adsabs/AstroCLIMB huggingface dataset:
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: The temperature ratio ﹩({\widetilde{T}}_{\nu }/{\widetilde{T}}_{{\nu }_{\mathrm{ref}}})﹩ between 800.2 MHz and the reference frequency ν ref = 700.0 MHz. To assess the impact of the intrinsic frequency variations in the sky, as described in Equation (20), we consider a simplified foreground model, denoted as “FG: freq−indep,” in which the spatial patterns at the reference frequency ν ref are held constant, while the average temperature corresponds to the actual temperature at each frequency. The standard, more complex foreground model is referred to as “FG: freq−dep.” The left panel displays the temperature ratio map using the original sky model, and the right panel illustrates the same for the simplified “FG: freq−indep” model. Notably, in the right panel, the observed noise originates from the independently added H I signal.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Comparison between the raw sky map (upper-left panel, with units of mK) and its convolution (upper-right panel) with the noisy beam Model-I (ε = 0.4%) B at 800.2 MHz. The middle-left panel demonstrates the residual map ﹩\delta {\widetilde{T}}_{\nu }﹩ caused by the beam noise δ B  ν  at ν = 800.2 MHz. The middle-right panel displays the residual H I map ﹩\delta {\widetilde{T}}_{\nu }^{\mathrm{HI}}﹩ at the same frequency and beam noise level. The bottom panel shows the variation of the temperature residual ﹩\delta {\widetilde{T}}_{\nu }({\hat{{\boldsymbol{n}}}}_{i})﹩ along the frequency axis.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: The illustration depicts our sky model at a frequency of 800.2 MHz. The highlighted region, which spans 130° ≤ R.A. ≤ 170° and 10° ≤ decl. ≤ 50°, represents the area chosen for our simulated data set.
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: NICER observations of a large stellar flare from a K-type giant star with Sherpa fits. Left: Four selected examples from the set of 127 spectra. Each spectrum is fitted with the same type of model (two thermal emission components), but each fit is independent with four parameters (temperature and volume emission measure, VEM, for both components). The color bar on the right shows the time of observation and indicates the flare decay over time (black to orange). Right: Fitted VEM (top) and temperature k T (center) over time (with k being the Boltzmann constant) for the two emission components (orange and blue). The fit quality (reduced χ 2) for each individual fit is shown in the bottom panel. For clarity, error bars are not shown.
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: Fitting luminosity vs. photon index relation for a sample of quasars (F. Shaban et al. 2022). Photon index data have asymmetric errors. Left: The upper panel displays the photon index (blue points with asymmetric errors for each source at a given luminosity) and the best-fit linear model (orange line). The lower panel shows the residuals. Right: Probability distribution from the bootstrap. The orange lines mark the median and 1σ uncertainties. The green dotted line marks the best-fit model assuming an average measurement error given by upper and lower errors.
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: Left: Chandra image of an X-ray cluster A2495 overlaid with the contours of the 2D fit assuming two beta2d model components with the same location of the center. Top right: the smoothed residuals with visible offset from the central location of the model. This cluster is known to have offsets between the central galaxy and the hot gas. Bottom right: the radial profiles of the surface brightness based on the fitted model components and the sum of the two components overlaid over the data and the background level.
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: An example of fitting multiple absorption lines to the optical spectrum. The model consisted of a polynomial (polynom1d) and four independent absorption lines (opticalgaussian parameterized by the position, width, and optical depth). The top panel shows the data points (blue) overplotted with the best-fit model with the residuals shown in the bottom panel.
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: Sherpa forward fitting. 1D or 2D data and complex models are defined. The best-fit parameters are found by a classic optimization method (violet box and red connectors) or model parameters are sampled from the posterior distribution with a Bayesian MCMC sampler (green box and connectors). The parameters with the confidence levels and the distributions can be visualized with the plotting and imaging options.
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: Modeling-trap map of an ACIS CCD with a 2D model defined as ﹩T(x,y)=({a}+{b}x)y+{c}y* {\exp }^{\tfrac{-{\left(x-x0\right)}^{2}}{2{\sigma }^{2}}}﹩. The left panel shows the data and the right panel shows the model image, with the color scale on the right in units of counts.
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: An optical spectrum (SDSS) fit with a model combined with a constant model (const1d) and a set of four Gaussian emission lines (gauss1d). The top panel shows the data (blue points) and the model fit (orange). The lower panel shows the residuals.
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: An example of Sherpa Python commands for fitting a polynomial to a 1D data array, d1, accessing Python objects (top) or using the UI (bottom).
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: Publications that used Sherpa (top panel) and the citations of these papers (bottom panel) over time until the end of 2023 (from the ADS library). The x-axis shows years and the vertical axis shows the number of publications.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Auto- and cross-power spectrum of SVD foreground removed map for noisy beam Model-I with ε = 0.4%. Here, we compare the results between our CBC method (solid lines) and the traditional approach (dashed and dotted lines). The upper panels show the autopower spectrum of the cleaned map δ T clean and the lower panels demonstrate the cross-correlation ratio with the injected H I signal. The middle panels show the cross-power spectrum between the δ T clean and the H I signal. The left column shows the case of the “FG: freq−indep” foreground model, and the right column corresponds to the case of the “FG: freq−dep” foreground model. As shown, our CBC method can reduce the autopower spectrum by a factor of a few and enhance the cross-correlation ratio by about 50%.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Frequency variation of the intrinsic temperature ratio ﹩{F}_{{\nu }_{\mathrm{ref}}\nu }^{T}﹩ for the “freq−dep” foreground model. With the reference frequency set at ν ref = 700 MHz, the amplitude of the factor F  T  gradually increases. Although this factor exhibits significant spatial variation, its frequency dependence remains quite smooth (right panel). This characteristic results in a comparatively lower impact on the final foreground removal process than the random beam noise we introduced creates.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Auto- and cross-power spectrum of cleaned map for noisy beam Model-II with ε = 1.6%. Unlike the case of Model-I, one can observe an increased oversubtraction at larger scales (k ≲ 0.15 h Mpc−1) in both the traditional and CBC methods, regardless of the foreground model considered. However, our CBC method continues to outperform the traditional approach at a similar level to that seen in Figure 10.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Similar to Figure 10, but for beam Model-I with smaller noise amplitude ε = 0.2%. Since ε is only half of the value used in Figure 10, the overall performance is better for both the traditional and CBC methods. Furthermore, while our CBC method continues to outperform the traditional approach, the correlation ratio for CBC stabilizes at f ∼ 0.8 compared to ∼0.6 for the traditional method, indicating a reduced improvement factor of about 1/3.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Autopower spectrum (upper panels) and cross-correlation ratio (lower panels) after SVD foreground removal, under the assumption that the relative beam ﹩{f}_{{\nu }_{\mathrm{ref}}\nu }({\boldsymbol{u}})={B}_{{\nu }_{\mathrm{ref}}}/{B}_{\nu }﹩ is precisely known, where ﹩{B}_{\nu }={B}_{\nu }^{G}+\delta {B}_{\nu }﹩. The left panels show the case of the “freq−indep” foreground model, and the right column corresponds to the case of the “freq−dep” foreground.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Similar to Figure 10, but for beam Model-I with higher noise amplitude ε = 0.6%. As expected, the overall performance has declined for both the traditional and CBC methods. The correlation ratio for CBC is f ∼ 0.45 at high k compared to ∼0.3 for the traditional method, demonstrating an improvement ratio of about 50%.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Similar to Figure 13, but here we assume a smaller ε = 0.8% for Model-II. As with Model-I, at this lower noise level, both the traditional and CBC methods perform well. The improvement factor for the CBC method is between 10% and a few percent.
Paper: Correlation-based Beam Calibration of 21 cm Intensity Mapping
Authors: Jiacheng Ding, Xin Wang, Ue-Li Pen, Xiao-Dong Li
Figure caption: Similar to Figure 13, but assuming ε = 2.4% for Model-II. At this noise level, the improvement for the CBC method is approximately one-third.
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: Sherpa’s place in the scientific Python ecosystem. Sherpa requires a numeric library (red box), and interfaces with several optional libraries for I/O, external model libraries, and visualization (blue boxes). It enables the fitting and modeling in several domain-specific packages (white boxes). The two large white arrows mark future implementation of interoperability with astropy.modeling and external optimizers. Infrastructure packages needed for developers, but not users, are marked with yellow boxes.
Paper: Sherpa: An Open-source Python Fitting Package
Authors: Aneta Siemiginowska, Douglas Burke, Hans Moritz Günther, Nicholas P. Lee, Warren McLaughlin, David A. Principe, Harlan Cheer, Antonella Fruscione, Omar Laurino, Jonathan McDowell, Marie Terrell
Figure caption: The color image of residuals from the best model shown in Figure 9. The color scale indicates σ values. The red boxes show the regions used for the 1D comparison plots shown in the right and bottom panels. The blue lines indicate the data and the orange lines the residuals.
Paper: The Chandra Source Catalog Release 2 Series
Authors: Ian N. Evans, Janet D. Evans, J. Rafael Martínez-Galarza, Joseph B. Miller, Francis A. Primini, Mojegan Azadi, Douglas J. Burke, Francesca M. Civano, Raffaele D'Abrusco, Giuseppina Fabbiano, Dale E. Graessle, John D. Grier, John C. Houck, Jennifer Lauer, Michael L. McCollough, Michael A. Nowak, David A. Plummer, Arnold H. Rots, Aneta Siemiginowska, Michael S. Tibbetts
Figure caption: Normalized histograms of the astrometric angular separations between the reference catalogs (SDSS DR15 for CSC 2.0 and Gaia DR3 for CSC 2.1) and crossmatched CSC master source positions for CSC 2.0 (red) and CSC 2.1 (blue), showing the overall improvement from matching release 2.1 astrometry to the Gaia-CRF3 reference frame.
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: Proper orbits of main-belt asteroids with relatively large errors (d > 100 m s–1). Problematic mean-motion resonances with Jupiter and Mars, secular resonances, and asteroid groups/families are labeled.
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: Proper (panels (A) and (B)) and osculating orbits (panels (C) and (D)) of main-belt asteroids. In the proper element space, various orbital structures come into focus.
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: Uncertainties of proper elements in terms of the d metric: new catalog (solid line), Novaković’s AFP catalog (dashed line).
Paper: Detecting Quasiperiodic Pulsations in Solar and Stellar Flares with a Neural Network
Authors: Sergey A. Belov, Dmitrii Y. Kolotkov, Valery M. Nakariakov, Anne-Marie Broomhall
Figure caption: Simultaneous plot of 2400 aligned and min–max scaled lightcurves from the synthetic data set (left panel) and min–max scaled lightcurves from a stellar flare catalog, observed with Kepler (L. A. Balona 2015).
Paper: Detecting Quasiperiodic Pulsations in Solar and Stellar Flares with a Neural Network
Authors: Sergey A. Belov, Dmitrii Y. Kolotkov, Valery M. Nakariakov, Anne-Marie Broomhall
Figure caption: Preprocessing of a flare lightcurve to produce two input channels for our FCN (see the architecture in Figure 3). Left panel: an initial flare time series (red curve) as the input channel 1, with the rise (black dashed curve) and decay (blue dashed–dotted curve) trends. Right panel: the detrended flare lightcurve as the input channel 2, consisting of the white-noise-padded rise phase (black curve) and the decay phase detrended with the Savitzky–Golay filter (blue curve).
Paper: Detecting Quasiperiodic Pulsations in Solar and Stellar Flares with a Neural Network
Authors: Sergey A. Belov, Dmitrii Y. Kolotkov, Valery M. Nakariakov, Anne-Marie Broomhall
Figure caption: Examples of synthetic lightcurves given by flare models: Equation (2), left; Equation (3), middle; and Equation (4), right. Top row: the initial flare profiles. Middle row: the initial flare profiles with added QPP (red curve), see Equation (5). Bottom row: the same as in the middle row but with added red and white noise (purple dashed curve), see Equation (6).
Paper: Detecting Quasiperiodic Pulsations in Solar and Stellar Flares with a Neural Network
Authors: Sergey A. Belov, Dmitrii Y. Kolotkov, Valery M. Nakariakov, Anne-Marie Broomhall
Figure caption: Left panel: the dynamics of the loss function during the training process for the training (red curve) and validation (blue dashed curve) data sets. Right panel: the dynamics of the accuracy score on the training (red curve) and validation (blue dashed curve) data sets during the training process.
Paper: Detecting Quasiperiodic Pulsations in Solar and Stellar Flares with a Neural Network
Authors: Sergey A. Belov, Dmitrii Y. Kolotkov, Valery M. Nakariakov, Anne-Marie Broomhall
Figure caption: Confusion matrices built using the synthetic test data set for 0.35, 0.5, 0.65, 0.75, 0.85, and 0.95 QPP-detection threshold values.
Paper: Detecting Quasiperiodic Pulsations in Solar and Stellar Flares with a Neural Network
Authors: Sergey A. Belov, Dmitrii Y. Kolotkov, Valery M. Nakariakov, Anne-Marie Broomhall
Figure caption: Architecture of the FCN for a QPP-detection task.
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: The past orbital convergence of secular angles 16–17 kyr ago indicates that the (9332) 1990SB1 family is extremely young. The nodal longitudes are shown by the red lines, and perihelion longitudes are shown in blue. The mean anomalies of family members, not shown here, converge at 16–17 kyr ago as well. All nine known family members are plotted here. This result was obtained without considering the Yarkovsky acceleration in the N-body integrator. A careful analysis including the Yarkovsky force is left for future work.
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: The past orbital convergence of secular angles 1.2–1.5 Myr ago indicates that the Moore–Sitterly family must be very young. The nodal longitudes are shown by the red line and perihelion longitudes in blue. All 17 known family members are plotted here. Note that this result was obtained without considering the Yarkovsky acceleration in the N-body integrator. A careful analysis including the Yarkovsky effect is left for future work.
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: Proper eccentricities and sine of proper inclinations for outer-belt asteroids (2.958 < a p < 3.8 au). Notable families are labeled: blue labels stand for families identified in this work and are not listed in Nesvorný et al. (2015), red labels are families known previously (not all previously known families are labeled).
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: Proper eccentricities and sine of proper inclinations for inner-belt asteroids (a p < 2.5 au). Notable families are labeled: blue labels stand for families identified in this work and are not listed in Nesvorný et al. (2015), red labels are families known previously (not all previously known families are labeled).
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: Osculating orbits of asteroids near the (9332) 1990SB1 family (MJD epoch 60400.0): the red dot is 9332, blue dots are small family fragments. The family is so compact that no background asteroids appear in any of these projections. Two family members, 2016DB45 and 2016EQ6, have practically identical orbits; the difference in the mean anomaly is only 0.6 deg. They appear as two overlapping dots in all projections.
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: Dynamical structure of the Veritas family: proper elements computed here (top panels) and proper elements from http://asteroids.matf.bg.ac.rs/fam/ (bottom panels).
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: Osculating orbits of asteroids near the Moore–Sitterly family (MJD epoch 60400.0): (2110) Moore–Sitterly and (44612) 1999 RP27 are shown by red dots, other family members by blue dots; gray dots show background asteroids.
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: Proper eccentricities and sine of proper inclinations for asteroids in the pristine zone (2.825 < a p < 2.958 au). Notable families are labeled: blue labels stand for families identified in this work and are not listed in Nesvorný et al. (2015), red labels are families known previously (not all previously known families are labeled).
Paper: Catalog of Proper Orbits for 1.25 Million Main-belt Asteroids and Discovery of 136 New Collisional Families
Authors: David Nesvorný, Fernando Roig, David Vokrouhlický, Miroslav Brož
Figure caption: Proper eccentricities and sine of proper inclinations for middle-belt asteroids (2.5 < a p < 2.825 au). Notable families are labeled: blue labels stand for families identified in this work and are not listed in Nesvorný et al. (2015), red labels are families known previously (not all previously known families are labeled).
Paper: Detecting Quasiperiodic Pulsations in Solar and Stellar Flares with a Neural Network
Authors: Sergey A. Belov, Dmitrii Y. Kolotkov, Valery M. Nakariakov, Anne-Marie Broomhall
Figure caption: Left panel: the histogram of the χ 2 criterion characterizing how good the slowly varying background flare trend is fitted by a parabolic function, for the stellar flare catalog of L. A. Balona (2015) based on the white-light Kepler data. Middle panel: a flare lightcurve and its parabolic trend. Right panel: a flare lightcurve with the parabolic trend removed.
Paper: Detecting Quasiperiodic Pulsations in Solar and Stellar Flares with a Neural Network
Authors: Sergey A. Belov, Dmitrii Y. Kolotkov, Valery M. Nakariakov, Anne-Marie Broomhall
Figure caption: Stellar flare lightcurves with QPP from C. E. Pugh et al. (2016) where the FCN did not find any QPP.
Paper: Detecting Quasiperiodic Pulsations in Solar and Stellar Flares with a Neural Network
Authors: Sergey A. Belov, Dmitrii Y. Kolotkov, Valery M. Nakariakov, Anne-Marie Broomhall
Figure caption: Stellar flare lightcurves with QPP from C. E. Pugh et al. (2016) where the FCN also found QPP.