dataesr/scientific-paragraphs-categorization huggingface dataset:
2008)  and those published in  Chatterjee et al. (2008, C08 hereafter)  for the period 1996-2004. Additionally, for the blazar PKS 1510-089 Rband photometric data were obtained from the VLBA-BU-BLAZAR blazar monitoring program for the period 2005-2018 13  (Jorstad & Marscher 2016). For the blazar 3C 279, additional R-band photometric data for the period 2018-2019 were obtained from the Skynet Robotic Telescope Network program (SKYNET) 14. There is a great deal of overlap between the SMARTS and the REM programs' data in the V , R, J, and K-11 http://www. astro. yale. edu/smarts/glast/home. php 12 http://users. utu. fi/kani/1m/ 13 https://www. bu. edu/blazars/VLBAproject. html 14 https://skynet. unc. edu bands and they complement each other well at V , R, and J-bands, with the pairs of magnitudes within 0.
1 mag on the same nights, consistent with the calibration uncertainties provided by these programs. Moreover, the R-band photometric data obtained from the VLBA-BU and the Tuorla monitoring programs are comparable to other datasets within the measurement uncertainties (<0. 1 mag) on the same nights. As a majority of these programs have mean sampling intervals >1-day, we have combined the datasets at V , R, and J-bands and average magnitudes were computed with 1-day binning intervals. This is done to avoid obtaining poor sampling on intranight timescales as well as to obtain homogeneous sampling on daily timescales. On the other hand, the K-band magnitudes from the SMARTS program has a magnitude offset ≥0. 1 mag from that provided by S16 on the same nights. On visual inspection, the K-band SMARTS light curve appears more variable than the S16 light curve on similar timescales.
Therefore, in this study, we use only the K-band data provided by S16. On a few occasions, fluxes at B, I, H, and K-bands were also obtained multiple times on a given night and we have averaged those observations over a 1-day binning interval. Next, the optical and infrared magnitudes, m, were converted to flux densities using m zero ×10 -0. 4× m , where m zero (=4063 Jy, 3636 Jy, 3064 Jy, 2635 Jy, 1590 Jy, 1020 Jy, and 600 Jy for the B, V , R, I, J, H, and K-bands, respectively) refer to zero-point magnitude fluxes of the photometric system  (Glass 1999). The errors in fluxes were derived using standard error propagation  (Bevington & Robinson 2003). The resulting optical and infrared-band light curves for the targets 3C 279 and PKS 1510-089 are presented in Figures  1(d, e ) and 2(d, e), respectively.
For the X-ray and GHz band radio light curves, these values range between ∼1. 4 and ∼3. 0 for the majority of the gathered light curves, except for the 14. 5 GHz UMRAO light curve for the target PKS 1510-089 for which it is 3. 4. As only relatively small (weeks) systematic gaps are expected in the RXTE, Swift, and the radio data because of pointing constraints due to the target's proximity to the Sun, these light curves can be considered better sampled than the optical ones. Since for the great majority of our data, these Var sp values are relatively small numbers, our cutting the spectrum down to 1/(2*T mean ) does not alter its shape. The sampling distribution and the corresponding spectral window functions of the observed light curves are shown in Appendices A. 1 and A. 2 for the blazars 3C 279 and PKS 1510-089, respectively.
Such an approach is possible solely due to the availability of the large number of light curves analyzed by us in this study. For this, we average all the PSD slopes at different radio and optical frequencies; this gives the mean PSD slope at two different synchrotron frequencies while the combination of two X-ray PSD slopes and one GeV PSD slope gives an estimate of the mean PSD slope at IC frequencies of the emission spectrum (see Section 1). As UMRAO 14. 5 GHz and OVRO 15. 0 GHz datasets are essentially the same  (Aller et al. 1985; Richards et al. 2011) , for the calculation of mean PSD slope at radio-synchrotron frequencies we did not use the OVRO estimates since the UMRAO 14. 5 GHz PSD covers a wider range of variability frequencies. For averaging, we used the PSD slopes which were successfully represented by the single power-law model (i.
RESULTS We have obtained long-term multiwavelength variability PSDs of two luminous blazars, 3C 279 and PKS 1510-089, using well-sampled (days/weeks sampling interval), long-duration (years-decades), datasets with good photometric accuracy (∼1-15%). The variability PSDs cover ∼2-4 decades in temporal frequency, corresponding to decades/years/months/days timescales, depending on the waveband. The key results of our detailed analysis are as follows: 1. For both of the targets, the radio variability PSDs are best described by the single power-law shapes with confidence ranging from ∼14% to ∼97% (Column 11; Table  2 ). The best-fit β range between ∼1. 4 and ∼2. 9 over the frequency range log 10 ν k (day -1 ) between -4. 2 and -1. 2, indicative of flicker to red-noise type statistical characters of variability (Table  2 ; Figures  5  and 6 ).
68 and -1. 03, closer to flicker noise. (Table  2 ; Figure  6 ). The variability PSDs at V , H, and K-bands do not provide acceptable fits to single power-law shapes for the blazar PKS 1510-089 (Table  2 ; Figure  6 ). The normalizations of these PSDs again are consistent with one another within 1-2σ uncertainties where the variability frequencies overlap (panels (b) and (e) of Figure  7 ). 3. For both blazars, the X-ray variability PSDs are well described by single power-law shapes with confidence ranging from ∼48% to ∼99% (Column 11; Table  2 ). The best-fit βs are between ∼1. 2 and ∼1. 5 over the frequency range log 10 ν k (day -1 ) between -3. 76 and -1. 08, indicative of nearly flickernoise variability (Table  2 ; Figures  5  and 6 ). The PSDs obtained using RXTE-PCA and Swift-XRT datasets for both of the targets complement each other well for overlapping variability frequencies (panels (c) and (f) of Figures 7).
Comparison of PSD slopes with the literature The γ-ray PSD slopes computed with the PSRESP technique used here for the blazars 3C 279 and PKS 1510-089 employing the decade-long light curves are consistent with those given in previous studies (β∼0. 6 and ∼1. 0 for 3C 279 and PKS 1510-089, respectively;  Meyer et al. 2019). Nilsson et al. (2018)  reported the optical R-band PSD slopes ∼1. 5 and ∼0. 9 for the blazars 3C 279 and PKS 1510-089, respectively which are also consistent within errors with our estimates. Chatterjee et al. (2008)  reported the PSD slopes for the blazar 3C 279 at X-ray, R-band optical, and radio frequencies as ∼2. 3, ∼1. 7, and ∼2. 3, respectively, using the decade-long RXTE-PCA data, R-band, and 14. 5 GHz UMRAO datasets for the period 1996-2007. Note that our longer duration X-ray, optical R-band and 14.
5 GHz light curves include datasets reported in  Chatterjee et al. (2008). The X-ray PSD slope obtained in their analysis of RXTE data is significantly steeper, β = 2. 3 ± 0. 3, as compared to our result of 1. 2±0. 3. Our X-ray PSD slope is corroborated by the Swift-XRT dataset over a similar timescale (β = 1. 4 ± 0. 7); therefore, this discrepancy cannot be due to the different lengths of light curves used in  Chatterjee et al. (2008)  and in our analyses for the PCA data. Good matches (within uncertainty), however, are found between the optical and radio PSD slopes between our analysis and that of  Chatterjee et al. (2008). Using the full-duration UMRAO light curves for both blazars,  Park & Trippe (2017)  reported the PSD slopes, β∼2. 5, ∼2. 2, and ∼1. 6 at 14. 5 GHz, 8. 0 GHz, and 4. 8 GHz, respectively, for 3C 279 and β∼1.
Comment on reported quasi-periodicities We now mention the reported QPOs on year-like timescales in γ-ray and optical/infrared light curves for the period 2005-2014 claimed by  Sandrinelli et al. (2016)  for the blazars 3C 279 (V , R, and K-bands) and PKS 1510-089 (V , R, J, and K-bands). We also use the same dataset presented in  Sandrinelli et al. (2016)  and supplement it with other datasets to increase the length of the analyzed light curves. Our γ-ray PSDs are well-represented by the single power-law spectral shapes with high confidence (p β ≥0. 76). Moreover, the optical/infrared PSDs of the blazar 3C 279 are represented by the single power-law spectral form adopted in our analysis with high confidence (p β >0. 6). For the blazar PKS 1510-089, the PSDs obtained at B, R, I, and Jbands are also well represented by a single power-law spectral form with decent confidence (p β > 0.
Key conclusions In summary, we have explored the statistical properties of blazar light curves by means of variability PSDs using good photometric quality (∼1-15% flux accuracy), extensive multiwavelength datasets (≥15 different frequencies covering 13 decades of the emission spectrum) available for the blazars 3C 279 and PKS 1510-089 over timescales exceeding a decade. The majority of PSDs show a good fit to single power-law spectral shapes over temporal frequencies ranging between ∼10 -4 and ∼10 -1 (day -1 ). However, we note that PSDs on temporal frequencies longer than 10 -2. 7 (day -1 ) are poorly sampled, providing only a few up to 10 cycles in the PSDs. The mean PSD slopes at radio and opticalsynchrotron emission frequencies are steeper from those at IC emission frequencies at ≥2σ level on timescales ranging from decades to weeks/days for the blazar 3C 279.
On similar broad timescales, the mean PSD slopes at radio and optical-synchrotron emission frequencies and at IC emission frequencies turn out to be statistically indistinguishable (≤2σ level) from one another for the blazar PKS 1510-089. The γ-ray variability PSDs covers ∼4 decades of the variability spectrum for these two blazars where the short-term variability PSDs connect smoothly to the long-term variability PSDs. The optical variability power spectrum covers ∼6 decades for the blazar 3C 279 and the normalization of intranight PSDs appears to be a simple extrapolation from longer timescales. 2018/29/B/ST9/02298. L. S. was supported by the Polish NCN grant 2016/22/E/ST9/00061. S. Z. acknowledges NCN grant 2018/29/B/ST9/01793. S. K. acknowledges support from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme under grant agreement No.
771282. A. G. acknowledges many discussions with FSSC help desk contact Nestor Mirabel regarding the LAT analysis. The Monte-Carlo simulations of the light curves have been performed at the Prometheus cluster of the Cyfronet PL grid under the computing grants 'lcsims2' and 'plglcsims'. A. G. thanks Micha l Ostrowski, Marek Sikora, and Stefan Wagner for useful discussions on the manuscript. This research has used data from the University of Michigan Radio Astronomy Observatory which was supported by the University of Michigan; research at this facility was supported in part by NSF grants AST-8021250, AST-8301234, AST-8501093, AST-8815678, AST-9120224, AST-9421979, AST-9617032, AST-9900723, AST-0307629, AST-0607523 and earlier awards, and by NASA under awards NNX09AU16G, NNX10AP16G, NNX11AO13G, and NNX13AP18G.
This publication makes use of data obtained at Metsähovi Radio Observatory, operated by Aalto University in Finland. This research has made use of data from the OVRO 40m monitoring program which was supported in part by NASA grants NNX08AW31G, NNX11A043G and NNX14AQ89G, and NSF grants AST-0808050 and AST-1109911 and private funding from Caltech and the MPIfR. This paper has made use of up-to-date SMARTS optical/near-infrared light curves that are available at www. astro. yale. edu/smarts/glast/home. php. The research at Boston University was supported by a number of NASA Fermi Guest Investigator grants, most recently 80NSSC20K1567. The material is based upon work supported by NASA under award number 80GSFC21M0002. The Fermi -LAT Collaboration acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis.
This work performed in part under DOE Contract DE-AC02-76SF00515. Facilities: Fermi, RXTE, Swift, SMARTS, REM, Tuorla, VLBA-BU-BLAZAR, SKYNET, MRO, OVRO, UMRAO An efficient diagnostic to discern the deleterious effects of Fourier transformation on the time series is to derive its spectral window function. The spectral window function measures the response of a Fourier transformation when applied on unevenly sampled time series. Specifically, it is 1 at the zeroth Fourier frequency and 0 at other frequencies for an evenly sampled time series (e. g. ,  Deeming 1975; Goyal 2020). Figures A. 1 and A. 2  present the sampling distribution and the corresponding spectral window functions for the long-term light curves of 3C 279 and PKS 1510-089, respectively. The spectral window functions corresponding to the Fermi-LAT curves for the targets show a uniform response across the frequency range as the LAT data are precisely sampled in 1-week intervals, and the other sampling values (14 and 21 days) are due to the missing TS<10 data points.
The majority of spectral window function curves corresponding to optical, infrared, and radio light curves exhibit broad/narrow peaks around ∼300 days timescale due to seasonal gaps in observation because of the source's proximity to the Sun. The sharpness of the peak in the spectral window function depends on the number of cycles covered. Therefore, those are more prominently seen in the few decades-long MRO and UMRAO radio light curves as compared to the nearly decade-long optical and infrared light curves. The spectral window function corresponding to the RXTE-PCA and Swift-XRT datasets also shows prominent peaks around the ∼300 days timescale, due to the scheduling constraints of these observations with the satellites due to the position of the Sun. Moreover, as expected, the spectral window functions related to the 3-hr integration bin show a rather uniform response across the frequency range as the light curves are without any periodic gaps, like those of weekly-binned LAT light curves.
Note-(1) the name of the blazar, (2-3) right ascension and declination from the NASA/IPAC Extragalactic Database (NED) (2019), (4) spectroscopic redshift. a Marziani et al. (1996); b Thompson et al. (1990), (5) mass of the SMBH. c Hayashida et al. (2015); d Rakshit (2020).
Figure1. Multiwavelength light curves of the blazar 3C 279. Panel (a) gives the 7-day integration bin γ-ray fluxes from the Fermi-LAT satellite (0. 1-300 GeV). The blue, red and green dashed vertical lines indicate three epochs for which 3-hr integration bin light curves are generated (panels a, b, and c of Figure3). Panel (b) shows the X-ray fluxes from the RXTE-PCA (3-20 keV) and Swift-XRT (0. 3-10 keV) satellites, respectively, while panel (c) gives the photon index derived by fitting a single power-law model to these datasets. Panels (d) and (e) give the B, V , R, I, J, H, and K-band fluxes from various, labeled, observing programs. Panel (f) gives radio fluxes, with noted offsets, from the Metsähovi (36. 9 GHz and 22. 0 GHz), OVRO (15 GHz) and UMRAO (14. 5 GHz, 8. 0 GHz, and 4. 8 GHz) monitoring programs, respectively.
ACKNOWLEDGMENTS We thank the journal referee for insightful and constructive comments, which improved the clarity and content of the manuscript. We thank our internal  Fermi-LAT reviewer ,  C. C. Cheung , for careful reading and several constructive comments on the manuscript. We also thank the  Fermi-LAT  publication board members,  Philippe Bruel  and  Matthew Kerr , for useful comments. A. G. acknowledges the financial support from the  Polish National Science Centre (NCN)  through the grant.
The GOLF instrument on board SoHO has been in operation for almost 25 years, but the ageing of the instrument has now strongly affected its performance, especially in the low-frequency pressure-mode (p-mode) region. At the end of the SoHO mission, the ground-based network BiSON will remain the only facility able to perform Sun-integrated helioseismic observations. Therefore, we want to assess the helioseismic performances of an échelle spectrograph such as SONG. The high precision of such an instrument and the quality of the data acquired for asteroseismic purposes call for an evaluation of the instrument's ability to perform global radialvelocity measurements of the solar disk. Data acquired during the Solar-SONG 2018 observation campaign at the Teide Observatory are used to study mid-and low-frequency p modes.
The layout of the paper is as follows. Section 2 presents the solar time series that were used for this work. We also give some details about the Solar-SONG data reduction method. In Sect. 3, the peak-bagging of the power spectral density (PSD) obtained from the time series is described. We use the peak-bagging results to compare GOLF and Solar-SONG performances in Sect. 4. Those results and the potential of Solar-SONG are discussed in Sect. 5. For a comparison, a detailed analysis of BiSON and HMI data is included in Appendix B.
GOLF data reduction Due to a loss in the counting rates measured by the photomultipliers resulting from normal ageing, the GOLF mean noise level has been increasing over the years  (García et al. 2005; Appourchaux et al. 2018)  in the high-and medium-frequency regions. Above 4 mHz and around 1 mHz, the photon noise power contribution dominates the PSD. We wanted to compare Solar-SONG performances not only to what GOLF performances are now, but also to what they used to be. We therefore selected 22 time series of the same length and all at similar epochs of the year in order to ensure that in each SoHO position on its orbit is comparable to what it was during the summer of 2018. We used GOLF time series calibrated using the method described in  García et al. (2005). GOLF measurements were obtained using the instrument's own time reference and not the SoHO main on-board time reference.
Solar-SONG data reduction In high-cadence mode, the SONG spectrograph acquires a spectrum approximately every 3. 5 s, with an exposure time below one second. The acquired spectrum covers 4400 to 6900 Å, with a pixel scale of 0. 022 Å, over 51 orders. However, for solar observations, we used an iodine cell to provide precise wavelength calibration; hence for those observations only 24 orders were used, covering the 4994 to 6208 Å range where the iodine cell imprint is present. The IDL iSONG  (Corsaro et al. 2012; Antoci et al. 2013 ) was used to compute the RVs for the solar data. The method that has been developed over the last few decades consists in dividing each order of the spectrum into so-called chunks and computing an RV for each of those chunks (see e. g. ,  Butler et al. 1996). Twenty-two chunks per order were used for Solar-SONG spectra.
Acknowledgements. The authors want to thank the anonymous referee for valuable comments that helped in improving the manuscript. This work is based on observations made at the Hertzsprung SONG telescope operated at the  Spanish Observatorio del Teide  on the island of Tenerife by the  Aarhus and Copenhagen Universities  and by the  Instituto de Astrofísica de Canarias. SoHO is a mission of international collaboration between  ESA  and  NASA. S. N. B. and R. A. G acknowledge the support from  PLATO  and GOLF CNES grants. Funding for Solar-SONG was provided by the  Excellence 'Severo Ochoa ' programme at the  IAC  and the  Ministry MINECO  under the program AYA-2016-76378-P. Funding for the  Stellar Astrophysics Centre  is provided by The  Danish National Research Foundation  (Grant  DNRF106 ).
Acknowledgment This project is supported by  Agence Nationale de la Recherche  under the contract  ANR-17-CE31-0019  (B. B. and J. N. ). This work was granted access to the HPC resources of CINES and IDRIS ( 2018-A0030506808 ,  2019-A0050506808 ,  2020-A0070506808  and  2020-A0080502271 ) by  GENCI. We also gratefully acknowledge the computing time granted on  SuperMUC-NG  (project  ID  pn72gi) by the  Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities at Garching near Munich and thank its staff  for their support. The authors are grateful to the colleagues of the CLS effort for having provided the gauge field ensembles used in the present work. This work is supported by the  Deutsche Forschungsgemeinschaft (DFG)  through the  Research Training Group "GRK 2149: Strong and Weak Interactions -from Hadrons to Dark Matter " (J.
We have quantified this difference by measuring longitudinal and lateral temperature differences and electric fields in two distinct experiments. In the first, a finite J Q was applied and the J e was kept equal to zero. In the second, a finite J e was injected without J Q. This led us to quantify the diagonal and off-diagonal components of the two conductivities, κ and κ. We also checked that the data respect Onsager reciprocity, which implies a unique thermoelectric tensor in equations (  2 ) and (  3 ), (See the supplement for details  [39] ). We found that the transverse thermal flow (but not the longitudinal one) is drastically affected by the particledriven flow of entropy represented by the thermoelectric term (See Thus, the corrected transverse thermal conductivity is larger than the measured one, which is itself larger than the expected one.
) µ 0 H ( T ) E 1 y E 1 x J Q ≠ 0 , J e = 0 ( a ) J Q = 0 , J e ≠ 0 ∇Τ 1 y ∇T 1 ( K m -1 ) µ 0 H ( T ) ( b ) -1 6 7 -1 6 6 -1 6 5 ∇Τ 1 x ∇T 1 x ( K m -1 ) E 2 y E 2 x E 2 ( V K -1 ) µ 0 H ( T ) ( c ) ∇Τ 2 y ∇Τ 2 x ∇T 2 ( K m -1 ) To determine the components of the 2 × 2 tensors σ , κ and α, we conducted two types of experiments (See Fig. S5 a  and d  ). In the first one, a heat current is applied along the x-axis ( J Q = 0) and electric current was kept at zero ( J e = 0). In the second one, an electric current is applied along x axis ( J e = 0) without heat current ( J Q = 0). In both experiments, we measured the temperature difference and the electric field along x and y axes at the same time. Fig. S5 b,c  and f,g  show typical data for both configurations at T= 19. 4 K for SrTiO 3-δ (n=1×10 18 cm -3 ).
ACKNOWLEDGMENTS N. F. would like to thank the  Johns Hopkins University  for hospitality during the early stages of this work. S. H. V. , N. F. , and  E. Barausse  acknowledge financial support provided under the European Union's  H2020   ERC Consolidator Grant  " GRavity from Astrophysical to Microscopic Scales " Grant Agreement No. GRAMS-815673. This work was supported by the  EU   Horizon 2020 Research and Innovation Programme  under the  Marie Sklodowska-Curie Grant  Agreement No. 101007855. S. H. V. acknowledges funding from the  Deutsche.
Observations Observations were acquired as part of project 2018. A. 00021. S (PI: Teague). Teague et al. (2022)  described the observational setup and calibration of these data in detail. We also combined this with data from project 2013. 1. 00387. S (PI: Guilloteau), originally presented in  Teague et al. (2016) , which has an almost identical correlator setup. In this paper, we focus on the CO and CS emission.
Integrated Intensity For the total integrated intensity, or zeroth-moment maps, we integrated the emission along the spectral axis using the Keplerian CLEAN mask to circumvent any sigma clipping. The CO zeroth-moment map has already been published in  Teague et al. (2022) , where a shadow was detected in the outer disk. These moment maps are shown in Figure  3  and compared to the 233 GHz continuum originally presented in  Macías et al. (2021)  to highlight the difference in the radial extent of the molecular gas and the mm-sized grains. We use the Python package GoFish (Teague 2019) to produce azimuthally averaged radial profiles of these zero moment maps. Emission from CO is detected out to 4 2 (252 au) and CS is detected out to 3 9 (235 au), while the continuum edge is around 1 65 (100 au;  Ilee et al.