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Dirac Cones and Room Temperature Polariton Lasing Evidenced in an Organic Honeycomb Lattice
No full textData to reproduce the figures of the publication: S. Betzold, J. Düreth, M. Dusel, M. Emmerling, A. Bieganowska, J. Ohmer, U. Fischer, S. Höfling, and S. Klembt, Dirac Cones and Room Temperature Polariton Lasing Evidenced in an Organic Honeycomb Lattice, Adv. Sci., e2400672 (2024), DOI: 10.1002/advs.202400672. This study demonstrates the realization of high-quality polariton graphene using organic semiconductors embedded in microcavities, achieving room-temperature polariton lasing and coherent condensation with precise control over coupling conditions. The findings pave the way for exploring complex systems, including 2D topological phenomena and non-Hermitian optics, at ambient conditions. Please read the 'README' file.S.B., J.D., M.D., M.E, S.H., and S.K. acknowledge financial support by the German Research Foundation (DFG) under Germany's Excellence Strategy–EXC2147 “ct.qmat” (project id 390858490) and HO 5194/12-1
Cogwheel phase cycling in population-detected optical coherent multidimensional spectroscopy
No full textAn integral procedure in every coherent multidimensional spectroscopy experiment is to suppress undesired background signals. For that purpose, one can employ a particular phase-matching geometry or phase cycling, a procedure that was adapted from nuclear magnetic resonance (NMR) spectroscopy. In optical multidimensional spectroscopy, phase cycling has been usually carried out in a “nested” fashion, where pulse phases are incremented sequentially with linearly spaced increments. Another phase-cycling approach which was developed for NMR spectroscopy is “cogwheel phase cycling,” where all pulse phases are varied simultaneously in increments defined by so-called “winding numbers”. Here we explore the concept of cogwheel phase cycling in the context of population-based coherent multidimensional spectroscopy. We derive selection rules for resolving and extracting fourth-order and higher-order nonlinear signals by cogwheel phase cycling and describe how to perform a numerical search for the winding numbers for various population-detected 2D spectroscopy experiments. We also provide an expression for a numerical search for nested phase-cycling schemes and predict the most economical schemes of both approaches for a wide range of nonlinear signals. The signal selectivity of the technique is demonstrated experimentally by acquiring rephasing and nonrephasing fourth-order signals of a laser dye by both phase-cycling approaches. We find that individual nonlinear signal contributions are, in most cases, captured with fewer steps by cogwheel phase cycling compared to nested phase cycling
Standardized Precipitation Evapotranspiration Index (spei)
No full textA drought measure specified using precipitation and evaporation.Standardized Precipitation Evapotranspiration Index: A precipitation and evapotranspiration(Hargreaves) anomaly is considered relativ to the mean of a reference period (1981-2010) and based on the underlying statistical distribution (Gamma). The anomlies are considered over different months (3, 6, 9, 12). (More information under: https://climate-indices.readthedocs.io/en/latest/
Thermodynamic Stability at the Two-Particle Level - Numerical results for the two-orbital Hubbard model
No full textThis dataset contains the DMFT/QMC results for the example of the two-orbital Hubbard model shown in the article "Thermodynamic Stability at the Two-Particle Level". It contains parameters, one-particle Green's functions, and observables in w2dynamics output format as well as patches for relevant functionality not contained in current versions of w2dynamics at the time of publication and scripts used for post-processing of the data and the creation of some of the graphs. For size reasons, the data files containing the corresponding two-particle Green's functions are split into multiple subdatasets whose identifiers are listed above.The data files are contained in directories named beta35 and beta50 for the inverse temperature used in the respective calculations, with files containing the two-particle Green's functions contained in the subdatasets listed above and indicated by names containing 'G2'. All calculations were performed for two-orbital Hubbard models on a Bethe lattice with density-density interaction with fixed ratios between the interaction coefficients. The individual file names contain the inverse temperature, e.g. '_b35_' for beta=35, Hubbard-U interaction strength, e.g. '_U1.44_' for U=1.44, and usually the chemical potential μ, e.g. '_mu1.26000_' for μ=1.26. The file name segment '_ma..._' present in some file names redundantly gives the difference of the used chemical potential from that necessary for half-filling. In the coexistence region, the phase of the solution depends on the procedure which is indicated by the name segment 'upward' / 'downward' / 'instable' (also sometimes shortened to just the initial letter) indicating the insulating or strongly correlated metallic phase, the weakly correlated metallic phase, and the unstable phase respectively. For some of the files containing unstable solutions, the targeted value of the quasiparticle weight Z calculated from the self-energy value at the first Matsubara frequency is given in the '_Ztarget..._' segment instead of an approximate value of the chemical potential (which is not preset as a fixed parameter for calculating unstable solutions). File names of files containing two-particle Green's functions additionally contain '_s..._' indicating separate calculations differing only in the used PRNG seed that allow further statistical post-processing beyond that done automatically by w2dynamics. n(mu) plots as shown in Figs. 2 and 3 of the article can be created using the script 'kappa_2band_create_mu_n_plot.py' by calling it with the appropriate arguments, e.g. using commands like `python kappa_2band_create_mu_n_plot.py -r "kappa_2band_bethe_dens_b35_U([0-9.]*)_([muZtarget0-9.]*).*hdf.*" --axisgroup 1 -k '' --imsiwsort --nmin 2.0 --nmax 2.08 --mumin 0.0 --mumax 0.15 --nmu --onecolsize *.hdf5.zst` in the beta35 directory to create a plot like in Fig. 2 and `python kappa_2band_create_mu_n_plot.py -r "kappa_2band_bethe_dens_b50_U([0-9.]*)_([muZtarget0-9.]*).*hdf.*" --axisgroup 1 -k '' --imsiwsort --nmin 2.0 --nmax 2.14 --mumin 0.0 --mumax 0.22 --nmu --onecolsize *.hdf5.zst` in the beta50 directory to create a plot like in Fig. 3. The script 'chi_d_orblt_diagonalize.py' can be used to compute and diagonalize the generalized susceptibility by passing a data file with the one-particle Green's function as argument after '--onepfile' and one with the corresponding two-particle Green's function after '--twopfile'. From the created .npz files, a plot like in Fig. 1 of the supplemental material can be created using the script 'chi_eigenbasis_multi_barcontribs.py' by calling it with the appropriate arguments, e.g. `python chi_eigenbasis_multi_barcontribs.py --force-centrosymm-contribs --onecolsize --bargraph 2 --beta 50 --hopping 0.5 --contrib real --barorder contrib kappa_2band_bethe_dens_b50_U1.4910_mu1.4924_u_chi_orblt.npz kappa_2band_bethe_dens_b50_U1.4915_mu1.4937_u_chi_orblt.npz kappa_2band_bethe_dens_b50_U1.4920_mu1.49510_u_chi_orblt.npz kappa_2band_bethe_dens_b50_U1.4930_mu1.49780_u_chi_orblt.npz kappa_2band_bethe_dens_b50_U1.50_mu1.51740_u_chi_orblt.npz --tickstrings '' '' '' '' ''` to create a similar plot showing the same data after the listed .npz files with the generalized susceptibility data have been created. Patches in the patch directory can be applied to w2dynamics 1.1.5 as published on GitHub to add functionality that allows performing calculations converging toward unstable solutions like those contained in this data set. This information is also contained in the markdown-formatted file README.md contained in the datasets.We are grateful for funding support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy through the Würzburg-Dresden Cluster of Excellence on Complexity and Topology in Quantum Matter ct.qmat (EXC 2147, Project ID 390858490) as well as through the Collaborative Research Center SFB 1170 ToCoTronics (Project ID 258499086)
Thermodynamic Stability at the Two-Particle Level - Numerical results for the two-orbital Hubbard model (beta = 50 two-particle functions, additional results for U = 1.46)
No full textThis dataset contains a part of the DMFT/QMC results for the example of the two-orbital Hubbard model shown in the article "Thermodynamic Stability at the Two-Particle Level". It contains additional statistically independent results (i.e. using multiple different PRNG seeds) for the two-particle Green's functions at inverse temperature beta = 50 and Hubbard interaction parameter U = 1.46. Other numerical results can be found in the main dataset listed under related identifiers and its other subdatasets.All data files are zstd-compressed HDF5 output files as generated by w2dynamics for worm-sampling calculations of the two-particle Green's functions of the auxiliary impurity problem of two-orbital Hubbard models on a Bethe lattice with density-density interaction with fixed ratios between the interaction coefficients at inverse temperature beta = 50 and Hubbard interaction parameter U = 1.46. The individual file names contain the chemical potential μ, e.g. '_mu1.33380_' for μ=1.3338, the letter 'u'(pward), 'd'(ownward), or 'i'(nstable) indicating a procedural detail that is related to the phase if the parameters of the solution are in the coexistence region (the corresponding phases are the insulating or strongly correlated metallic one, the weakly correlated metallic one, and the unstable metallic one respectively), and a PRNG seed index, e.g. '_s2_' for index 2. More detailed descriptions and instructions can be found in the included readme file or the technical remarks on the main dataset.We are grateful for funding support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy through the Würzburg-Dresden Cluster of Excellence on Complexity and Topology in Quantum Matter ct.qmat (EXC 2147, Project ID 390858490) as well as through the Collaborative Research Center SFB 1170 ToCoTronics (Project ID 258499086)
Climate Indicators: Dry Days (dd)
No full textcdo -yearsum -ltc,1 RR.nc out.ncNumber of dry days: Let RRt be the daily precipitation amount on day t. For dd count all days in one year where RRt < 1mm
Climate Indicators: Heat Wave Duration Index (hwdi)
No full textcdo -eca_hwdi TX.nc -ydrunmean,5 TXref.nc out.ncDuration of Heat Wave: Let TXt be the daily maximum temperature on day t and TX’t be the climatological average of a running 5 day mean (1981-2010), then Hwdi is the longest period of consecutive days (≥ 6 days) in one year, where TXt > TX’t +5°C
Crop Indicators: Soybean_L
No full textCrop water need: Let ET0i be the daily potential evapotranspiration [mm] for day i, and Kc(pl,ph) the crop factor per plant pl and phase ph, and s(pl,ph) and e(pl,ph) the corresponding start and end day of pl and ph then CWN(pl,ph) is the daily average of the product of Kc(pl,ph) and ET0i, for s(pl,ph)≤ i < e(pl,ph) and s(pl,IS) = climatological ons (of rs1). Water deficit: Let CWN(pl,ph)i be the daily crop water need and efftpi the daily effective precipitation (which is 0mm for daily precipitation < 6.5mm, is 75mm for daily precipitation ≥ 75mm, and else the daily precipitation RRi), then Ir(pl,ph) is the daily average of the difference of cwn and efftp per plant and phase. Water balance: Let ETi be the daily actual evapotranspiration [mm] (calculated by the daily surface latent heat flux) then WA is the daily average of the difference of the daily precipitation and ETi per plant and phase
Consecutive Dry Days in Rainy Season (cddrs)
No full textcdo -eca_cdd RRmask.nc out.ncDuration of dry period in rainy season: Let RRt be the daily precipitation amount on day t. For cddrs count the largest number of consecutive days in one year where RRt < 1mm, but only in rainy season which means rs1_ons≤t≤rs1_ces or rs2_ons≤t≤rs2_ces, for climatological rs1_ons, rs1_ces, rs2_ons, rs2_ces
Dry Days (dd)
No full textcdo -yearsum -ltc,1 RR.nc out.ncNumber of dry days: Let RRt be the daily precipitation amount on day t. For dd count all days in one year where RRt < 1mm