arXiv subjects

Dan Schmidt

Publications and source records attributed to Dan Schmidt.

8 recordsLinked to original sources

arXiv2026

Broad-band High-Energy Resolution Hard X-ray Spectroscopy using Transition Edge Sensors at SPring-8

We have succeeded in operating a transition-edge sensor (TES) spectrometer and evaluating its performance at the SPring-8 synchrotron X-ray light source. The TES spectrometer consists of a 240 pixel National Institute of Standards and Technology (NIST) TES system, and 220 pixels are operated simultaneously with an energy resolution of $4$~eV at 6~keV at a rate of about 1~c/s/pixel. The tolerance for high count rates is evaluated in terms of energy resolution and live time fraction, leading to an empirical compromise of about 2 x 10^3 c/s/all pixels with an energy resolution of 5 eV at 6 keV. By utilizing the TES's wide-band spectroscopic capability, simultaneous multi-element analysis is demonstrated for a standard sample. We conducted X-ray absorption near-edge structure (XANES) analysis in fluorescence mode using the TES spectrometer. The excellent energy resolution of the TES enabled us to detect weak fluorescence lines from dilute samples and trace elements that have previously been difficult to resolve due to the nearly overlapping emission lines of other dominant elements. The neighboring lines of As K alpha and Pb L alpha2 of the standard sample were clearly resolved and the XANES of Pb L alpha2 was obtained. Moreover, the X-ray spectrum from the small amount of Fe in aerosols was distinguished from the spectrum of a blank target, which helps us to understand the targets and the environment. These results are the first important step for the application of high resolution TES-based spectroscopy at hard X-ray synchrotron facilities.

astro-ph.IM↗

arXiv2026

Imaging the Superconducting Proximity Effect in S-S'-S Transition Edge Sensors

Proximity effects at superconducting interfaces, between different superconductors (S-S') or between superconductors and normal metals (S-N), are fundamental to the performance of superconducting electronics, yet only few experiments have directly probed the spatial structure of proximity effects within a device. This is particularly relevant for transition edge sensors (TESs), where the interplay of direct and inverse proximity effects governs detector sensitivity. Here, we use scanning superconducting interference device (SQUID) susceptometry to directly image the local diamagnetic response in functional S-S'-S TES structures. We resolve long range proximity coupling extending over tens of micrometers, revealing that the local transition temperature is dramatically tuned by neighboring regions, being either enhanced by superconducting (S) leads or suppressed by normal metal (N) contacts. Our observations are quantitatively supported by Ginzburg Landau modeling of the device geometry and calculations of the temperature dependent diamagnetism based on self-consistent Usadel equations. By providing spatially resolved measurements of the interplay of proximity effects in TES devices, this work establishes a framework for understanding and controlling superconducting states in heterogeneous superconducting structures.

cond-mat.supr-con↗

arXiv2025

Impact of embedded $^{163}$Ho on the performance of the transition-edge sensor microcalorimeters of the HOLMES experiment

We present a detailed investigation of the performance of transition-edge sensor (TES) microcalorimeters with $^{163}$Ho atoms embedded by ion implantation, as part of the HOLMES experiment aimed at neutrino mass determination. The inclusion of $^{163}$Ho atoms introduces an excess heat capacity due to a pronounced Schottky anomaly, which can affect the detector's energy resolution, signal height, and response time. We fabricated TES arrays with varying levels of $^{163}$Ho activity and characterized their performance in terms of energy resolution, decay time constants, and heat capacity. The intrinsic energy resolution was found to degrade with increasing $^{163}$Ho activity, consistent with the expected scaling of heat capacity. From the analysis, we determined the specific heat capacity of $^{163}$Ho to be $(2.9 \pm 0.4 \mathrm{(stat)} \pm 0.7 \mathrm{(sys)})$ J/K/mol at $(94 \pm 1)$\,mK, close to the literature values for metallic holmium. No additional long decay time constants correlated with $^{163}$Ho activity were observed, indicating that the excess heat capacity does not introduce weakly coupled thermodynamic systems. These results suggest that our present TES microcalorimeters can tolerate $^{163}$Ho activities up to approximately 5 Bq without significant performance degradation. For higher activities, reducing the TES transition temperature is necessary to maintain energy resolution. These findings provide critical insights for optimizing TES microcalorimeters for future neutrino mass experiments and other applications requiring embedded radioactive sources. The study also highlights the robustness of TES technology in handling implanted radionuclides while maintaining high-resolution performance.

physics.ins-det↗

arXiv2025

A high-speed, high-resolution Transition Edge Sensor spectrometer for soft X-rays at the Advanced Photon Source

This project explores the design and development of a transition edge sensor (TES) spectrometer for resonant soft X- ray scattering (RSXS) measurements developed in collaboration between Argonne National Laboratory (ANL) and the National Institute of Standards and Technology (NIST). Soft X-ray scattering is a powerful technique for studying the electronic and magnetic properties of materials on a microscopic level. However, the lack of high-performance soft X-ray spectrometers has limited the potential of this technique. TES spectrometers have the potential to overcome these limitations due to their high energy resolution, high efficiency, and broad energy range. This project aims to optimize the design of a TES spectrometer for RSXS measurements and more generally soft X-ray spectroscopy at the Advanced Photon Source (APS) 29-ID, leading to improved understanding of advanced materials. We will present a detailed description of the instrument design and implementation. The spectrometer consists of a large array of approximately 250 high-speed and high-resolution pixels. The pixels have saturation energies of approximately 1 keV, sub-ms pulse duration and energy resolution of approximately 1 eV. The array is read out using microwave multiplexing chips with MHz bandwidth per channel, enabling efficient data throughput. To facilitate measurement of samples in situ under ultra-high vacuum conditions at the beamline, the spectrometer is integrated with an approximately 1 m long snout.

physics.ins-det↗

arXiv2019

Self-Play Learning Without a Reward Metric

The AlphaZero algorithm for the learning of strategy games via self-play, which has produced superhuman ability in the games of Go, chess, and shogi, uses a quantitative reward function for game outcomes, requiring the users of the algorithm to explicitly balance different components of the reward against each other, such as the game winner and margin of victory. We present a modification to the AlphaZero algorithm that requires only a total ordering over game outcomes, obviating the need to perform any quantitative balancing of reward components. We demonstrate that this system learns optimal play in a comparable amount of time to AlphaZero on a sample game.

cs.LG↗

arXiv2019

Noisier2Noise: Learning to Denoise from Unpaired Noisy Data

We present a method for training a neural network to perform image denoising without access to clean training examples or access to paired noisy training examples. Our method requires only a single noisy realization of each training example and a statistical model of the noise distribution, and is applicable to a wide variety of noise models, including spatially structured noise. Our model produces results which are competitive with other learned methods which require richer training data, and outperforms traditional non-learned denoising methods. We present derivations of our method for arbitrary additive noise, an improvement specific to Gaussian additive noise, and an extension to multiplicative Bernoulli noise.

eess.IV↗

arXiv2019

Monotone Learning with Rectified Wire Networks

We introduce a new neural network model, together with a tractable and monotone online learning algorithm. Our model describes feed-forward networks for classification, with one output node for each class. The only nonlinear operation is rectification using a ReLU function with a bias. However, there is a rectifier on every edge rather than at the nodes of the network. There are also weights, but these are positive, static, and associated with the nodes. Our "rectified wire" networks are able to represent arbitrary Boolean functions. Only the bias parameters, on the edges of the network, are learned. Another departure in our approach, from standard neural networks, is that the loss function is replaced by a constraint. This constraint is simply that the value of the output node associated with the correct class should be zero. Our model has the property that the exact norm-minimizing parameter update, required to correctly classify a training item, is the solution to a quadratic program that can be computed with a few passes through the network. We demonstrate a training algorithm using this update, called sequential deactivation (SDA), on MNIST and some synthetic datasets. Upon adopting a natural choice for the nodal weights, SDA has no hyperparameters other than those describing the network structure. Our experiments explore behavior with respect to network size and depth in a family of sparse expander networks.

cs.LG↗

arXiv2016

Proactive Message Passing on Memory Factor Networks

We introduce a new type of graphical model that we call a "memory factor network" (MFN). We show how to use MFNs to model the structure inherent in many types of data sets. We also introduce an associated message-passing style algorithm called "proactive message passing"' (PMP) that performs inference on MFNs. PMP comes with convergence guarantees and is efficient in comparison to competing algorithms such as variants of belief propagation. We specialize MFNs and PMP to a number of distinct types of data (discrete, continuous, labelled) and inference problems (interpolation, hypothesis testing), provide examples, and discuss approaches for efficient implementation.

cs.AI↗