Heavily obscured AGN: an ideal laboratory to study the early co-evolution of galaxies and black holes

Obscured AGN are a crucial ingredient to understand the full growth history of super massive black holes and the coevolution with their host galaxies, since they constitute the bulk of the BH accretion. In the distant Universe, many of them are hosted by submillimeter galaxies (SMGs), characterized by a high production of stars and a very fast consumption of gas. Therefore, the analysis of this class of objects is fundamental to investigate the role of the ISM in the early coevolution of galaxies and black holes. We present a multiwavelength study of a sample of six obscured X-ray selected AGN at z>2.5 in the CDF-S, detected in the far-IR/submm bands. We performed the X-ray spectral analysis based on the 7Ms Chandra dataset, which provides the best X-ray spectral information currently available for distant AGN. We were able to place constraints on the obscuring column densities and the intrinsic luminosities of our targets. Moreover, we built up the UV to FIR spectral energy distributions (SEDs) by combining the broad-band photometry from CANDELS and the Herschel catalogs, and analyzed them by means of an SED decomposition technique. Therefore, we derived important physical parameters of both the host galaxy and the AGN. In addition, we obtained, through an empirical calibration, the gas mass in the host galaxy and assessed the galaxy sizes in order to estimate the column density associated with the host ISM. The comparison of the ISM column densities with the values measured from the X-ray spectral analysis pointed out that the contribution of the host ISM to the obscuration of the AGN emission can be substantial, ranging from ~10% up to ~100% of the value derived from the X-ray spectra. The absorption may occur at different physical scales in these sources and, in particular, the medium in the host galaxy is an ingredient that should be taken into account, since it may have a relevant role in driving the early co-evolution of galaxies with their black holes.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Rational algorithms for the decomposition of Feynman integrals via intersection theory

The decomposition of Feynman integrals into a basis of independent master integrals is an essential ingredient of high-precision theoretical predictions, that often represents a major bottleneck when processes with a high number of loops and legs are involved. In this thesis we present a new algorithm for the decomposition of Feynman integrals into master integrals with the formalism of intersection theory. Intersection theory is a novel approach that allows to decompose Feynman integrals into master integrals via projections, based on a scalar product between Feynman integrals called intersection number. We propose a new purely rational algorithm for the calculation of intersection numbers of differential $n-$forms that avoids the presence of algebraic extensions. We show how expansions around non-rational poles, which are a bottleneck of existing algorithms for intersection numbers, can be avoided by performing an expansion in series around a rational polynomial irreducible over $\mathbb{Q}$, that we refer to as $p(z)-$adic expansion. The algorithm we developed has been implemented and tested on several diagrams, both at one and two loops.