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most citedswordfish: Efficient Forecasting of New Physics Searches without Monte Carlo

3 citations · 4 across the 2 of their papers we have counts for

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hep-ph2020

Transient Radio Signatures from Neutron Star Encounters with QCD Axion Miniclusters

Thomas D. P. Edwards, Bradley J. Kavanagh, Luca Visinelli +1

The QCD axion is expected to form dense structures known as axion miniclusters if the Peccei-Quinn symmetry is broken after inflation. Miniclusters that have survived until today w…

hep-ph2019

Radio Signal of Axion-Photon Conversion in Neutron Stars: A Ray Tracing Analysis

Mikaël Leroy, Marco Chianese, Thomas D. P. Edwards +1

Axion dark matter can resonantly convert into photons in the magnetospheres of neutron stars (NSs). It has recently been shown that radio observations of nearby NSs can therefore p…

hep-ph2018

Digging for Dark Matter: Spectral Analysis and Discovery Potential of Paleo-Detectors

Thomas D. P. Edwards, Bradley J. Kavanagh, Christoph Weniger +5

Paleo-detectors are a recently proposed method for the direct detection of Dark Matter (DM). In such detectors, one would search for the persistent damage features left by DM--nucl…

hep-ph2018

Statistical challenges in the search for dark matter

Sara Algeri, Melissa van Beekveld, Nassim Bozorgnia +19

The search for the particle nature of dark matter has given rise to a number of experimental, theoretical and statistical challenges. Here, we report on a number of these statistic…

hep-ph2018

Dark Matter Model or Mass, but Not Both: Assessing Near-Future Direct Searches with Benchmark-free Forecasting

Thomas D. P. Edwards, Bradley J. Kavanagh, Christoph Weniger

Forecasting the signal discrimination power of dark matter (DM) searches is commonly limited to a set of arbitrary benchmark points. We introduce new methods for benchmark-free for…

hep-ph20173 cited

swordfish: Efficient Forecasting of New Physics Searches without Monte Carlo

Thomas D. P. Edwards, Christoph Weniger

We introduce swordfish, a Monte-Carlo-free Python package to predict expected exclusion limits, the discovery reach and expected confidence contours for a large class of experiment…