Tools to project the sensitivity of paleo detectors.
This code requires paleoSpec for the generation of the background and signal spectra. You can get it from https://github.com/sbaum90/paleoSpec
If you use paleoSens for your work, please cite arXiv:1806.05991, arXiv:1811.06844, arXiv:1906.05800, and arXiv:2106.06559.
This module can compute the projected (90% confidence level) exclusion limits and the (5 sigma) dissovery limit of a paleo-detector to the usual WIMP signal from the ambient dark matter halo.
Usage: Call as python3 WIMP_reach.py RUNFILE, where RUNFILE is the name (without the .py file extension!) of the runfile. See WIMP_default_runfile.py for an example of the syntax and the parameters which should be entered.
Jupyter Notebooks demonstrating how to use the package and many RUNFILE examples be found in the "ExamplesAndTests_Wimp_reach" folder.
Per default, WIMP_reach uses a Poisson-likelihood to calculate the sensitivity. It allows one to incorporate external (Gaussian) constraints on the nuissance parameters via the ext_* parameters in the RUNFILE. If the optional parameter "Gaussian_likelihood" is set to Gaussian_likelihood = True in the RUNFILE, the sensitivity is instead calculated by assuming a normal distribution of the number of events in each bin. Per default, the code will compute the variance in each bin from the Poisson error only. However, an additional parameter rel_bkg_sys can be included. If this parameter is set to a value different from 0, the code will project the sensitivity by including an additional RELATIVE systematic error of the number of background events in each bin. Hence, in this case the variance of the number of events in the i-th bin is set by var_i = N_i + (rel_bkg_sys * N_i^bkg)^2 where N_i is the number of events in the i-th bin, and N_i^bkg the number of background events in the i-th bin. Note that for the exclusion limit, N_i = N_i^bkg, while for the discovery reach, they differ by the contribution of the signal to the Asimov data. Finally, note that if Gaussian_likelihood != True the code uses a Poisson likelihood and the rel_bkg_sys parameter is ignored.
These folders contain the code used to get the results presented in arXiv:2107.02812. More specifically the code provided can be used to calculate the projected discovery reach for a series of paleo-detectors to the presence of substructure (a dark disk or subhalo transit). Note that we specifically ask whether the dark matter substructure in question can be discriminated from the smooth Milky Way halo signal.
Both codes work similarly to WIMP_reach.py, and can be called by running python3 darkdisk_reach.py RUNFILE or python3 subhalo_reach.py RUNFILE where RUNFILE is the name (without the .py file extension!) of the runfile. We have included a number of example runfiles to demonstrate the versatility of the code. Our fiducial results from arXiv:2107.02812 are labeled as runfile-fiducial.py in each of the folders. Each folder also contains an example jupyter notebook with plotted results.
Like WIMP_reach.py, both substructure codes contain the option to add in additional relative systematic errors (see description above). The only difference is that the rel_bkg_sys variable in the runfile is replaced with Relative_background_systematics.