Impure Thoughts on Inelastic Dark Matter

The inelastic dark matter scenario was proposed to reconcile the DAMA annual modulation with null results from other experiments. In this scenario, WIMPs scatter into an excited state, split from the ground state by an energy delta comparable to the available kinetic energy of a Galactic WIMP. We note that for large splittings delta, the dominant scattering at DAMA can occur off of thallium nuclei, with A~205, which are present as a dopant at the 10^-3 level in NaI(Tl) crystals. For a WIMP mass m~100GeV and delta~200keV, we find a region in delta-m-parameter space which is consistent with all experiments. These parameters in particular can be probed in experiments with thallium in their targets, such as KIMS, but are inaccessible to lighter target experiments. Depending on the tail of the WIMP velocity distribution, a highly modulated signal may or may not appear at CRESST-II.Comment: 3 pages, 1 figure, accepted for publication in Physical Review Letter

Supernova neutrino physics with xenon dark matter detectors: A timely perspective

Dark matter detectors that utilize liquid xenon have now achieved tonne-scale targets, giving them sensitivity to all flavours of supernova neutrinos via coherent elastic neutrino-nucleus scattering. Considering for the first time a realistic detector model, we simulate the expected supernova neutrino signal for different progenitor masses and nuclear equations of state in existing and upcoming dual-phase liquid xenon experiments. We show that the proportional scintillation signal (S2) of a dual-phase detector allows for a clear observation of the neutrino signal and guarantees a particularly low energy threshold, while the backgrounds are rendered negligible during the supernova burst. XENON1T (XENONnT and LZ; DARWIN) experiments will be sensitive to a supernova burst up to 25 (35; 65) kpc from Earth at a significance of more than 5 sigma, observing approximately 35 (123; 704) events from a 27 Msun supernova progenitor at 10 kpc. Moreover, it will be possible to measure the average neutrino energy of all flavours, to constrain the total explosion energy, and to reconstruct the supernova neutrino light curve. Our results suggest that a large xenon detector such as DARWIN will be competitive with dedicated neutrino telescopes, while providing complementary information that is not otherwise accessible.Comment: 19 pages, 9 figures. Minor revisions compared to original version. Matches version published in Phys. Rev.

Investigation of Time Varying Nuclear Decay Rates

Since the discovery of radioactive decay, radioactive decay rates have consistently shown to be constant. Recently, groups of researchers around the world have noticed variation in the decay rates of different radioactive isotopes, while other groups have noticed no such effect. If the variation is truly varying decay rates, this would imply groundbreaking new physics and would have implications for practices such as carbon dating. More sophisticated experiments are required to determine if the variations are truly new physics or systematic effects inherent to nuclear decay experiments. We are building an experiment where activity data from various radioactive sources will be taken with NaI(Tl) detectors at different places around the world (Purdue University, Nationaal Instituut voor Kernfysica en Hoge-Energiefysica [NIKHEF], University of Zurich, one more to be decided). Previous studies have not used our data acquisition methods which will allow for a richer analysis to check for time variations. At this point, all components have been acquired and construction of the experiment is underway. I have performed several tests on our new sodium iodide detectors and used the results to determine the optimum operating voltage test. This test will be performed on each of the detectors to determine the voltage at which each one should be operated. Currently I am performing more tests to determine the amount of lead shielding needed between each detector. Our work will be very important in determining systematic sources of error in nuclear decay experiments and solving the puzzle of modulating radioactive decay rates

Machine Learning in XENON1T Analysis

In process of analyzing large amounts of quantitative data, it can be quite time consuming and challenging to uncover populations of interest contained amongst the background data. Therefore, the ability to partially automate the process while gaining additional insight into the interdependencies of key parameters via machine learning seems quite appealing. As of now, the primary means of reviewing the data is by manually plotting data in different parameter spaces to recognize key features, which is slow and error prone. In this experiment, many well-known machine learning algorithms were applied to a dataset to attempt to semi-automatically identify known populations, and potentially identify other features of interest such as detector artefacts. Additionally, using the results of the machine learning process it became possible to cross-check the results of the XENON1T selection cuts. Clustering algorithms were used to segment the dataset into populations, which then recursively split those into additional subpopulations. Upon capturing a subpopulation, a classifier was trained and used to predict if other data could potentially belong to the same population. From this process, it was observed that there were two clustering algorithms that were capable of identifying the electronic recoil band accurately. It was also seen that a few XENON1T selection cuts may need relaxed. These algorithms may be able to be used to tweak the cuts, or continue in search of artefacts. The process of automating the analysis stage by means of machine learning could be further extended by automating the recognition of waveforms using neural networks