Aldo Romani

In particle physics, experiments generate substantial amounts of data that can be difficult to process without preliminary scaling. To avoid losing potentially crucial data, experimental collaborations are studying novel techniques for real-time data processing to extract features for further physics analysis. A common approach, especially in neutrino physics, is to use FPGAs for data acquisition and pre-processing. This paper presents an advanced Real-Time Wiener deconvolution algorithm designed to leverage the processing capabilities of the FPGA integrated into the readout boards of the Jiangmen Underground Neutrino Observatory (JUNO). The goal is to enable real-time reconstruction of the signal generated by photomultiplier tubes (PMTs) when neutrino interactions are detected. By exploiting online reconstruction of the signal generated by PMTs, we expect to improve the detection of low-energy depositions, such as those produced by transient astrophysical phenomena. These depositions are usually not saved because of the significant background that affects the low end of the energy spectrum, which would result in a large trigger rate, hence a large amount of data required for storage. This paper presents the features of the algorithm, including its ability to manage high-throughput data streams with minimal latency, adaptability, and resilience in discerning the characteristics of input data. Performance is evaluated on a JUNO electronic board. This study further demonstrates the potential of FPGA-based solutions for neutrino physics.

Emerald green, a copper acetoarsenite pigment, enriched the 19th-century and 20th-century artists’ palettes and caused darkening and fragilities in paintings. The accepted alteration mechanism involves the oxidation of triarsenite ions [(As ₃ O ₆ ) ³⁻ ] to arsenates (AsO ₄ ³⁻ ), although questions about the promoting factors and the origin of the oxidized arsenic remain unanswered. This study investigates the primary environmental parameters inducing alteration in the oil binder and elucidates the associated degradation pathways through a multiscale analytical approach, including noninvasive spectroscopic techniques and synchrotron radiation–based x-ray methods. By combining results from a historical oil painting by James Ensor (1860 to 1949) and artificially aged paint mock-ups, we identified ultraviolet A–visible light and humidity (relative humidity ≥ 95%) as key driving factors, inducing a dual degradation pathway: Light promotes surface-stratified arsenic oxidation (As ³⁺ → As ⁵⁺ ) resulting in amorphous As ⁵⁺ -rich compounds, whereas a dark, high moisture environment favors arsenolite (As ₂ O ₃ ) crystallization. In addition, a noninvasive analytical strategy is proposed for monitoring the conservation state of emerald green paints in historical artworks.

In this study, we introduce a simple but effective model, combined with a classification system for light sources based on their spectral profile approximated by the relative power distribution in the red, green and blue regions, which successfully fits experimental data of pigment colour changes in an oil binder under various lighting conditions. This model is experimentally validated by artificial ageing tests on two sets of model samples made of historical pigments (strontium yellow and Prussian blue mixed with lead white) using three white light sources (two WLEDs and a xenon light source). The model accurately fits colour shifts by analysing changes in International Commission on Illumination L*a*b* values. The results highlight that integrating the spectral classification of the light sources with the colour change model provides deeper insights into the causes of paint colour changes than traditional methods that rely solely on total irradiance. This combined approach enables precise prediction of long-term colour changes by exploiting the spectral characteristics of light sources.