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[ascl:2307.012]
mnms: Map-based Noise ModelS

Atkins, Zachary; Duivenvoorden, Adriaan J.; Coulton, William R.; Qu, Frank J.; Aiola, Simone; Calabrese, Erminia; Chesmore, Grace E.; Choi, Steve K.; Devlin, Mark J.; Dunkley, Jo; Hervías-Caimapo, Carlos; Guan, Yilun; La Posta, Adrien; Li, Zack; Louis, Thibaut; Madhavacheril, Mathew S.; Moodley, Kavilan; Naess, Sigurd; Nati, Federico; Niemack, Michael D.; Page, Lyman; Puddu, Roberto; Salatino, Maria; Sifón, Cristóbal; Staggs, Suzanne T.; Vargas, Cristian; Vavagiakis, Eve M.; Wollack, Edward J.

mnms (Map-based Noise ModelS) creates map-based models of Simons Observatory Atacama Cosmology Telescope (ACT) data. Each model supports drawing map-based simulations from data splits with independent realizations of the noise or equivalent, similar to an independent set of time-domain sims. In addition to the ability to create on-the-fly simulations, mnms also includes ready-made scripts for writing a large batch of products to disk in a dedicated SLURM job.

[ascl:2309.015]
bskit: Bispectra from cosmological simulation snapshots

bskit, built upon the nbodykit (ascl:1904.027) simulation analysis package, measures density bispectra from snapshots of cosmological N-body or hydrodynamical simulations. It can measure auto or cross bispectra in a user-specified set of triangle bins (that is, triplets of 3-vector wavenumbers). Several common sets of bins are also implemented, including all triangle bins for specified k_min and k_max, equilateral triangles between specified k_min and k_max, isosceles triangles, and squeezed isosceles triangles.

[ascl:2312.008]
CompressedFisher: Library for testing Fisher forecasts

The CompressedFisher library tests whether Fisher forecasts using simulated components are converged. The library contains tools to compute standard Fisher estimates, estimate the level of bias due to the finite number of simulations, and compute the compressed Fisher information. Typical usage of CompressedFisher requires two ensembles of simulations: one set of simulations is given at the fiducial parameters (𝜃) to estimate the covariance matrix. The second is a set of simulated derivatives; these can either be in the form of realizations of the derivatives themselves or simulations evaluate at a set of point in the neighborhood of the fiducial point that the code can use to estimate the derivatives.