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DarkSide-50 at Gran Sasso underground laboratory (LNGS), Italy, is a direct dark matter search experiment based on a liquid argon TPC. DS-50 has completed its first dark matter run using atmospheric argon as target. The detector performances and the results of the first physics run are presented in this proceeding.

The DarkSide experiment is designed for the direct detection of Dark Matter with a double phase liquid Argon TPC operating underground at Laboratori Nazionali del Gran Sasso. The TPC is placed inside a 30 tons liquid organic scintillator sphere, acting as a neutron veto, which is in turn installed inside a 1 kt water Cherenkov detector. The current detector is running since November 2013 with a 50 kg atmospheric Argon fill and we report here the first null results of a Dark Matter search for a (1422 ± 67) kg.d exposure. This result correspond to a 90% CL upper limit on the WIMP-nucleon cross section of 6.1 × 10-44 cm2 (for a WIMP mass of 100 GeV c2) and it's currently the most sensitive limit obtained with an Argon target.

We report the first results of DarkSide-50, a direct search for dark matter operating in the underground Laboratori Nazionali del Gran Sasso (LNGS) and searching for the rare nuclear recoils possibly induced by weakly interacting massive particles (WIMPs). The dark matter detector is a Liquid Argon Time Projection Chamber with a (46.4+-0.7) kg active mass, operated inside a 30 t organic liquid scintillator neutron veto, which is in turn installed at the center of a 1 kt water Cherenkov veto for the residual flux of cosmic rays. We report here the null results of a dark matter search for a (1422+-67) kg d exposure with an atmospheric argon fill. This is the most sensitive dark matter search performed with an argon target, corresponding to a 90% CL upper limit on the WIMP-nucleon spin-independent cross section of 6.1x10^-44 cm^2 for a WIMP mass of 100 GeV/c^2.

It is generally inferred from astronomical measurements that Dark Matter (DM) comprises approximately 27\\% of the energy-density of the universe. If DM is a subatomic particle, a possible candidate is a Weakly Interacting Massive Particle (WIMP), and the DarkSide-50 (DS) experiment is a direct search for evidence of WIMP-nuclear collisions. DS is located underground at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy, and consists of three active, embedded components; an outer water veto (CTF), a liquid scintillator veto (LSV), and a liquid argon (LAr) time projection chamber (TPC). This paper describes the data acquisition and electronic systems of the DS detectors, designed to detect the residual ionization from such collisions.

Using a fully automated cryogenic wafer prober, we measured superconductor fabrication process control monitors and simple integrated circuits on 200 mm wafers at 4.4 K, including SQIF-based magnetic field sensors, SQUID-based circuits for measuring inductors, Nb/Al-AlOx/Nb Josephson junctions, test structures for measuring critical current of superconducting wires and vias, resistors, etc., to demonstrate the feasibility of using the system for characterizing niobium superconducting devices and integrated circuits on a wafer scale. Data on the wafer-scale distributions of the residual magnetic field, junction tunnel resistance, energy gap, inductance of multiple Nb layers, critical currents of interlayer vias are presented. Comparison with existing models is made. The wafers were fabricated in the SFQ5ee process, the fully planarized process with eight niobium layers and a layer of kinetic inductors, developed for superconductor electronics at MIT Lincoln Laboratory. The cryogenic wafer prober was developed at HPD/ FormFactor, Inc.

Spins in solid-state materials, molecules, and other chemical systems have the potential to impact the fields of quantum sensing, communication, simulation, and computing. In particular, color centers in diamond, such as negatively charged nitrogen vacancy (NV\\(^-\\)) and silicon vacancy centers (SiV\\(^-\\)), are emerging as quantum platforms poised for transition to commercial devices. A key enabler stems from the semiconductor-like platform that can be tailored at the time of growth. The large growth parameter space makes it challenging to use intuition to optimize growth conditions for quantum performance. In this paper, we use supervised machine learning to train regression models using different synthesis parameters in over 100 quantum diamond samples. We train models to optimize NV\\(^-\\) defects in diamond for high sensitivity magnetometry. Importantly, we utilize a magnetic-field sensitivity figure of merit (FOM) for NV magnetometry and use Bayesian optimization to identify critical growth parameters that lead to a 300% improvement over an average sample and a 55% improvement over the previous champion sample. Furthermore, using Shapley importance rankings, we gain new physical insights into the most impactful growth and post-processing parameters, namely electron irradiation dose, diamond seed depth relative to the plasma, seed miscut angle, and reactor nitrogen concentration. As various quantum devices can have significantly different material requirements, advanced growth techniques such as plasma-enhanced chemical vapor deposition (PE-CVD) can provide the ability to tailor material development specifically for quantum applications.