The identification of an artist's palette through the application of non-invasive techniques is a challenging goal due to the huge variety of artistic materials that constitutes a painting. An effective approach is to combine several techniques providing complementary information in order to minimise the risk of misinterpreting the data. In this paper, we propose a multi-analytical method comprising three non-invasive mapping techniques, namely Reflectance Imaging Spectroscopy (RIS), Macro-X-Ray Fluorescence (MA-XRF) and Fluorescence Lifetime Imaging (FLI), for the study of a fourteenth-century painting by Pietro Lorenzetti from the Uffizi Gallery collection. For the low-cost and time-saving interpretation and integration of the data provided by the different techniques, a purposely developed software for multivariate statistical analysis was used. FLI data were acquired with a prototype applied for the first time on a work of art, and the data were processed with a method based on phasor analysis. The information obtained was discussed within a multidisciplinary team of experts on painting materials and data processing belonging to both the scientific and the conservation community.
Large radio and mm–wave telescopes use very sensitive detectors requiring cryogenic cooling to reduce detector noise. Pulse Tubes (PT) cryocoolers are widely used to reach temperatures of a few K, defining the base temperature of further sub–K stages. This technology represents an effective solution for continuous operation, featuring high stability and reduced vibration levels on the detectors. However, the compressor used to operate the PT is a significant source of microphonics and electrical noise, making its use at the focus of large steerable telescopes not advisable. This calls for long flexible helium lines between the compressor, operated at the base of the radio telescope, and the cold–head, mounted in the receivers cabin with the receiver detectors. The distance between the receiver cabin and the base can be >100 m long for large radio telescopes. In the framework of our development of the MIllimetric Sardinia radio Telescope Receiver based on Array of Lumped elements kids (MISTRAL), a W–band camera working at the Gregorian focus of the 64 m aperture Sardinia Radio Telescope (SRT) with an array of Lumped Elements Kinetic Inductance Detectors (LEKID), we have developed a cryogenic system based on a PT refrigerator as the first cooling stage. Here we describe the MISTRAL cryogenic system and focus on the validation of the use of a commercial PT Cryocooler with 100 m helium lines running from the cold head to the compressor unit. The configuration allows us to operate the 0.9 W PT reaching below 4.2 K with 0.5 W dissipation.
The recent advances in machine learning algorithms have boosted the application of these techniques to the field of condensed matter physics, in order e.g. to classify the phases of matter at equilibrium or to predict the real-time dynamics of a large class of physical models. Typically in these works, a machine learning algorithm is trained and tested on data coming from the same physical model. Here we demonstrate that unsupervised and supervised machine learning techniques are able to predict phases of a non-exactly solvable model when trained on data of a solvable model. In particular, we employ a training set made by single-particle correlation functions of a non-interacting quantum wire and by using principal component analysis, k-means clustering, t-distributed stochastic neighbor embedding and convolutional neural networks we reconstruct the phase diagram of an interacting superconductor. We show that both the principal component analysis and the convolutional neural networks trained on the data of the non-interacting model can identify the topological phases of the interacting model. Our findings indicate that non-trivial phases of matter emerging from the presence of interactions can be identified by means of unsupervised and supervised techniques applied to data of non-interacting systems.
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