Detecting and Correcting Gain Jumps in TES Microcalorimeters

Daniel S. Swetz; Daniel T. Becker; Joel N. Ullom; Joseph W. Fowler; Mark W. Keller; Thomas A. Baker

REVIEW 4 major objections 4 minor 20 references

Detecting and Correcting Gain Jumps in TES Microcalorimeters

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This paper shows that gain jumps in gamma-ray TES microcalorimeters can be reset during acquisition with brief bias-current spikes and corrected offline by clustering pulse heights into discrete gain states, removing false lines from spectr

desk verdict First published method for TES gain-jump detection/correction, plausible and honest, but quantitative support is thin; worth refereeing as a methods contribution. read the letter →

arxiv 2509.04675 v1 pith:PQE4L6JA submitted 2025-09-04 physics.ins-det physics.data-an

Thomas A. Baker , Daniel T. Becker , Joseph W. Fowler , Mark W. Keller , Daniel S. Swetz , Joel N. Ullom This is my paper · ORCID

classification physics.ins-detphysics.data-an PACS 85.25.Oj07.85.Nc

keywords transition-edgesensormicrocalorimetergainjumpgamma-rayspectroscopydataanalysisbiasresetclusteringfalsepeaks

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper develops two automated methods for dealing with "gain jumps" in gamma-ray microcalorimeters based on transition-edge sensors: a live reset using a brief bias-current spike, and an offline algorithm that identifies the discrete gain states present in recorded data. These jumps, abrupt shifts in detector gain, introduce false peaks into spectra if ignored. The authors show that their bias-spike procedure returns detectors to a consistent gain state with little dead time, and that the offline algorithm labels each pulse with its gain state, allowing per-state energy calibration that removes the false peaks. If the methods work as claimed, they give experimental users a largely unsupervised way to protect spectral quality in a common failure mode.

What carries the argument

The load-bearing mechanism is the offline gain-state detection pipeline: after drift removal, a bilateral filter smooths the pulse-height stream while preserving edges, inflection points in the smoothed trace mark candidate gain jumps, and a variant of agglomerative clustering merges adjacent regions that are statistically consistent and then clusters non-adjacent regions with indistinguishable means. This yields a per-pulse gain-state label. For live reset, the 'bias spike'—a brief excursion to the normal branch with a slow, stepped return to the bias point—re-establishes a reproducible gain state.

What would settle it

Feed the offline algorithm synthetic data with known gain jumps plus a slow linear drift that has not been removed, and check whether the algorithm reports spurious gain states; alternatively, select a pulse-height range containing two closely spaced spectral lines and see whether the algorithm still separates states correctly. A clear failure in either test would show that the piecewise-constant single-line assumption is a practical limit rather than a mere convenience.

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Extended reading notes

Core claim

The central claim is that gain jumping in TES microcalorimeters—abrupt discrete shifts in gain that cannot be removed by baseline drift correction—can be both prevented and corrected automatically. During acquisition, briefly driving the TES into its normal state with the bias current resets the device to a consistent gain state, at a dead-time cost of about 0.6% when applied at 10-minute intervals. After acquisition, an offline algorithm takes a single reference spectral line, divides the pulse-height stream into regions at candidate jump locations, merges neighboring regions whose means are statistically indistinguishable, and clusters non-adjacent regions that share a gain state; the resu

Load-bearing premise

The offline algorithm assumes the user can specify a pulse-height range containing exactly one spectral line, with all continuous drift already removed, so that the line is piecewise-constant with discontinuities only at gain jumps; if a second line or residual drift enters that range, the region-merging step will misclassify drift or blended lines as gain states.

Editorial extensions

If this is right

Bias spikes at 10-minute intervals return detectors to a consistent gain state with only about 0.6% dead time, making live reset practical for long acquisitions.
The offline detection algorithm requires no prior knowledge of the number of gain states, their separation, or detector resolution, so the same parameters transfer across different detectors.
Per-state energy calibration removes duplicate peaks that would otherwise appear as false elemental lines or distort concentration estimates.
Correcting by calibrating each state separately is robust to the nonlinear energy dependence of gain-state spacing, unlike linearly rescaling pulse heights.
The offline algorithm can be applied to existing datasets, recovering spectral quality from data that was previously degraded by gain jumps.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The offline algorithm's requirement of an isolated spectral line suggests it could be extended to other detector types—X-ray or metallic magnetic calorimeters—that exhibit discrete gain states, provided a clean reference line exists; the paper notes it expects X-ray compatibility.
Pairing the bias-spike reset with a real-time jump detector could make the reset immediate rather than periodic, cutting the time a detector spends in a degraded state; the paper lists online detection as an open need.
Because gain-state spacing depends nonlinearly on energy, multi-line data could map that dependence and predict state labels across lines, catching cases where a jump vanishes in one spectral line but appears in another.
The region-merging and clustering approach could be reused as a general tool for identifying discrete level shifts in any long pulse-height time series where a reference signal is available.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is the first published treatment of automatic gain-jump detection and correction for TES microcalorimeters, and the authors deserve credit for saying so plainly and for shipping a procedure that looks workable. Read it as a methods proposal, not as a demonstrated performance claim, because the quantitative support is thinner than the title implies.

What's new: the bias-spike reset (driving the TES normal during acquisition) and the offline algorithm that finds gain-state boundaries, clusters regions into states, and lets you calibrate each state separately. The components are standard (bilateral filtering, agglomerative clustering), but the application is new and the description is concrete enough to reimplement. The paper also does the right thing by stating its assumptions explicitly, including the one that the reference line is clean and piecewise-constant. The discussion of why linear rescaling of gain states doesn't work (nonlinear energy dependence) is useful.

Where it's soft: the synthetic test is reported as "to within acceptable limits" with no error rates, no confusion matrices, no sensitivity to the user-set parameters. The real-data validation is qualitative, and the clean corrected spectrum is partially reassuring but also partly a consequence of the method itself, since calibrating each identified state to the same line removes duplicate peaks by construction. The load-bearing assumption about no residual drift is not stress-tested; the paper's own discussion admits that tolerant parameters can turn continuous drift into spurious gain states. Line blending for large jumps is also conceded as a limitation. None of this makes the method wrong, but it means the paper doesn't yet demonstrate that the algorithm is robust for the "minimal user supervision" use case it motivates. No code or data is provided, which makes independent checking harder.

Who it's for: people running TES arrays who have seen gain jumps and need a place to start. Practitioners will find the algorithm description and the bias-spike trick valuable even without quantitative guarantees. It deserves a serious referee; I'd ask the authors for numbers from the synthetic tests, a sensitivity analysis, and ideally a release of the code, but I would not desk-reject it.

Referee Report

4 major / 4 minor

Summary. The paper addresses gain jumps in TES microcalorimeters with two contributions: a live-acquisition reset method that briefly drives the TES normal via a bias-current 'spike,' and an offline algorithm that detects gain jumps and identifies unique gain states in recorded pulse-height data, allowing per-state energy calibration. The offline algorithm (Section IV-A) assumes a single isolated reference spectral line that is piecewise constant after prior drift correction. The authors test the algorithm on synthetic data and apply it to 48 hours of 153Gd data from a 68-detector array, showing a corrected spectrum with no visible duplicate peaks. The paper explicitly notes that the impact analysis is primarily qualitative.

Significance. If validated quantitatively, the contributions would fill a genuine gap: the authors are correct that no published methods exist for resetting or correcting gain jumps in TES microcalorimeters, and gain jumps can seriously degrade spectra for non-expert users. The bias-spike reset is attractive in its simplicity and low dead time (~0.6%), and the offline algorithm is inventive in combining a bilateral filter with agglomerative clustering. The paper also explicitly uses the same parameter values across all detectors, which supports the claim that the algorithm is not heavily tuned. However, the current validation is qualitative: the synthetic-data check reports no numerical accuracy, no confusion matrices, and no sensitivity analysis, and the real-data demonstration is partly self-confirming because calibrating each detected gain state to the same reference line removes duplicate peaks by construction. The Discussion is commendably honest about limitations, but those limitations bear directly on the central claim of robust automated detection. The paper is a useful contribution, but the load-bearing validation needs strengthening.

major comments (4)

[§IV-B] The synthetic-data test is described in a single sentence: the algorithm 'reproduced the expected solution to within acceptable limits, and the overall accuracy was comparable to what a human researcher might obtain through manual analysis.' No quantitative results are given—no accuracy, precision, recall, confusion matrix, or error bars. Because Section IV-A's piecewise-constant single-line assumption is foundational, the paper needs a quantitative sensitivity analysis over residual drift amplitude, line separation, gain-state spacing, background level, and event count. Without these numbers, the robustness claim for unsupervised use is not supported.
[§IV-B, Fig. 4] The real-data demonstration is partially self-confirming. The correction method works by dividing data into detected gain states and calibrating each state to the same reference line; this procedure necessarily removes duplicate peaks attributable to state-to-state gain shifts. Therefore, a clean corrected spectrum does not by itself demonstrate that the detected state boundaries and labels were correct. An independent check—for example, comparing detected jump times to independently identified events, or using a second spectral line as a cross-check—would strengthen the claim substantially. The paper does not provide such a check.
[§V, Discussion] The Discussion concedes that parameters can be chosen so that 'slight continuous drift' is detected 'as if it were composed of gain jumps.' This is a direct admission of a failure mode for the Section IV-A assumption that all continuous drift has been removed. The paper does not quantify how much residual drift is tolerated or provide a test with realistic drift residuals. Similarly, the Discussion notes that 'very large gain jumps may cause even a well-isolated line to interfere with other lines,' but no synthetic or real-data example of this case is given. These limitations are load-bearing because the algorithm treats every remaining discontinuity as a gain-state boundary.
[§IV-A, step 3] The algorithm relies on several user-adjustable parameters: minimum region size, threshold separation, state-clustering threshold, bilateral filter kernel widths, and (in the live method) bias spike interval. The paper states the same parameters were used for all detectors, which is encouraging, but no sensitivity analysis is presented. Since the central claim is 'automated' detection that requires minimal user supervision, the authors should show how the output changes for reasonable variations in these parameters, or at least provide a quantitative robustness test for a representative parameter range.

minor comments (4)

[§II] The description of the synthetic data generation is brief. It would be helpful to state the number of synthetic datasets generated and the parameter ranges actually explored, rather than only listing the parameter axes.
[Fig. 3] The color coding for gain states is reused when there are many states (e.g., 13 states in the first panel). A legend or explicit description of the color mapping would improve readability.
[Fig. 4] The figure caption states 'Count per 10 eV bin' but the vertical axis label shows 'Count per 10 eV bin' with a logarithmic scale. The y-axis label appears as '100 101 102 ...' which is a log scale, but this is not stated. A brief note on the log scale and binning would help.
[§I] The sentence 'We are not aware of any prior research published on this hypothesis' is a literature claim that is hard to verify. A more precise statement about what was searched would be useful, though this is not essential.

Circularity Check

1 steps flagged · score 4.0 of 10

Real-data corrected-spectrum demonstration is self-confirming; the detection algorithm itself has independent (though qualitative) synthetic testing.

self definitional [Section IV-B, real-data test / Fig. 4 caption]
""To produce the corrected spectrum, we divided the data from each detector into separate gain states, performed energy calibration on each state separately, and summed the results. ... The result contains no sign of duplicate peaks and is a high-quality spectrum, showing that our approach was successful.""

Per-state energy calibration to the same reference line (103.18 keV) maps every detected gain-state group's peak to the same calibrated energy. If the algorithm over-splits a true state into spurious ones, each group is calibrated separately and contributes a peak at 103.18 keV, so the summed spectrum shows no duplicate peak by construction. The absence of duplicate peaks in Fig. 4 is therefore guaranteed by the correction procedure, not by correct state boundaries. It does not independently validate the detected jump locations; the external check is the synthetic test, reported only qualitatively ('to within acceptable limits') with no quantitative error rates.

full rationale

The detection algorithm itself (bilateral-filter inflection points, agglomerative region clustering, relabeling) is a self-contained segmentation method and is not circular: it does not use the answer to construct the answer. The synthetic-data test with known jump timings and amplitudes, although described only as reproducing the expected solution 'to within acceptable limits,' provides an external check and keeps the central detection claim from being purely tautological. The main circularity is in the real-data validation: the corrected spectrum in Fig. 4 is produced by calibrating each detected gain state separately to the same reference line. Any partition into groups, if each group is calibrated to 103.18 keV, will place a peak at 103.18 keV in every group, so the disappearance of duplicate peaks is baked into the correction step rather than demonstrating that the state labels are correct. The paper itself concedes that the impact analysis is 'primarily qualitative' (Section V) and lists the load-bearing assumptions and limitations (single isolated spectral line, all continuous drift removed, possible gain jumps that vanish in the reference line, and limited jump-location precision) in Sections IV-A and V; these are honest limitations, not additional circular steps. There is no load-bearing self-citation chain: reference [14] (Fowler et al.) supplies baseline drift correction as an external method, and reference [16] (Bennett et al.) supports only a speculative physical hypothesis, not the algorithm's inputs. Overall, the partial circularity in the real-data demonstration warrants a moderate score, but the core detection method retains independent content.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The algorithm's behavior is governed by hand-chosen thresholds and smoothing widths, with no sensitivity analysis. The method also assumes ideal data: a single isolated line with drift already removed. These assumptions are loaded in rather than derived. No new physical entities are introduced.

free parameters (5)

minimum region size = 5 samples
User-specified threshold used in region merging (Section IV-A step 3d). Determines which small regions are merged and therefore where jumps are placed. No sensitivity analysis is given.
threshold separation = 5.0 standard errors of the mean
User-specified multiple of standard error used to decide whether adjacent regions belong to different gain states. Set to 5.0 for all detectors; no sensitivity analysis.
state-clustering threshold = 1.0
User-specified multiple of dispersion used in step 4 to decide whether two non-adjacent regions belong to the same gain state. Hand-chosen; no sensitivity analysis.
bilateral filter kernel widths = spatial 4 and 2 samples; range 4 sigma and sigma across two passes
Chosen smoothing parameters in step 2. They affect inflection-point detection and hence jump location estimates; no justification or variation study is provided.
bias spike interval = 10 minutes
Fixed interval chosen for the live reset procedure, producing about 0.6 percent dead time. Not optimized against jump occurrence statistics.

assumptions (5)

domain assumption The selected pulse-height range contains exactly one spectral line.
Stated in Section IV-A. If the range contains multiple lines, the selection step will mix different energies and bias jump detection.
domain assumption All continuous drift has been removed so the line is piecewise-constant with discontinuities only at gain jumps.
Stated in Section IV-A. Residual drift will be misidentified as gain states; this is a load-bearing premise for the clustering algorithm.
domain assumption The line shape is Gaussian for the purpose of estimating standard deviations.
Stated in Section IV-A as a mathematical convenience. Deviations are assumed not to matter but are not tested.
domain assumption The chosen reference spectral line reveals all gain jumps that occur.
The paper's own Discussion notes that nonlinear energy dependence of gain-state separation could cause a jump to vanish on one line while appearing on another, in which case the algorithm would miss it.
domain assumption Bias spikes return the TES to a consistent gain state.
The reset effectiveness is observed empirically in Section III, not derived from a model. The paper acknowledges that more investigation would be needed to confirm behavior.

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Cite this review

Pith. "Pith review of Detecting and Correcting Gain Jumps in TES Microcalorimeters." pith.science (2026). https://pith.science/paper/PQE4L6JA

@misc{pith2026250904675,
  author       = {Pith},
  title        = {Pith review of: Detecting and Correcting Gain Jumps in TES Microcalorimeters},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PQE4L6JA}},
  note         = {Machine review of arXiv:2509.04675}
}

read the original abstract

Arrays of microcalorimeters based on transition-edge sensors (TESs) are being actively deployed to laboratories all over the world. A TES microcalorimeter array produces very large quantities of data and users of these devices have varying levels of experience, so it is important to provide robust software for data acquisition and analysis that can function with minimal user supervision. This software should be capable of addressing common phenomena that can adversely affect spectrum quality. Gain jumping is one such phenomenon that is characterized by abrupt changes in the gain of a device. Left unaddressed, gain jumps can degrade spectra by introducing false peaks. We are not aware of any previously published methods for resetting gain jumps during data acquisition or existing algorithms for correcting data that is degraded by gain jumps. We have developed automated methods for detecting and correcting gain jumps in gamma-ray TES microcalorimeters. We present a procedure for resetting gain jumps during a live data acquisition that involves briefly driving the TES into its normal state using the bias current. We also describe an algorithm for locating gain jumps and identifying unique gain states within existing microcalorimeter data. Finally, we provide a possible approach for correcting gain jumps after they have been identified.

Figures

Figures reproduced from arXiv: 2509.04675 by the authors.

**Figure 3.** Approximate pulse energies from three different detectors, color-coded [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

**Figure 4.** Spectral lines for 153Gd at 97.43 and 103.18 keV before and after applying our correction method using the 103.18 keV line. The uncorrected spectrum was produced by separately calibrating and adding the results from 68 separate detectors. To produce the corrected spectrum, we divided the data from each detector into separate gain states, performed energy calibration on each state separately, and summed the results. … view at source ↗

Discussion (0). Continue with ORCID to comment.

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Works this paper leans on

20 extracted references · 19 canonical work pages

[1]

Large-area microcalorimeter detectors for ultra-high-resolution X-ray and gamma-ray spectroscopy,

M. K. Bacrania, A. S. Hoover, P. J. Karpius, M. W. Rabin, C. R. Rudy, D. T. V o, J. A. Beall, D. A. Bennett, W. B. Doriese, G. C. Hilton, R. D. Horansky, K. D. Irwin, N. Jethava, E. Sassi, J. N. Ullom, and L. R. Vale, “Large-area microcalorimeter detectors for ultra-high-resolution X-ray and gamma-ray spectroscopy,” IEEE Transactions on Nuclear Science , ...

work page 2009
[2]

Determination of plutonium isotopic content by microcalorimeter gamma-ray spectroscopy,

A. S. Hoover, R. Winkler, M. W. Rabin, D. T. V o, J. N. Ullom, D. A. Bennett, W. B. Doriese, J. W. Fowler, R. D. Horansky, D. R. Schmidt, L. R. Vale, and K. Schaffer, “Determination of plutonium isotopic content by microcalorimeter gamma-ray spectroscopy,” IEEE Transactions on Nuclear Science , vol. 60, p. 681–688, Apr. 2013

work page 2013
[3]

Improved plutonium and americium photon branching ratios from microcalorimeter gamma spectroscopy,

M. D. Yoho, K. E. Koehler, D. T. Becker, D. A. Bennett, M. H. Carpenter, M. P. Croce, J. D. Gard, J. A. B. Mates, D. J. Mercer, N. J. Ortiz, D. R. Schmidt, C. M. Smith, D. S. Swetz, A. D. Tollefson, J. N. Ullom, L. R. Vale, A. L. Wessels, and D. T. V o, “Improved plutonium and americium photon branching ratios from microcalorimeter gamma spectroscopy,” Nu...

work page 2020
[4]

Improved non- destructive isotopic analysis with practical microcalorimeter gamma spectrometers,

M. Croce, D. Becker, K. E. Koehler, and J. Ullom, “Improved non- destructive isotopic analysis with practical microcalorimeter gamma spectrometers,” Journal of Nuclear Materials Management , vol. 49, no. 3, pp. 108–113, 2021

work page 2021
[5]

Few-electron highly charged muonic Ar atoms verified by electronic K x rays,

T. Okumura, T. Azuma, D. A. Bennett, W. B. Doriese, M. S. Durkin, J. W. Fowler, J. D. Gard, T. Hashimoto, R. Hayakawa, Y . Ichinohe, P. Indelicato, T. Isobe, S. Kanda, D. Kato, M. Katsuragawa, N. Kawa- mura, Y . Kino, N. Kominato, Y . Miyake, K. M. Morgan, H. Noda, G. C. O’Neil, S. Okada, K. Okutsu, N. Paul, C. D. Reintsema, T. Sato, D. R. Schmidt, K. Shi...

arXiv 2024
[6]

First application of superconducting transition-edge sensor microcalorimeters to hadronic atom X-ray spectroscopy,

S. Okada, D. A. Bennett, C. Curceanu, W. B. Doriese, J. W. Fowler, J. D. Gard, F. P. Gustafsson, T. Hashimoto, R. S. Hayano, S. Hirenzaki, J. P. Hays-Wehle, G. C. Hilton, N. Ikeno, M. Iliescu, S. Ishimoto, K. Itahashi, M. Iwasaki, T. Koike, K. Kuwabara, Y . Ma, J. Marton, H. Noda, G. C. O’Neil, H. Outa, C. D. Reintsema, M. Sato, D. R. Schmidt, H. Shi, K. ...

work page 2016
[7]

Proof-of-principle experiment for testing strong-field quantum electrodynamics with exotic atoms: High precision X-ray spec- troscopy of muonic neon,

T. Okumura, T. Azuma, D. Bennett, I. Chiu, W. Doriese, M. Durkin, J. Fowler, J. Gard, T. Hashimoto, R. Hayakawa, G. Hilton, Y . Ichinohe, P. Indelicato, T. Isobe, S. Kanda, M. Katsuragawa, N. Kawamura, Y . Kino, K. Mine, Y . Miyake, K. Morgan, K. Ninomiya, H. Noda, G. O’Neil, S. Okada, K. Okutsu, N. Paul, C. Reintsema, D. Schmidt, K. Shimomura, P. Strasse...

work page 2023
[8]

High-resolution high-speed microwave-multiplexed low temperature microcalorimeters for the HOLMES experiment,

B. Alpert, D. Becker, D. Bennet, M. Biasotti, M. Borghesi, G. Gallucci, M. De Gerone, M. Faverzani, E. Ferri, J. Fowler, J. Gard, A. Gi- achero, J. Hays–Wehle, G. Hilton, J. Mates, A. Nucciotti, A. Orlando, G. Pessina, A. Puiu, C. Reintsema, D. Schmidt, D. Swetz, J. Ullom, and L. Vale, “High-resolution high-speed microwave-multiplexed low temperature micr...

work page 2019

Show all 20 references

[9]

Transition-edge sensor pixel parameter design of the microcalorimeter array for the X-ray Integral Field Unit on Athena,

S. J. Smith, J. S. Adams, S. R. Bandler, G. L. Betancourt-Martinez, J. A. Chervenak, M. P. Chiao, M. E. Eckart, F. M. Finkbeiner, R. L. Kelley, C. A. Kilbourne, A. R. Miniussi, F. S. Porter, J. E. Sadleir, K. Sakai, N. A. Wakeham, E. J. Wassell, W. Yoon, D. A. Bennett, W. B. D...

2016
[10]

511-CAM mission: a pointed 511 keV gamma-ray telescope with a focal plane detector made of stacked transition edge sensor microcalorimeter arrays,

F. Shirazi, E. Gau, M. A. Hossen, D. Becker, D. R. Schmidt, D. S. Swetz, D. Bennett, D. Braun, F. Kislat, J. D. Gard, J. Mates, J. Weber, N. R. Cavero, S. Chun, L. Lisalda, A. T. West, B. Dev, F. Ferrer, R. Bose, J. N. Ullom, and H. Krawczynski, “511-CAM mission: a pointed 511...

2023
[11]

ASCENT: a balloon-borne hard x-ray imaging spectroscopy telescope using transition edge sensor microcalorimeter detectors,

F. Kislat, D. Becker, D. Bennett, A. Dasgupta, J. Fowler, C. L. Fryer, J. Gard, E. Gau, D. Gurgew, K. Harmon, T. Hayashi, S. Heatwole, M. A. Hossen, H. Krawczynski, R. J. Lanzi, J. Legere, J. A. B. Mates, M. McConnell, J. Nagy, T. Okajima, T. Sato, D. Schmidt, S. Spooner, D. S...

2023
[12]

Signal processing for microcalorimeters,

A. E. Szymkowiak, R. L. Kelley, S. H. Moseley, and C. K. Stahle, “Signal processing for microcalorimeters,” Journal of Low Temperature Physics, vol. 93, pp. 281–285, Nov. 1993

1993
[13]

Correcting gain drift in TES detectors for future X-ray satellite missions,

S. J. Smith, M. C. Witthoeft, J. S. Adams, S. R. Bandler, S. Beaumont, J. A. Chervenak, R. S. Cumbee, M. E. Eckart, F. M. Finkbeiner, S. V . Hull, R. L. Kelley, C. A. Kilbourne, M. A. Leutenegger, F. S. Porter, K. Sakai, N. A. Wakeham, and E. J. Wassell, “Correcting gain drift...

2023
[14]

The practice of pulse processing,

J. W. Fowler, B. K. Alpert, W. B. Doriese, Y . I. Joe, G. C. O’Neil, J. N. Ullom, and D. S. Swetz, “The practice of pulse processing,” Journal of Low Temperature Physics, vol. 184, pp. 374–381, July 2016

2016
[15]

Ultra-fast kinematic vortices in mesoscopic superconductors: the effect of the self- field,

L. R. Cadorim, A. de Oliveira Junior, and E. Sardella, “Ultra-fast kinematic vortices in mesoscopic superconductors: the effect of the self- field,” Scientific Reports, vol. 10, p. 18662, Oct. 2020

2020
[16]

Phase- slip lines as a resistance mechanism in transition-edge sensors,

D. A. Bennett, D. R. Schmidt, D. S. Swetz, and J. N. Ullom, “Phase- slip lines as a resistance mechanism in transition-edge sensors,” Applied Physics Letters, vol. 104, no. 4, 2014

2014
[17]

Wessels, Advances in Superconducting Gamma-Ray Microcalorime- ters to Improve Non-Destructive Analysis of Nuclear Materials

A. Wessels, Advances in Superconducting Gamma-Ray Microcalorime- ters to Improve Non-Destructive Analysis of Nuclear Materials . PhD thesis, University of Colorado Boulder, 2022

2022
[18]

Simultaneous readout of 128 X-ray and gamma-ray transition-edge microcalorimeters using microwave SQUID multiplexing,

J. A. B. Mates, D. T. Becker, D. A. Bennett, B. J. Dober, J. D. Gard, J. P. Hays-Wehle, J. W. Fowler, G. C. Hilton, C. D. Reintsema, D. R. Schmidt, D. S. Swetz, L. R. Vale, and J. N. Ullom, “Simultaneous readout of 128 X-ray and gamma-ray transition-edge microcalorimeters usin...

2017
[19]

Bilateral filtering: Theory and applications,

S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, “Bilateral filtering: Theory and applications,” Foundations and Trends in Computer Graph- ics and Vision , vol. 4, no. 1, p. 1–75, 2008

2008
[20]

Hierarchical cluster- ing,

B. S. Everitt, S. Landau, M. Leese, and D. Stahl, “Hierarchical cluster- ing,” in Cluster Analysis, ch. 4, pp. 71–110, Wiley, 5th ed., 2011. This article has been accepted for publication in IEEE Transactions on Applied Superconductivity. This is the author's version which has...

2011

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Large-area microcalorimeter detectors for ultra-high-resolution X-ray and gamma-ray spectroscopy,

M. K. Bacrania, A. S. Hoover, P. J. Karpius, M. W. Rabin, C. R. Rudy, D. T. V o, J. A. Beall, D. A. Bennett, W. B. Doriese, G. C. Hilton, R. D. Horansky, K. D. Irwin, N. Jethava, E. Sassi, J. N. Ullom, and L. R. Vale, “Large-area microcalorimeter detectors for ultra-high-resolution X-ray and gamma-ray spectroscopy,” IEEE Transactions on Nuclear Science , ...

work page 2009

[2] [2]

Determination of plutonium isotopic content by microcalorimeter gamma-ray spectroscopy,

A. S. Hoover, R. Winkler, M. W. Rabin, D. T. V o, J. N. Ullom, D. A. Bennett, W. B. Doriese, J. W. Fowler, R. D. Horansky, D. R. Schmidt, L. R. Vale, and K. Schaffer, “Determination of plutonium isotopic content by microcalorimeter gamma-ray spectroscopy,” IEEE Transactions on Nuclear Science , vol. 60, p. 681–688, Apr. 2013

work page 2013

[3] [3]

Improved plutonium and americium photon branching ratios from microcalorimeter gamma spectroscopy,

M. D. Yoho, K. E. Koehler, D. T. Becker, D. A. Bennett, M. H. Carpenter, M. P. Croce, J. D. Gard, J. A. B. Mates, D. J. Mercer, N. J. Ortiz, D. R. Schmidt, C. M. Smith, D. S. Swetz, A. D. Tollefson, J. N. Ullom, L. R. Vale, A. L. Wessels, and D. T. V o, “Improved plutonium and americium photon branching ratios from microcalorimeter gamma spectroscopy,” Nu...

work page 2020

[4] [4]

Improved non- destructive isotopic analysis with practical microcalorimeter gamma spectrometers,

M. Croce, D. Becker, K. E. Koehler, and J. Ullom, “Improved non- destructive isotopic analysis with practical microcalorimeter gamma spectrometers,” Journal of Nuclear Materials Management , vol. 49, no. 3, pp. 108–113, 2021

work page 2021

[5] [5]

Few-electron highly charged muonic Ar atoms verified by electronic K x rays,

T. Okumura, T. Azuma, D. A. Bennett, W. B. Doriese, M. S. Durkin, J. W. Fowler, J. D. Gard, T. Hashimoto, R. Hayakawa, Y . Ichinohe, P. Indelicato, T. Isobe, S. Kanda, D. Kato, M. Katsuragawa, N. Kawa- mura, Y . Kino, N. Kominato, Y . Miyake, K. M. Morgan, H. Noda, G. C. O’Neil, S. Okada, K. Okutsu, N. Paul, C. D. Reintsema, T. Sato, D. R. Schmidt, K. Shi...

arXiv 2024

[6] [6]

First application of superconducting transition-edge sensor microcalorimeters to hadronic atom X-ray spectroscopy,

S. Okada, D. A. Bennett, C. Curceanu, W. B. Doriese, J. W. Fowler, J. D. Gard, F. P. Gustafsson, T. Hashimoto, R. S. Hayano, S. Hirenzaki, J. P. Hays-Wehle, G. C. Hilton, N. Ikeno, M. Iliescu, S. Ishimoto, K. Itahashi, M. Iwasaki, T. Koike, K. Kuwabara, Y . Ma, J. Marton, H. Noda, G. C. O’Neil, H. Outa, C. D. Reintsema, M. Sato, D. R. Schmidt, H. Shi, K. ...

work page 2016

[7] [7]

Proof-of-principle experiment for testing strong-field quantum electrodynamics with exotic atoms: High precision X-ray spec- troscopy of muonic neon,

T. Okumura, T. Azuma, D. Bennett, I. Chiu, W. Doriese, M. Durkin, J. Fowler, J. Gard, T. Hashimoto, R. Hayakawa, G. Hilton, Y . Ichinohe, P. Indelicato, T. Isobe, S. Kanda, M. Katsuragawa, N. Kawamura, Y . Kino, K. Mine, Y . Miyake, K. Morgan, K. Ninomiya, H. Noda, G. O’Neil, S. Okada, K. Okutsu, N. Paul, C. Reintsema, D. Schmidt, K. Shimomura, P. Strasse...

work page 2023

[8] [8]

High-resolution high-speed microwave-multiplexed low temperature microcalorimeters for the HOLMES experiment,

B. Alpert, D. Becker, D. Bennet, M. Biasotti, M. Borghesi, G. Gallucci, M. De Gerone, M. Faverzani, E. Ferri, J. Fowler, J. Gard, A. Gi- achero, J. Hays–Wehle, G. Hilton, J. Mates, A. Nucciotti, A. Orlando, G. Pessina, A. Puiu, C. Reintsema, D. Schmidt, D. Swetz, J. Ullom, and L. Vale, “High-resolution high-speed microwave-multiplexed low temperature micr...

work page 2019