Machine learning methods for spectroscopic information recovery under ultrafast photon pileup | Paper Citation Record

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This paper cites The BELLA Center hundred terawatt laser system for photon sources and user experiments,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup The BELLA Center hundred terawatt laser system for photon sources and user experiments,

Reference 1

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Observation fff844c1-cba1-492c-ae95-e711795b475a · outbound

This paper cites Development of the MeV Thomson-scattered gamma ray source using laser plasma accelerators at the BELLA Center,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Development of the MeV Thomson-scattered gamma ray source using laser plasma accelerators at the BELLA Center,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:13.266029Z

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Observation b1ddd8c6-da73-43d0-9e53-a1e600b8df0b · outbound

This paper cites Principles and applications of x-ray light sources driven by laser wakefield acceleration,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Principles and applications of x-ray light sources driven by laser wakefield acceleration,

Reference 3

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Observation 92c70ff0-f7fd-45e2-86c5-f1d410ab6536 · outbound

This paper cites Impact of monoenergetic photon sources on nonproliferation applications final report,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Impact of monoenergetic photon sources on nonproliferation applications final report,

Reference 4

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Observation 6e0304e5-59c3-4c6d-a653-caa55817357b · outbound

This paper cites Resonance fluorescence in nuclei,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Resonance fluorescence in nuclei,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:13.216114Z

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Observation ae5be606-dc9c-44e9-9e48-5a690596011c · outbound

This paper cites Investigation of nuclear structure by resonance fluorescence scattering,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Investigation of nuclear structure by resonance fluorescence scattering,

Reference 6

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Observation 2e1d2fbd-2ac0-41ab-a036-be62d474ab83 · outbound

This paper cites Transmission-based detection of nuclides with nuclear resonance fluorescence using a quasimonoener- getic photon source,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Transmission-based detection of nuclides with nuclear resonance fluorescence using a quasimonoener- getic photon source,

Reference 7

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Observation 0203b688-b48f-4cdf-aa30-dccb74e8b3dd · outbound

This paper cites Nuclear resonance fluorescence for nuclear materials assay,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Nuclear resonance fluorescence for nuclear materials assay,

Reference 8

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Observation 3c4ff34a-f1ef-4e8d-90a0-b8bdd450077e · outbound

This paper cites Physical cryptographic verification of nuclear warheads,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Physical cryptographic verification of nuclear warheads,

Reference 9

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Observation 70110ecf-7a7d-474c-a5a2-2be6a1c8d212 · outbound

This paper cites Experimental demonstration of an isotope-sensitive warhead verification technique using nuclear resonance fluorescence,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Experimental demonstration of an isotope-sensitive warhead verification technique using nuclear resonance fluorescence,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:13.133248Z

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Observation 62e076a5-2669-44f0-acb5-f6075f383f1a · outbound

This paper cites Rapid interrogation of special nuclear materials by combining scattering and transmission nuclear resonance fluorescence spectroscopy,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Rapid interrogation of special nuclear materials by combining scattering and transmission nuclear resonance fluorescence spectroscopy,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:13.116979Z

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Observation d9adc877-5371-4784-bc44-3a6ba0f913c7 · outbound

This paper cites Isotope-sensitive imaging of special nuclear materials using computer tomography based on scattering nuclear resonance fluorescence,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Isotope-sensitive imaging of special nuclear materials using computer tomography based on scattering nuclear resonance fluorescence,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:13.100967Z

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Observation e531b32a-88ac-4e27-8918-f7d818f5bd9e · outbound

This paper cites Review of high energy x- ray computed tomography for non-destructive dimensional metrology of large metallic advanced manufactured components,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Review of high energy x- ray computed tomography for non-destructive dimensional metrology of large metallic advanced manufactured components,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:13.084905Z

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This paper cites The Next Generation of MeV Energy X-ray Sources for use in the Inspection of Additively Manufactured Parts for Industry.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup The Next Generation of MeV Energy X-ray Sources for use in the Inspection of Additively Manufactured Parts for Industry

Reference 14

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Observation 238b7c4d-666f-4aaa-9024-514c83e2bfe4 · outbound

This paper cites Fundamental limitations of dual energy x-ray scanners for cargo content atomic number discrimination,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Fundamental limitations of dual energy x-ray scanners for cargo content atomic number discrimination,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:13.068711Z

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Observation c0bd180b-c4eb-4bae-8d2f-0e10941bbe1b · outbound

This paper cites Aγ-ray tracking algorithm for the GRETA spectrometer,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Aγ-ray tracking algorithm for the GRETA spectrometer,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:13.040625Z

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Observation 9ec18634-820e-4a41-9e84-53c87d72c93a · outbound

This paper cites Bayesian reconstruction of photon interaction sequences for high-resolution PET detectors,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Bayesian reconstruction of photon interaction sequences for high-resolution PET detectors,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.909756Z

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Observation d578e8dc-73b3-42f2-a08b-ade0936b9928 · outbound

This paper cites Pileup mitigation with machine learning (PUMML),.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Pileup mitigation with machine learning (PUMML),

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.893227Z

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Observation 6d0591c6-311c-4c9d-8db8-5df1010117f5 · outbound

This paper cites A deep learning approach to correctly identify the sequence of coincidences in cross-strip CZT detectors,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup A deep learning approach to correctly identify the sequence of coincidences in cross-strip CZT detectors,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.877020Z

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Observation ac09da19-64fe-4200-986e-f60092ca1a4c · outbound

This paper cites Experimental evaluation of convolutional neural network-based inter-crystal scattering recovery for high-resolution PET detectors,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Experimental evaluation of convolutional neural network-based inter-crystal scattering recovery for high-resolution PET detectors,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.860082Z

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Observation 7234acf5-3d2b-4973-a061-0f1f22677d59 · outbound

This paper cites Classical and machine learning methods for event reconstruction in NeuLAND,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Classical and machine learning methods for event reconstruction in NeuLAND,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.842792Z

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Observation 334eb24c-0aee-4784-a701-c02281183c50 · outbound

This paper cites Low activity tritium detection in CCDs using deep learning techniques,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Low activity tritium detection in CCDs using deep learning techniques,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.826283Z

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Observation ed8408cf-5989-46e3-8b85-a58f500bd8a6 · outbound

This paper cites Geant4—a simulation toolkit,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Geant4—a simulation toolkit,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.808160Z

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This paper cites Imaging and radiography with nuclear resonance fluorescence and effective-Z (EZ-3D™) determination; SNM detection using prompt neutrons from photon induced fission,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Imaging and radiography with nuclear resonance fluorescence and effective-Z (EZ-3D™) determination; SNM detection using prompt neutrons from photon induced fission,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.791737Z

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This paper cites M400 base specifications,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup M400 base specifications,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.776052Z

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Observation 573171b8-02a9-4edf-af16-e6be3cbeec78 · outbound

This paper cites Large-volume cad- mium zinc telluride modules for safeguards verification of unirradiated nuclear material,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Large-volume cad- mium zinc telluride modules for safeguards verification of unirradiated nuclear material,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.759599Z

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Observation 3f769758-aa3e-47dd-b685-96f3129df233 · outbound

This paper cites Many-electron singularity in x-ray photoe- mission and x-ray line spectra from metals,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Many-electron singularity in x-ray photoe- mission and x-ray line spectra from metals,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.743425Z

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Observation c6faa27b-0318-4630-8b23-8e447e7580ab · outbound

This paper cites Gamma-ray peak shapes from cadmium zinc telluride detectors,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Gamma-ray peak shapes from cadmium zinc telluride detectors,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.725740Z

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Observation 111a3139-9149-4d8b-9751-2ba31bb56d7c · outbound

This paper cites A peak shape function building and comparison of cadmium zinc telluride detector for gamma-ray spectrum,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup A peak shape function building and comparison of cadmium zinc telluride detector for gamma-ray spectrum,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.708318Z

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Observation f14a915b-1f5f-4f30-a8e6-a69087d31ae5 · outbound

This paper cites Charge sharing in common-grid pixelated CdZnTe detectors,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Charge sharing in common-grid pixelated CdZnTe detectors,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.689661Z

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Observation 8744fac0-0bd4-4cf8-a20a-1395f50bbc24 · outbound

This paper cites Signal modeling of charge sharing effect in simple pixelated CdZnTe detector,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Signal modeling of charge sharing effect in simple pixelated CdZnTe detector,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.669085Z

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This paper cites PointNet: Deep learning on point sets for 3D classification and segmentation,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup PointNet: Deep learning on point sets for 3D classification and segmentation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.651673Z

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Observation 67b6a7ee-a9bc-4884-aba1-4de090fafde5 · outbound

This paper cites PointNet++: Deep hierarchical feature learning on point sets in a metric space,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup PointNet++: Deep hierarchical feature learning on point sets in a metric space,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.633793Z

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Observation 6de9b483-5c2a-43b9-8ef1-41ae58bff59f · outbound

This paper cites On the representation power of set pooling networks,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup On the representation power of set pooling networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.615048Z

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Observation ec069e51-7d7a-417a-93a0-ade0b066ee87 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup How Powerful are Graph Neural Networks?

Reference 35

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Observation ab717edf-b506-4334-ad3c-ae78bf419ffa · outbound

This paper cites eqnn-jax,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup eqnn-jax,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.599065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 64494843-dc0d-4521-ace2-c10745a00b3a · outbound

This paper cites JAX: composable transformations of Python+NumPy programs,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup JAX: composable transformations of Python+NumPy programs,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-14T04:14:12.203003Z

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This paper cites Compiling machine learning programs via high-level tracing,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Compiling machine learning programs via high-level tracing,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.570213Z

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Observation 6b005ed4-9371-4a98-bcf2-cdc549454913 · outbound

This paper cites Adam: A method for stochastic optimization,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Adam: A method for stochastic optimization,

Reference 39

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This paper cites Axiomatic attribution for deep networks,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Axiomatic attribution for deep networks,

Reference 40

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This paper cites Data-driven performance optimization of gamma spectrometers with many channels,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Data-driven performance optimization of gamma spectrometers with many channels,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.529575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e5549a02-b08e-42e7-a74e-ee7290df6a39 · outbound

This paper cites Entry A002866 in the on-line encyclopedia of integer sequences,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Entry A002866 in the on-line encyclopedia of integer sequences,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.510380Z

Source-reported events for the cited work

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Observation 6dfe2b70-348f-4de1-9f94-07bf433a7a2a · outbound

This paper cites Entry A000670 in the on-line encyclopedia of integer sequences,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Entry A000670 in the on-line encyclopedia of integer sequences,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.492869Z

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Observation f5d033d5-66bc-41db-bdd1-d52296d0dec3 · outbound

This paper cites Entry A000262 in the on-line encyclopedia of integer sequences,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Entry A000262 in the on-line encyclopedia of integer sequences,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.473600Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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This paper cites Entry A000110 in the on-line encyclopedia of integer sequences,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Entry A000110 in the on-line encyclopedia of integer sequences,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.454455Z

Source-reported events for the cited work

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This paper cites Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.436702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b9fe5f53-1259-4389-8c00-580778aa7876 · outbound

This paper cites Evaluating and calibrating uncertainty prediction in regression tasks,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Evaluating and calibrating uncertainty prediction in regression tasks,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-14T04:14:12.257913Z

Source-reported events for the cited work

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Observation 8c38531c-a94e-49a6-870b-c252f8a7affe · outbound

This paper cites Accurate uncertainties for deep learning using calibrated regression,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Accurate uncertainties for deep learning using calibrated regression,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-14T04:14:12.262917Z

Source-reported events for the cited work

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Observation d49b05e0-6fac-4f40-9fc9-165bd2d9e7ec · outbound

This paper cites Uncertainty quantification in scientific machine learning: Methods, 14 metrics, and comparisons,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Uncertainty quantification in scientific machine learning: Methods, 14 metrics, and comparisons,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:14:12.393878Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e42612ce-8a92-4429-8720-640ad65eb40d · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-14T04:14:12.274020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0455d86a-82a1-4db4-ac6e-ac81901e4ca3 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup Adam: A Method for Stochastic Optimization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-14T04:14:12.217801Z

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