adsabs/AstroCLIMB huggingface dataset:
Paper: Orbital Decay of Double White Dwarfs: Beyond Gravitational-wave Radiation Effects
Authors: G. A. Carvalho, R. C. dos Anjos, J. G. Coelho, R. V. Lobato, M. Malheiro, R. M. Marinho, J. F. Rodriguez, J. A. Rueda, R. Ruffini
Figure caption: Difference in the intrinsic time-domain phase evolution between the two models ﹩{\rm{\Delta }}{Q}_{\omega }\equiv {Q}_{\omega }^{\mathrm{GW}}-{Q}_{\omega }^{\mathrm{UIM}}﹩, for a binary with M = 1.2  M ⊙, q = 1, η = 100, and two selected values of the magnetic field B = 8 × 107 G (solid curves), B = 2 × 108 G (dashed curves). The different colors correspond to different initial values of α used in the numerical integration. The intrinsic time-domain phase parameter encapsulates two of the intrinsic observable obtained from GW data. Since ΔQ  ω  ≫ 1, the UIM waveform gets out of phase with respect to the waveform in the case of only GW radiation. A sufficiently large dephasing can be detected by LISA for appropriate conditions (see main text for details).
Paper: Orbital Decay of Double White Dwarfs: Beyond Gravitational-wave Radiation Effects
Authors: G. A. Carvalho, R. C. dos Anjos, J. G. Coelho, R. V. Lobato, M. Malheiro, R. M. Marinho, J. F. Rodriguez, J. A. Rueda, R. Ruffini
Figure caption: EM power as a function of orbital angular frequency given by Equation (15), for selected values of the primary’s magnetic field. In this example, we set η = 100, and the binary is mass-symmetric with M 1 = M 2 = 0.6 M ⊙. The mass–radius relation is taken from Carvalho et al. (2018). For comparison, we also show the GW power (black dashed curve) given by Equation (4).
Paper: Orbital Decay of Double White Dwarfs: Beyond Gravitational-wave Radiation Effects
Authors: G. A. Carvalho, R. C. dos Anjos, J. G. Coelho, R. V. Lobato, M. Malheiro, R. M. Marinho, J. F. Rodriguez, J. A. Rueda, R. Ruffini
Figure caption: Constraint on the binary masses and magnetic field of the primary in ZTF J1539+5027. We have used the orbital period and decay rate reported in Burdge et al. (2019) and listed in Table 1.
Paper: The Solar Cycle Dependence of In Situ Properties of Two Types of Interplanetary CMEs during 1999–2020
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Chi Ma, Lidong Xia
Figure caption: The yearly median speeds of FCs and NFCs (top panel), and histograms of speed for FCs and NFCs during different phases of solar cycles 23 (middle panel) and 24 (bottom panel). The SSNs are denoted by black lines on the top panel, and the red, blue, and green represent FCs, NFCs, and solar wind, respectively. The solid vertical lines in panels (b1)–(b4) and (c1)–(c4) denote the median speed, and the dotted vertical lines represent lower and upper quartiles.
Paper: The Solar Cycle Dependence of In Situ Properties of Two Types of Interplanetary CMEs during 1999–2020
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Chi Ma, Lidong Xia
Figure caption: The yearly median Q Fe of FCs and NFCs (top panel) and Q Fe histograms for FCs and NFCs during different phases of solar cycles 23 (middle panel) and 24 (bottom panel). The SSNs are denoted by black lines on the top panel, and the red, blue, and green represent FCs, NFCs, and solar wind, respectively. The solid vertical lines in panels (b1)–(b4) and (c1)–(c4) denote the median Q Fe, and the dotted vertical lines represent lower and upper quartiles.
Paper: The Solar Cycle Dependence of In Situ Properties of Two Types of Interplanetary CMEs during 1999–2020
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Chi Ma, Lidong Xia
Figure caption: The yearly numbers (top panel), proportions (middle panel), and duration (bottom) of FCs and NFCs. The SSNs are denoted by black lines in the top panel, and red and blue represent FCs and NFCs, respectively.
Paper: The Solar Cycle Dependence of In Situ Properties of Two Types of Interplanetary CMEs during 1999–2020
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Chi Ma, Lidong Xia
Figure caption: The yearly median FIP bias of FCs and NFCs (top panel) and FIP bias histograms for FCs and NFCs during different phases of solar cycles 23 (middle panel) and 24 (bottom panel). The SSNs are denoted by black lines on the top panel, and the red, blue, and green represent FCs, NFCs, and solar wind, respectively. The solid vertical lines in panels (b1)–(b4) and (c1)–(c4) denote the median FIP bias, and the dotted vertical lines represent lower and upper quartiles.
Paper: The Solar Cycle Dependence of In Situ Properties of Two Types of Interplanetary CMEs during 1999–2020
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Chi Ma, Lidong Xia
Figure caption: The CCs between the SSNs and the parameters inside FCs (red), NFCs (blue), solar wind (green), and ICMEs as a whole (black).
Paper: The Solar Cycle Dependence of In Situ Properties of Two Types of Interplanetary CMEs during 1999–2020
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Chi Ma, Lidong Xia
Figure caption: The yearly median A He of FCs and NFCs (top panel) and A He histograms for FCs and NFCs during different phases of solar cycles 23 (middle panel) and 24 (bottom panel). The SSNs are denoted by black lines on the top panel, and the red, blue, and green represent FCs, NFCs, and solar wind, respectively. The solid vertical lines in panels (b1)–(b4) and (c1)–(c4) denote the median A He, and the dotted vertical lines represent lower and upper quartiles.
Paper: The Solar Cycle Dependence of In Situ Properties of Two Types of Interplanetary CMEs during 1999–2020
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Chi Ma, Lidong Xia
Figure caption: The yearly median O7+/O6+ of FCs and NFCs (top panel) and O7+/O6+ histograms for FCs and NFCs during different phases of solar cycles 23 (middle panel) and 24 (bottom panel). The SSNs are denoted by black lines on the top panel, and the red, blue, and green represent FCs, NFCs, and solar wind, respectively. The solid vertical lines in panels (b1)–(b4) and (c1)–(c4) denote the median O7+/O6+, and the dotted vertical lines represent lower and upper quartiles.
Paper: Stellar Obliquity from Spot Transit Mapping of Kepler-210
Authors: Adriana Valio, Alexandre Araújo
Figure caption: Differential rotation profile of Kepler-210. The orange square represents the rotation period at the transit latitude of Kepler-210 b (−32.°9), whereas the horizontal dashed line represents the average rotation period of 12.338 days. The blue squares are the estimates of the rotation period of spots occulted by the oblique orbit of Kepler-210 c in latitude bins of 10°. The dashed blue curve is the fit resulting from the five blue squares, whereas the solid blue curve is the result of all six rotational periods (orange and blue squares) from spots occulted by Kepler-210 b and Kepler-210 c.
Paper: Stellar Obliquity from Spot Transit Mapping of Kepler-210
Authors: Adriana Valio, Alexandre Araújo
Figure caption: Map of spot longitude in time from the transits of Kepler-210 b in a reference frame that rotates with the star with a period of 12.44 days, which is the best calculated rotation period at the transit latitude of −32.°9. The flux deficit due to the spots at a given longitude is shown in the bottom panel.
Paper: Stellar Obliquity from Spot Transit Mapping of Kepler-210
Authors: Adriana Valio, Alexandre Araújo
Figure caption: Sum of the area of all the spots located within the longitude interval bins for the spots occulted from the transits of planets Kepler-210 b (violet) and Kepler-201 c (orange). A dip may be seen in the distribution of spots from Kepler-210 c at around − 37°.
Paper: Stellar Obliquity from Spot Transit Mapping of Kepler-210
Authors: Adriana Valio, Alexandre Araújo
Figure caption: Light curve of a double transit of both planets Kepler-210 b and Kepler-210 c. The central time is 960.83432 BKJD. The model of the double transits is represented by the red curve.
Paper: Multiple Transitions in Vacuum Dark Energy and H 0 Tension
Authors: Hossein Moshafi, Hassan Firouzjahi, Alireza Talebian
Figure caption: A schematic view of the evolution of dark energy ﹩{\rho }_{{}_{X}}﹩ compared to total energy energy density ρ. Left: at early times ﹩{\rho }_{{}_{X}}\ll \rho ﹩ and ﹩{H}_{X}^{-1}\gg {H}^{-1}﹩ so the FLRW horizon is within one patch of the dS horizon associated to dark energy. Middle: around the time of transition, ﹩{\rho }_{{}_{X}}\sim \rho ﹩ and ﹩{H}_{X}^{-1}\sim {H}^{-1}﹩. This is the time when the effects of dark energy become important. Right: long after the transition, many patches of the dS horizon associated with ﹩{\rho }_{{}_{X}}﹩ enter the FLRW horizon and the effects of ﹩{\rho }_{{}_{X}}﹩ are represented by a phenomenological fluid with the equation of state w.
Paper: Intermediate-mass Black Holes on the Run from Young Star Clusters
Authors: Elena González Prieto, Kyle Kremer, Giacomo Fragione, Miguel A. S. Martinez, Newlin C. Weatherford, Michael Zevin, Frederic A. Rasio
Figure caption: Top panel: cumulative BBH merger rate for BHs, computed using Equation (1). Bottom panel: comoving merger rate calculated with Equation (2). We label as stellar mergers cases where both component masses are less than 40.5 M ⊙. The green and magenta lines show the merger rates for BBHs with at least one component in the “mass gap” (40.5–120 M ⊙) or an IMBH (M > 120 M ⊙), respectively.
Paper: Multiple Transitions in Vacuum Dark Energy and H 0 Tension
Authors: Hossein Moshafi, Hassan Firouzjahi, Alireza Talebian
Figure caption: Contour plots for Two-step models for H 0 vs. Ω m  based on CMB+BAO+SN data, comparing the effect of the second step’s position z c,2 with fixed f 1, f 2 > 0. Note that the shaded area shows the measurement of H 0 done by the SH0ES team and its 1σ error (Riess et al. 2022).
Paper: Multiple Transitions in Vacuum Dark Energy and H 0 Tension
Authors: Hossein Moshafi, Hassan Firouzjahi, Alireza Talebian
Figure caption: Contour plots for One-step models with different values of f 1 for H 0 vs. Ω m  based on CMB+BAO+SN data. Note that the shaded area shows the measurement of H 0 done by the SH0ES team and its 1σ error (Riess et al. 2022).
Paper: Multiple Transitions in Vacuum Dark Energy and H 0 Tension
Authors: Hossein Moshafi, Hassan Firouzjahi, Alireza Talebian
Figure caption: One-dimensional likelihoods for One-step models with different values of f 1 for H 0 based on CMB+BAO+SN data. Note that the shaded area shows the measurement of H 0 done by the SH0ES team and its 1σ error (Riess et al. 2022).
Paper: Multiple Transitions in Vacuum Dark Energy and H 0 Tension
Authors: Hossein Moshafi, Hassan Firouzjahi, Alireza Talebian
Figure caption: Contour plots for One-step models for H 0 vs. Ω m  based on CMB+BAO+SN data. Note that the shaded area shows the measurement of H 0 done by the SH0ES team and its 1σ error (Riess et al. 2022).
Paper: Multiple Transitions in Vacuum Dark Energy and H 0 Tension
Authors: Hossein Moshafi, Hassan Firouzjahi, Alireza Talebian
Figure caption: One-dimensional likelihoods for One-step models for H 0 based on “CMB+BAO+SN” data. Note that the shaded area shows the measurement of H 0 done by the SH0ES team and its 1σ error (Riess et al. 2022).
Paper: Multiple Transitions in Vacuum Dark Energy and H 0 Tension
Authors: Hossein Moshafi, Hassan Firouzjahi, Alireza Talebian
Figure caption: One-dimensional likelihoods for Two-step models for H 0 based on CMB+BAO+SN data, comparing the effect of the second step’s position z c,2 with fixed f 1, f 2 > 0.
Paper: MeV–GeV Gamma-Ray Emission from SNR G327.1–1.1 Discovered by the Fermi-LAT
Authors: Jordan Eagle, Daniel Castro, Tea Temim, Jean Ballet, Patrick Slane, Joseph Gelfand, Matthew Kerr, Marco Ajello
Figure caption: Supernova remnant SNR G327.1–1.1 as observed by the Molonglo Observatory Synthesis Telescope at 843 MHz (red), Chandra X-rays between 0.5 and 7 keV (green), and the High Energy Stereoscopic System (H.E.S.S.) TeV and Fermi-LAT best-fit positions indicated by their 95% uncertainty radii, R 95 = 0.°035 and R 95 = 0.°079 for the TeV and MeV–GeV positions, respectively. The faint radio SNR shell is outlined as the red-dashed circle.
Paper: MeV–GeV Gamma-Ray Emission from SNR G327.1–1.1 Discovered by the Fermi-LAT
Authors: Jordan Eagle, Daniel Castro, Tea Temim, Jean Ballet, Patrick Slane, Joseph Gelfand, Matthew Kerr, Marco Ajello
Figure caption: Unsmoothed Chandra ACIS-I image of the pulsar wind nebula (PWN) region (2.′6 across) with the “body” and “head” regions used for the spectral extraction, which are chosen to be the same regions of the radio relic (“body”) and radio finger (“head”) spectral extraction regions used in Ma et al. (2016; see their Figure 2, bottom right panel).
Paper: A Direct Measurement of Galaxy Major and Minor Merger Rates and Stellar Mass Accretion Histories at Z < 3 Using Galaxy Pairs in the REFINE Survey
Authors: Christopher J. Conselice, Carl J. Mundy, Leonardo Ferreira, Kenneth Duncan
Figure caption: The total (see text) measured pair fraction, f pair, for galaxies at M * > 1011 M ⊙ down to a stellar mass ratio of 1:10. Measurements are made in the UDS (red circles), UltraVISTA (green triangles), VIDEO (blue squares), and GAMA (gold cross) survey fields. A power law (black solid line) is fit to the data points with best-fitting parameters described in the text. The lower dashed line shows the major merger pair fraction for the same stellar mass range from M17. The two orange points at higher redshifts are the merger fractions for minor mergers from Bluck et al. (2012). The purple shaded region shows the 1σ variation in the pair fraction as measured by using 24 light cones based on the H15 semianalytical model. The solid line is the best-fit line for the total merger history, and the minor mergers are shown by the cyan dotted–dashed line.
Paper: A Direct Measurement of Galaxy Major and Minor Merger Rates and Stellar Mass Accretion Histories at Z < 3 Using Galaxy Pairs in the REFINE Survey
Authors: Christopher J. Conselice, Carl J. Mundy, Leonardo Ferreira, Kenneth Duncan
Figure caption: Comparison between the total merger pair fraction history (solid line) along with the major mergers (dashed line), and derived minor mergers, shown as a dotted–dashed cyan line and the points. These are from the best fits to these quantities, showing that minor mergers are more common up to z ∼ 3 than major mergers are. We furthermore plot the Man et al. (2016) results for measured minor merger fractions from CANDELS as the black solid points. Also shown are the model predictions as discussed in Figure 2 and the text.
Paper: A Direct Measurement of Galaxy Major and Minor Merger Rates and Stellar Mass Accretion Histories at Z < 3 Using Galaxy Pairs in the REFINE Survey
Authors: Christopher J. Conselice, Carl J. Mundy, Leonardo Ferreira, Kenneth Duncan
Figure caption: The comparison between best-fit photometrically derived redshifts, z phot, and spectroscopically measured redshifts, z spec, in the (a) UDS, (b) VIDEO, (c) COSMOS, and (d) GAMA regions, similar to that shown in M17. Shown within parenthesis are the number of science-quality spectroscopic redshifts within each field that we use for calibrating the photometric redshifts. The normalized median absolute deviation, average offset, and outlier fraction of our photometric redshifts are discussed in more detail in M17.
Paper: Reclassifying Swift Gamma-Ray Bursts with Diverse Duration Distributions
Authors: Q. Deng, Z.-B. Zhang, X.-J. Li, H.-Y. Chang, X.-L. Zhang, H.-Y. Zhen, H. Sun, Q. Pan, X.-F. Dong
Figure caption: The fractions f NC of noncollapsars are plotted against T 90 for five data sets at the 68% confidence levels (shaded regions). The horizontal black lines represent a probability of f NC = 0.5. The vertical lines in each panel mark the duration threshold T th (solid lines) together with the 1σ scatter (dashed lines) when f NC = 0.5 is fulfilled. The cyan lines are the cumulative fractions of T 90.
Paper: Reclassifying Swift Gamma-Ray Bursts with Diverse Duration Distributions
Authors: Q. Deng, Z.-B. Zhang, X.-J. Li, H.-Y. Chang, X.-L. Zhang, H.-Y. Zhen, H. Sun, Q. Pan, X.-F. Dong
Figure caption: Hardness ratio log HR32 vs. log T 90 (left subpanel) and histograms of log HR32 for SGRBs and LGRBs (right subpanel) plotted for each data set. The two black vertical boundaries are the same as in Figure 4, and their averages are identified with the vertical purple lines. Different kinds of GRBs including SN/GRBs, XRFs, and GW/FRB-associated GRBs are illustrated in the inserts.
Paper: Reclassifying Swift Gamma-Ray Bursts with Diverse Duration Distributions
Authors: Q. Deng, Z.-B. Zhang, X.-J. Li, H.-Y. Chang, X.-L. Zhang, H.-Y. Zhen, H. Sun, Q. Pan, X.-F. Dong
Figure caption: The differential T 90 distributions of five data sets. The dashed lines denote the components of noncollapsars (green) and collapsars (purple), respectively. The solid lines stand for the best fits by the probability density function of Equation (1).
Paper: Reclassifying Swift Gamma-Ray Bursts with Diverse Duration Distributions
Authors: Q. Deng, Z.-B. Zhang, X.-J. Li, H.-Y. Chang, X.-L. Zhang, H.-Y. Zhen, H. Sun, Q. Pan, X.-F. Dong
Figure caption: The T 90 distributions of different samples of Swift GRBs. Data sets I–V are marked with (a)–(e), respectively. A multiple-Gauss model has been used to fit to data. The vertical lines indicate the boundary of any two adjacent components.
Paper: Reclassifying Swift Gamma-Ray Bursts with Diverse Duration Distributions
Authors: Q. Deng, Z.-B. Zhang, X.-J. Li, H.-Y. Chang, X.-L. Zhang, H.-Y. Zhen, H. Sun, Q. Pan, X.-F. Dong
Figure caption: The intrinsic duration T 90,i  distributions of five samples of Swift GRBs with known redshift in the rest frame. Different data sets are marked with solid, dashed, dashed–dotted, dashed–dotted-dotted, and dotted lines individually.
Paper: Reclassifying Swift Gamma-Ray Bursts with Diverse Duration Distributions
Authors: Q. Deng, Z.-B. Zhang, X.-J. Li, H.-Y. Chang, X.-L. Zhang, H.-Y. Zhen, H. Sun, Q. Pan, X.-F. Dong
Figure caption: HR32 evolving with T 90 for different components of prompt γ-ray emissions in Figure 1.
Paper: LISA Gravitational Wave Sources in a Time-varying Galactic Stochastic Background
Authors: Matthew C. Digman, Neil J. Cornish
Figure caption: Comparison of whitened residual power ﹩{({w}_{{nm}}^{\mathrm{AE}})}^{2}/{S}_{{nm},\mathrm{model}}^{\mathrm{AE}}﹩ between the left panel (a constant model of the galactic stochastic background) and center (our cyclostationary model for a 2 yr simulated data set). The cyclostationary model is a significant improvement, with residuals statistically well approximated as Gaussian. Right: we show the whitened residuals of the cyclostationary component after turning off instrumental noise completely, i.e., ﹩{({w}_{{nm},\mathrm{gal}}^{\mathrm{AE}})}^{2}/{S}_{{nm},\mathrm{gal}}^{\mathrm{AE}}﹩. At frequencies around 2 mHz, the signal is still very well approximated as cyclostationary, while at the tails of the spectrum, a slight deviation from perfect cyclostationarity is apparent. This deviation is of limited practical significance because the spectrum falls off rapidly compared to the instrumental noise at those frequencies. Additionally, it will not be possible to separate the instrumental and galactic contributions to the noise spectrum in real data.
Paper: LISA Gravitational Wave Sources in a Time-varying Galactic Stochastic Background
Authors: Matthew C. Digman, Neil J. Cornish
Figure caption: Histogram comparison of the number of detectable galactic binaries between a constant model of the galactic stochastic background and cyclostationary model for our 1 yr simulated data set. The models disagree on the threshold S/N > 7 for 241 binaries, out of 7273 total predicted detectable by the cyclostationary model, vs. 7470 in the constant model. Because the binaries are physically highly clustered at the galactic center, the relative increase in detection efficiency far from the galactic center is significantly greater than the loss of efficiency near the galactic center. Therefore, the overall observing volume is larger in the cyclostationary model, despite the lower number of galactic binaries detected. The source of the observed structure is explored in Figure 6.
Paper: LISA Gravitational Wave Sources in a Time-varying Galactic Stochastic Background
Authors: Matthew C. Digman, Neil J. Cornish
Figure caption: Improvement of galactic confusion noise spectrum with observation time in the S/Nthresh = 7 cyclostationary model, with the best-fit spectrum from Equation (9). Our fits are all performed intrinsically in the wavelet domain, but over this frequency range we approximate ﹩{S}^{\mathrm{AE}}(f)\simeq \langle {S}_{m}^{\mathrm{AE}}\rangle {dt}﹩, dt ≃ 30.075 s, to enable easier comparison to frequency domain results. Overall the model produces an excellent fit to the overall shape of the spectrum.
Paper: LISA Gravitational Wave Sources in a Time-varying Galactic Stochastic Background
Authors: Matthew C. Digman, Neil J. Cornish
Figure caption: Effective range to an injected supermassive black hole binary with f RD = 2 mHz as a function of chirp time using the galactic background from the 2 yr simulated data set. While the sensitivity varies with time, averaged over the course of 1 yr the total observing volume is about 20% larger. The minimum at t  c  ≃ 0.73 corresponds to the closest passage of LISA’s maximum sensitivity to the galactic anticenter.
Paper: LISA Gravitational Wave Sources in a Time-varying Galactic Stochastic Background
Authors: Matthew C. Digman, Neil J. Cornish
Figure caption: Modulation in the amplitude of the galactic background r AE  n  from Equation (6) as a function of time for 2 yr of simulated LISA data. The amplitude in the A channel varies by a factor of ≃7, while the amplitude in the E channel varies by a factor of ≃4, and the mean amplitude across the two channels varies by a factor of ≃5, from ≃0.32 to ≃1.67. The model includes the first five harmonics of 1 yr as described in Equation (7), although in this case the amplitudes of the first two harmonics dominate the other three by more than an order of magnitude.
Paper: LISA Gravitational Wave Sources in a Time-varying Galactic Stochastic Background
Authors: Matthew C. Digman, Neil J. Cornish
Figure caption: Comparison of S/N obtained in our 1 yr simulated data set for a 2 mHz galactic binary with all-sky-averaged S/N ≃ 10. Each pixel is an average of 50 random realizations of the inclination, polarization, and binary phase at each HEALPix pixel. The total volume sensitive to the test binary is ≃20% larger. However, the bulk of the galactic binaries predicted by the population synthesis model are found near the galactic center, resulting in the cyclostationary model detecting a lower number of galactic binaries, as shown in Figure 5.
Paper: LISA Gravitational Wave Sources in a Time-varying Galactic Stochastic Background
Authors: Matthew C. Digman, Neil J. Cornish
Figure caption: Illustration of the injection procedure used to generate in Figures 8 and 9. For this figure, we superpose the simulated signals for an injected SMBHB at each of the four chirp times presented in Figure 8 with a whitened galactic+instrument noise spectrum from the constant noise model. The source has a total mass chosen such that f RD = 2 mHz, and a mean S/N2 = 10,000 across all realizations of the chirp time and sky location.
Paper: LISA Gravitational Wave Sources in a Time-varying Galactic Stochastic Background
Authors: Matthew C. Digman, Neil J. Cornish
Figure caption: Convergence of the iterative procedure described in Section 2.2 for a 2 yr galactic stochastic gravitational-wave spectrum. The final converged spectrum was first reached on iteration 17; iterations 5–16 were omitted to improve the clarity of the plot. The procedure has already suppressed 90% of S/N > 7 binaries by iteration 4, and the final seven iterations are spent deciding on the inclusion of only ∼10 binaries with S/N ≃ 7.