FORTE satellite constraints on ultra-high energy cosmic particle fluxes

The FORTE (Fast On-orbit Recording of Transient Events) satellite records bursts of electromagnetic waves arising from near the Earth's surface in the radio frequency (RF) range of 30 to 300 MHz with a dual polarization antenna. We investigate the possible RF signature of ultra-high energy cosmic-ray particles in the form of coherent Cherenkov radiation from cascades in ice. We calculate the sensitivity of the FORTE satellite to ultra-high energy (UHE) neutrino fluxes at different energies beyond the Greisen-Zatsepin-Kuzmin (GZK) cutoff. Some constraints on supersymmetry model parameters are also estimated due to the limits that FORTE sets on the UHE neutralino flux. The FORTE database consists of over 4 million recorded events to date, including in principle some events associated with UHE neutrinos. We search for candidate FORTE events in the period from September 1997 to December 1999. The candidate production mechanism is via coherent VHF radiation from a UHE neutrino shower in the Greenland ice sheet. We demonstrate a high efficiency for selection against lightning and anthropogenic backgrounds. A single candidate out of several thousand raw triggers survives all cuts, and we set limits on the corresponding particle fluxes assuming this event represents our background level.Comment: added a table, updated references and Figure 8, this version is submitted to Phys. Rev.

Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance

Published in Handbook of financial time series, 2008, https://doi.org/10.1007/978-3-540-71297-8_22</p

Maximum likelihood and Gaussian estimation of continuous time models in finance

Ministry of Education, Singapore under its Academic Research Funding Tier

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed