Doppler-free spectroscopy in driven three-level systems

We demonstrate two techniques for studying the features of three-level systems driven by two lasers (called control and probe), when the transitions are Doppler broadened as in room-temperature vapor. For $\Lambda$-type systems, the probe laser is split to produce a counter-propagating pump beam that saturates the transition for the zero-velocity atoms. Probe transmission then shows Doppler-free peaks, which can even have sub-natural linewidth. For V-type systems, the transmission of the control beam is detected as the probe laser is scanned. The signal shows Doppler-free peaks when the probe laser is resonant with transitions for the zero-velocity group. Both techniques greatly simplify the study of three-level systems since theoretical predictions can be directly compared without complications from Doppler broadening and the presence of multiple hyperfine levels in the spectrum.Comment: 6 pages, 5 figure

Precise measurement of hyperfine intervals using avoided crossing of dressed states

We demonstrate a technique for precisely measuring hyperfine intervals in alkali atoms. The atoms form a three-level $\Lambda$ system in the presence of a strong control laser and a weak probe laser. The dressed states created by the control laser show significant linewidth reduction. We have developed a technique for Doppler-free spectroscopy that enables the separation between the dressed states to be measured with high accuracy even in room-temperature atoms. The states go through an avoided crossing as the detuning of the control laser is changed from positive to negative. By studying the separation as a function of detuning, the center of the level-crossing diagram is determined with high precision, which yields the hyperfine interval. Using room-temperature Rb vapor, we obtain a precision of 44 kHz. This is a significant improvement over the current precision of ~ 1 MHz.Comment: 4 pages, 4 figures. To be published shortly in Europhysics Letter

Precise measurement of hyperfine structure in the 5P3/25P_{3/2} state of 85^{85}Rb

We demonstrate a technique to measure hyperfine structure using a frequency-stabilized diode laser and an acousto-optic modulator locked to the frequency difference between two hyperfine peaks. We use this technique to measure hyperfine intervals in the $5P_{3/2}$ state of $^{85}$Rb and obtain a precision of 20 kHz. We extract values for the magnetic-dipole coupling constant $A=25.038(5)$ MHz and the electric-quadrupole coupling constant $B=26.011(22)$ MHz. These values are a significant improvement over previous results.Comment: 4 pages, 4 figure

Observation of the nuclear magnetic octupole moment of 173^{173}Yb from precise measurements of hyperfine structure in the 3P2{^3P}_2 state

We measure hyperfine structure in the metastable ${^3P}_2$ state of $^{173}$Yb and extract the nuclear magnetic octupole moment. We populate the state using dipole-allowed transitions through the ${^3P}_1$ and ${^3S}_1$ states. We measure frequencies of hyperfine transitions of the ${^3P}_2 \rightarrow {^3S}_1$ line at 770 nm using a Rb-stabilized ring cavity resonator with a precision of 200 kHz. Second-order corrections due to perturbations from the nearby ${^3P}_1$ and ${^1P}_1$ states are below 30 kHz. We obtain the hyperfine coefficients as: $A=-742.11(2)$ MHz, $B=1339.2(2)$ MHz, which represent two orders-of-magnitude improvement in precision, and $C=0.54(2)$ MHz. From atomic structure calculations, we obtain the nuclear moments: quadrupole $Q=2.46(12)$ b and octupole $\Omega=-34.4(21)$ b\,$\times \mu_N$.Comment: 5 pages, 1 figur

Identifying Parkinson’s Patients: A Functional Gradient Boosting Approach

Parkinson’s, a progressive neural disorder, is difficult to identify due to the hidden nature of the symptoms associated. We present a machine learning approach that uses a definite set of features obtained from the Parkinson’s Progression Markers Initiative (PPMI) study as input and classifies them into one of two classes: PD (Parkinson’s disease) and HC (Healthy Control). As far as we know this is the first work in applying machine learning algorithms for classifying patients with Parkinson’s disease with the involvement of domain expert during the feature selection process. We evaluate our approach on 1194 patients acquired from Parkinson’s Progression Markers Initiative and show that it achieves a state-of-the-art performance with minimal feature engineering