Search for light dark matter candidates with the DarkSide-20k liquid argon time projection chamber


Dark Matter is one of the main puzzles in fundamental physics and Weakly Interacting Massive Particles (WIMP) are among the best-motivated dark matter particle candidates. As of today, the most sensitive experimental technique to discover the WIMPs in the mass range from 1 GeV/c2 to 10 TeV/c2 is the dual phase Time Projection Chamber (TPC) filled with noble liquids. The “dual-phase” approach has the main advantage to provide simultaneous access to the ionization and to the scintillation signals. This enables accurate reconstruction of the event topology and powerful discrimination between electronic and nuclear recoils, the latter being the signal expected from WIMP dark matter candidates. These features, together with the scalability to massive detectors, make liquid argon (LAr) a compelling target for WIMPs of mass >20 GeV/c2. 

LAr can further extend its physics potential by looking at the ionization channel only, dropping the scintillation one. Indeed, the efficiency of detecting a single ionization electron is much higher than that of a scintillation photon. The price is the loss of particle identification based on the scintillation pulse shape, but the gain is in the detection threshold. Such a low threshold opens up many opportunities enabling the search for very light candidates of WIMPs (10 MeV/c2 - 1 GeV/c2 mass range), sterile neutrinos, and axions.  Moreover, that energy region is sensitive to neutrino interactions via coherent elastic scattering with nuclei, a reaction characterized by the highest cross section for MeV-range neutrinos, such as from the Sun and galactic and extragalactic core-collapse supernovae.  

As part of the DarkSide program, the APC team coordinates data reconstruction, simulation, and sensitivity studies. In particular, the APC team led the recent data analyses using the 50-kg LAr DarkSide-50 experiment, which have recently improved existing limits on interactions of <4-GeV/c2 mass WIMPs.  At the same time, the APC team has developed a highly optimized python-based data reconstruction framework for the next-generation DarkSide-20k experiment. This is a 50-ton LAr TPC designed to search for WIMPs with masses above 20 GeV/c2, currently under construction at the Gran Sasso National Laboratories. 

The PhD candidate will focus his/her work on extending the DarkSide-20k physics potential to "light" signals, id est those induced by light dark matter particles (low-mass WIMPs, sterile neutrinos, dark photons, and axions) and by core-collapse supernova neutrinos. He/she will import signal models already coded for the DarkSide-50 analyses into the DarkSide-20k framework and investigate the background components affecting the low energy region with Monte Carlo simulation. At  the same time, the candidate will model the LAr ionization and detector responses to nuclear and electronic recoils in the keV range.  

In the second year, the candidate will adapt the existing multivariate profile likelihood (HistFactory-based) framework, developed for “standard” high-mass WIMPs, to the low-energy regime. Such a framework will allow the candidate to investigate the impact of the background on the sensitivity and to explore selection criteria and software techniques aimed at mitigating it. The PhD candidate will develop a likelihood estimator to extract the event position along the drift field (z) exploiting the electron diffusion along the drift, and a machine learning approach for the xy one. The work will be developed on Monte Carlo samples, and then tested on real data from various small-scale setups, some already operational, others being finalized. Among these, the PhD candidate will participate in the on-site commissioning, calibration and data taking from the Proto-1 setup. 

In the third year, the candidate will determine the sensitivities to the different light signals. The third year also coincides with the start of DarkSide-20k data acquisition. As the candidate will have already acquired all the needed expertise on event reconstruction, selection criteria, and  sensitivity tools, he/she will participate in the analysis of the first data. 



Davide Franco






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