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Author |
n_TOF Collaboration (Sosnin, N.V. et al.); Babiano-Suarez, V.; Caballero, L.; Domingo-Pardo, C.; Ladarescu, I.; Tain, J.L. |
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Title |
Measurement of the 77Se(n,gamma) cross section up to 200 keV at the n_TOF facility at CERN |
Type |
Journal Article |
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Year |
2023 |
Publication |
Physical Review C |
Abbreviated Journal |
Phys. Rev. C |
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Volume |
107 |
Issue |
6 |
Pages |
065805 - 9pp |
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Abstract |
The 77Se(n,gamma) reaction is of importance for 77Se abundance during the slow neutron capture process in massive stars. We have performed a new measurement of the 77Se radiative neutron capture cross section at the Neutron Time-of-Flight facility at CERN. Resonance capture kernels were derived up to 51 keV and cross sections up to 200 keV. Maxwellian-averaged cross sections were calculated for stellar temperatures between kT = 5 keV and kT = 100 keV, with uncertainties between 4.2% and 5.7%. Our results lead to substantial decreases of 14% and 19% in 77Se abundances produced through the slow neutron capture process in selected stellar models of 15M0 and 2M0, respectively, compared to using previous recommendation of the cross section. |
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Address |
[V. Sosnin, N.; Lederer-Woods, C.; Garg, R.; Dietz, M.; Murphy, A. St. J.; Lonsdale, S.; Woods, P. J.] Univ Edinburgh, Sch Phys & Astron, Edinburgh, Scotland, Email: nsosnin@ed.ac.uk |
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Amer Physical Soc |
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English |
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ISSN |
2469-9985 |
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Notes |
WOS:001023903800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5599 |
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Author |
Tortajada, S.; Albiol, F.; Caballero, L.; Albiol, A.; Leganes-Nieto, J.L. |
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Title |
A portable geometry-independent tomographic system for gamma-ray, a next generation of nuclear waste characterization |
Type |
Journal Article |
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Year |
2023 |
Publication |
Scientific Reports |
Abbreviated Journal |
Sci Rep |
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Volume |
13 |
Issue |
1 |
Pages |
12284 - 10pp |
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Abstract |
One of the main activities of the nuclear industry is the characterisation of radioactive waste based on the detection of gamma radiation. Large volumes of radioactive waste are classified according to their average activity, but often the radioactivity exceeds the maximum allowed by regulators in specific parts of the bulk. In addition, the detection of the radiation is currently based on static detection systems where the geometry of the bulk is fixed and well known. Furthermore, these systems are not portable and depend on the transport of waste to the places where the detection systems are located. However, there are situations where the geometry varies and where moving waste is complex. This is especially true in compromised situations.We present a new model for nuclear waste management based on a portable and geometry-independent tomographic system for three-dimensional image reconstruction for gamma radiation detection. The system relies on a combination of a gamma radiation camera and a visible camera that allows to visualise radioactivity using augmented reality and artificial computer vision techniques. This novel tomographic system has the potential to be a disruptive innovation in the nuclear industry for nuclear waste management. |
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Address |
[Tortajada, Salvador; Albiol, Francisco; Caballero, Luis] Univ Valencia, CSIC, Inst Fis Corpuscular, E-46980 Paterna Valencia, Spain, Email: s.tortajada@ific.uv.es |
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Publisher |
Nature Portfolio |
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English |
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ISSN |
2045-2322 |
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Notes |
WOS:001041587900052 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5612 |
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Author |
Balibrea-Correa, J.; Lerendegui-Marco, J.; Calvo, D.; Caballero, L.; Babiano, V.; Ladarescu, I.; Redondo, M.L.; Tain, J.L.; Tolosa, A.; Domingo-Pardo, C.; Calvino, F.; Casanovas, A.; Tarifeño-Saldivia, A.; Alcayne, V.; Cano-Ott, D.; Martinez, T.; Guerrero, C.; Barbagallo, M.; Macina, D.; Bacak, M. |
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Title |
A first prototype of C6D6 total-energy detector with SiPM readout for neutron capture time-of-flight experiments |
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Journal Article |
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Year |
2021 |
Publication |
Nuclear Instruments & Methods in Physics Research A |
Abbreviated Journal |
Nucl. Instrum. Methods Phys. Res. A |
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Volume |
985 |
Issue |
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Pages |
164709 - 8pp |
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Keywords |
Silicon photomultiplier; Radiation detectors; Time-of-flight; Radiative capture; Total energy detector; Pulse-height weighting technique |
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Abstract |
Low efficiency total-energy detectors (TEDs) are one of the main tools for neutron capture cross section measurements utilizing the time-of-flight (TOF) technique. State-of-the-art TEDs are based on a C6D6 liquid-scintillation cell optically coupled to a fast photomultiplier tube. The large photomultiplier tube represents yet a significant contribution to the so-called neutron sensitivity background, which is one of the most conspicuous sources of uncertainty in this type of experiments. Here we report on the development of a first prototype of a TED based on a silicon-photomultiplier (SiPM) readout, thus resulting in a lightweight and much more compact detector. Apart from the envisaged improvement in neutron sensitivity, the new system uses low voltage (+28 V) and low current supply (-50 mA), which is more practical than the-kV supply required by conventional photomultipliers. One important difficulty hindering the earlier implementation of SiPM readout for this type of detector was the large capacitance for the output signal when all pixels of a SiPM array are summed together. The latter leads to long pulse rise and decay times, which are not suitable for time-of-flight experiments. In this work we demonstrate the feasibility of a Schottky-diode multiplexing readout approach, that allows one to preserve the excellent timing properties of SiPMs, hereby paving the way for their implementation in future neutron TOF experiments. |
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Address |
[Balibrea-Correa, J.; Lerendegui-Marco, J.; Calvo, D.; Caballero, L.; Babiano, V; Ladarescu, I; Redondo, M. Lopez; Tain, J. L.; Tolosa, A.; Domingo-Pardo, C.] Univ Valencia, Inst Fis Corpuscular, CSIC, Valencia, Spain, Email: dacaldia@ific.uv.es; |
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Elsevier |
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English |
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ISSN |
0168-9002 |
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Notes |
WOS:000592358200019 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4638 |
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Author |
Schaffter, T. et al; Albiol, F.; Caballero, L. |
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Title |
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms |
Type |
Journal Article |
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Year |
2020 |
Publication |
JAMA Network Open |
Abbreviated Journal |
JAMA Netw. Open |
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Volume |
3 |
Issue |
3 |
Pages |
e200265 - 15pp |
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Keywords |
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Abstract |
Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results Overall, 144231 screening mammograms from 85580 US women (952 cancer positive <= 12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166578 examinations from 68008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. Question How do deep learning algorithms perform compared with radiologists in screening mammography interpretation? Findings In this diagnostic accuracy study using 144231 screening mammograms from 85580 women from the United States and 166578 screening mammograms from 68008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks; including clinical data and prior mammograms did not improve artificial intelligence performance. However, combining best-performing artificial intelligence algorithms with single-radiologist assessment demonstrated increased specificity. Meaning Integrating artificial intelligence to mammography interpretation in single-radiologist settings could yield significant performance improvements, with the potential to reduce health care system expenditures and address resource scarcity experienced in population-based screening programs. This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms. |
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Address |
[Schaffter, Thomas; Hoff, Bruce; Yu, Thomas; Neto, Elias Chaibub; Friend, Stephen; Guinney, Justin] Sage Bionetworks, Computat Oncol, Seattle, WA USA, Email: gustavo@us.ibm.com |
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Publisher |
Amer Medical Assoc |
Place of Publication |
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English |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2574-3805 |
ISBN |
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Conference |
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Notes |
WOS:000519249800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4683 |
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