Datensätze

This repository contains data and numerical simulation code for Sunami et. al., "Universal scaling of the dynamic BKT transition in quenched 2D Bose gases". The data is stored in the format of hdf5 (Hierarchical Data Format), which can be read from standard programming environment such as python (h5py), matlab (`h5read`), and many others. Numerical simulation scripts consist of functions.c, MonteCarloSampling.cpp and phase12_den_sample_time.cpp. Please see the section for numerical simulation in the Supplementary Material. The description for each hdf5 file in this repository is provided in readme.txt.

Raw spectral data for publication "Unambiguous Calibration of Power Dependence in Ratiometric Luminescent Nanothermometry Through Multiple Intensity Ratios and Symbolic Regression"

This dataset contains Level1B-equivalent data products LRI1B and a newly derived Range Thermal Coupling product RTC1B. The data is supplementary to this publication: Misfeldt et al., 2023.

In this dataset derived on 2022-11-15, we provide the most significant outcome of the above publication, which is the newly derived LRI1B-v52 data product. We use a model for the cavity length (and thus the laser frequency) where the cavity resonance frequency exponentially converges to a final value. We furthermore provide the corresponding Range Thermal Coupling (RTC) data product.

The data set contains the data underlying the fiber link performance evaluation published in Koke et al 2019 New J. Phys. 21 123017, doi.org/10.1088/1367-2630/ab5d95 The experimental setup and the methodology used is explained in this publication.

1. Introduction

Welcome to the documentation for the code, data, and plotting scripts corresponding to the paper titled "Method Comparison for Simulating Non-Gaussian Beams and Diffraction for Precision Interferometry". This repository contains resources to replicate the simulation results presented in the paper. Please carefully review the instructions below to ensure the successful execution of the code, data generation, and plotting.

2. Dependencies

Before running the code, it is necessary to install IfoCAD (version 2022/10, git commit adf19a5b) in your environment. Please note, that IfoCAD is currently prepared for publication. However, because IfoCAD is not yet publically available, we provide here likewise the data generated by IfoCAD. Here is the introduction page of IfoCAD: www.aei.mpg.de/ifocad. Additionally, the plotting scripts require the installation of gnuplot.

3. Directory Structure

The code, data, and plotting scripts are organized according to the structure of the paper's sections. Each section corresponds to a specific folder. Here is a brief overview of the file structure: Section2/: Code, data, and plotting scripts for Section 2 Section3/: Code, data, and plotting scripts for Section 3 ...

4. Running the Code

Step 1:

Install Dependencies Ensure IfoCAD is installed. The open-source release is anticipated soon. Also, make sure gnuplot is installed for the plotting scripts.

Step 2:

Run the Code Navigate to the specific section folder and to run the code.

5. Generating Data

After running the code, the generated data will be stored in the file path configured in the code. Utilize this data for further analysis and visualization.

6. Plotting Scripts

Each section's plotting scripts are located in their respective folders. Run these scripts sequentially to generate plots consistent with those presented in the paper. Make sure to have gnuplot installed for proper execution.

For any issues or questions during usage, feel free to contact us.

Thank you for using our resources!

The enclosed data set provides additional data on the measurements presented in the publication entitled "Optical frequency ratio of a 171Yb+ single-ion clock and a 87Sr lattice clock" in Metrologia (DOI: 10.1088/1681-7575/abc86f).

The simulated LLR data are round-trip travel times between Earth station and lunar retro-reflector. And the simulated DLLR data are range differences between the ranges measured by the same station to different lunar reflectors within a short time interval. For the simulated DLLR data, the measurement time for the second reflector minus the measurement time for the first reflector is 1.5 minutes. The folders "ReflectorBaselines", "TimespanStations" and "NewReflectors" contain the simulated LLR and DLLR data used for different analysis cases. The type of the simulated data (LLR or DLLR) and the corresponding case are shown in the name of the simulated data files. Below are the contents of the different columns of the simulated LLR and DLLR data. The columns of the simulated DLLR data contain the following information: 1. station name 2. measurment time t1 for reflector 1 [JD-2440000.0] 3. reflector 1 name 4. reflector 2 name 5. range difference [km] 6. accuracy [nm] 7. temperature [Celsius degree] 8. pressure [hPa] 9. wavelength [nm] 10. humidity [%] The columns of the simulated LLR data contain the following information: 1. station name 2. measurement time for a reflector [JD-2440000.0] 3. reflector name 4. round-trip travel time [s] 5. accuracy [ns] 6. temperature [Celsius degree] 7. pressure [hPa] 8. wavelength [nm] 9. humidity [%] More details about the data and their analysis can be found in: 1. Zhang M. (2023): Characteristics and Benefits of Differential Lunar Laser Ranging, PhD thesis at Leibniz Universität Hannover, in press 2. Zhang M.; Müller J.; Biskupek L. (2023) : Benefit by combining Lunar Laser Ranging and Differential Lunar Laser Ranging, Astronomy & Astrophysics, in review

QuantumFrontiers_HLSST_SLR_COMB2021s is an updated series of monthly gravity field models based on high-low satellite-to-satellite (HLSST) tracking and satellite laser ranging (SLR) data up to degree and order 60. The time series is Kalman-filtered. The combination of HLSST and SLR data is done on the normal equation level using Variance Component Estimation. The series spans from 2003 to 2021 and thus covers the entire period between GRACE and GRACE Follow-On. It is therefore a prime candidate to bridge the data gap between these two satellite mission considering long-wavelength features on a global scale. The model has been developed with data contributions from the Astronomical Institute, University Bern (AIUB), the Institute of Geodesy, Theoretical Geodesy and Satellite Geodesy, Graz University of Technology, the Institute for Geodesy, Leibniz University Hannover and the European Space Agency. More details on the processing can be found in "Time-Variable Gravity Signal in Greenland Revealed by High-Low Satellite-to-Satellite Tracking" (Weigelt et al, 2013, doi.org/10.1002/jgrb.50283) Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2123 QuantumFrontiers – 390837967.

QuantumFrontiers_HLSST_SLR_COMB2019s is a series of monthly gravity field models based on high-low satellite-to-satellite (HLSST) tracking and satellite laser ranging (SLR) data up to degree and order 60. The combination of HLSST and SLR data is done on the normal equation level using Variance Component Estimation. The series spans from 2003 to 2018 and thus covers the entire period between GRACE and GRACE Follow-On. It is therefore a prime candidate to bridge the data gap between these two satellite mission considering long-wavelength features on a global scale. The model has been developed with data contributions from the Astronomical Institute, University Bern (AIUB), the Institute of Geodesy, Theoretical Geodesy and Satellite Geodesy, Graz University of Technology, the Institute for Geodesy, Leibniz University Hannover and the European Space Agency. More details on the processing can be found in "Time-Variable Gravity Signal in Greenland Revealed by High-Low Satellite-to-Satellite Tracking" (Weigelt et al, 2013, doi.org/10.1002/jgrb.50283) Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2123 QuantumFrontiers – 390837967.

Lunar Laser Ranging (LLR) measures the distance between observatories on Earth and retro-reflectors on Moon since 1969. From the mesurements it is possible to estimate the Earth Rotation Parameters (ERP) using LLR data. For the determination of the published ERP data 28093 LLR normal points for the time span April 1970 - April 2021 were used. The analysis was done with the LUNAR software of the Insitute of Geodesy (IfE). The data contains 32 files which show the estimated values and their 3-sigma uncertainties of the Earth Rotation Phase (ΔUT1) and the terrestrial pole offsets (xp and yp). The data is devided in 3 subsets according to the minimum number of normal points (NPs) per night (e.g. 5, 10, or 15 NPs). Each subset contains files with ERPs calculated from NPs observed from either only APOLLO (USA), or only OCA (France), or all LLR observatories. For APOLLO, the case of 15 NPs per night was not considered. Each case has four individual files - one each for results of the estimation of ΔUT1, xp, yp, and xp & yp.

Differential Lunar Laser Ranging (DLLR) is a new concept which will be implemented at the Table Mountain Observatory of JPL. A DLLR station will measure a lunar reflector at time t1 to get a range measurement. After a short switching interval (e.g., 1.5 min), the same station at time t2 will measure another reflector to get another range. The DLLR observation will be obtained by the difference of the two consecutive range measurements, called lunar range difference. As no actual DLLR data is available now, we did the DLLR simulation based on modifying the LLR simulation software of Hofmann (2017). The simulated DLLR data is simulated according to the real Lunar Laser Ranging (LLR) 28093 normal points from 1970 to 2021 provided by the stations McDonald, MLRS1, MLRS2, OCA, WLRS, MLRO, APOLLO and LURE for the reflectors Apollo 11, Apollo 14, Apollo 15, Lunokhod 1 and 2. The data distribution, the assumed stations and reflectors used for DLLR simulation are the same as those of the real LLR data group. We simulated four DLLR data groups, two with 1.5-minute switching interval, two with 15-minute switching interval. Each switching interval group includes two subgroups with different observation accuracies. One is the same as the LLR range accuracy and another one is 200 times improved based on LLR range accuracy. For this DLLR simulation, the used temperature, pressure, wavelength and humidity of the station are the same for the two measurement time points t1 and t2.

QuantumFrontiers_HLSST_SLR_COMB2019s is a series of monthly gravity field models based on high-low satellite-to-satellite (HLSST) tracking and satellite laser ranging (SLR) data up to degree and order 60. The combination of HLSST and SLR data is done on the normal equation level using Variance Component Estimation. The series spans from 2003 to 2018 and thus covers the entire period between GRACE and GRACE Follow-On. It is therefore a prime candidate to bridge the data gap between these two satellite mission considering long-wavelength features on a global scale. The model has been developed with data contributions from the Astronomical Institute, University Bern (AIUB), the Institute of Geodesy, Theoretical Geodesy and Satellite Geodesy, Graz University of Technology, the Institute for Geodesy, Leibniz University Hannover and the European Space Agency. More details on the processing can be found in "Time-Variable Gravity Signal in Greenland Revealed by High-Low Satellite-to-Satellite Tracking" (Weigelt et al, 2013, doi.org/10.1002/jgrb.50283) Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2123 QuantumFrontiers – 390837967.