2018-09-20
04:22 |
Identifying the relevant dependencies of the neural network response on characteristics of the input space
/ Wunsch, Stefan (KIT, Karlsruhe, EKP ; CERN) ; Friese, Raphael (KIT, Karlsruhe, EKP) ; Wolf, Roger (KIT, Karlsruhe, EKP) ; Quast, Günter (KIT, Karlsruhe, EKP)
The relation between the input and output spaces of neural networks (NNs) is investigated to identify those characteristics of the input space that have a large influence on the output for a given task. For this purpose, the NN function is decomposed into a Taylor expansion in each element of the input space. [...]
arXiv:1803.08782.-
2018-09-17 - 6 p.
- Published in : Comput. Softw. Big Sci. 2 (2018) 5
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2018-09-19
06:08 |
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2018-09-19
06:08 |
Updated fits to the present $b \to s \ell ^+\ell ^-$ data
/ Mahmoudi, F (Lyon, IPN ; CERN) ; Hurth, T (U. Mainz, PRISMA) ; Neshatpour, S (IPM, Tehran)
We discuss the observed deviations in b → s`+` − processes from the Standard Model predictions and present global fits for both hadronic effects and the new physics description of these anomalies. We investigate whether the different anomalies can be described by a consistent new physics effect. [...]
CERN-TH-2018-074.-
2018 - 11 p.
- Published in : Acta Phys. Pol. B 49 (2018) 1267-1277
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In : XXIV Cracow Epiphany Conference on Advances in Heavy Flavour Physics, Cracow, Poland, 9 - 12 Jan 2018, pp.1267-1277
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2018-09-19
06:08 |
Updating the MACHO fraction of the Milky Way dark halo with improved mass models
/ Calcino, Josh (Queensland U.) ; Garcia-Bellido, Juan (Madrid, IFT) ; Davis, Tamara M. (Queensland U.)
Recent interest in primordial black holes as a possible dark matter candidate has motivated the reanalysis of previous methods for constraining massive astrophysical compact objects in the Milky Way halo and beyond. In order to derive these constraints, a model for the dark matter distribution around the Milky Way must be used. [...]
arXiv:1803.09205; CERN-TH-2018-051; CERN-TH-2018-051.-
2018 - 18 p.
- Published in : Mon. Not. R. Astron. Soc. 479 (2018) 2889-2905
Fulltext: PDF; External links: 00006 The efficiency functions for the MACHO Collaboration 5.7 year (criteria A) and EROS-2 surveys, showing the efficiency with which each survey could detect lenses as a function of the Einstein ring crossing time \citep{alcock2000,eros2007}.; 00001 The contour plot for the constraints on the power-law dark halo model. The rather triangular contours for the $q$ parameter are a result of the hard-wall prior forcing each step in the MCMC chain to only sample models that are self-consistent (see section \ref{sec:plm}). A large shift in the disk properties ($a_d,\, M_d$), results in only a small shift in the parameters for the dark halo fit ($v_a,\, R_d,\, \beta$).; 00009 The optical depth $\tau$ along the line of sight towards the LMC for the low and high $\Sigma_\odot$ prior, as well for the standard model.; 00000 The expected number of microlensing events the EROS-2 survey should have seen if all DM was in the form of MACHOs with some mass $m$. The shaded regions represent the 2-$\sigma$ uncertainty propagated from the MWRC fit in Figure \ref{fig:rot_plot}, and also includes the uncertainty in the shape of the MW dark halo. Here we have plotted the distribution in the expected number of events with both the low and high $\Sigma_\odot$ prior that was used in fitting the MWRC. As one might expect, the low $\Sigma_\odot$ prior on the disk fit results in a larger dark halo, which results in a higher expected number of events.; 00010 The same as Figure \ref{fig:er_nevs}, but using the MACHO Collaboration 5.7 year efficiency.; 00003 The EROS-2 standard model constraints for an extended mass function and clustered PBHs. For $\sigma=0.5$, the constraints closely follow the monochromatic mass constraints, however the constraints shift to lower masses for broad distributions. In the bottom right, an example of a lognormal distribution with parameters $[\mu,\, \sigma]=[10M_\odot,\, 0.5]$ is plotted. A vertical line drawn from the peak of the distribution intercepts the $\sigma = 0.5$ constraint (in green) at $f_{\textrm{PBH}}=0.475$, meaning that this distribution is excluded from contributing more than 47.5\% of DM at 95\% c.l. A PBH distribution with $[\mu,\, \sigma]=[10M_\odot,\, 2]$, or ever $[\mu,\, \sigma]=[10M_\odot,\, \sqrt{2}]$, could still make up the entirety of DM.; 00002 The exclusion plots using the EROS-2 bright stars sample, in which no microlensing events were detected \citep{eros2007}, and the MACHO Collaboration 5.7 year results, with 10 events observed \citep{alcock2000,bennett2005}. The standard model constraints for each survey are plotted as solid lines, overlapping their respective updated constraint at high mass. The updated constraints for each survey are plotted as the shaded regions for both the low and high $\Sigma_\odot$ prior. The uncertainty from the MWRC fit (Figure \ref{fig:rot_plot}) is propagated to produce these shaded regions, but they also represent the uncertainty in the shape of the MW dark halo, as discussed in section \ref{sec:mwq}. The region enclosed within any of the U-shaped lines/contours is excluded at the 95\% confidence interval.; 00011 The best fit plots and 2-$\sigma$ uncertainties (shaded) for the power-law model with both the low $\Sigma_\odot$ (LP) and high $\Sigma_\odot$ (HP) priors described in section \ref{sec:sfdc}. Our fixed model of the bulge is included in the combined and best fit constraints. The ``Combined'' shaded region is constructed using equation \ref{eq:vrot}. The difference between the combined rotation curve for the low and high prior cases is not discernible at the resolution of this plot, so we only show a single combined shaded region rather than one for each prior. The effect of the two priors is noticeable between the disk and dark halo contributions, where the high prior case makes a slightly more massive disk and therefore a slightly lighter dark halo in the inner regions of the rotation curve.; 00004 The exclusion plots for the EROS-2 (left) and MACHO (right) surveys when considering an extended mass function. The shaded regions plotted represent a combination of the low and high $\sigma_\odot$ priors in Figure \ref{fig:er_up}. The $x$-axis represents the peak mass $\mu$ for the extended mass distribution constraints. The standard model constraints for the monochromatic (Delta function) masses is plotted in blue (`SM Consts'), and only slightly deviates from the extended mass distribution with a small dispersion $\sigma=0.5$. It can be noted that the constraints from both surveys weaken substantially for broader mass distributions for the updated constraints. On the right hand side of each plot we have plotted the mass distributions for $\mu = = 10\,M_\odot$ and $\sigma=[0.5, \sqrt{2}, 2]$, but have scaled them by $e^{\frac{1}{2}\sigma^2}$ so that they are visible on the $y$-scale of this Figure.; 00008 The exclusion plots for the EROS-2 (left) and MACHO (right) surveys when considering clustered PBHs. The $x$-axis represents the peak mass $\mu$ for the extended mass distribution that makes the cluster of PBH (see equation \ref{eq:newms}). The standard model constraints for the monochromatic (Delta function) masses is plotted in blue (`SM Consts'). A larger cluster size $N_{\textrm{cl}}$ is more effective at shifting the microlensing constraints towards lower masses due to the higher combined mass of the cluster. A clustered PBH distribution is plotted to the right with parameters $N_{\rm cl}=10$, $\mu = 1\, M_\odot$, $\sigma = 0.5$.; 00007 The dependence of the phase-space density $F(E, L_z)$ on the parameters $\beta$ and $q$. The shaded regions represent the parameter space where $F(E, L_z)$ is positive definite, and hence is the allowed region. When the power-law model produces a falling rotation curve, the allowed region of halo shape decreases. The MWRC is best described by a model that produces a falling rotation curve at large radii. As a consequence of this, we will not be able to fully encapsulate the uncertainty in the shape of the dark halo using the power-law model.; 00005 The contour plot for the constraints on the semi-isothermal dark halo model. The blue and red regions are the fits for the high and low $\Sigma_\odot$ priors respectively.; 00012 As with Figure \ref{fig:ISO}, but for the NFW dark halo profile.; 00008 The exclusion plots for the EROS-2 (left) and MACHO (right) surveys when considering an extended mass function. The shaded regions plotted represent a combination of the low and high $\sigma_\odot$ priors in Figure \ref{fig:er_up}. The $x$-axis represents the peak mass $\mu$ for the extended mass distribution constraints. The standard model constraints for the monochromatic (delta function) masses are plotted as a blue solid line, and only slightly deviate from the extended mass distribution with a small dispersion $\sigma=0.5$. It can be noted that the constraints from both surveys weaken substantially for broader mass distributions for the updated constraints. On the right hand side of each plot we have plotted the mass distributions for $\mu = 10\,M_\odot$ and $\sigma=[0.5, \sqrt{2}, 2]$, but have scaled them by $e^{\frac{1}{2}\sigma^2}$ so that they are visible on the $y$-scale of this figure.; 00009 The exclusion plots for the EROS-2 (left) and MACHO (right) surveys when considering clustered PBHs. The $x$-axis represents the peak mass $\mu$ for the extended mass distribution that makes the cluster of PBH (see equation \ref{eq:newms}). The standard model constraints for the monochromatic (delta function) masses is plotted in blue. A larger cluster size $N_{\textrm{cl}}$ is more effective at shifting the microlensing constraints towards lower masses due to the higher combined mass of the cluster. A clustered PBH distribution is plotted to the right with parameters $N_{\rm cl}=10$, $\mu = 1\, M_\odot$, $\sigma = 0.5$.; 00001 The best fit plots and 2-$\sigma$ uncertainties (shaded) for the power-law model with both the low $\Sigma_\odot$ (LP) and high $\Sigma_\odot$ (HP) priors described in section \ref{sec:sfdc}. Our fixed model of the bulge is included in the combined and best fit constraints. The ``Combined'' shaded region is constructed using equation \ref{eq:vrot}. The difference between the combined rotation curve for the low and high prior cases is not discernible at the resolution of this plot, so we only show a single combined shaded region rather than one for each prior. The effect of the two priors is noticeable between the disk and dark halo contributions, where the high prior case makes a slightly more massive disk and therefore a slightly lighter dark halo in the inner regions of the rotation curve.; 00012 The contour plot for the constraints on the power-law dark halo model. The rather triangular contours for the $q$ parameter are a result of the hard-wall prior forcing each step in the MCMC chain to only sample models that are self-consistent (see section \ref{sec:plm}). A large shift in the disk properties ($a_d,\, M_d$), results in only a small shift in the parameters for the dark halo fit ($v_a,\, R_d,\, \beta$).; 00003 The efficiency functions for the MACHO Collaboration 5.7 year (criteria A) and EROS-2 surveys, showing the efficiency with which each survey could detect lenses as a function of the Einstein ring crossing time \citep{alcock2000,eros2007}.; 00000 The dependence of the phase-space density $F(E, L_z)$ on the parameters $\beta$ and $q$. The shaded regions represent the parameter space where $F(E, L_z)$ is positive definite, and hence is the allowed region. When the power-law model produces a falling rotation curve, the allowed region of halo shape decreases. The MWRC is best described by a model that produces a falling rotation curve at large radii. As a consequence of this, we will not be able to fully encapsulate the uncertainty in the shape of the dark halo using the power-law model.; 00006 The optical depth $\tau$ along the line of sight towards the LMC for the low and high $\Sigma_\odot$ prior, as well for the standard model.; 00004 The expected number of microlensing events the EROS-2 survey should have seen if all DM was in the form of MACHOs with some mass $M$. The shaded regions represent the 2-$\sigma$ uncertainty propagated from the MWRC fit in Figure \ref{fig:rot_plot}, and also includes the uncertainty in the shape of the MW dark halo. Here we have plotted the distribution in the expected number of events with both the low and high $\Sigma_\odot$ prior that was used in fitting the MWRC. As one might expect, the low $\Sigma_\odot$ prior on the disk fit results in a larger dark halo, which results in a higher expected number of events.; 00005 The same as Figure \ref{fig:er_nevs}, but using the MACHO Collaboration 5.7 year efficiency.; 00002 The EROS-2 standard model constraints for an extended mass function and clustered PBHs. For $\sigma=0.5$, the constraints closely follow the monochromatic mass constraints (orange dotted line), however the constraints shift to lower masses for broader distributions. In the bottom right, an example of a lognormal distribution with parameters $[\mu,\, \sigma]=[10M_\odot,\, 0.5]$ is plotted. A vertical line drawn from the peak of the distribution intercepts the $\sigma = 0.5$ constraint (in green, right solid line) at $f_{\textrm{PBH}}=0.475$, meaning that this distribution is excluded from contributing more than 47.5\% of DM at 95\% c.l. A PBH distribution with $[\mu,\, \sigma]=[10M_\odot,\, 2]$, or even $[\mu,\, \sigma]=[10M_\odot,\, \sqrt{2}]$, could still make up the entirety of DM.; 00007 The exclusion plots using the EROS-2 bright stars sample (bottom set of curves), in which no microlensing events were detected \citep{eros2007}, and the MACHO Collaboration 5.7 year results (top set of curves), with 10 events observed \citep{alcock2000,bennett2005}. The standard model constraints for each survey are plotted as solid lines, overlapping their respective updated constraint at high mass. The updated constraints for each survey are plotted as the shaded regions for both the low and high $\Sigma_\odot$ prior. The uncertainty from the MWRC fit (Figure \ref{fig:rot_plot}) is propagated to produce these shaded regions, but they also represent the uncertainty in the shape of the MW dark halo, as discussed in section \ref{sec:mwq}. The region enclosed within any of the U-shaped lines/contours is excluded at the 95\% confidence interval.; 00010 The contour plot for the constraints on the semi-isothermal dark halo model. The blue and red regions are the fits for the high and low $\Sigma_\odot$ priors respectively.; 00011 As with Figure \ref{fig:ISO}, but for the NFW dark halo profile.
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2018-09-18
09:36 |
20 gas gaps Multigap Resistive Plate Chamber: Improved rate capability with excellent time resolution
/ Liu, Z (CERN ; World Lab., Geneva) ; Carnesecchi, F (INFN, Bologna ; U. Bologna, DIFA ; Enrico Fermi Ctr., Rome) ; Rodriguez, O M (World Lab., Geneva) ; Williams, M C S (CERN ; INFN, Bologna ; U. Bologna, DIFA ; Gangneung-Wonju Natl. U.) ; Zichichi, A (CERN ; INFN, Bologna ; U. Bologna, DIFA ; Enrico Fermi Ctr., Rome) ; Zuyeuski, R (World Lab., Geneva ; Enrico Fermi Ctr., Rome)
A 20 gas gaps multigap resistive plate chamber (MRPC) was built with thin (0.28 mm) glass sheets and 0.16 mm gas gap size. This chamber reaches 97% efficiency at 18.4 kV and a time resolution of 32 ps (sigma) at an instantaneous particle flux around 2.5 kHz/cm$^2$. [...]
2018 - 5 p.
- Published in : Nucl. Instrum. Methods Phys. Res., A 908 (2018) 383-387
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2018-09-18
09:36 |
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2018-09-13
04:32 |
Precise mirror alignment and basic performance of the RICH detector of the NA62 experiment at CERN
/ Anzivino, G. (Perugia U. ; INFN, Perugia) ; Barbanera, M. (INFN, Perugia) ; Bizzeti, A. (Modena U. ; INFN, Florence) ; Brizioli, F. (Perugia U. ; INFN, Perugia) ; Bucci, F. (INFN, Florence) ; Cassese, A. (INFN, Florence ; Florence U.) ; Cenci, P. (INFN, Perugia) ; Checcucci, B. (INFN, Perugia) ; Ciaranfi, R. (INFN, Florence) ; Duk, V. (INFN, Perugia ; Birmingham U.) et al.
The Ring Imaging Cherenkov detector is crucial for the identification of charged particles in the NA62 experiment at the CERN SPS. The detector commissioning was completed in 2016 by the precise alignment of mirrors using reconstructed tracks. [...]
arXiv:1809.04026.-
2018-07-16 - 13 p.
- Published in : JINST 13 (2018) P07012
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2018-09-13
04:32 |
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2018-09-13
04:32 |
Investigating efficient methods for computing four-quark correlation functions
/ Abdel-Rehim, Abdou (SUNY, Utica/Rome ; Cyprus Inst.) ; Alexandrou, Constantia (Cyprus Inst. ; Cyprus U.) ; Berlin, Joshua (Frankfurt U.) ; Dalla Brida, Mattia (INFN, Milan Bicocca ; Milan Bicocca U.) ; Finkenrath, Jacob (Cyprus Inst.) ; Wagner, Marc (Frankfurt U.)
We discuss and compare the efficiency of various methods, combinations of point-to-all propagators, stochastic timeslice-to-all propagators, the one-end trick and sequential propagators, to compute two-point correlation functions of two-quark and four-quark interpolating operators of different structure including quark-antiquark type, mesonic molecule type, diquark-antidiquark type and two-meson type. Although we illustrate our methods in the context of the $a_0(980)$, they can be applied for other multi-quark systems, where similar diagrams appear. [...]
arXiv:1701.07228; CERN-TH-2017-112.-
2017-11 - 25 p.
- Published in : Comput. Phys. Commun. 220 (2017) 97-121
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2018-09-11
04:29 |
Dual-Readout Calorimetry
/ Lee, Sehwook (Kyungpook Natl. U.) ; Livan, Michele (INFN, Pavia ; Pavia U.) ; Wigmans, Richard (Texas Tech.)
In the past 20 years, dual-readout calorimetry has emerged as a technique for measuring the properties of high-energy hadrons and hadron jets that offers considerable advantages compared with the instruments that are currently used for this purpose in experiments at the high-energy frontier. In this paper, we review the status of this experimental technique and the challenges faced for its further development..
arXiv:1712.05494.-
2018-04-27 - 40 p.
- Published in : Rev. Mod. Phys. 90 (2018) 025002
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