5,074 research outputs found
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 -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 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 state of 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 state of Rb and obtain a
precision of 20 kHz. We extract values for the magnetic-dipole coupling
constant MHz and the electric-quadrupole coupling constant
MHz. These values are a significant improvement over previous
results.Comment: 4 pages, 4 figure
Observation of the nuclear magnetic octupole moment of Yb from precise measurements of hyperfine structure in the state
We measure hyperfine structure in the metastable state of
Yb and extract the nuclear magnetic octupole moment. We populate the
state using dipole-allowed transitions through the and
states. We measure frequencies of hyperfine transitions of the 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 and states are below 30 kHz. We obtain the
hyperfine coefficients as: MHz, MHz, which
represent two orders-of-magnitude improvement in precision, and
MHz. From atomic structure calculations, we obtain the nuclear moments:
quadrupole b and octupole b\,.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
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