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Author |
Majumdar, A.; Papoulias, D.K.; Srivastava, R.; Valle, J.W.F. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Physics implications of recent Dresden-II reactor data |
Type |
Journal Article |
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Year |
2022 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
106 |
Issue |
9 |
Pages |
093010 - 14pp |
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Abstract ![sorted by Abstract field, descending order (down)](img/sort_desc.gif) |
Prompted by the recent Dresden-II reactor data, we examine its implications for the determination of the weak mixing angle, paying attention to the effect of the quenching function. We also determine the resulting constraints on the unitarity of the neutrino mixing matrix, as well as on the most general type of nonstandard neutral-current neutrino interactions. |
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Address |
[Majumdar, Anirban; Srivastava, Rahul] Indian Inst Sci Educ & Res Bhopal, Dept Phys, Bhopal Bypass Rd, Bhopal 462066, India, Email: anirban19@iiserb.ac.in; |
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Amer Physical Soc |
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English |
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ISSN |
2470-0010 |
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Notes |
WOS:000917769000005 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5469 |
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Permanent link to this record |
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Author |
Bernal, N.; Munoz-Albornoz, V.; Palomares-Ruiz, S.; Villanueva-Domingo, P. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Current and future neutrino limits on the abundance of primordial black holes |
Type |
Journal Article |
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Year |
2022 |
Publication |
Journal of Cosmology and Astroparticle Physics |
Abbreviated Journal |
J. Cosmol. Astropart. Phys. |
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Volume |
10 |
Issue |
10 |
Pages |
068 - 38pp |
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Keywords |
neutrino detectors; primordial black holes |
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Abstract ![sorted by Abstract field, descending order (down)](img/sort_desc.gif) |
Primordial black holes (PBHs) formed in the early Universe are sources of neutrinos emitted via Hawking radiation. Such astrophysical neutrinos could be detected at Earth and constraints on the abundance of comet-mass PBHs could be derived from the null observation of this neutrino flux. Here, we consider non-rotating PBHs and improve constraints using Super-Kamiokande neutrino data, as well as we perform forecasts for next-generation neutrino (Hyper-Kamiokande, JUNO, DUNE) and dark matter (DARWIN, ARGO) detectors, which we compare. For PBHs less massive than " few x 1014 g, PBHs would have already evaporated by now, whereas more massive PBHs would still be present and would constitute a fraction of the dark matter of the Universe. We consider monochromatic and extended (log-normal) mass distributions, and a PBH mass range spanning from 1012 g to ti 1016 g. Finally, we also compare our results with previous ones in the literature. |
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[Bernal, Nicolas] New York Univ Abu Dhabi, POB 129188, Abu Dhabi, U Arab Emirates, Email: nicolas.bernal@uan.edu.co; |
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IOP Publishing Ltd |
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English |
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ISSN |
1475-7516 |
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Notes |
WOS:000882783900003 |
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no |
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yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5412 |
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Author |
Martinelli, M.; Scarcella, F.; Hogg, N.B.; Kavanagh, B.J.; Gaggero, D.; Fleury, P. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Dancing in the dark: detecting a population of distant primordial black holes |
Type |
Journal Article |
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Year |
2022 |
Publication |
Journal of Cosmology and Astroparticle Physics |
Abbreviated Journal |
J. Cosmol. Astropart. Phys. |
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Volume |
08 |
Issue |
8 |
Pages |
006 - 47pp |
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Keywords |
dark matter theory; gravitational waves / experiments; gravitational waves / sources; primordial black holes |
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Abstract ![sorted by Abstract field, descending order (down)](img/sort_desc.gif) |
Primordial black holes (PBHs) are compact objects proposed to have formed in the early Universe from the collapse of small-scale over-densities. Their existence may be detected from the observation of gravitational waves (GWs) emitted by PBH mergers, if the signals can be distinguished from those produced by the merging of astrophysical black holes. In this work, we forecast the capability of the Einstein Telescope, a proposed third-generation GW observatory, to identify and measure the abundance of a subdominant population of distant PBHs, using the difference in the redshift evolution of the merger rate of the two populations as our discriminant. We carefully model the merger rates and generate realistic mock catalogues of the luminosity distances and errors that would be obtained from GW signals observed by the Einstein Telescope. We use two independent statistical methods to analyse the mock data, finding that, with our more powerful, likelihood-based method, PBH abundances as small as fPBH approximate to 7 x 10(-6) ( fPBH approximate to 2 x 10(-6)) would be distinguishable from f(PBH) = 0 at the level of 3 sigma with a one year (ten year) observing run of the Einstein Telescope. Our mock data generation code, darksirens, is fast, easily extendable and publicly available on GitLab. |
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[Martinelli, Matteo] INAF Osservatorio Astron Roma, Via Frascati 33, I-00040 Rome, Italy, Email: matteo.martinelli@inaf.it; |
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IOP Publishing Ltd |
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English |
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1475-7516 |
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Notes |
WOS:000911612900001 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5461 |
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Permanent link to this record |
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Author |
Hirn, J.; Garcia, J.E.; Montesinos-Navarro, A.; Sanchez-Martin, R.; Sanz, V.; Verdu, M. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
A deep Generative Artificial Intelligence system to predict species coexistence patterns |
Type |
Journal Article |
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Year |
2022 |
Publication |
Methods in Ecology and Evolution |
Abbreviated Journal |
Methods Ecol. Evol. |
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Volume |
13 |
Issue |
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Pages |
1052-1061 |
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Keywords |
artificial intelligence; direct interactions; generative adversarial networks; indirect interactions; species coexistence; variational AutoEncoders |
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Abstract ![sorted by Abstract field, descending order (down)](img/sort_desc.gif) |
Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge. |
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Address |
[Hirn, Johannes; Enrique Garcia, Jose; Sanz, Veronica] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: miguel.verdu@ext.uv.es |
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Publisher |
Wiley |
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English |
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2041-210x |
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Notes |
WOS:000765239700001 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5155 |
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Permanent link to this record |
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Author |
Garcia Navarro, J.E.; Fernandez-Prieto, L.M.; Villaseñor, A.; Sanz, V.; Ammirati, J.B.; Diaz Suarez, E.A.; Garcia, C. |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
Type |
Journal Article |
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Year |
2022 |
Publication |
Seismological Research Letters |
Abbreviated Journal |
Seismol. Res. Lett. |
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Volume |
93 |
Issue |
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Pages |
2529-2542 |
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Abstract ![sorted by Abstract field, descending order (down)](img/sort_desc.gif) |
Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures. |
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no |
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yes |
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yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5500 |
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Permanent link to this record |
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Author |
AbdusSalam, S.S. et al; Eberhardt, O. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Simple and statistically sound recommendations for analysing physical theories |
Type |
Journal Article |
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Year |
2022 |
Publication |
Reports on Progress in Physics |
Abbreviated Journal |
Rep. Prog. Phys. |
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Volume |
85 |
Issue |
5 |
Pages |
052201 - 11pp |
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Keywords |
particle physics; statistics; methodology |
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Abstract ![sorted by Abstract field, descending order (down)](img/sort_desc.gif) |
Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with statistically unsound ad hoc methods, involving intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these procedures, suggesting where possible simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide any physicists lacking comprehensive statistical training with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden. Our examples can be reproduced with the code publicly available at Zenodo. |
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Address |
[AbdusSalam, Shehu S.; Fowlie, Andrew] Shahid Beheshti Univ, Dept Phys, Tehran, Iran, Email: andrew.j.fowlie@njnu.edu.cn |
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IOP Publishing Ltd |
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English |
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ISSN |
0034-4885 |
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Notes |
WOS:000791574900001 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5221 |
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Author |
PANDA Collaboration (Davi, F. et al); Diaz, J. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Technical design report for the endcap disc DIRC |
Type |
Journal Article |
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Year |
2022 |
Publication |
Journal of Physics G |
Abbreviated Journal |
J. Phys. G |
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Volume |
49 |
Issue |
12 |
Pages |
120501 - 128pp |
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Keywords |
technical design report; particle identification; Cherenkov detector; PANDA |
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Abstract ![sorted by Abstract field, descending order (down)](img/sort_desc.gif) |
PANDA (anti-proton annihiliation at Darmstadt) is planned to be one of the four main experiments at the future international accelerator complex FAIR (Facility for Antiproton and Ion Research) in Darmstadt, Germany. It is going to address fundamental questions of hadron physics and quantum chromodynamics using cooled antiproton beams with a high intensity and and momenta between 1.5 and 15 GeV/c. PANDA is designed to reach a maximum luminosity of 2 x 10(32) cm(-2) s. Most of the physics programs require an excellent particle identification (PID). The PID of hadronic states at the forward endcap of the target spectrometer will be done by a fast and compact Cherenkov detector that uses the detection of internally reflected Cherenkov light (DIRC) principle. It is designed to cover the polar angle range from 5 degrees to 22 degrees and to provide a separation power for the separation of charged pions and kaons up to 3 standard deviations (s.d.) for particle momenta up to 4 GeV/c in order to cover the important particle phase space. This document describes the technical design and the expected performance of the novel PANDA disc DIRC detector that has not been used in any other high energy physics experiment before. The performance has been studied with Monte-Carlo simulations and various beam tests at DESY and CERN. The final design meets all PANDA requirements and guarantees sufficient safety margins. |
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[Davi, F.] Univ Politecn Marche Ancona, Ancona, Italy, Email: muschmidt@uni-wuppertal.de |
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IOP Publishing Ltd |
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English |
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ISSN |
0954-3899 |
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Notes |
WOS:000928188400001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5476 |
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Permanent link to this record |
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Author |
Giare, W.; Renzi, F.; Melchiorri, A.; Mena, O.; Di Valentino, E. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Cosmological forecasts on thermal axions, relic neutrinos, and light elements |
Type |
Journal Article |
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Year |
2022 |
Publication |
Monthly Notices of the Royal Astronomical Society |
Abbreviated Journal |
Mon. Not. Roy. Astron. Soc. |
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Volume |
511 |
Issue |
1 |
Pages |
1373-1382 |
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Keywords |
cosmic background radiation; cosmological parameters; dark matter; early Universe; cosmology: observations |
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Abstract ![sorted by Abstract field, descending order (down)](img/sort_desc.gif) |
One of the targets of future cosmic microwave background (CMB) and baryon acoustic oscillation measurements is to improve the current accuracy in the neutrino sector and reach a much better sensitivity on extra dark radiation in the early Universe. In this paper, we study how these improvements can be translated into constraining power for well-motivated extensions of the standard model of elementary particles that involve axions thermalized before the quantum chromodynamics (QCD) phase transition by scatterings with gluons. Assuming a fiducial Lambda cold dark matter cosmological model, we simulate future data for Stage-IV CMB-like and Dark Energy Spectroscopic Instrument (DESI)-like surveys and analyse a mixed scenario of axion and neutrino hot dark matter. We further account also for the effects of these QCD axions on the light element abundances predicted by big bang nucleosynthesis. The most constraining forecasted limits on the hot relic masses are m(a) less than or similar to 0.92 eV and n-ary sumation m(nu) less than or similar to 0.12 eV at 95 per cent Confidence Level, showing that future cosmic observations can substantially improve the current bounds, supporting multimessenger analyses of axion, neutrino, and primordial light element properties. |
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Address |
[Giare, William; Melchiorri, Alessandro] Univ Roma La Sapienza, Phys Dept, Ple Aldo Moro 2, I-00185 Rome, Italy, Email: william.giare@gmail.com |
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Oxford Univ Press |
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English |
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0035-8711 |
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Notes |
WOS:000770034000012 |
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no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5192 |
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Permanent link to this record |
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Author |
Roser, J.; Barrientos, L.; Bernabeu, J.; Borja-Lloret, M.; Muñoz, E.; Ros, A.; Viegas, R.; Llosa, G. |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Joint image reconstruction algorithm in Compton cameras |
Type |
Journal Article |
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Year |
2022 |
Publication |
Physics in Medicine and Biology |
Abbreviated Journal |
Phys. Med. Biol. |
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Volume |
67 |
Issue |
15 |
Pages |
155009 - 15pp |
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Keywords |
Compton camera; compton imaging; hadron therapy; image reconstruction; LM-MLEM; Monte Carlo simulations; multi-layer compton telescope |
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Abstract ![sorted by Abstract field, descending order (down)](img/sort_desc.gif) |
Objective. To demonstrate the benefits of using an joint image reconstruction algorithm based on the List Mode Maximum Likelihood Expectation Maximization that combines events measured in different channels of information of a Compton camera. Approach. Both simulations and experimental data are employed to show the algorithm performance. Main results. The obtained joint images present improved image quality and yield better estimates of displacements of high-energy gamma-ray emitting sources. The algorithm also provides images that are more stable than any individual channel against the noisy convergence that characterizes Maximum Likelihood based algorithms. Significance. The joint reconstruction algorithm can improve the quality and robustness of Compton camera images. It also has high versatility, as it can be easily adapted to any Compton camera geometry. It is thus expected to represent an important step in the optimization of Compton camera imaging. |
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Address |
[Roser, J.; Barrientos, L.; Bernabeu, J.; Borja-Lloret, M.; Munoz, E.; Ros, A.; Viegas, R.; Llosa, G.] CSIC UV, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: Jorge.Roser@ific.uv.es |
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IOP Publishing Ltd |
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English |
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0031-9155 |
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Notes |
WOS:000827830200001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5298 |
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Permanent link to this record |
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Author |
Borys, D. et al; Brzezinski, K. |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
ProTheRaMon-a GATE simulation framework for proton therapy range monitoring using PET imaging |
Type |
Journal Article |
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Year |
2022 |
Publication |
Physics in Medicine and Biology |
Abbreviated Journal |
Phys. Med. Biol. |
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Volume |
67 |
Issue |
22 |
Pages |
224002 - 15pp |
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Keywords |
proton therapy; GATE; Monte Carlo simulations; J-PET; medical imaging |
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Abstract ![sorted by Abstract field, descending order (down)](img/sort_desc.gif) |
Objective. This paper reports on the implementation and shows examples of the use of the ProTheRaMon framework for simulating the delivery of proton therapy treatment plans and range monitoring using positron emission tomography (PET). ProTheRaMon offers complete processing of proton therapy treatment plans, patient CT geometries, and intra-treatment PET imaging, taking into account therapy and imaging coordinate systems and activity decay during the PET imaging protocol specific to a given proton therapy facility. We present the ProTheRaMon framework and illustrate its potential use case and data processing steps for a patient treated at the Cyclotron Centre Bronowice (CCB) proton therapy center in Krakow, Poland. Approach. The ProTheRaMon framework is based on GATE Monte Carlo software, the CASToR reconstruction package and in-house developed Python and bash scripts. The framework consists of five separated simulation and data processing steps, that can be further optimized according to the user's needs and specific settings of a given proton therapy facility and PET scanner design. Main results. ProTheRaMon is presented using example data from a patient treated at CCB and the J-PET scanner to demonstrate the application of the framework for proton therapy range monitoring. The output of each simulation and data processing stage is described and visualized. Significance. We demonstrate that the ProTheRaMon simulation platform is a high-performance tool, capable of running on a computational cluster and suitable for multi-parameter studies, with databases consisting of large number of patients, as well as different PET scanner geometries and settings for range monitoring in a clinical environment. Due to its modular structure, the ProTheRaMon framework can be adjusted for different proton therapy centers and/or different PET detector geometries. It is available to the community via github (Borys et al 2022). |
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Address |
[Borys, Damian] Silesian Tech Univ, Dept Syst Biol & Engn, Gliwice, Poland, Email: damin.borys@polsl.pl |
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Publisher |
IOP Publishing Ltd |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0031-9155 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:000885248200001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5416 |
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Permanent link to this record |