the companion. This discovery further reinforces the improved efficiency of targeted direct-imaging campaigns informed by long-baseline, precision stellar astrometry.

astro-ph.SR↗

arXiv2023

Chemical Environment Adaptive Learning for Optical Band Gap Prediction of Doped Graphitic Carbon Nitride Nanosheets

This study presents a novel Machine Learning Algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications, however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' local chemical environment underlying complex molecular structures. Our benchmark results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development.

physics.chem-ph↗

arXiv2023

Post-processing CHARIS integral field spectrograph data with PyKLIP

We present the pyKLIP-CHARIS post-processing pipeline, a Python library that reduces high contrast imaging data for the CHARIS integral field spectrograph used with the SCExAO project on the Subaru Telescope. The pipeline is a part of the pyKLIP package, a Python library dedicated to the reduction of direct imaging data of exoplanets, brown dwarfs, and discs. For PSF subtraction, the pyKLIP-CHARIS post-processing pipeline relies on the core algorithms implemented in pyKLIP but uses image registration and calibrations that are unique to CHARIS. We describe the pipeline procedures, calibration results, and capabilities in processing imaging data acquired via the angular differential imaging and spectral differential imaging observing techniques. We showcase its performance on extracting spectra of injected synthetic point sources as well as compare the extracted spectra from real data sets on HD 33632 and HR 8799 to results in the literature. The pipeline is a python-based complement to the SCExAO project supported, widely used (and currently IDL-based) CHARIS data post-processing pipeline (CHARIS DPP) and provides an additional approach to reducing CHARIS data and extracting calibrated planet spectra.

astro-ph.IM↗