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Author |
Easa, H.; Gregoire, T.; Stolarski, D.; Cosme, C. |
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Title |
Baryogenesis and dark matter in multiple hidden sectors |
Type |
Journal Article |
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Year |
2024 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
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Volume |
109 |
Issue |
7 |
Pages |
075003 - 29pp |
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Abstract |
We explore a mechanism for producing the baryon asymmetry and dark matter in models with multiple hidden sectors that are Standard -Model -like but with varying Higgs mass parameters. If the field responsible for reheating the Standard Model and the exotic sectors carries an asymmetry, it can be converted into a baryon asymmetry using the standard sphaleron process. A hidden sector with positive Higgs mass squared can accommodate dark matter with its baryon asymmetry, and the larger abundance of dark matter relative to baryons is due to dark sphalerons being active all the way down the hidden sector QCD scale. This scenario predicts that dark matter is clustered in large dark nuclei and gives a lower bound on the effective relativistic degrees of freedom, Delta N eff greater than or similar to 0 .05 , which may be observable in the nextgeneration cosmic microwave background experiment CMB-S4. |
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Address |
[Easa, Hassan; Gregoire, Thomas; Stolarski, Daniel; Cosme, Catarina] Carleton Univ, Ottawa Carleton Inst Phys, 1125 Colonel Dr, Ottawa, ON K1S 5B6, Canada, Email: Hassaneasa@cmail.carleton.ca; |
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Publisher |
Amer Physical Soc |
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English |
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ISSN |
2470-0010 |
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Notes |
WOS:001224349300001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial  |
6129 |
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Author |
CALICE Collaboration (Lai, S. et al); Irles, A. |
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Title |
Software compensation for highly granular calorimeters using machine learning |
Type |
Journal Article |
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Year |
2024 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
19 |
Issue |
4 |
Pages |
P04037 - 28pp |
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Keywords |
Large detector-systems performance; Pattern recognition; cluster finding; calibration and fitting methods; Performance of High Energy Physics Detectors |
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Abstract |
A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied. |
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Address |
[Lai, S.; Utehs, J.; Wilhahn, A.] Georg August Univ Gottingen, Phys Inst 2, Friedrich Hund Pl 1, D-37077 Gottingen, Germany, Email: jack.rolph@desy.de |
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Publisher |
IOP Publishing Ltd |
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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 |
1748-0221 |
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Conference |
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Notes |
WOS:001230094600001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6128 |
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Author |
LHCb Collaboration (Aaij, R. et al); Jaimes Elles, S.J.; Jashal, B.K.; Martinez-Vidal, F.; Oyanguren, A.; Rebollo De Miguel, M.; Sanderswood, I., Zhuo, J. |
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Title |
Amplitude Analysis of the B0 -> K*0 μ+μ- Decay |
Type |
Journal Article |
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Year |
2024 |
Publication |
Physical Review Letters |
Abbreviated Journal |
Phys. Rev. Lett. |
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Volume |
132 |
Issue |
13 |
Pages |
131801 - 13pp |
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Abstract |
An amplitude analysis of the B-0 -> K*(0) mu(+)mu(-) decay is presented using a dataset corresponding to an integrated luminosity of 4.7 fb(-1) of pp collision data collected with the LHCb experiment. For the first time, the coefficients associated to short-distance physics effects, sensitive to processes beyond the standard model, are extracted directly from the data through a q(2)-unbinned amplitude analysis, where q(2) is the mu(+)mu(-) invariant mass squared. Long-distance contributions, which originate from nonfactorizable QCD processes, are systematically investigated, and the most accurate assessment to date of their impact on the physical observables is obtained. The pattern of measured corrections to the short-distance couplings is found to be consistent with previous analyses of b- to s-quark transitions, with the largest discrepancy from the standard model predictions found to be at the level of 1.8 standard deviations. The global significance of the observed differences in the decay is 1.4 standard deviations. |
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Address |
[Dettori, F.; Fujii, Y.; Hadavizadeh, T.; Lane, J. J.; Litvinov, R.; Manca, G.; Saitta, B.; Ward, J. A.] Monash Univ, Sch Phys & Astron, Melbourne, Australia |
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Publisher |
Amer Physical Soc |
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 |
0031-9007 |
ISBN |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:001201992300011 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6127 |
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Author |
Ferrer-Sanchez, A.; Martin-Guerrero, J.; Ruiz de Austri, R.; Torres-Forne, A.; Font, J.A. |
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Title |
Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics |
Type |
Journal Article |
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Year |
2024 |
Publication |
Computer Methods in Applied Mechanics and Engineering |
Abbreviated Journal |
Comput. Meth. Appl. Mech. Eng. |
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Volume |
424 |
Issue |
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Pages |
116906 - 18pp |
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Keywords |
Riemann problem; Euler equations; Machine learning; Neural networks; Relativistic hydrodynamics |
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Abstract |
We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss. The performance of our methodology is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with the GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative l(2) error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference ''ground truth'', and with the corresponding error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by the GA-PINN model is comparable to that obtained with a shock-capturing scheme, achieving a performance superior to that of the baseline PINN algorithm in general. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved variables, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN methodology discussed in this paper could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions. |
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Address |
[Ferrer-Sanchez, Antonio; Martin-Guerrero, JoseD.] ETSE UV, Elect Engn Dept, IDAL, Avgda Univ S-N, Valencia 46100, Spain, Email: Antonio.Ferrer-Sanchez@uv.es |
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Corporate Author |
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Thesis |
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Publisher |
Elsevier Science Sa |
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 |
0045-7825 |
ISBN |
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Conference |
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Notes |
WOS:001221797400001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
6126 |
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Author |
Chen, M.C.; Li, X.Q.; Liu, X.G.; Medina, O.; Ratz, M. |
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Title |
Modular invariant holomorphic observables |
Type |
Journal Article |
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Year |
2024 |
Publication |
Physics Letters B |
Abbreviated Journal |
Phys. Lett. B |
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Volume |
852 |
Issue |
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Pages |
138600 - 13pp |
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Keywords |
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Abstract |
In modular invariant models of flavor, observables must be modular invariant. The observables discussed so far in the literature are functions of the modulus tau and its conjugate, (tau) over bar. We point out that certain combinations of observables depend only on tau , i.e. are meromorphic, and in some cases even holomorphic functions of tau. These functions, which we dub “invariants” in this Letter, are highly constrained, renormalization group invariant, and allow us to derive many of the models' features without the need for extensive parameter scans. We illustrate the robustness of these invariants in two existing models in the literature based on modular symmetries, Gamma(3) and Gamma(5). We find that, in some cases, the invariants give rise to robust relations among physical observables that are independent of tau. Furthermore, there are instances where additional symmetries exist among the invariants. These symmetries are relevant phenomenologically and may provide a dynamical way to realize symmetries of mass matrices. |
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Address |
[Chen, Mu-Chun; Li, Xueqi; Liu, Xiang-Gan; Ratz, Michael] Univ Calif Irvine, Dept Phys & Astron, Irvine, CA 92697 USA, Email: muchunc@uci.edu; |
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Corporate Author |
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Thesis |
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Publisher |
Elsevier |
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 |
0370-2693 |
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:001221253800001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6125 |
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