adsabs/AstroCLIMB huggingface dataset:
Paper: A Broadband Solar Radio Dynamic Spectrometer Working in the Millimeter-wave Band
Authors: Shang ZiQian, Xu Ke, Liu Yang, Wu Zhao, Lu Guang, Zhang YuanYuan, Zhang Lei, Su YanRui, Chen Yao, and Yan FaBao
Figure caption: (a) The first LNA noise figure. (b) The gain of the analog unit. Each 1 GHz bandwidth has a similar shape due to the effect of the filter, and the gain decreases with increasing frequency.
Paper: A Broadband Solar Radio Dynamic Spectrometer Working in the Millimeter-wave Band
Authors: Shang ZiQian, Xu Ke, Liu Yang, Wu Zhao, Lu Guang, Zhang YuanYuan, Zhang Lei, Su YanRui, Chen Yao, and Yan FaBao
Figure caption: The linearity of the 10 channels. The system linearity is determined according to the power of the broadband white noise signal outputted by the vector signal generator and the value of the digital receiver. The different slopes of the 10 channels are caused by the different gains between the channels.
Paper: A Broadband Solar Radio Dynamic Spectrometer Working in the Millimeter-wave Band
Authors: Shang ZiQian, Xu Ke, Liu Yang, Wu Zhao, Lu Guang, Zhang YuanYuan, Zhang Lei, Su YanRui, Chen Yao, and Yan FaBao
Figure caption: (a) In the system stability test, the system has poor stability during the first 2.5 hr of instrument operation. With prolonged working, the gain is relatively stable when the heat is volatilized by the conduction-cooling device and the heat generated by the instrument is basically in equilibrium. (b) The gain of the analog unit at different temperatures, including the insertion loss of the test radio frequency cable. An analog signal source (Keysight N5183B) is used as an input and a signal analyzer (Keysight N9040B) reads the output power. Every temperature change is kept in the high–low temperature test chamber for 0.5 hr.
Paper: A Broadband Solar Radio Dynamic Spectrometer Working in the Millimeter-wave Band
Authors: Shang ZiQian, Xu Ke, Liu Yang, Wu Zhao, Lu Guang, Zhang YuanYuan, Zhang Lei, Su YanRui, Chen Yao, and Yan FaBao
Figure caption: The 35.25 GHz observation data under different weather conditions. Except for August 24 and September 3, which were clear days, the other days were accompanied by different weather conditions. During sunny days, the transient fluctuations are most likely caused by the gain variation, and the small spikes may be caused by the ambience (e.g., clouds). The long-term changes may be caused by the gain fluctuation or the environmental change (e.g., changes in elevation and ambient temperature).
Paper: A Broadband Solar Radio Dynamic Spectrometer Working in the Millimeter-wave Band
Authors: Shang ZiQian, Xu Ke, Liu Yang, Wu Zhao, Lu Guang, Zhang YuanYuan, Zhang Lei, Su YanRui, Chen Yao, and Yan FaBao
Figure caption: The scan of the Sun. The upper panel shows the spectrum of the 5 GHz bandwidth and the lower panel shows the intensity of the 10 channels.
Paper: A Broadband Solar Radio Dynamic Spectrometer Working in the Millimeter-wave Band
Authors: Shang ZiQian, Xu Ke, Liu Yang, Wu Zhao, Lu Guang, Zhang YuanYuan, Zhang Lei, Su YanRui, Chen Yao, and Yan FaBao
Figure caption: (a) The dynamic spectrum is not consistent under the influence of flatness. (b) After normalization, the dynamic spectrum eliminates the effect of gain. (c) The uncalibrated solar radio flux.
Paper: A Broadband Solar Radio Dynamic Spectrometer Working in the Millimeter-wave Band
Authors: Shang ZiQian, Xu Ke, Liu Yang, Wu Zhao, Lu Guang, Zhang YuanYuan, Zhang Lei, Su YanRui, Chen Yao, and Yan FaBao
Figure caption: (a) The 5 GHz bandwidth dynamic spectrum for the Sun and the Moon where the flatness is optimized. The spectra of dark blue, light blue, and yellow are the background of the sky, the Moon, and the Sun, respectively. (b) The radiation intensity of the Sun and the Moon corresponding to (a).
Paper: A Broadband Solar Radio Dynamic Spectrometer Working in the Millimeter-wave Band
Authors: Shang ZiQian, Xu Ke, Liu Yang, Wu Zhao, Lu Guang, Zhang YuanYuan, Zhang Lei, Su YanRui, Chen Yao, and Yan FaBao
Figure caption: Solar radio flux intensity was calibrated by using the new Moon on 2021 July 10 and August 8. Since the difference between the Moon and Moon background is small, the small fluctuations in the observation lead to a large difference in the Sun-to-Moon ratio.
Paper: H-band Light Curves of Milky Way Cepheids via Difference Imaging
Authors: Tarini Konchady, Ryan J. Oelkers, David O. Jones, Wenlong Yuan, Lucas M. Macri, Erik R. Peterson, and Adam G. Riess
Figure caption: A representative science image (left) of the RX Cam field, with the Cepheid near the center. The differenced image (right) is the result of subtracting the convolved reference image from the science image. The color scale is inverted for clarity. North is up and east is to the left.
Paper: H-band Light Curves of Milky Way Cepheids via Difference Imaging
Authors: Tarini Konchady, Ryan J. Oelkers, David O. Jones, Wenlong Yuan, Lucas M. Macri, Erik R. Peterson, and Adam G. Riess
Figure caption: Top left: a 500 × 400 pixel cutout of the RX Cam reference frame. Top right: a 3D visualization of the 5 × 5 δ-function kernel used to convolve the reference frame (top left) to match a typical science frame (bottom left). Bottom left: a cutout of the same area for a typical science frame in the RX Cam image sequence. Bottom right: residuals after subtraction. All images are displayed using the same logarithmic stretch; colors have been inverted for clarity.
Paper: H-band Light Curves of Milky Way Cepheids via Difference Imaging
Authors: Tarini Konchady, Ryan J. Oelkers, David O. Jones, Wenlong Yuan, Lucas M. Macri, Erik R. Peterson, and Adam G. Riess
Figure caption: Photometric precision of light curves obtained via difference imaging (colored points) and the expected precision from photon statistics (solid line) for a representative field (RX Cam). The color of each point indicates distance from the edge of the frame, showing greater precision in the central area.
Paper: H-band Light Curves of Milky Way Cepheids via Difference Imaging
Authors: Tarini Konchady, Ryan J. Oelkers, David O. Jones, Wenlong Yuan, Lucas M. Macri, Erik R. Peterson, and Adam G. Riess
Figure caption: Raw (open red circles) and detrended (left) and phased (center) light curves (filled blue circles) and models (dashed red lines), plus reference images (right, ﹩4^{\prime} ﹩ on a side, inverted for clarity) of our target Cepheids. The mean magnitudes used in the left column were taken from 2MASS PSC. Phased light curves are plotted relative to the mean magnitude of each light curve, and the models are shown with 1σ intervals (shaded red regions). Observations taken within 2.4 hr were averaged into a single epoch. Periods were taken from Riess et al. (2021). The black diamonds in the V0386 Cyg light curves correspond to an epoch that was excluded due to a significant difference in defocused PSF size compared to all other images.
Paper: H-band Light Curves of Milky Way Cepheids via Difference Imaging
Authors: Tarini Konchady, Ryan J. Oelkers, David O. Jones, Wenlong Yuan, Lucas M. Macri, Erik R. Peterson, and Adam G. Riess
Figure caption: Results of the bootstrap procedure to compare Cepheid amplitudes derived from our work (red) and from (Monson & Pierce 2011, black). Top: Binned and phase-folded light curves; solid lines show the best-fit templates. The red and black shaded regions represent the 1σ intervals. Bottom: Results of 1000 bootstrap samplings (with replacement) to estimate the uncertainty on the best-fit amplitudes. We find agreement between the two data sets at the 1.1–1.4σ level.
Paper: H-band Light Curves of Milky Way Cepheids via Difference Imaging
Authors: Tarini Konchady, Ryan J. Oelkers, David O. Jones, Wenlong Yuan, Lucas M. Macri, Erik R. Peterson, and Adam G. Riess
Figure caption: Comparison of corrections to “mean light” for the random-phase HST F160W observations of the seven MW Cepheids discussed in this work, derived from ground-based VI and H light curves, respectively.
Paper: Mid-infrared Outbursts in Nearby Galaxies (MIRONG). II. Optical Spectroscopic Follow-up
Authors: Yibo Wang, Ning Jiang, Tinggui Wang, Lin Yan, Zhenfeng Sheng, Liming Dou, Jiani Ding, Zheng Cai, Luming Sun, Chenwei Yang, and Xinwen Shu
Figure caption: We show the continuum fitting of the spectrum of J1647+3843 before (SDSS, top panel) and after (DBSP, bottom panel) an MIR outburst as an example. The original SDSS spectrum is plotted in red and the fitted stellar continuum is shown in blue. For the DBSP spectrum taken after the outburst, an additional reddened power-law component (yellow) has been also included in the fitting. The shadow regions show the prominent telluric absorption windows, which were masked in the fitting procedure.
Paper: Mid-infrared Outbursts in Nearby Galaxies (MIRONG). II. Optical Spectroscopic Follow-up
Authors: Yibo Wang, Ning Jiang, Tinggui Wang, Lin Yan, Zhenfeng Sheng, Liming Dou, Jiani Ding, Zheng Cai, Luming Sun, Chenwei Yang, and Xinwen Shu
Figure caption: The left, middle, and right panels show the Hβ, Hα, and [Fe VII]λ6087 fitting results respectively. The red curves in the three panels represent the original emission lines data while the blue curves represent the best-fitting model. Narrow and broad components are displayed with green and purple curves. The black dashed line models the residual local continuum, and only a few spectra’s Hα and Hβ fitting need this component as shown in the figure to improve the fitting.
Paper: Mid-infrared Outbursts in Nearby Galaxies (MIRONG). II. Optical Spectroscopic Follow-up
Authors: Yibo Wang, Ning Jiang, Tinggui Wang, Lin Yan, Zhenfeng Sheng, Liming Dou, Jiani Ding, Zheng Cai, Luming Sun, Chenwei Yang, and Xinwen Shu
Figure caption: We show the spectral evolution of J1442+5558, including the whole spectral evolution (top panel, continuum subtracted) and the evolution of the Hα region (bottom panel).
Paper: Mid-infrared Outbursts in Nearby Galaxies (MIRONG). II. Optical Spectroscopic Follow-up
Authors: Yibo Wang, Ning Jiang, Tinggui Wang, Lin Yan, Zhenfeng Sheng, Liming Dou, Jiani Ding, Zheng Cai, Luming Sun, Chenwei Yang, and Xinwen Shu
Figure caption: We show the comparison of the W1-band variability amplitude ΔW1 (left panel), W1 peak luminosity LW1 (middle panel), and dust temperature Tdust (right panel). All data were drawn from Paper I, in which the dust temperature was picked out from results fitting with dust absorption coefficient.
Paper: Mid-infrared Outbursts in Nearby Galaxies (MIRONG). II. Optical Spectroscopic Follow-up
Authors: Yibo Wang, Ning Jiang, Tinggui Wang, Lin Yan, Zhenfeng Sheng, Liming Dou, Jiani Ding, Zheng Cai, Luming Sun, Chenwei Yang, and Xinwen Shu
Figure caption: Spectral evolution of variable sources. For the source J1111+5923, the original flux calibration of Lick-180318 was not good, and we took the recalibrated one by making the narrow Hα flux the same as that of DBSP-200213 because they have similar observation conditions.
Paper: Mid-infrared Outbursts in Nearby Galaxies (MIRONG). II. Optical Spectroscopic Follow-up
Authors: Yibo Wang, Ning Jiang, Tinggui Wang, Lin Yan, Zhenfeng Sheng, Liming Dou, Jiani Ding, Zheng Cai, Luming Sun, Chenwei Yang, and Xinwen Shu
Figure caption: Spectral evolution of variable sources. For the source J1133+6701, the recalibration was not applied for DBSP_180511 because of the bad S/N of its [O III]λ5007.
Paper: Mid-infrared Outbursts in Nearby Galaxies (MIRONG). II. Optical Spectroscopic Follow-up
Authors: Yibo Wang, Ning Jiang, Tinggui Wang, Lin Yan, Zhenfeng Sheng, Liming Dou, Jiani Ding, Zheng Cai, Luming Sun, Chenwei Yang, and Xinwen Shu
Figure caption:  Spectral evolution of variable sources.
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: Upper panel: equatorial coordinates of RRLs identified from three surveys in our sample. The black squares are RRLs from the Gaia catalog, red circles are RRLs from ASAS-SN catalog, and blue triangles are RRLs from ZTF catalog. Lower panel: the distributions of the Galactic longitude (l) and latitude (b) for all RRLs in our sample.
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: The distributions of metallicity, radial velocity, and proper motions are given in the upper panels, while distributions of their uncertainties are shown in the lower panels. Dashed lines (red colored values) are the peak values in the distributions.
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: A comparison of the photometrically derived metallicties (black squares with black lines (uncertainties)) and spectroscopically determined metallicities (red circles with red lines (uncertainties)) is given in the upper panel. The discrepancies between the uncertainties of the two are shown in the lower panel. It is obvious that most of the spectroscopically determined metallicities have smaller uncertainties compared to photometrically derived metallicities (the majority have uncertainties of 0.22 dex).
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: The cumulative distribution function of 3417 RRLs is given in the upper panel. The lower panel is the color map to show the metallicity distribution of the RRLs sample in the r and z plane. r is the Galactocentric distance in the Cartesian coordinate system, and z is the Galactic vertical height.
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: Distribution of the azimuthal (in the direction of the Galactic rotation) velocity (Vϕ) vs. metallicity ([Fe/H]). With the total 3417 RRL stars, it is weighted for each grid cell of the parameter space. The magenta area is the thin disk population, the green area is the thick disk population, and the orange area is the halo population including the GS (Sausage-like) population (see the text for further discussion).
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: Density maps for metal-poor and metal-rich stars in the parameter spaces of Vϕ—Galactic vertical height ∣z∣ (upper panels) and Vϕ—Galactocentric distance in the Galactic plane R (lower panels). Here, RRLs with [Fe/H] > −0.7 dex are metal-rich stars, and RRLs with [Fe/H] < −0.7 dex are metal-poor stars.
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: Spatial distributions: all RRLs of the sample, halo RRLs, thick disk RRLs, and thin disk RRLs in the r–z plane, respectively.
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: Azimuthal velocity and metallicity distributions of RRLs selected from the thick disk, thin disk, and halo, respectively. Red lines are the best-fit lines.
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: Best-fit values from the Gaussian model fitting results for the three main components.
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: Azimuthal velocity and metallicity distributions of the sample from the halo including the GS-like component.
Paper: The Milky Way Revealed by Variable Stars. I. Sample Selection of RR Lyrae Stars and Evidence for Merger History
Authors: Iminhaji Ablimit, Gang Zhao, Uy. Teklimakan, Jian-Rong Shi, and Kunduz Abdusalam
Figure caption: Anisotropy of the GS-like (radial) component and its fraction in the total halo sample.
Paper: Preparing to Discover the Unknown with Rubin LSST: Time Domain
Authors: Xiaolong Li, Fabio Ragosta, William I. Clarkson, and Federica B. Bianco
Figure caption: Distribution of median time gaps between observations in different nights for 86 simulations of the Rubin LSST Wide Fast Deep Survey OpSim v1.5 (see Section 2.4). Here, observations are spatially grouped by healpixel using resolution parameter NSIDE = 64, with pixel area 0.84 square degrees (roughly corresponding to 55 arcminute resolution). The top row shows the distribution of gaps between observations of the same field in any filter, and the rows beneath show the time gaps between observations in the same filter, as labeled. The distributions are normalized by peak height, and the modal value over the 10 yr survey simulation across all OpSims is indicated to the right of each distribution, along with the number (in parentheses) of observations in a 1.5 day-wide bin around the peak value. The distributions are smoothed via kernel density estimation (using the Python Seaborn package with default settings). The breadth of the distribution (and at times its complex shape with multiple peaks) demonstrates how simulated Rubin LSST cadences, all developed in compliance with the SRD requirements that make specifications for each filter, can still be very different even in core features such as the median time gap, with ranges that can span tens of days (e.g., z band). For reference, the median time gap for the baseline survey baseline_v1.5 is 22, 18, 7, 9, 11, 19 for ugrizy, respectively, and 3 days for images in any filter. This figure is discussed in Section 1. Also see Bianco et al. (2022).
Paper: Preparing to Discover the Unknown with Rubin LSST: Time Domain
Authors: Xiaolong Li, Fabio Ragosta, William I. Clarkson, and Federica B. Bianco
Figure caption: The distribution of all time gaps for the baseline_v1.5 OpSim. The triangle of plots on the left shows all time gaps between different filters (which enable the measurement of color) within 1.5 hr. The column of plots on the right shows the distribution of time gaps in the same filter for the 10 yr survey, which enables the measurement of brightness changes. The filters are indicated in each quadrant: from u to y moving from top to bottom and left to right. All histograms are normalized, but the intensity of the color is proportional to the total number of observations in that filter pair, as indicated by the color bar. In each quadrant, the value of DKL is reported (see Section 3.1). We note that the majority of observations are taken with adjacent filters, which gives a narrow leverage on the spectral energy distribution, and less power to measure color. Color is in fact better measured with filters that are more separated in wavelength, for example g-i or r-z, as described in Bianco et al. (2019).
Paper: Preparing to Discover the Unknown with Rubin LSST: Time Domain
Authors: Xiaolong Li, Fabio Ragosta, William I. Clarkson, and Federica B. Bianco
Figure caption: Time gaps in g − i (left) and g − g (right) from baseline_v1.5 compared to the “ideal” distribution, plotted in red. Shown is a uniform distribution in log space up to 1.5 hr for the colors (left, with the y-axis in units of 1000 observations) and up to 10 yr for the light-curve shape (right, with the y-axis in units of 1 million observations). See Section 3.1.
Paper: Preparing to Discover the Unknown with Rubin LSST: Time Domain
Authors: Xiaolong Li, Fabio Ragosta, William I. Clarkson, and Federica B. Bianco
Figure caption: Figures of merit FoMtGaps for the OpSim v1.5 runs based on the distribution of time gaps. The FoMtGaps is calculated as described in Equation (3). The plot on the left (a) shows the FoM for repeat visits in the same filter. The plot on the right (b) shows the value for observations in pairs of different filters. Each OpSim is presented as a bar whose length corresponds to the value of the FoM: the FoMs for different filters are concatenated horizontally. For example, on the left, the different color bars represent the time gap FoM for different filters from u to y. The OpSims are sorted by the total FoM. In panel (a) the bars grow toward the left, in panel (b) the grow toward the right, so that asymmetries in the plot can give intuition on the overall distribution of the two different metrics across the set of the OpSims. See Section 3.1.
Paper: Preparing to Discover the Unknown with Rubin LSST: Time Domain
Authors: Xiaolong Li, Fabio Ragosta, William I. Clarkson, and Federica B. Bianco
Figure caption: Distribution of depth for images in three different OpSims. The survey specifications are indicated by the a gray band (minimum requirement to stretch goal) and the vertical line (design specification) as per Ivezić & the LSST Science Collaboration (2013), their Table 6, for each filter (as indicated in the top left of each panel). Some OpSims are designed to include in the WFD short exposures and may perform well in metrics based on number of exposures taken. But shallower images generate lower signal-to-noise ratio measurements and only allow for the exploration of a smaller volume of the universe. This would have a negative impact on the discovery of anomalies if it came at the cost of long exposures. We show the distribution of 5σ depth for images in three OpSims: the baseline (blue filled histogram), twilight_neo_mod1, and short_exp_2ns_1expt. twilight_neo_mod1 and short_exp_2ns_1expt have additional short-exposures leading to a bimodal distribution. However, while for short_exp_2ns_1expt OpSim the distribution of faint (fainter than ∼21.5 in each band) images is similar to the that of the baseline, twilight_neo_mod1 has fewer faint images in the r, i, and z bands, and more in the y band. See Section 4.
Paper: Preparing to Discover the Unknown with Rubin LSST: Time Domain
Authors: Xiaolong Li, Fabio Ragosta, William I. Clarkson, and Federica B. Bianco
Figure caption: Ranking of OpSims based on the depth of the exposure as discussed in Section 4. u60, which produces a 60 s u-band exposure instead of the standard 30 s, extends the observed volume slightly in the u band and ranks highly in this metric; however, it was performing poorly in both FoMtGaps (Figures 5(a) and (b)). Some (but not all) short_exp and twilight OpSims perform poorly, indicating the short exposures come at the cost of high-quality, deep, exposures at least in some filters. Otherwise, for the most part, the families of OpSims are clustered together in this diagram, all with a similar FoMdepth score: 90% of the OpSims generate values within 10% of each other in this metric.
Paper: Preparing to Discover the Unknown with Rubin LSST: Time Domain
Authors: Xiaolong Li, Fabio Ragosta, William I. Clarkson, and Federica B. Bianco
Figure caption: Maps of the footprint FoMs for four OpSims: from left to right, baseline_v1.5, footprint_big_sky, twilight_new_mod1, and wfd_depth_scale0.99. In the top panel, the colored heapixels (N = 16) are the locations where the number of observations in g and r within a 2 day interval is greater than the median count for such observations in baseline_1.5: the metric for extragalactic footprint (FoMEG). The bottom panel shows the results for FoMGal where each heapixels is weighted by the corresponding star count. The footprint_big_sky extends the footprint to higher latitude than baseline_1.5. The twilight allocates additional visits near twilight. See Section 5.