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Register a new userWhat to Expect When Youre Expecting: Femtoscopy at the LHC
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Mike Lisa
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A huge systematics of femtoscopic measurements have been used over the past 20 years to characterize the system created in heavy ion collisions. These measurements cover two orders of magnitude in energy, and with LHC beams imminent, this range will be extended by more than another order of magnitude. Here, I discuss theoretical expectations of femtoscopy of $A+A$ and $p+p$ collisions at the LHC, based on Boltzmann and hydrodynamic calculations, as well as on naive extrapolation of existing systematics.
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Dynamical models allow us to connect the motion of a set of tracers to the underlying gravitational potential, and thus to the total (luminous and dark) matter distribution. They are particularly useful for understanding the mass and spatial distribution of dark matter (DM) in a galaxy. Globular clusters (GCs) are an ideal tracer population in dynamical models, since they are bright and can be found far out into the halo of galaxies. We aim to test how well Jeans-Anisotropic-MGE (JAM) models using GCs (positions and line-of-sight velocities) as tracers can constrain the mass and radial distribution of DM halos. For this, we use the E-MOSAICS suite of 25 zoom-in simulations of L* galaxies. We find that the DM halo properties are reasonably well recovered by the JAM models. There is, however, a strong correlation between how well we recover the mass and the radial distribution of the DM and the number of GCs in the galaxy: the constraints get exponentially worse with fewer GCs, and at least 150 GCs are needed in order to guarantee that the JAM model will perform well. We find that while the data quality (uncertainty on the radial velocities) can be important, the number of GCs is the dominant factor in terms of the accuracy and precision of the measurements. This work shows promising results for these models to be used in extragalactic systems with a sample of more than 150 GCs.
Many dynamical models of the Milky Way halo require assumptions that the distribution function of a tracer population should be independent of time (i.e., a steady state distribution function) and that the underlying potential is spherical. We study the limitations of such modelling by applying a general dynamical model with minimal assumptions to a large sample of galactic haloes from cosmological $N$-body and hydrodynamical simulations. Using dark matter particles as dynamical tracers, we find that the systematic uncertainties in the measured mass and concentration parameters typically have an amplitude of 25% to 40%. When stars are used as tracers, however, the systematic uncertainties can be as large as a factor of $2-3$. The systematic uncertainties are not reduced by increasing the tracer sample size and vary stochastically from halo to halo. These systematic uncertainties are mostly driven by underestimated statistical noise caused by correlated phase-space structures that violate the steady state assumption. The number of independent phase-space structures inferred from the uncertainty level sets a limiting sample size beyond which a further increase no longer significantly improves the accuracy of dynamical inferences. The systematic uncertainty level is determined by the halo merger history, the shape and environment of the halo. Our conclusions apply generally to any spherical steady-state model.
The smartphone and laptop can be unlocked by face or fingerprint recognition, while neural networks which confront numerous requests every day have little capability to distinguish between untrustworthy and credible users. It makes model risky to be traded as a commodity. Existed research either focuses on the intellectual property rights ownership of the commercialized model, or traces the source of the leak after pirated models appear. Nevertheless, active identifying users legitimacy before predicting output has not been considered yet. In this paper, we propose Model-Lock (M-LOCK) to realize an end-to-end neural network with local dynamic access control, which is similar to the automatic locking function of the smartphone to prevent malicious attackers from obtaining available performance actively when you are away. Three kinds of model training strategy are essential to achieve the tremendous performance divergence between certified and suspect input in one neural network. Extensive experiments based on MNIST, FashionMNIST, CIFAR10, CIFAR100, SVHN and GTSRB datasets demonstrated the feasibility and effectiveness of the proposed scheme.
Identical particle correlations at fixed multiplicity are considered by means of quantum canonical ensemble of finite systems. We calculate one-particle momentum spectra and two-particle Bose-Einstein correlation functions in the ideal gas by using a recurrence relation for the partition function. Within such a model we investigate the validity of the thermal Wicks theorem and its applicability for decomposition of the two-particle distribution function. The dependence of the Bose-Einstein correlation parameters on the average momentum of the particle pair is also investigated. Specifically, we present the analytical formulas that allow one to estimate the effect of suppressing the correlation functions in a finite canonical system. The results can be used for the femtoscopy analysis of the A+A and p+p collisions with selected (fixed) multiplicity.
A study of energy behavior of the pion spectra and interferometry scales is carried out for the top SPS, RHIC and for LHC energies within the hydrokinetic approach. The main mechanisms that lead to the paradoxical, at first sight, dependence of the interferometry scales with an energy growth, in particular, a decrease $R_{out}/R_{side}$ ratio, are exposed. The hydrokinetic predictions for the HBT radii at LHC energies are compared with the recent results of the ALICE experiment.
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