Results for “FLAG” — Page 17

NASA NTRSDate not supplied

Qualification of Domestic Lyocell Based Phenolic Impregnated Carbon Ablator (PICA-D) for NASA Missions

Phenolic Impregnated Carbon Ablator (PICA) is a low-density ablator that has been used as the planetary entry heatshield for several NASA missions since the late 90’s. Its low density and efficient performance characteristics have proven effective for use from Discovery to Flag-ship class missions, from Sample Return missions such as Stardust, OSIRIS-REx to large Mars Lander missions such as Mars Science Lab (MSL) and Mars 2020. The rayon-based carbon precursor raw material used in PICA preform manufacturing has experienced multiple supply chain issues. The challenge involved in finding a replacement fiber source is in processing as well as in the final performance of the ablator. Each replacement necessitates the requalification of the PICA. This has happened at least twice in the past 25 years, and a third substitution is now needed. Due to the obsolescence of the input foreign rayon fiber source, a new variant of PICA has been developed using a domestic rayon-like fiber source, Lyocell. Due to its flight heritage and proven capability, PICA is baselined as the Thermal Protection System (TPS) for Dragonfly and Mars Sample Return Sample Retrieval Lander mission and is being considered for the backshell of the Mars Sample Return Earth Entry System. All three missions are due to be launched between 2026 and 2028. The challenge this time is to ensure the PICA made with domestic material is a suitable replacement to the heritage PICA used in MSL and Mars 2020 so that the design of the heatshield can be matured without much risk. Results are presented from the recent efforts of 22 PICA-D billets that were Lot Acceptance Tested. Thermal, mechanical, and representative environment arc-jet tests have been conducted. Testing of PICA-Domestic (PICA-D) indicates very comparable performance with respect to “heritage” PICA materials and thus PICA-D is expected to be a sustainable and nearly a “drop-in” replacement solution for future NASA missions.

Matt Gasch↗

NASA NTRSDate not supplied

Qualification of Domestic Lyocell Based Phenolic Impregnated Carbon Ablator (PICA-D) for NASA Missions

Phenolic Impregnated Carbon Ablator (PICA) is a low-density ablator that has been used as the planetary entry heatshield for several NASA missions since the late 90’s. Its low density and efficient performance characteristics have proven effective for use from Discovery to Flag-ship class missions, from Sample Return missions such as Stardust, OSIRIS-REx to large Mars Lander missions such as Mars Science Lab (MSL) and Mars 2020. The rayon-based carbon precursor raw material used in PICA preform manufacturing has experienced multiple supply chain issues. The challenge involved in finding a replacement fiber source is in processing as well as in the final performance of the ablator. Each replacement necessitates the requalification of the PICA. This has happened at least twice in the past 25 years, and a third substitution is now needed. Due to the obsolescence of the input foreign rayon fiber source, a new variant of PICA has been developed using a domestic rayon-like fiber source, Lyocell. Due to its flight heritage and proven capability, PICA is baselined as the Thermal Protection System (TPS) for Dragonfly and Mars Sample Return Sample Retrieval Lander mission and is being considered for the backshell of the Mars Sample Return Earth Entry System. All three missions are due to be launched between 2026 and 2028. The challenge this time is to ensure the PICA made with domestic material is a suitable replacement to the heritage PICA used in MSL and Mars 2020 so that the design of the heatshield can be matured without much risk. Results are presented from the recent efforts of 22 PICA-D billets that were Lot Acceptance Tested. Thermal, mechanical, and representative environment arc-jet tests have been conducted. Testing of PICA-Domestic (PICA-D) indicates very comparable performance with respect to “heritage” PICA materials and thus PICA-D is expected to be a sustainable and nearly a “drop-in” replacement solution for future NASA missions.

PICA↗

NASA NTRSDate not supplied

Distributed Target Tracking With Optimal Data Migration

The paper presents an Extended Kalman Filter based framework for airborne target tracking using dynamic information fusion from multi-modal sensors with geodiversity. First, the algorithm execution location is determined using an optimal data migration strategy, next the sensors information is dynamically fused at each estimation instance using validity flag for each sensor reading, finally the target estimation is updated based on the fused innovation vector. The approach is applied to synthetic data generated from the radar and camera models located on the ground for the simulated target flight in Reflection simulation environment.

Distributed sensing↗

NASA NTRSDate not supplied

Decoupling the Effects of Anthropogenic Emission Reductions from the Meteorology and Natural Emissions in TROPOMI NO 2 Retrievals During the 2020 COVID-19 Lockdowns

Satellite measurements during the COVID-19 lockdowns that began in 2020 revealed unprecedented reductions in NO 2 tropospheric vertical column densities (VCD). These reductions have largely been attributed to reduced anthropogenic emissions associated with abrupt decreases in road traffic and other power consuming business activities. Although decreased emissions tended to be the main contributor to the observed NO 2 VCD reduction, meteorological variability also played a role. Whereas the observed VCD changes were predominantly negative in places where public health policies were strictly enforced, meteorology had both positive and negative effects over short time intervals. Here, we present results from a global study of the NO 2 reductions aimed at disentangling the meteorological and natural emission variability from the anthropogenic emissions over the world’s most populated megacities. For this study, NASA’s TROPOMI NO 2 algorithm was used in conjunction with the Global Modeling Initiative (GMI) chemical transport model to separate the contributions due emissions and meteorology. A priori NO 2 profiles were generated from two GMI simulations performed for 2020 at a resolution of 0.25° longitude x 0.25° latitude. The first simulation used updated, COVID-impacted NOX emissions based on Forster et al. (2020), while the second simulation used the 2019 emissions with the 2020 meteorology, referred to here as 2020BAU. The 2020BAU data set allowed for the decoupling of the emission component from the meteorology. When compared to the same period in 2019, NO 2 column amounts during the lockdowns were reduced in 35 out of 36 cities. While reduced emissions contributed most to the observed total change in NO 2 during the lockdowns, the effects of meteorology were significant, ranging between 40% (Chennai) and 15% (Beijing). In China, an increase in NO 2 levels due to meteorology were observed in five out of the seven cities considered in the study. Use of different a-priori NO 2 profiles from the two simulations in our TROPOMI retrievals allowed for a determination of retrieval errors that ranged from -1.7% to -11.0%. We used the quality assurance flag > 0.75 to select the highest quality scenes in the study period. Using the GMI, we estimated the sampling biases to be in the range -11% to 10% of the total change for the cities in our study.

Brad Fisher↗

NASA NTRSDate not supplied

A New Level 3 Aerosol Product for SAGE III/ISS

- A new Level 3 SAGE III/ISS aerosol product is produced that now includes aerosol/cloud flags. - Cloud screening algorithm is developed based on a method proposed by Thomason and Vernier (2013) with some modifications. - Frequent volcanic and PyroCb events during SAGE III/ISS era (2017-present) makes it challenging to implement a cloud screening algorithm. - We use version 5.2 of SAGE III/ISS for all our analyses.

Mahesh Kovilakam↗

NASA NTRSDate not supplied

M2-SCREAM: A Stratospheric Composition Reanalysis of Aura MLS

Stratospheric composition and transport patterns are changing because of increasing greenhouse gas concentrations and declining levels of ozone depleting substances. Superimposed on forced trends are: dynamical variability at a range of time scales and unforeseen perturbations such as those resulting from injections of volcanic material and smoke from PyroCb events. Chemical reanalyses can be powerful tools for studying the composition of the stratosphere and disentangling forced responses from internal variability. This presentation introduces a new chemical reanalysis of stratospheric constituents developed and produced at NASA’s Global Modeling and Assimilation Office (GMAO) using the recently developed GMAO Constituent Data Assimilation System. Called the MERRA-2 Stratospheric Composition Reanalysis with Aura MLS (M2-SCREAM), this reanalysis consists of assimilated global three-dimensional fields of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO3) and nitrous oxide (N2O) mixing ratios and covers the period since the beginning of MLS observations in September 2004 to the present. M2-SCREAM assimilates version 4.2 MLS profiles of the five constituents alongside total ozone column from the Ozone Monitoring Instrument. The dynamics and tropospheric water vapor are constrained by assimilated meteorological fields from MERRA-2. The assimilated constituent fields and meteorology are provided to users at a 50-km horizontal resolution and a three-hourly frequency. Monthly analysis uncertainties and flags are also provided to the users. M2-SCREAM is an accurate and dynamically consistent high-resolution data record of the five constituents, all of which are of primary importance to stratospheric chemistry and transport studies. We will present a description of M2-SCREAM and selected results of a process-based evaluation of this product using independent data. As an illustration, we will discuss the perturbation to stratospheric composition from the eruption of Hunga Tonga–Hunga Ha'apai in January 2022 as seen in the reanalysis. We will also propose potential scientific applications of M2-SCREAM and outline plans for an upcoming comprehensive composition reanalysis that is being developed at NASA GMAO.

MERRA-2↗

NASA NTRSDate not supplied

Development of Advanced Conformal and Soft Pica for Future Nasa Missions and Commercial Space

Phenolic Impregnated Carbon Ablator (PICA) is a low-density ablator that has been used as the planetary entry heatshield for several NASA missions since the late 90’s. Its low density and efficient performance characteristics have proven effective for use from Discovery to Flag-ship class missions, from Sample Return missions such as Stardust, OSIRIS-REx to large Mars Lander missions such as MSL and Mars 2020. Continued development of a more mass efficient and compliant versions of PICA that use a carbon felt as the substrate will be presented. The felt based versions of PICA known as Conformal PICA (C-PICA) and Soft PICA (S-PICA), are very comparable in terms of peak heat-flux capabilities compared to standard PICA. The lower density and of C-PICA and S-PICA further show a much higher thermal protection efficiency and hence more mass savings. The compliant nature of these materials allows for use on a wide variety of substrates and therefore makes it very attractive for commercial low earth orbit missions as well as NASA’s aerocapture missions to Ice Giants. NASA is currently working with several commercial partners (Varda and Inversion Space) to Technology Transfer C/S-PICA variants for commercial LEO missions. Thermal, mechanical, and representative environment arc-jet tests have been conducted comparing the performance of Conformal and Soft-PICA materials.

Matthew J Gasch↗

NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) is an occultation instrument that acquires measurements of aerosols and gases within the Earth’s stratosphere and upper troposphere. SAGE III/ISS provides level 2 solar species products for aerosol extinction (9 channels), nitrogen dioxide (NO2), ozone (O3), and water vapor (H2O). The level 2 products currently provide three O3 profiles based on differing retrievals. The first O3 profile is based on measurements at short wavelengths within the Hartley-Huggins band (MesO3), the second O3 profile is based on measurements made at visible wavelengths within the Chappius band (MLR O3), and the final profile is found using a more SAGE II like approach (AO3). The SAGE III/ISS also provides level 2 lunar species products for ozone (O3), nitrogen dioxide (NO2), and nitrogen trioxide (NO3). Version 5.3 of the SAGE III/ISS retrieval algorithm introduces improvements that affect the level 2 data products. The largest change to the solar algorithm is the implementation of disturbance monitoring package (DMP) corrections to improve pointing accuracy. The DMP is comprised of a miniature inertial measurement unit that measures rotation in inertial space using ring laser gyroscopes oriented about three orthogonal axes which can be used to correct pointing errors caused by mechanical disturbances. A major change common to solar and lunar algorithms is meteorological input from the coarser 42 level Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model data to the 72 level MERRA-2 model data. The final major change involves improving the automated quality assurance (QA) algorithm to recover events that were withheld from the public release because differences between the AO3 and MLR O3 for some events after the eruption of Tonga–Hunga Haʻapai. Other changes for v5.3 include minor bug fixes as well as restoration of some data quality flags that were removed in v5.21. This presentation presents the impacts of these changes as well as overall observations of interest from v5.3 level 2 data products.

Carter Hulsey↗

NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) is an occultation instrument that acquires measurements of aerosols and gases within the Earth’s stratosphere and upper troposphere. SAGE III/ISS provides level 2 solar species products for aerosol extinction (9 channels), nitrogen dioxide (NO2), ozone (O3), and water vapor (H2O). The level 2 products currently provide three O3 profiles based on differing retrievals. The first O3 profile is based on measurements at short wavelengths within the Hartley-Huggins band (MesO3), the second O3 profile is based on measurements made at visible wavelengths within the Chappius band (MLR O3), and the final profile is found using a more SAGE II like approach (AO3). The SAGE III/ISS also provides level 2 lunar species products for ozone (O3), nitrogen dioxide (NO2), and nitrogen trioxide (NO3). Version 5.3 of the SAGE III/ISS retrieval algorithm introduces improvements that affect the level 2 data products. The largest change to the solar algorithm is the implementation of disturbance monitoring package (DMP) corrections to improve pointing accuracy. The DMP is comprised of a miniature inertial measurement unit that measures rotation in inertial space using ring laser gyroscopes oriented about three orthogonal axes which can be used to correct pointing errors caused by mechanical disturbances. A major change common to solar and lunar algorithms is meteorological input from the coarser 42 level Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model data to the 72 level MERRA-2 model data. The final major change involves improving the automated quality assurance (QA) algorithm to recover events that were withheld from the public release because differences between the AO3 and MLR O3 for some events after the eruption of Tonga–Hunga Haʻapai. Other changes for v5.3 include minor bug fixes as well as restoration of some data quality flags that were removed in v5.21. This presentation presents the impacts of these changes as well as overall observations of interest from v5.3 level 2 data products.

Carter Hulsey↗

NASA NTRS2021

Exep: Environmental Testing of High-Actuator-Count Mems Deformable Mirrors for Space-Based Applications

Coronagraph instruments on future space telescopes will rely on deformable mirrors to create high contrast images for the direct detection of exoplanets. As part of the NASA Exoplanet Explorations Program’s coronagraph technology development efforts, two sets of Micro-electro-mechanical systems (MEMS) deformable mirrors manufactured by Boston Micromachines Corporation were exposed to vibration and thermal cycles representative of launch conditions. The first set were 952-actuator Kilo-DMs that successfully demonstrated 100% actuator survival and achieved ~1e-8 contrast after the environment test. The second round used an engineering-grade 2048-actuator deformable mirror on which few changes were identified after the environmental test. However, each actuator that changed behavior was flagged as anomalous beforehand or was directly adjacent to a defective actuator. From this result, we hypothesized that typical actuators on a science grade deformable mirror are robust to environmental testing. A third set of 2048-actuator deformable mirror have been procured for a planned test to characterize the deformable mirrors using interferometric measurements, contrast results on a new in-air coronagraph testbed, and infrared microscopy on the internal structure of the MEMS devices.

Lam, Charlie↗

NASA NTRS2023

Aurora Detection From Nighttime Lights for Earth and Space Science Applications

This research leverages data from the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer (VIIRS) instrument onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. We demonstrate the value of mining the VIIRS DNB for aurora and describe our use of unsupervised machine learning to create a binary mask for aurora occurrence. This mask can be used to flag aurora-contaminated observations for NASA's nighttime lights products for Earth science applications. The identification of auroral regions can also be used for Space Weather applications, for example, for comparison with aurora forecast model and with other satellite- or ground-based aurora observations. The DNB is a broadband channel that is sensitive to wavelengths from 500 to 900 nm, which covers most of the visible light spectrum, and as the name implies, captures light even at night with a sensitivity at the nanowatt level. This band is suitable for aurora observations since the light emitted by the aurora tends to be dominated by emissions from atomic oxygen, resulting in a greenish glow at a wavelength of 557.7 nm, especially at an altitude of 110 km. This study compares the global nighttime derived aurora regions for 17 and 18 March with the NOAA Space Weather Prediction Center's (SWPC) probability product for the St. Patrick's Day geomagnetic storm in 2015. VIIRS sensors are slated to be added to the next generation of polar-orbiting operational satellites. Our novel automated approach to aurora identification opens up an efficient way to leverage this unique data source.

Aurora↗

NASA NTRS2023

Pypromice: A Python Package for Processing Automated Weather Station Data

The pypromice Python package is for processing and handling observation datasets from automated weather stations (AWS). It is primarily aimed at users of AWS data from the Geological Survey of Denmark and Greenland (GEUS), which collects and distributes in situ weather station observations to the cryospheric science research community. Functionality in pypromice is primarily handled using two key open-source Python packages, xarray (Hoyer & Hamman, 2017) and pandas (The pandas development team, 2020). A defined processing workflow is included in pypromice for transforming original AWS observations (Level 0, L0) to a usable, CF-convention-compliant dataset (Level 3, L3) (Figure 1). Intermediary processing levels (L1,L2) refer to key stages in the workflow, namely the conversion of variables to physical measurements and variable filtering (L1), cross-variable corrections and user-defined data flagging and fixing (L2), and derived variables (L3). Information regarding the station configuration is needed to perform the processing, such as instrument calibration coefficients and station type (one-boom tripod or two-boom mast station design, for example), which are held in a toml configuration file. Two example configuration files are provided with pypromice , which are also used in the package’s unit tests. More detailed documentation of the AWS design, instrumentation, and processing steps are described in Fausto et al. (2021).

pypromice↗

NASA NTRS2023

An Analysis of Degradation in Low-Cost Particulate Matter Sensors

Low-cost sensors (LCSs) are increasingly being used to measure fine particulate matter (PM 2.5 ) concentrations in cities around the world. One of the most commonly deployed LCSs is PurpleAir with ∼15 000 sensors deployed in the United States, alone. PurpleAir measurements are widely used by the public to evaluate PM 2.5 levels in their neighborhoods. PurpleAir measurements are also increasingly being integrated into models by researchers to develop large-scale estimates of PM 2.5 . However, the change in sensor performance over time has not been well studied. It is important to understand the lifespan of these sensors to determine when they should be serviced or replaced, and when measurements from these devices should or should not be used for various applications. This paper fills this gap by leveraging the fact that: (1) each PurpleAir sensor is composed of two identical sensors and the divergence between their measurements can be observed, and (2) there are numerous PurpleAir sensors within 50 meters of regulatory monitors allowing for the comparison of measurements between these instruments. We propose empirically-derived degradation outcomes for the PurpleAir sensors and evaluate how these outcomes change over time. On average, we find that the number of ‘flagged’ measurements, where the two sensors within each PurpleAir sensor disagree, increases with time to ∼4% after 4 years of operation. Approximately, 2 percent of all PurpleAir sensors were permanently degraded. The largest fraction of permanently degraded PurpleAir sensors appeared to be in the hot and humid climate zone, suggesting that sensors in these locations may need to be replaced more frequently. We also find that the bias of PurpleAir sensors, or the difference between corrected PM2.5 levels and the corresponding reference measurements, changed over time by −0.12 μg m −3 (95% CI: −0.13 μg m −3 , −0.10 μg m −3 ) per year. The average bias increases dramatically after 3.5 years. Further, climate zone is a significant modifier of the association between degradation outcomes and time.

Priyanka deSouza↗

NASA NTRSDate not supplied

Anomaly Detection in Flight Operational Data Using Deep Learning

In this session, we demonstrate two recently developed deep learning models for anomaly detection in flight operational data by the Data Sciences Group at NASA Ames Research Center. The first model is Convolutional Variational Auto-Encoder (CVAE) [1], which is an unsupervised deep encoder-decoder model, designed specifically for finding anomalies in heterogeneous multivariate time series data. We will demonstrate its application to finding anomalies in streaming data from NASA’s Digital Information Platform’s Fuser source. CVAE identifies data instances that are not representative of expected nominal behavior as anomalous. Since it is an unsupervised approach, the flagged anomalies will need to be reviewed by the subject matter experts (SMEs) for validation and labeling and is designed to assist with vulnerability discovery within Safety Monitoring System programs. The second model is Robust and Explainable Semi-supervised Anomaly Detection (RESAD) model [2], which builds on CVAE to allow learning from both minimally labeled data (previously reviewed by the SMEs) as well as majority unlabeled data. RESAD takes advantage of graph theoretic techniques to propagate the labels from the labeled data to the unlabeled data based on a pre-defined similarity metric and structures the learned feature space from flight time-series so that data of the same class would cluster tightly together. This model characteristic is enabled by training with an augmented loss function and allows learning of a more informative feature space for down-stream tasks such as search and active learning. We demonstrate RESAD using data from the NASA DASHlink project [3].

anomaly detection↗

NASA NTRS2023

Plant Disease Detection: Exploring Applications in Hyperspectral Imaging and Machine Learning for Agriculture

The threat of crop disease on food security and agriculture is projected to escalate, leading to reduced crop yields, global economic loss, and endangered food availability to vulnerable populations. Early identification of plant disease is crucial to combatting the crisis but traditional manual methods for disease detection are laborious and may miss early signs of infection. The proposed solution suggests equipping a drone with a hyperspectral camera to collect images and analyzing the data with neural networks trained to flag and classify infected plants. This approach offers a faster, more accurate, and potentially more cost-effective alternative to current practices. While the expensive and complex nature of hyperspectral imaging (HSI) may be an obstacle to the adoption of the drone, rapidly advancing technologies compounded with rental-based usage may make these tools simpler, cheaper, and more accessible to a wider audience. The research further discusses the potential for automated flight paths and the expansion of disease detection to a broader range of crops.

plant disease detection↗

NASA NTRSDate not supplied

Homogenized Ground-Based and Profile Ozone Datasets From the TOAR-II/HEGIFTOM Project: Methods and Station Trends

Within the framework of the second phase of the Tropospheric Ozone Assessment Report (TOAR-II), it was recognized that an essential first step for deriving accurate trends from the ground-based networks that monitor ozone in the free troposphere is putting the measurements on the same basis with respect to absolute references and processing methods. The relevant procedures are referred to as “harmonization” or “homogenization”. The TOAR II working group, “HEGIFTOM” (Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements), has carried out harmonization for five types of network (Figure below) instruments (mid-1990s to 2020): ozonesondes, commercial aircraft IAGOS landing/takeoff profiles, Fourier-Transform Infrared spectrometer (FTIR), tropospheric Lidar, and Brewer/Dobson Umkehr. First, we summarize the homogenization effort for each network that provides new quality-assessed ozone profile or segment (partial column) data sets, including uncertainty estimates and quality flags. Second, with the HEGIFTOM datasets forming the basis for a global assessment of tropospheric ozone column trends, results derived with various trend detection algorithms will be presented. The ultimate goal is evaluation of the consistency of the calculated trends among different techniques at selected stations and/or regions.

ozone↗

NASA NTRSDate not supplied

Advancing Air Mobility: Few-Shot Learning in Airspace Research and Development

The advancement of Air Mobility, particularly in the context of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM), represents a transformative shift in aviation's role in modern society. A comprehensive understanding of requirement consistency is paramount for fostering interoperability, standardization, and cost-effectiveness within airspace systems. This paper introduces a novel approach utilizing a pretrained Sentence Transformers model and few-shot learning to address this crucial aspect task of flagging potentially inconsistent requirements. Few-shot learning supports the development of this future through ensuring the accuracy and consistency of identified requirements with little human oversight. This approach offers a promising solution to the challenges of requirement consistency identification in airspace systems. By harnessing the power of advanced NLP techniques with fine-tuned models, stakeholders can enhance efficiency, accuracy, and scalability; ultimately fostering improved interoperability, standardization, and cost-effectiveness in airspace management.

Natural Language Processing↗

NASA NTRSDate not supplied

Qualification of A Transition Edge Sensor Based Anti-Coincidence Detector for A Probe Class Mission

The Line Emission Mapper (or LEM) is an X-ray probe class mission aimed to study the physics of galaxy formation, with a large field-of-view, high-resolution transition-edge sensor (TES) microcalorimeter. Because of the diffuse and faint nature of LEM’s targets, a high-fidelity anticoincidence detector (anti-co) is essential to reduce instrument background below the level of sky background. Lying directly behind the LEM sensor, the anti-co will detect ~3 counts per second per square centimeter from background galactic cosmic rays and the secondary particles they induce to detect and flag any events that could be confused with a science photon in the 0.3-2 keV signal band of LEM. Our design spans an active region 4-cm in diameter. It consists of 12 quasiparticle-trap-assisted electrothermal-feedback (QET) channels, each with 100 parallel TESes with common bias lines. The TES is a narrow strip of Mo/Au bilayer. We present fabrication processes that maintain the transition temperature (T c ) in Mo/Au bilayer structures across the active region. We further present a new design based on a a-Si/W TES layer that has previously achieved reasonable uniformity in lab testing.

x-ray↗