Advanced techniques for diagnostics and control applied to particle accelerators

201 p.

Fitting BICEP2 with defects, primordial gravitational waves and dust

In this work we discuss the possibility of cosmic defects being responsible for the B-mode signal measured by the BICEP2 collaboration. We also allow for the presence of other cosmological sources of B-modes such as inflationary gravitational waves and polarized dust foregrounds, which might contribute to or dominate the signal. On the one hand, we find that defects alone give a poor fit to the data points. On the other, we find that defects help to improve the fit at higher multipoles when they are considered alongside inflationary gravitational waves or polarized dust. Finally, we derive new defect constraints from models combining defects and dust. This proceeding is based on previous works [1, 2].

Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed