arXiv subjects

Scott Chapman

Publications and source records attributed to Scott Chapman.

At least 19 recordsLinked to original sources

arXiv2026

CCAT: The Prime-Cam Instrument for the Fred Young Submillimeter Telescope -- Overview and Status

Prime-Cam is a first-generation science instrument for the CCAT Observatory's six-meter aperture Fred Young Submillimeter Telescope (FYST), under construction at an elevation of 5600 m on Cerro Chajnantor in Chile's Atacama Desert. Prime-Cam will deliver over ten times greater mapping speed at submillimeter wavelengths than current facilities for unprecedented broadband and spectroscopic measurements in windows between 1.4 -- 0.3 mm (220 -- 850 GHz). When fully populated, Prime-Cam will field over 100,000 kinetic inductance detectors across seven independently optimized instrument modules. With Prime-Cam, the CCAT Collaboration will address a suite of science goals, from Big Bang cosmology, to galaxy evolution and star formation over cosmic time. Prime-Cam is scheduled for integration in FYST in late 2026, followed by a year of early science observations with the 280 and 350 GHz instrument modules. We discuss the design and in-lab testing of the 1.8-m diameter Prime-Cam receiver and 280 GHz instrument module, and give an update on deployment status and early science plans.

astro-ph.IM↗

arXiv2026

CCAT: a two-octave 1.024 GHz KID readout featuring an overlap-channel polyphase synthesis filter bank on RFSoC

Next-generation submillimeter instruments require gigahertz-scale readout bandwidths to support the growing detector counts of large microwave kinetic inductance detector (KID) arrays. Designed to meet the readout bandwidth and tone-capacity requirements of the CCAT Prime-Cam 850 GHz and 410 GHz instrument modules, we developed our second-generation (Gen2) KID readout system on a Xilinx ZCU111 radio frequency system-on-chip (RFSoC), based on a parallelized overlap-channel polyphase synthesis filter bank and a companion wideband receiving channelizer. This two-octave architecture reads out four independent RF networks, each with 1.024 GHz instantaneous bandwidth and up to 2048 detectors. The polyphase synthesis doubles the baseline channel count and bandwidth, and enables on-the-fly control of individual tone frequency, amplitude and phase, facilitating optimized KID biasing and providing a gateway to tone tracking. These capabilities may also be useful more broadly across Prime-Cam and in similar frequency-division multiplexed (FDM) readout systems. We present the design in DSP simulation, and measurements in RF loopback as well as with two KID test chips, one spanning the full two-octave band. Loopback measurements demonstrate comparable noise performance between the first-generation (Gen1) baseline and Gen2; preliminary on/off-resonance measurements indicate detector-noise-limited operation for the majority of channels; and the digital channel crosstalk is measured and discussed. We report FPGA resource and power utilization and assess scalability toward future large-format KID instruments.

astro-ph.IM↗

arXiv2026

CCAT: Characterization of the first science-grade MKID array for the Prime-Cam 850 GHz module

The Fred Young Submillimeter Telescope (FYST) is a 6-meter crossed-Dragone telescope developed by the CCAT collaboration. Sited at 5600 m on Cerro Chajnantor in the Atacama Plateau of Chile, FYST aims to provide superior atmospheric transmission and a wide field of view for submillimeter observations. Prime-Cam is a first-generation instrument for FYST designed to house up to seven separate instrument modules. Among these, the 850 GHz module represents the highest-frequency band and is optimized for ultra-sensitive broadband polarimetry and imaging. This module is designed to deploy ~38,000 polarization-sensitive lumped-element titanium-nitride (TiN) microwave kinetic inductance detectors (MKIDs) across three arrays. Thus the 850 GHz module will have the most submillimeter MKIDs in a single instrument module to date. These detectors have adopted a novel two-octave design to maximize the multiplexing achieved using a RFSoC readout system. We review the design parameters and fabrication process for the first 850 GHz science-grade array. The preliminary measurements of the array show a fabrication yield of 99%, highlighting the successful application of the design and fabrication process. We further present the cryogenic characterization of the first full MKID array developed for the Prime-Cam 850 GHz module. Multiple tests were performed on the devices including resonator frequency mapping, quality factor measurements, optical load sweeps and noise performance. From these measurements, we discuss the measured optical efficiency, sensitivity, and uniformity across the array and the expected on-sky performance of the module. The 850 GHz module will be deployed for observing in 2027.

astro-ph.IM↗

arXiv2026

CCAT: Design and Characterization of the 350 GHz Instrument Module

The CCAT Collaboration's Prime-Cam instrument will soon be deployed to the Fred Young Submillimeter Telescope (FYST) in Chile's Atacama Desert. Featuring prominently in Prime-Cam's calibration and early science observations will be the 350 GHz instrument module, a broadband camera that will field more than 10,000 microwave kinetic inductance detectors (KIDs) across three detector arrays. Forecasts show this module will be capable of making the most sensitive to-date measurements of polarized dust emission over a large fraction of the sky at this frequency, enabling new galactic polarization science and improved understanding of cosmological foregrounds. In this work we discuss the design of the 350 GHz instrument module, covering aspects of the optics, readout, and detector arrays. We then report on the results of in-lab testing of the fully-integrated module, achieving stable cryogenic performance with a 100 mK focal plane, high detector yield, and a passband comparable to designed specifications. Upon completion of these tests, this module was shipped to the telescope site in Chile for integration in Prime-Cam.

astro-ph.IM↗

arXiv2026

CCAT: Optical Design of the 410 GHz Prime-Cam Module

Prime-Cam is a first-generation instrument for the Fred Young Submillimeter Telescope (FYST), enabling wide-field, multi-frequency observations for cosmology, line-intensity mapping, and galaxy studies. We present an optical performance study for a candidate 410 GHz broadband module designed to field approximately 21,000 polarisation-sensitive kinetic inductance detectors (KIDs). The three-lens silicon design was adapted from the SO LATR design and used for the existing 280 and 350 GHz Prime-Cam instrument modules, as well as this study. At 410 GHz, a shorter wavelength places tighter demands on wavefront quality and beam shape. Using Ansys Zemax OpticStudio and Huygens PSF analysis, we evaluate candidate module positions, compare 350 and 410 GHz performance, and assess field-dependent Strehl ratio, ellipticity, and encircled-energy behaviour. A preliminary tolerancing study, using inverse increment and Monte Carlo methods, tests sensitivity to selected alignment perturbations.

astro-ph.IM↗

arXiv2026

Assessing the large-scale angular clustering of UNIONS Lyman Break Galaxies via cross-correlations

Lyman-break galaxies (LBGs), selected via the strong spectral break blueward of the Lyman limit, are powerful tracers of large-scale structure at redshifts $z>2$. In this work, we assess the feasibility of using LBGs selected from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) multi-band photometric catalog as cosmological probes of the high-redshift Universe using two-point statistics. We demonstrate that spatially varying imaging systematics, driven by variations in PSF depth, seeing across the UNIONS footprint, limit robust measurements of the LBG auto-angular power spectrum on large scales, even after correcting the LBG field with linear or non-linear mitigation techniques. This study shows that clustering analyses of faint galaxy samples close to survey depth are challenging. We therefore turn to cross-correlation measurements with external tracers, in particular the \textit{Planck} CMB lensing convergence and quasars from DESI DR1 and \textit{Quaia}, which are less sensitive to the angular imaging systematics. Using both data and mock catalogues, we demonstrate that the LBG--CMB lensing cross-power spectrum can be measured more robustly than the auto-spectrum, with an amplitude consistent with theoretical predictions. Residual systematics primarily manifest as excess variance at large angular scales, without introducing a significant bias in the recovered signal. Taken together, these results establish UNIONS-selected LBGs as reliable tracers for cross-correlation cosmology at $z\sim 2.5$, and highlight cross-correlation techniques as a powerful and robust avenue for extracting cosmological information from photometric high-redshift galaxy samples in the presence of complex imaging systematics.

astro-ph.CO↗

arXiv2026

Benchmarking Vision-Language Models for Microscopic Plant Image Understanding

Microscopic imaging provides essential visual evidence for studying plant biology and pathology at the cellular and subcellular levels. However, existing benchmarks on vision-language models primarily focus on macroscopic plant imagery, while the microscopic domain remains underexplored. To address this gap, we present PlantMicro, a comprehensive benchmark for evaluating vision-language models (VLMs) in microscopic plant imagery. PlantMicro integrates more than 5,000 images collected across diverse hosts, biological domains, and imaging modalities. Building on this diversity, we design a set of complementary tasks that capture different facets of microscopic image understanding. To support these tasks, we construct over 9,000 VQA pairs that systematically evaluate the capabilities of VLMs. Experiments on PlantMicro show that current VLMs struggle with fine-grained recognition and biologically grounded reasoning. For example, GPT-5 achieves 34.93% accuracy on the pathogen classification task, which is only modestly above the random-guessing baseline. The results highlight a significant gap in current VLMs' ability to comprehend plant microscopic images. PlantMicro provides a standardized foundation for advancing VLMs toward reliable and comprehensive microscopy-level plant understanding.

cs.CV↗

arXiv2026

It's Not Just Star Formation: A trend of low dark matter densities in the Andromeda dwarf galaxy system

Dynamical mass modeling of Andromeda (M31) dwarf spheroidal (dSph) galaxies has revealed a growing trend of lower central dark matter (DM) densities than predicted by pure DM structure formation in Lambda Cold Dark Matter ($\Lambda$CDM) cosmology simulations and lower than most Milky Way (MW) satellites. So far, however, only four of the 35 confirmed M31 dSphs have been successfully mass modeled. In this second paper of a series, we aim to better understand growing Local Group (LG) dSph patterns by mass modeling seven more M31 dSphs: Andromeda I, III, V, VII, IX, XXXI, and XXXII. We update the kinematics of each dwarf and estimate their central dark matter densities at 150 pc using the dynamical Jeans modeling tool, GravSphere. We also update their DM halo mass, $M_{\rm{200}}$, via abundance matching. We find Andromeda III and V to have central DM densities in line with $\Lambda$CDM expectations, resembling dSphs around the Milky Way. The remaining five dwarfs have anomalously low central densities, continuing a growing trend seen for M31 satellites. We investigate each dwarf's star formation history and find that star formation-induced `DM heating' is disfavored as the sole explanation of these lower central densities. We consider the effect of tides and halo concentration scatter on these systems and predict that they should be on more plunging orbits than their denser counterparts. If this prediction is misaligned with the data, it could necessitate new physics beyond the Standard Cosmological Model.

astro-ph.GA↗

arXiv2026

CCAT: Silicon-Platelet Feedhorns for Submillimeter Wavelengths

Silicon-platelet feedhorn arrays are an established technology at millimeter wavelengths that, for some applications, can provide significant advantages over traditional direct-machined metal feedhorns. The Prime-Cam focal planes operating in the 350 GHz ($\sim$860 $\mathrm{\mu}$m) and 850 GHz ($\sim$350 $\mathrm{\mu}$m) bands are anticipated to carry the first silicon-platelet feedhorn arrays to operate fully at submillimeter wavelengths, representing a significant step forward in the application of this technology. In particular, the feedhorns designed for operation in the 850 GHz band represent a 3x increase in frequency compared to previously demonstrated and deployed devices of this type. Here we present a demonstration of silicon-platelet feedhorns at these submillimeter wavelengths, including in-lab performance characterization. We present fabrication metrology, room-temperature beammaps, and cryogenic optical efficiency measurements where the feedhorns are coupled to prototype CCAT Prime-Cam detectors. We show that feedhorn performance measurements are well matched to simulation and compare that performance directly to traditional, direct-machined metal feedhorns.

astro-ph.IM↗

arXiv2026

Learning to Synergize Semantic and Geometric Priors for Limited-Data Wheat Disease Segmentation

Wheat disease segmentation is fundamental to precision agriculture but faces severe challenges from significant intra-class temporal variations across growth stages. Such substantial appearance shifts make collecting a representative dataset for training from scratch both labor-intensive and impractical. To address this, we propose SGPer, a Semantic-Geometric Prior Synergization framework that treats wheat disease segmentation under limited data as a coupled task of disease-specific semantic perception and disease boundary localization. Our core insight is that pretrained DINOv2 provides robust category-aware semantic priors to handle appearance shifts, which can be converted into coarse spatial prompts to guide SAM for the precise localization of disease boundaries. Specifically, SGPer designs disease-sensitive adapters with multiple disease-friendly filters and inserts them into both DINOv2 and SAM to align their pretrained representations with disease-specific characteristics. To operationalize this synergy, SGPer transforms DINOv2-derived features into dense, category-specific point prompts to ensure comprehensive spatial coverage of all disease regions. To subsequently eliminate prompt redundancy and ensure highly accurate mask generation, it dynamically filters these dense candidates by cross-referencing SAM's iterative mask confidence with the category-specific semantic consistency derived from DINOv2. Ultimately, SGPer distills a highly informative set of prompts to activate SAM's geometric priors, achieving precise and robust segmentation that remains strictly invariant to temporal appearance changes. Extensive evaluations demonstrate that SGPer consistently achieves state-of-the-art performance on wheat disease and organ segmentation benchmarks, especially in data-constrained scenarios.

cs.CV↗

arXiv2026

StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components

Sorghum is a globally important cereal grown widely in water-limited and stress-prone regions. Its strong drought tolerance makes it a priority crop for climate-resilient agriculture. Improving water-use efficiency in sorghum requires precise characterisation of stomatal traits, as stomata control of gas exchange, transpiration and photosynthesis have a major influence on crop performance. Automated analysis of sorghum stomata is difficult because the stomata are small (often less than 40 $\mu$m in length in grasses such as sorghum) and vary in shape across genotypes and leaf surfaces. Automated segmentation contributes to high-throughput stomatal phenotyping, yet current methods still face challenges related to nested small structures and annotation bottlenecks. In this paper, we propose a semi-supervised instance segmentation framework tailored for analysis of sorghum stomatal components. We collect and annotate a sorghum leaf imagery dataset containing 11,060 human-annotated patches, covering the three stomatal components (pore, guard cell and complex area) across multiple genotypes and leaf surfaces. To improve the detection of tiny structures, we split high-resolution microscopy images into overlapping small patches. We then apply a pseudo-labelling strategy to unannotated images, producing an additional 56,428 pseudo-labelled patches. Benchmarking across semantic and instance segmentation models shows substantial performance gains: for semantic models the top mIoU increases from 65.93% to 70.35%, whereas for instance models the top AP rises from 28.30% to 46.10%. These results demonstrate that combining patch-based preprocessing with semi-supervised learning significantly improves the segmentation of fine stomatal structures. The proposed framework supports scalable extraction of stomatal traits and facilitates broader adoption of AI-driven phenotyping in crop science.

cs.CV↗

arXiv2025

CCAT: Optical Responsivity, Noise, and Readout Optimization of KIDs for Prime-Cam

The Prime-Cam instrument on the Fred Young Submillimeter Telescope (FYST) at the CCAT Observatory will conduct sensitive millimeter to submillimeter surveys for a range of astrophysical and cosmological sciences. Prime-Cam will use kinetic inductance detectors (KIDs) sensitive to multiple frequency bands spanning 280-850 GHz. With over 100,000 sensors under development, these KID arrays will soon form the largest submillimeter focal plane ever built. With fixed microwave tones probing amplitude and phase modulations in the KIDs due to incoming radiation, challenges arise in determining the optimal readout settings, especially under varying atmospheric loading. Realizing the science goals of FYST requires operating the detectors at optimal performance and determining accurate responsivities, which depend on readout tone placement and power. To address these challenges, we present laboratory measurements of sample pixels from the 280 GHz TiN and Al arrays using a blackbody cold load to simulate observing conditions. These measurements probe detector responsivity and noise across varying optical loading, tone power, and tone placement, providing the foundation to guide in situ calibration and operation of the >100,000 KIDs. We characterize detector sensitivity via the Noise Equivalent Power (NEP) as a function of readout tone power and placement, and measure the impact of detuning due to varying optical power on the NEP. Our test setup and methodology will inform the commissioning of Prime-Cam, in situ detector calibration procedures, the cadence of probe tone resetting, and potential design refinements for future arrays, supporting FYST's planned first light in 2026.

astro-ph.IM↗

arXiv2025

CCAT: Design, Implementation, and Testing of a System to Read Out over 10,000 280 GHz KIDs using RFSoC Electronics

Over the past decade, kinetic inductance detectors (KIDs) have emerged as a viable superconducting technology for astrophysics at millimeter and submillimeter wavelengths. KIDs spanning 210 - 850 GHz across seven instrument modules will be deployed in the Prime-Cam instrument of CCAT Observatory's Fred Young Submillimeter Telescope at an elevation of 5600 m on Cerro Chajnantor in Chile's Atacama Desert. The natural frequency-division multiplexed readout of KIDs allows hundreds of detectors to be coupled to a single radio frequency (RF) transmission line, but requires sophisticated warm readout electronics. The FPGA-based Xilinx ZCU111 radio frequency system on chip (RFSoC) offers a promising and flexible solution to the challenge of warm readout. CCAT uses custom packaged RFSoCs to read out KIDs in the Prime-Cam instrument. Each RFSoC can simultaneously read out four RF channels with up to 1,000 detectors spanning a 512 MHz bandwidth per channel using the current firmware. We use five RFSoCs to read out the >10,000 KIDs in the broadband 280 GHz instrument module. Here, we describe and demonstrate the readout hardware, software and pipeline for the RFSoC system. We present a detector position map of the 280 GHz module focal plane and preliminary averaged spectral responses of a small subset of detectors from the TiN and first Al arrays. These measurements demonstrate our ability to simultaneously readout thousands of detectors, validate the end-to-end performance of the readout and optical systems, and represent a critical step toward reading out the ~100,000 KIDs in Prime-Cam in its future full capacity configuration.

astro-ph.IM↗

arXiv2025

CCAT: Magnetic Sensitivity Measurements of Kinetic Inductance Detectors

The CCAT Observatory is a ground-based submillimeter to millimeter experiment located on Cerro Chajnantor in the Atacama Desert, at an altitude of 5,600 meters. CCAT features the 6-meter Fred Young Submillimeter Telescope (FYST), which will cover frequency bands from 210 GHz to 850 GHz using its first-generation science instrument, Prime-Cam. The detectors used in Prime-Cam are feedhorn-coupled, lumped-element superconducting microwave kinetic inductance detectors (KIDs). The telescope will perform wide-area surveys at speeds on the order of degrees per second. During telescope operation, the KIDs are exposed to changes in the magnetic field caused by the telescope's movement through Earth's magnetic field and internal sources within the telescope. We present and compare measurements of the magnetic sensitivity of three different CCAT KID designs at 100 mK. The measurements are conducted in a dilution refrigerator (DR) with a set of room temperature Helmholtz coils positioned around the DR. We discuss the implications of these results for CCAT field operations.

astro-ph.IM↗

arXiv2025

Dynamic Orchestration of Multi-Agent System for Real-World Multi-Image Agricultural VQA

Agricultural visual question answering is essential for providing farmers and researchers with accurate and timely knowledge. However, many existing approaches are predominantly developed for evidence-constrained settings such as text-only queries or single-image cases. This design prevents them from coping with real-world agricultural scenarios that often require multi-image inputs with complementary views across spatial scales, and growth stages. Moreover, limited access to up-to-date external agricultural context makes these systems struggle to adapt when evidence is incomplete. In addition, rigid pipelines often lack systematic quality control. To address this gap, we propose a self-reflective and self-improving multi-agent framework that integrates four roles, the Retriever, the Reflector, the Answerer, and the Improver. They collaborate to enable context enrichment, reflective reasoning, answer drafting, and iterative improvement. A Retriever formulates queries and gathers external information, while a Reflector assesses adequacy and triggers sequential reformulation and renewed retrieval. Two Answerers draft candidate responses in parallel to reduce bias. The Improver refines them through iterative checks while ensuring that information from multiple images is effectively aligned and utilized. Experiments on the AgMMU benchmark show that our framework achieves competitive performance on multi-image agricultural QA.

cs.CV↗

arXiv2025

CCAT: Mod-Cam Cryogenic Performance and its Impact on 280 GHz KID Array Noise

The CCAT Observatory's Fred Young Submillimeter Telescope (FYST) is designed to observe submillimeter astronomical signals with high precision, using receivers fielding state-of-the-art kinetic inductance detector (KID) arrays. Mod-Cam, a first-light instrument for FYST, serves as a testbed for instrument module characterization, including detailed evaluation of thermal behavior under operating conditions prior to deploying modules in the larger Prime-Cam instrument. Prime-Cam is a first generation multi-band, wide-field camera for FYST, designed to field up to seven instrument modules and provide unprecedented sensitivity across a broad frequency range. We present results from two key laboratory characterizations: an "optically open" cooldown to validate the overall thermal performance of the cryostat, and a "cold load" cooldown to measure the effect of focal plane temperature stability on detector noise. During the optically open test, we achieved stable base temperatures of 1.5 K on the 1 K stage and 85 mK at the detector stage. In the cold load configuration, we measured a detector focal plane RMS temperature stability of 3.2e-5 K. From this stability measurement, we demonstrate that the equivalent power from focal plane thermal fluctuations is only 0.0040% of a 5pW incident photon power for aluminum detectors and 0.0023% for titanium-nitride detectors, a negligible level for CCAT science goals. This highlights the success of the cryogenic system design and thermal management.

astro-ph.IM↗

arXiv2025

CCAT: Mod-Cam Readout Overview and Flexible Stripline Performance

The CCAT Observatory's primary science instrument, Prime-Cam, is nearing readiness for deployment to the Fred Young Submillimeter Telescope (FYST) in the Atacama Desert in northern Chile. When fully deployed, Prime-Cam will field approximately 100,000 kinetic inductance detectors (KIDs) across seven instrument modules making both broadband and polarimetric measurements. Meanwhile, in-lab characterization of the first CCAT instrument module, a 280 GHz broadband camera fielding over 10,000 KIDs, is currently underway in the testbed instrument Mod-Cam. Both Mod-Cam and Prime-Cam will employ 46 cm long low-thermal-conductivity flexible circuits ("stripline") between 4 K and 300 K to connect large-format arrays of multiplexed KIDs in each instrument module to readout electronics. The 280 GHz camera currently installed in Mod-Cam uses six of these striplines to read out its over 10,000 detectors. We have examined the thermal and electrical performance of the stripline installed in Mod-Cam. We begin by characterizing the OFHC copper in the stripline traces, allowing for the estimation of thermal loading through these flexible circuits in their configurations in both Mod-Cam and Prime-Cam. We then directly measure the thermal conductivity of the stripline, finding it is best described by $kA = 22\pm6~T^{0.84\pm0.09}~\mathrm{\mu~W~m~K^{-1}}$ for temperature ranges of 6 K < T < 20 K and $kA~=~0.6\pm0.3~T^{-0.4\pm0.1}~\mathrm{mW~m~K^{-1}}$ for ranges from 20 K < T < 80 K. Following our thermal characterizations, we report on the transmission and crosstalk properties of the Mod-Cam readout chain, isolating elevated crosstalk to SMP-SMA transition printed circuit boards (PCBs) that interface with the stripline. This finding validates the stripline circuit as a viable high-density cabling option for large-format array readout.

astro-ph.IM↗

arXiv2025

Augment to Segment: Tackling Pixel-Level Imbalance in Wheat Disease and Pest Segmentation

Accurate segmentation of foliar diseases and insect damage in wheat is crucial for effective crop management and disease control. However, the insect damage typically occupies only a tiny fraction of annotated pixels. This extreme pixel-level imbalance poses a significant challenge to the segmentation performance, which can result in overfitting to common classes and insufficient learning of rare classes, thereby impairing overall performance. In this paper, we propose a Random Projected Copy-and-Paste (RPCP) augmentation technique to address the pixel imbalance problem. Specifically, we extract rare insect-damage patches from annotated training images and apply random geometric transformations to simulate variations. The transformed patches are then pasted in appropriate regions while avoiding overlaps with lesions or existing damaged regions. In addition, we apply a random projection filter to the pasted regions, refining local features and ensuring a natural blend with the new background. Experiments show that our method substantially improves segmentation performance on the insect damage class, while maintaining or even slightly enhancing accuracy on other categories. Our results highlight the effectiveness of targeted augmentation in mitigating extreme pixel imbalance, offering a straightforward yet effective solution for agricultural segmentation problems.

cs.CV↗