dataesr/scientific-paragraphs-categorization huggingface dataset:
g. Desvignes et al. 2016). For MSPs for which data from more than one PTA were available, the IPTA data combination increased the time-span of the MSP data, as well as their cadence and observing-frequency coverage. Increased time-span and cadence allows improved sampling of orbits at longer and shorter periods, respectively. They also lead to better characterisation of low-and high-frequency noise properties. Noise mitigation is further aided by improved observing-Table  1. General characteristics of the IPTA DR 1. 0 data  (Verbiest et al. 2016)  for the MSPs used in this study (note that PSR J0437-4715 was not used to derive mass limits of solarsystem bodies; see Section 4. 1). For each pulsar we note the total time-span, T , the average cadence, the number of telescopes contributing data, and the weighted root-mean-square (RMS) of the timing residuals (after subtracting the waveform of the DM variations) The residual RMS was derived using the planetary ephemeris DE421.
By comparison to the IPTA DR 1 data, the one change we have made is related to PSR J1713+0747. The large number of TOAs (19972) would make the current analysis significantly computationally expensive. This large number of TOAs primarily stems from NANOGrav data, which are not averaged over the observing frequency band, resulting in one TOA per frequency channel. To reduce the computational cost for PSR J1713+0747 we employed the tempo2 routine AverageData and produced an average TOA for each epoch per observing frequency band by summing up all channels across the frequency band.
Single-pulsar timing and noise analysis As with other applications of PTAs, constraining the masses of known or unknown bodies in orbit around the SSB requires good characterisation of the noise in individual pulsar data (see  Cordes 2013; Verbiest & Shaifullah 2018 , for reviews on sources of noise in pulsar timing), as noise components may have significant power at frequencies related to a planetary orbit. Insufficient accounting of the noise can lead to significant bias on the measured values of the timing parameters and their uncertainties  (Coles et al. 2011; van Haasteren & Levin 2013). CHM10 pointed out these effects in the context of constraining planets' masses and specifically did not include one of the four pulsars they used, PSR J0437-4715, when estimating the mass error of Mars.
Specifically, CHM10 argued that its noise model was not sufficient to account for spectral features close to the orbital frequency of Mars, and including this pulsar would thus bias the solution for the specific planet. For the work presented in this paper, for each pulsar we created different timing and noise models for each SSE. The initial phase-coherent timing models were obtained using the timing software tempo2  (Hobbs, Edwards & Manchester 2006). tempo2 uses a previously derived timing model (which could be as simple as the pulsar discovery position and rotational frequency) and iteratively performs a leastsquares fit of the model to the TOAs until the reduced chi-squared of the residuals is minimized. tempo2 applies a linearised approximation to calculate the small, linear offsets of model parameters from the pre-fit value (see also  Edwards, Hobbs & Manchester 2006).
The least-squares fit can be unweighted or weighted according to the TOA uncertainties. Throughout this work, our timing solutions use weighted fits. These initial individual pulsar-timing mod-els do not include parameters related to errors in the SSE or any noise components. We then employed temponest  (Lentati et al. 2014)  to perform a Bayesian (simultaneous) timing and noise analysis, with the same noise modelling used, for example, in  Caballero et al. (2016). temponest samples the joint parameter space of the timing and noise parameters using Multinest  (Feroz et al. 2009 ), a Bayesian inference algorithm based on nested sampling (see  Skilling 2004) , while evaluating the timing model at each point of the parameter space using the tempo2 algorithms. Before proceeding to the correlated-signal analysis, we produced the final noise models employing the analysis package that we use to make the search for errors in the SSE in order to have a consistent mathematical noise-model parametrization.
IMCCE and JPL provide libraries that contain modules and functions that read in the data from the ephemerides and calculate the positions and velocities of the SSPSs for given times. IMCCE and JPL provide the calceph 1  (Gastineau et al. 2015 ) and 1 http://www. imcce. fr/fr/presentation/equipes/ASD/inpop/calceph/ spice 2 libraries respectively. Having confirmed that both libraries give completely consistent results, we used the calceph in all related work, except the calculations regarding mass errors of ABOs, as discussed in Section 4. 2. 1.
Analysis method for unknown solar-system bodies The approximation used in CHM10 can also be employed in the case where instead of errors in the SSE's reference mass of the SSPS, we consider the mass of UMOs, for which we then also need to model the dynamics of their motion. Such an analysis is beneficial for different reasons. Firstly, it gives the potential to PTAs to probe the masses and dynamics of any object in orbit around the SSB and to impose constraints on physical parameters of proposed or hypothetical objects (see Section 4. 3), such as Planet Nine  (Brown & Batygin 2016)  or dark matter in the solar system with specified mass distributions  (Loeb & Zaldarriaga 2005; Pitjev & Pitjeva 2013; Pitjeva & Pitjev 2013). In this study we focus on a simple model which assumes small bodies in Keplerian orbits around the SSB in order to probe to first order the sensitivity of the real PTA data set to orbiting masses in the solar system.
ANALYSES AND RESULTS The analysis with our implementation of the CHM10 method used ten different SSEs, five from IMCCE (designation "INPOP") and five from JPL (designation "DE"). An overview of the SSEs we employed can be found in Table  2. Before proceeding to searches for correlated SSE-error signals across pulsars, we performed some preliminary searches for errors in masses of SSPSs using single-pulsar data to check the effects of the noise model we select and to compare the performance of our implementations of the CHM10 method with that of tempo2. We also made a first-order comparison of the effects on pulsar timing from choosing a different SSE during the analysis. We tested whether using the noise model described in Section 3. 1 produced significantly different results than when using the more complex models published in  Lentati et al.
The MSPs are listed in order of sensitivity (best to worse) in the interval a ∈  [5, 17]. Given the data set's cadence and time-span, this is the interval where the analysis performance is expected to impact mostly on our results. MSPs in boldface were selected to derive the mass constraints of SSPS, ABOs and UMOs (see discussion in main text). (2013) also demonstrated that the ability of the noise models included in the timing analysis to prevent leakage of residuals in astrometric parameters depends on the total timespan of the pulsar data set. Therefore, a given SSE may perform differently in terms of the residual RMS for pulsars with different time-spans, even when their true noise properties are similar, since de-correlating pulsar noise, SSE residuals and astrometric parameters requires sufficient data length.
In the presence of lowfrequency noise, if the models underestimate the noise levels, one would expect to see significant detections of δm for planets with periods longer than the data set's time-span. The results are as expected, with the uncertainties on δm of the ice giants being orders of magnitude larger than the rest of the planets. Using the results of the analysis, we derived the mass constraints of the planetary systems in the solar system using the IPTA DR 1 and the ten SSEs employed in this study. The results are summarized in Table  4. Since the so- lar gravitational parameter is a fitted quantity in the SSEs, and is therefore different in each case, we express all results as ratios of the planetary gravitational parameters as derived using a specific SSE (superscript SSE ) with re-spect to the nominal solar gravitational parameter, GM N ⊙ = 1.
To evaluate our results, we compare them with the results from CHM10 and with the current best estimates 4 (CBEs) adopted by the International Astronomical Union (IAU) for the planet-moons systems. The CBEs, denoted with the CBE superscript, are selected from the literature and are derived directly from spacecraft data. For the comparison, we also expressed the CBE results with respect to the nominal solar gravitational parameter. We note again, that such an approach does not change our results within the uncertainties due to the current data precision, but we follow this practice to allow better comparisons with future results and follow the recommended best practices by the IAU. Compared to CHM10, the mass constraints have improved by factors of  5. 7, 8. 5, 20, 6. 7 and 4  for the planetary systems of Mercury, Venus, Mars, Jupiter and Saturn, respectively.
Asteroid-belt objects The main asteroid belt hosts small bodies with masses that reach up to order 10 -10 M ⊙. With the IPTA DR 1 having sensitivity to mass errors of the order 10 -11 M ⊙ -10 -10 M ⊙ between the orbits of Mars and Jupiter (see also next section), it is logical to attempt constraining the masses of the largest bodies of the main belt. This is the first time that PTA data are used to derive mass constraints on ABOs. As our data are only beginning to be sensitive to ABO masses, in this work we perform a pilot study and use only one SSE. Future work with more sensitive data can focus more on comparisons between the pulsar-timing constraints on ABO mass using different SSEs. We employed the SSE DE435 together with additional, high-precision positional data for the ABOs from the New Horizons SPICE Data Archive 5 , which were used for the New Horizons spacecraft mission.
This is indeed the case in our analysis, with the upper limits from the blind search being ∼2 to 14 times higher. One could also extend the upper-limit analysis to wider orbits in order to retrieve, for example, an upper limit on the mass of Planet Nine. In GLC18, the results using simulated data show that the precision of the IPTA DR 1 is not sufficient to give informative constraints on the mass of Planet Nine. We therefore did not attempt this, but reserve such effort for future work. As discussed in GLC18, the results from this type of analysis directly provide upper limits on the presence of any type of massive objects in orbit around the SSB. As such,   our results are also applicable to more exotic objects such as dark matter clumps  (Loeb & Zaldarriaga 2005)  or cosmic strings  (Blanco-Pillado et al.
DISCUSSION AND CONCLUSIONS In the work described in this paper we have employed previously published methods on a subset of the first IPTA data release in order to constrain the masses of solar-system bodies using ten different SSEs, five from IMCCE and five from JPL. Using a new computational implementation of the method first described in CHM10, we have derived new mass constraints for the SSPSs, which were found to be statistically consistent using all ten SSEs. While the biases from the SSE reference values appear consistent for each SSPS, the results appear to be dominated by data noise. Within the uncertainties, our results are in agreement with the CBEs from the IAU which overall have significantly lower uncertainties. For the first time, PTA data were also used to significantly constrain the masses of the most massive ABOs.
A Bayesian method from GLC18 was also employed for the first time on real data to provide generic sensitivity limits on the mass of UMOs in the solar system using pulsar timing. The new mass constraints on all planetary systems show improvements of factors 4 to 20 from the last work that used the same method, namely CHM10, emphasizing the fact that increasing the precision, cadence, frequency coverage and time-span of the pulsar-timing data allows for constant improvements of PTA sensitivity to potential errors in SSEs. As such, the IPTA greatly serves this research since the combination of independent data sets improves the data overall in all these aspects. As noted in  Caballero (2018) , the use of the IPTA combined data improved the sensitivity to planetary masses by factors up to ∼ 4 by comparison to only using EPTA data, when using the same pulsars in both cases.
Additionally, the IPTA combined data set also allowed more options with regards to choosing MSPs for the analyses and this study has benefited from using a larger and different sample of pulsars than CHM10. We note that constraining planetary masses with pulsar-timing data helps us cross-check the data quality and pulsar noise models using information on physical properties that are measured completely independently. Large deviations from the SSE's reference masses or unexplained signals present only in one pulsar's data, which are not detected with multi-pulsar searches for correlated signals, can indicate insufficiencies of the noise models or possible systematics in the data of a given pulsar. In this paper we have additionally demonstrated with real data the ability of algorithms that search for UMOs of any type in the solar system, to provide generic mass sensitivity curves using pulsar timing.
While with certain limitations, the GLC18 code applied in this paper highlights differences between SSEs. As we saw in Section 4. 3, the main differences in UMO-mass sensitivity curves between DE421, DE435 and INPOP17A appear in the asteroid belt and around Jupiter. While the details of the differences between SSEs are beyond the scope of this study, we note that these results may be due to changes in the way that ABO masses and their perturbations on each other and on Mars are estimated, as well as recent updates in the positional data of Mars, Jupiter and Saturn that IMCCE and JPL have been implementing (e. g. Viswanathan et al. 2017; Folkner et al. 2014). For example, when estimating perturbations of the ABOs on the orbit of Mars, both DE421 and DE435 used data for the 343 ABOs identified to be dominant.
However, while in DE421 only eleven ABO masses were individually calculated (for the rest either the initial values were kept fixed or values were fixed to approximate values derived densities assumed per taxonomic class), for DE435 (also the case for DE430) the individual masses were calculated for all 343 ABOs. Future work with more precise data sets could focus more on the effects of such difference on pulsar timing and applications. As noted in CHM10, since PTAs are sensitive to the total mass of the SSPSs, if PTAs in the future measure differences in the masses with statistical significance, those differences may reflect differences in the masses or total number of moons taken into account when estimating the position of the planet-moons barycentre and total mass estimations. Although the GLC18 algorithm is not directly applicable to bodies in orbit around major planets, the differences in the sensitivity curves around semi-major axis values close to planetary orbits may still be associated with such errors.
The mass of UMOs at these semi-major axis values, may also reflect differences in the SSEs regarding the positional data of the planets and moons, since with the applied methodology such effects could potentially be absorbed by the UMO mass parameter. These results underline the potential of pulsar timing and PTA research to also provide feedback and independent checks to groups developing SSEs, and add information in the future for SSE development. While at the precision that the IPTA DR 1 can probe the masses of SSPSs we have confirmed that the SSEs give consistent results, we found that for any given MSP the timing residuals resulting from using different SSEs can vary at different levels. For individual pulsars we have noted that the differences between the RMS deviations of residuals formed using various SSEs (see Fig.
As such we will regularly have to check the impact of SSE updates on pulsar-timing and PTA applications and compare our results to updated CBEs. Pulsartiming results, on the other hand, are also able to show strong improvements over time. As was noted already in CHM10, pulsar timing has the benefit of being able to improve on mass constraints with more accumulation of data, even if the data quality remains constant. The example of Saturn is important to highlight, since within the next ten years our data sets will be long enough to fully sample its orbit, which has a period of 29. 5 yr. This will allow us to fully de-correlate signals from Saturn from those of timing and noise parameters and the uncertainties of the Saturnian δm will be very significantly reduced. General predictions on ex-pected improvements in probing parameters of solar-system bodies with future instruments can be made using generic sensitivity curves for UMOs.
In GLC18, it was shown that regular observations of 20-40 MSPs with future radio telescopes, which may achieve timing residuals of average RMS levels of order ∼ 100 ns for 20 yr, can potentially improve our mass constraints on the Jovian system by another two orders of magnitude, at levels below the current constraints by space missions. Therefore, despite the anticipated improvements in the CBE mass values of the Jovian system as a result of the analysis of data by the JUNO and JUICE space missions, our predictions are indicative of the potential for interesting results that can be produced in the future with respect to solar-system studies using PTA data. Finally, we comment on the relation between research into SSEs, solar-system studies and GW searches with PTAs. Results of studies such as the present can give hints on which GW frequencies one can expect most of the differences in the limits by PTAs when using different SSEs.
This is indeed the case and already GWB upper limits and detection statistics are being affected by the choice of SSEs, which leads to the need of introducing SSE-related parameters in the model in order to mitigate such effects  (Arzoumanian et al. 2018). Vice versa, one expects the presence of a GWB to influence our results when trying to constrain planetary masses with PTA data. As discussed in Section 1, with more MSPs of high data precision, a GWB and a δm signal should be distinguishable on the basis of their different angular correlation. Further work is underway to understand the impact of SSE selection to the timing and noise models and to bridge the systematics of each SSE to a given analysis. Early attempts to do this included non-physical, generic error vectors of the position of the SSB  (Deng et al.
ACKNOWLEDGMENTS Part of this work is based on observations with the 100-m telescope of the  Max-Planck-Institut für Radioastronomie (MPIfR) at Effelsberg. The Nançay Radio Observatory is operated by the  Paris Observatory , associated to the  French Centre National de la Recherche Scientifique (CNRS). Pulsar research at the  Jodrell Bank Centre for Astrophysics  and the observations using the Lovell Telescope are supported by a consolidated grant from the  STFC  in the UK. The Westerbork Synthesis Radio Telescope is operated by the  Netherlands Institute for Radio Astronomy (ASTRON)  with support from The  Netherlands Foundation for Scientific Research NWO. The  Green Bank Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc.
The Arecibo Observatory is operated by  SRI International  under a cooperative agreement with the  NSF  ( AST-1100968 ), and in alliance with  Ana G. Méndez-Universidad Metropolitana  and the  Universities Space Research Association. The Parkes radio telescope is part of the  Australia Telescope National Facility  which is funded by the  Australian Government  for operation as a National Facility managed by  CSIRO. This work was supported by the  MPG  funding for the Max-Planck Partner Group. Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration (NASA). Work at  NRL  is supported by  NASA. We acknowledge financial support from " Programme National de Cosmologie and Galaxies" (PNCG)  and from " Programme National Gravitation, Références, Astronomie, Métrologie " (PNGRAM) funded by  CNRS/INSU-IN2P3-INP ,  CEA  and  CNES, France.
We acknowledge financial support from the project " Opening a new era in pulsars and compact objects science with MeerKat" in the context of INAF  grant " SKA-CTA 2016 ". Part of this research was funded by the  Australian Research Council Centre of Excellence for Gravitational Wave Discovery (Oz-Grav) ,  CE170100004. The  Flatiron Institute  is supported by the  Simons Foundation. The computation was performed using the Hercules cluster at the  Max Planck Computing and Data Facility, Garching , the Dirac cluster in KIAA and the Tianhe II supercomputer at Guangzhou. RNC acknowledges the support of the  International Max Planck Research School Bonn/Cologne  and the  Bonn-Cologne Graduate School  for part of this work. KJL is sup-ported by the  National Natural Science Foundation of China  (Grant No.
11373011 ). KJL is supported by  XDB23010200 ,  National Basic Research Program of China, 973 Program ,  2015CB857101  and  NSFC U15311243 ,  11690024. We are also supported by the  MPG  funding for the Max-Planck Partner Group. PL acknowledges the support of the  International Max Planck Research School Bonn/Cologne. GD and KL acknowledge financial support by the  European Research Council (ERC)  for the  ERC Synergy Grant BlackHole-Cam  under contract no. 610058. ZA, AB, SB-S, SC, JMC, PD, TD, EF, NG-D, PG, MTL, TJWL, ANL, DRL, RSL, DRM, MAM, STWM, CMFM, DJN, NTP, TTP, SMR, XS, JS, IS, DRS, KS, JKS, SRT, RvH are members of the  NANOGrav Physics Frontiers Center , which is supported by  NSF  award  1430284. GJ and GS acknowledge support from the  Netherlands Organisation for Scientific Research NWO  ( TOP2.
Crystallography A cubic single crystal of DUT-48 was placed into a glass capillary (d = 0. 3 mm) with small amount of the mother liqueur. Data was collected at BL14. 2 beamline of the MX-facility at BESSY-II synchrotron, operated by Helmholtz-Zentrum Berlin für Materialien und Energie. Single crystal structure of DUT-48: C34H20Cu2N2O10, 743. 6 g mol  -1  , cubic, Fm3 " m, a = 40. 490 (5)   Å, V = 66381(23) Å 3 , Z = 24, ρcal = 0. 444 g. cm -3 , 3870 independent reflections observed, R1 = 0. 0748 [I>2σ(I)], wR2= 0. 2286 (all data) and GoF 1. 121. This data can be obtained free of charge from the Cambridge Crystallographic Data Centre under entry CCDC-1827897 via www. ccdc. cam. ac. uk/data_request/cif. Additional experimental details can be obtained from ESI.
Simulation Details The behavior and mechanical properties of the representative ligands were investigated for the corresponding acid via Density Functional Theory(DFT) optimizations using the CRYSTAL14 software  30  with localized TZVP basis sets  31  and the hybrid exchange-correlation functional PBE0  32  Long-range dispersion corrections were included via the Grimme ''D2'' approach  33  Stress-strain curves were subsequently produced using the methodology described previously  26  Grand Canonical Monte Carlo(GCMC) simulations were employed to simulate the adsorption and enthalpy of n-butane using the RASPA2. 0 code. 34 Frameworks were treated by the UFF forcefield  35  and n-butane the united-atom TraPPe force field  36  Molecular dynamics (MD) simulations used a modified MOF-FF force field  37  recently adapted to lammps  38  by Boyd et al.