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- Data Compression and Inference in Cosmology with Self-Supervised Machine Learning. Details are described in arXiv:2308.09751 (2023).
- Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation. Details are described in arXiv:2208.13796 (2022).
- Measurement of the effective density slope of the perturber in the JVAS B1938+666 lens system. Details are described in MNRAS, Vol. 516, Issue 1 (2022) [arXiv:2206.10635].
- Substructure Detection Reanalyzed: Dark Perturber shown to be a Line-of-Sight Halo. The analysis is described in MNRAS, Vol. 515, Issue 3 (2022) [arXiv:2112.00749].
- Flow-Based Likelihoods for Non-Gaussian Inference. Details are described in “Flow-Based Likelihoods for Non-Gaussian Inference”, Phys. Rev. D 102, 103507 (2020) [arXiv:2007.05535]
- Code to compute Cosmic Microwave Background and large-scale structure observables in the presence of massive neutrinos or any light massive relics (LiMR). Details are described in “Accurately Weighing Neutrinos with Cosmological Surveys“, Phys. Rev. D 103, 023503 (2021) [arXiv:2006.09395].
- Interactive version of the relative contribution of line-of-sight halos and subhalos to the convergence power spectrum for different source and lens redshifts, and many known lensing systems. Details are described in “Quantifying the Line-of-Sight Halo Contribution to the Dark Matter Convergence Power Spectrum from Strong Gravitational Lenses”, Phys. Rev. D 102, 063502 (2020) [arXiv:2006.07383].
- Hierarchical component separation algorithm, primarily aimed towards use on the Cosmic Microwave Background. Details are described in “A Novel CMB Component Separation Method: Hierarchical Generalized Morphological Component Analysis”, MNRAS, Vol 494, Issue 1 (2020) [arXiv:1910.08077].
- Code to find scale-dependent galaxy bias in a fast and accurate way. Details of the code are described in J. Muñoz and C. Dvorkin, “Efficient Computation of Galaxy Bias with Neutrinos and Other Relics”, Phys. Rev. D98, 043503 (2018) [arXiv:1805.11623].
- Power Spectrum of Dark Matter Substructure in Strong Gravitational Lenses. Details are described in “On the Power Spectrum of Dark Matter Substructure in Strong Gravitational Lenses”, Phys. Rev. D97, 023001 (2018) [arXiv:1707.04590] and “Gravitational Lensing and the Power Spectrum of Dark Matter Substructure: Insights from the ETHOS N-body Simulations”, Phys. Rev. D98, 103517 (2018) [arXiv:1809.00004].
- Multi-component likelihood code used in BICEP2/Keck Array and Planck joint analysis. Details of the code are described in P. Ade et al. (including C. Dvorkin), “A Joint Analysis of BICEP2/Keck Array and Planck Data”, Phys. Rev. Lett. 114, 101301 (2015) [arXiv:1502.00612].
- Principal components (PC) of the Generalized Slow Roll source function: eigenfunctions, mean values and covariance matrix of PC amplitudes.
- Update for WMAP 9-year likelihood code (2013).
- Optimized WMAP7 likelihood code for multicore systems (x40 times faster). The optimization of the code and the fast approximate techniques for describing the low-l polarization information are described in detail in Appendix A of C. Dvorkin & W. Hu, Phys. Rev. D82,043513 (2010). [arXiv:1007.0215].
