H. Erik Swanson

Torsion Balance Test of Spin Coupled Forces

The extraordinary sensitivity of the torsion balance has made it a valuable tool to test symmetries in nature and to search for new weak macroscopic forces. Most torsion balance experiments employ unpolarized test bodies either of different composition to test the universality of free fall or of special geometry to test for violations of the 1/r2 law of gravity.1 There are several motivations, however, to perform similar torsion balance measurements with spin polarized test bodies: to help elucidate the role of spin in gravitation, to search for new forces mediated by pseudoscalar bosons, and to perform a precise test of Lorentz (rotational) and CPT invariance. We have constructed an electron spin polarized test body and have completed a first round of measurements with this test body mounted on our EotWash II torsion balance2 In this paper, we present our results and interpret them as new limits on the strength and range of macroscopic spin coupled interactions and as a new limit on Lorentz and CPT invariance violation. Moody and Wilczek3 have shown that the exchange of a mixed parity boson (such as the axion, a proposed 0– particle with a small admixture of 0+) leads to a CP-violating potential between two point particles of the form:

Removal of zero-point drift from AB data and the statistical cost

Often the result of a scientific experiment is given by the difference of measurements in two configurations, denoted by A and B. Since the measurements are not obtained simultaneously, the drift of the zero point can bias the result. In practice measurement patterns are used to minimize this bias. The time sequence AB followed by BA, for example, would cancel a linear drift in the average difference A − B. We propose taking data with an alternating series ABAB, and removing the drift with a post hoc analysis. We present an analysis method that removes the bias from the result for a drift up to polynomial order p. A statistical cost function c(N) is introduced to compare the uncertainty in the end result with that from using a raw data average. For a data set size N > 30 the statistical cost is negligible. For N < 30 the cost is plotted as a function of N and filter order p and the trade-off between the size of the data set and p is discussed.