adsabs/AstroCLIMB huggingface dataset:
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: PSD of the count rate summed over the energy channels. It was computed using Welch’s method with a segment length of ∼650 s and median averaging. The PSD is averaged over 1000 data segments with at least 60 minutes of continuous, trigger-free data. The spectrum shows a steep increase at low frequencies, indicative of red noise fluctuations in the background, which limit our ability to increase the averaging window. The red line marks the 10−2 Hz scale, showing that significant background fluctuations are already dominating the Poisson noise in timescales longer than 100 s. As a result, the validity of our background estimation is limited to shorter timescales, further shortening the validity of the matched filtering results to the order of a few seconds.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Timing glitch test. The figure shows a histogram of the timing glitch test statistic for 2014 triggers with a duration of less than 0.5 s. Overlaid are the triggers that were classified by the GBM catalog as GRBs. The distribution seems to fit a normal distribution (with a small SNR-dependent bias), justifying the 5σ selection of the threshold.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Trigger distribution and pastro after applying energy, time, and localization cut. We take triggers with 40 ≤ Epeak ≤ 2000 keV, 0.016 ≤ t ≤ 3.606 s, and ﹩{{ \mathcal B }}_{\oplus },{{ \mathcal B }}_{\odot }\lt 1﹩.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Local background estimation example. The figure shows a simulated signal with noise and the local background estimation using a standard rolling mean, a rolling mean with a gap, and a second-order filter. It is clear that when a burst occurs, the gapped version captures the real background more accurately at the time of the burst. Furthermore, by applying the second-order filter (see main text and Appendix C), we improve the estimation, especially where the background varies in a nonlinear fashion.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Background fitting. After obtaining the background estimator (Equation (7)), we smooth out background fluctuations by fitting a polynomial to the selected energy channels. The fitting ranges are chosen in featureless segments of the spectrum.
Paper: Discovering Cataclysmic Variables from the Rubin Observatory LSST
Authors: D. A. H. Buckley, Y. Tampo, P. Szkody, M. Motsoaledi, S. Scaringi, M. Lochner, N. Rawat, J. P. Marais, B. van Soelen, S. Macfarlane, A. van Dyk
Figure caption: Observed magnitude at the first detection in outburst vs. the outburst amplitude from the r-band counterpart in the simulated light curves of WZ Sge–type DNe. The vertical and horizontal lines show the typical saturation magnitude in the LSST and the outburst amplitudes of the input light curves, respectively. In case there is no detection before the outburst in the r band, we assume the upper limit rlow ≥ 24.0 mag and give a lower limit on the outburst amplitude. Only the samples in the thin-disk distribution model whose outbursts are detected within the first 7 days are plotted.
Paper: Discovering Cataclysmic Variables from the Rubin Observatory LSST
Authors: D. A. H. Buckley, Y. Tampo, P. Szkody, M. Motsoaledi, S. Scaringi, M. Lochner, N. Rawat, J. P. Marais, B. van Soelen, S. Macfarlane, A. van Dyk
Figure caption: Top left: the number of LSST visits in the whole 10 yr. Detectability dependence on the equatorial coordinate for micronovae (upper right), WZ Sge stars (middle), and AM Her stars (bottom) of the thin-disk distribution model. The description of each plot and category is presented in the text.
Paper: Discovering Cataclysmic Variables from the Rubin Observatory LSST
Authors: D. A. H. Buckley, Y. Tampo, P. Szkody, M. Motsoaledi, S. Scaringi, M. Lochner, N. Rawat, J. P. Marais, B. van Soelen, S. Macfarlane, A. van Dyk
Figure caption: Numbers of detection categories on various system parameters in the thin-disk distribution model. Galactic latitude b (degrees; left), Galactic extinction E(B − V) (magnitude; middle), and distance (kiloparsecs; right) of WZ Sge–type DNe (top row), polars (middle row), and micronovae (bottom row).
Paper: Discovering Cataclysmic Variables from the Rubin Observatory LSST
Authors: D. A. H. Buckley, Y. Tampo, P. Szkody, M. Motsoaledi, S. Scaringi, M. Lochner, N. Rawat, J. P. Marais, B. van Soelen, S. Macfarlane, A. van Dyk
Figure caption: r-band input light curves of WZ Sge-type DNe (top left), micronovae (top right), and polars (bottom), in units of absolute magnitude.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Top row: the left and right panels show the injected and detected component mass distributions of NSs for the Gaussian and double Gaussian mass models, respectively. The black solid lines indicate the true mass distributions (see Table 2), and histograms correspond to the source-frame masses of 5 and 50 NSs detected with SNR ≥ 20. Bottom row: the left and right panels display the luminosity distance distributions of the detected events for the same mass models. The black lines represent the underlying true distributions, while the histograms show results for 5 and 50 detected NSs with SNR ≥ 20.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Same as Figure 2, but using 50 events.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Comparison of the inferred population and cosmological parameters from five GW events, following the double Gaussian mass distribution, detected by the LIGO-Virgo detectors. The black solid lines show the injected values of the corresponding parameters. The 90% credible intervals are also mentioned for each of the respective marginalized 1D posteriors.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Comparison of the inferred population and cosmological parameters from five events, based on the Gaussian mass distribution, detected by the LIGO-Virgo detectors. The label “Population” refers to the Bayesian analysis considering only mass and redshift information (m1, m2, z), while the other label “EoS+Population” also includes tidal parameters (m1, m2, z, Λ1, Λ2). The black solid lines indicate the injected values of the corresponding parameters. The 90% credible intervals are shown for each of the respective marginalized 1D posteriors.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Same as Figure 4, but using 50 events.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Structure of the C I 1.0693 μm line observed in the prepeak NIR spectra of SNe Ia. The rest wavelength of the C I 1.0693 μm and Mg II 1.0927 μm lines are given by the dashed vertical lines. The spectra are divided into three groups and color coded according to the structure of the absorption feature. The first group includes SNe that lack any absorption features (blue), the second group displays a single “V” absorption feature in the 1 μm region (green) associated with high-velocity Mg II 1.0927 μm and finally are spectra displaying a “W” feature (red) associated with the presence of magnesium and unburnt carbon in the ejecta.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Nebular-phase emission features of the He I 1.083 μm/Paγ 1.094 μm, O I 1.129 μm/Fe I 1.142 μm, Paβ 1.282 μm, and Si I 1.644 μm lines of the three SNe II SN 2020jfo, SN 2020qmp and SN 2022acko. In the He I 1.083 μm/Paγ 1.094 μm and O I 1.129 μm /Fe I 1.142 μm plots are centered on the He I 1.083 μm and O I 1.129 μm lines respectively. Dashed vertical lines denote the rest wavelength of the individual emission lines. Features were fit with either a single or double Lorentzian function to determine the FWHM of the emission lines.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: FWHM of the strong nebular-phase emission lines across the four spectra of SNe 2020jfo, 2020qmp, and 2022acko. The average FWHM of each line is given by the black data, with the individual SN FWHM given by the colored data corresponding to the SNe colors in Figure 8. The hydrogen envelope is located at larger velocities than all lines except the Fe I 1.142 μm line, which are brought up significantly by SN 2020jfo and SN 2020qmp, suggesting a potential for mixing out of iron group elements in these SNe, differences in metallicities, or asymmetries with the explosion.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: HISS spectra of SNe Ia ordered by number of observations and phase, with the spectral phase shown above each spectrum. All spectra are shown in the rest frame, and the spectral classification is given for each object. Spectra have been smoothed using a Gaussian filter with σ = 3, shown by the solid black line, with the raw spectra shown by the gray line. Telluric regions are shown by the gray vertical regions.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Line profile of the He I 1.083 μm feature, used in determining the strength of the SN II. All spectra were taken between +50 and 130 days after maximum light, when the absorption component was still visible. Left: strong SNe II SN 2022mit, SN 2022dml, SN 2021qvr, and SN 2021adlw, all of which possess an pEW > 50 Å. These SNe lack a visible high-velocity component to the helium feature. Right: the weak SN II SN 2021tiq.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: The CO first overtone can be identified in ground-based NIR spectra of CCSNe in the weeks to months following maximum light. CO was not identified in all SNe shown; those SNe with confident identification of CO are marked with an *.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Same as Figure B1 but for SNe II observed at t ≥ +150 days after maximum light.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Same as Figure B1 but for SNe II observed before +150 days after maximum light.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Same as Figure B1 but for SE-SNe.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: The self-attention computation mechanism of the Swin Transformer. (a) The input image. (b) The image is segmented into uniformly distributed, nonoverlapping patches, with self-attention calculated independently within each window (W-MSA). (c) The windows are shifted to form new windows. (d) Regions extending beyond the image boundaries are filled using the remaining portions of the image, enabling self-attention calculations for the SW-MSA. Through shifting windows, the Swin Transformer could effectively capture features of celestial object images and obtain positions of these celestial objects with relatively high positional precision.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: The figure illustrates the diagram of the neural network for ordinary source detection. The feature for faint source detection is derived by combining the 2D spatial distribution of photons with a 3D point cloud representation generated via a multiview projection strategy.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: The figure clearly illustrates the detection results both prior to and following the integration step. Figure a depicts the results of direct integration, where it is evident that several duplicate detection candidates are produced by the two detection algorithms. Once the integration of the detection results is completed, these duplicate sources are consolidated into a single source.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: The figure compares the photon count distribution in a 3D data cube before (a) and after (b) our preprocessing step. The initial data in (a) exhibits prominent cruciform artifacts caused by bright sources. Panel (b) shows the result after these regions are masked, yielding a data cube free of these structures. The detection and removal step effectively eliminates cruciform arms, thereby increasing the detection efficiency for faint targets.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: This figure shows the structure of the neural network for ordinary source detection and the data flow in the ordinary source detection. To enable adaptive detection, the proposed neural network utilizes a dual-input architecture. It processes not only features from the candidate image but also a confidence level derived from a set of reference targets.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: This figure shows the relationship between recall and precision for different flux intervals. Since the detection results cannot provide specific flux intervals, the P-R rate is shared across different flux intervals. Although dim targets have a smaller P-R rate, our method could still obtain an acceptable P-R rate for targets with different fluxes.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: The exposure time and orientation of these images are different. However, most targets with moderate brightness (some of which are difficult to discern by the human eye) can be detected by our framework without tuning of hyperparameters, such as the detection threshold.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: This figure shows the detection results on the standard data set (R.A. from 330∘ to 20∘, decl. from 2∘ to 46∘) with 228 frames of observation images (from 2024 December 19 to December 26).
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: The figure displays the loss curve for the ultrabright and bright source detection neural network on the left panel, while the right panel illustrates the loss curve for the ordinary source detection neural network. Both of these neural networks are converged after training.
Paper: Influence of Magnetic Complexity of Active Regions on Solar Wind Properties during Solar Cycles 23 and 24
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Limei Yan, Qi Liu, Lidong Xia
Figure caption: The QFe in the three types of AR solar wind. The layout is the same as that of Figure 6. As the magnetic complexity of the AR increases, the QFe in the AR solar wind also increases.
Paper: Influence of Magnetic Complexity of Active Regions on Solar Wind Properties during Solar Cycles 23 and 24
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Limei Yan, Qi Liu, Lidong Xia
Figure caption: The FIP bias of the three types of AR solar wind. The layout is the same as that of Figure 6. The FIP bias is increased with the complexity of the source region magnetic field structure.
Paper: Influence of Magnetic Complexity of Active Regions on Solar Wind Properties during Solar Cycles 23 and 24
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Limei Yan, Qi Liu, Lidong Xia
Figure caption: The AHe in the three types of AR solar wind. The layout is the same as that of Figure 3. The AHe in complex AR solar wind is significantly higher than that in α and β AR solar wind.
Paper: Influence of Magnetic Complexity of Active Regions on Solar Wind Properties during Solar Cycles 23 and 24
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Limei Yan, Qi Liu, Lidong Xia
Figure caption: Panels (a), (b), and (c) show the histogram comparisons of O7+/O6+, QFe, and FIP bias for all AR solar wind before and after the ACE/SWICS anomaly, respectively. The dashed lines indicate the corresponding mean values.
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: Example of a 4FGL proposed counterpart that is not confirmed by , which proposed a second blazar. The 4FGL counterpart (lower SED on the right) has a strong radio but has no infrared, optical, or X-ray flux; the SED of 3HSP J021205.7-255758, the  counterpart, clearly fits well with that of an HSP counterpart.
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: The most common situation exemplified by the case of 4FGL J2221.8+3358, where  and 4FGL/4LAC identify the same γ-ray blazar. Left:  proposes a single counterpart coincident with the 4LAC counterpart (yellow circle). The purple ellipse in the figure represents the 4FGL error region. Right: The SED of CRATES J222152+3335844, the counterpart of 4FGL J2221.8+3358.
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: Example of identification of a previously unassociated source (4FGLJ0122.4+1034). Left: the 4FGLJ0122.4+1034 elliptical error region and ’s counterpart. Right: the SED of ’s candidate (1FLAT J012223.6+103213).
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: Distribution of the 4FGL signif_avg significance parameter for the confirmed blazars and the newly discovered.
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: The distribution of the synchrotron-peak frequency in the sample. Altogether, we show a total blazar population of 1696 (43.0%) LSP, 925 (23.5%) ISP, and 1322 (33.5%) HSP.
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: The distribution of the flux Energy_Flux100 for the same, different, and new blazars.
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: Sky map of 4FGL/4LAC sources (light gray) along with the 421 new blazars discovered (yellow star symbols) and the sources assigned a different association (light blue circles).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: The “fundamental plane” of black hole activity for the complete sample (top), RQ (left), and RL (right) sources separately. The x-axis is defined following the respective best-fit parameters.
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 1427+480, PG 1435−067, PG 1440+356, PG 1444+406, PG 1512+370 (RL), and PG 1519+226 (the source is the weak one at the center of the image). The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).