5,308 research outputs found
Commissioning of the ATLAS Pixel Detector
The ATLAS pixel detector is a high precision silicon tracking device located
closest to the LHC interaction point. It belongs to the first generation of its
kind in a hadron collider experiment. It will provide crucial pattern
recognition information and will largely determine the ability of ATLAS to
precisely track particle trajectories and find secondary vertices. It was the
last detector to be installed in ATLAS in June 2007, has been fully connected
and tested in-situ during spring and summer 2008. It is currently in a
commissioning phase using cosmic-ray events. We present the highlights of the
past and future commissioning activities of the ATLAS pixel system.Comment: Poster at ICHEP08, Philadelphia, USA, July 2008. 3 pages, LaTeX, 2
eps figure
Large deviations and continuity estimates for the derivative of a random model of on the critical line
In this paper, we study the random field \begin{equation*} X(h) \circeq
\sum_{p \leq T} \frac{\text{Re}(U_p \, p^{-i h})}{p^{1/2}}, \quad h\in [0,1],
\end{equation*} where is an i.i.d. sequence of
uniform random variables on the unit circle in . Harper (2013)
showed that is a good model for the large values of
when is large, if
we assume the Riemann hypothesis. The asymptotics of the maximum were found in
Arguin, Belius & Harper (2017) up to the second order, but the tightness of the
recentered maximum is still an open problem. As a first step, we provide large
deviation estimates and continuity estimates for the field's derivative
. The main result shows that, with probability arbitrarily close to ,
\begin{equation*} \max_{h\in [0,1]} X(h) - \max_{h\in \mathcal{S}} X(h) = O(1),
\end{equation*} where a discrete set containing points.Comment: 7 pages, 0 figur
Extremes of the two-dimensional Gaussian free field with scale-dependent variance
In this paper, we study a random field constructed from the two-dimensional
Gaussian free field (GFF) by modifying the variance along the scales in the
neighborhood of each point. The construction can be seen as a local martingale
transform and is akin to the time-inhomogeneous branching random walk. In the
case where the variance takes finitely many values, we compute the first order
of the maximum and the log-number of high points. These quantities were
obtained by Bolthausen, Deuschel and Giacomin (2001) and Daviaud (2006) when
the variance is constant on all scales. The proof relies on a truncated second
moment method proposed by Kistler (2015), which streamlines the proof of the
previous results. We also discuss possible extensions of the construction to
the continuous GFF.Comment: 30 pages, 4 figures. The argument in Lemma 3.1 and 3.4 was revised.
Lemma A.4, A.5 and A.6 were added for this reason. Other typos were corrected
throughout the article. The proof of Lemma A.1 and A.3 was simplifie
Poisson-Dirichlet statistics for the extremes of a log-correlated Gaussian field
We study the statistics of the extremes of a discrete Gaussian field with
logarithmic correlations at the level of the Gibbs measure. The model is
defined on the periodic interval , and its correlation structure is
nonhierarchical. It is based on a model introduced by Bacry and Muzy [Comm.
Math. Phys. 236 (2003) 449-475] (see also Barral and Mandelbrot [Probab. Theory
Related Fields 124 (2002) 409-430]), and is similar to the logarithmic Random
Energy Model studied by Carpentier and Le Doussal [Phys. Rev. E (3) 63 (2001)
026110] and more recently by Fyodorov and Bouchaud [J. Phys. A 41 (2008)
372001]. At low temperature, it is shown that the normalized covariance of two
points sampled from the Gibbs measure is either or . This is used to
prove that the joint distribution of the Gibbs weights converges in a suitable
sense to that of a Poisson-Dirichlet variable. In particular, this proves a
conjecture of Carpentier and Le Doussal that the statistics of the extremes of
the log-correlated field behave as those of i.i.d. Gaussian variables and of
branching Brownian motion at the level of the Gibbs measure. The method of
proof is robust and is adaptable to other log-correlated Gaussian fields.Comment: Published in at http://dx.doi.org/10.1214/13-AAP952 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Is the Riemann zeta function in a short interval a 1-RSB spin glass ?
Fyodorov, Hiary & Keating established an intriguing connection between the
maxima of log-correlated processes and the ones of the Riemann zeta function on
a short interval of the critical line. In particular, they suggest that the
analogue of the free energy of the Riemann zeta function is identical to the
one of the Random Energy Model in spin glasses. In this paper, the connection
between spin glasses and the Riemann zeta function is explored further. We
study a random model of the Riemann zeta function and show that its two-overlap
distribution corresponds to the one of a one-step replica symmetry breaking
(1-RSB) spin glass. This provides evidence that the local maxima of the zeta
function are strongly clustered.Comment: 20 pages, 1 figure, Minor corrections, References update
- …