adsabs/AstroCLIMB huggingface dataset:
Paper: Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science
Authors: Megan E. Schwamb, R. Lynne Jones, Peter Yoachim, Kathryn Volk, Rosemary C. Dorsey, Cyrielle Opitom, Sarah Greenstreet, Tim Lister, Colin Snodgrass, Bryce T. Bolin, Laura Inno, Michele T. Bannister, Siegfried Eggl, Michael Solontoi, Michael S. P. Kelley, Mario Jurić, Hsing Wen Lin, 省文 林, Darin Ragozzine, Pedro H. Bernardinelli, Steven R. Chesley, Tansu Daylan, Josef Ďurech, Wesley C. Fraser, Mikael Granvik, Matthew M. Knight, Carey M. Lisse, Renu Malhotra, William J. Oldroyd, Audrey Thirouin, Quanzhi Ye, 泉志 叶
Figure caption: Impact on other solar system metrics due to the inclusion of a low-SE solar system twilight microsurvey (v2.2 simulations). The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. The baseline simulation does not include twilight low-SE observations. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. Simulation legend: twi_neo_repeatX_Y_npZ_v2.2_10yrs for microsurvey with X repeat visits in Y filter(s) per pointing per twilight observed where Z = 1 (on every night), 2 (one night on/one night off), 3 (one night on/two nights off), 4 (one night on/three nights off), 5 (four nights on/four nights off), 6 (three nights on/four nights off), 7 (two nights on/four nights off).
Paper: Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science
Authors: Megan E. Schwamb, R. Lynne Jones, Peter Yoachim, Kathryn Volk, Rosemary C. Dorsey, Cyrielle Opitom, Sarah Greenstreet, Tim Lister, Colin Snodgrass, Bryce T. Bolin, Laura Inno, Michele T. Bannister, Siegfried Eggl, Michael Solontoi, Michael S. P. Kelley, Mario Jurić, Hsing Wen Lin, 省文 林, Darin Ragozzine, Pedro H. Bernardinelli, Steven R. Chesley, Tansu Daylan, Josef Ďurech, Wesley C. Fraser, Mikael Granvik, Matthew M. Knight, Carey M. Lisse, Renu Malhotra, William J. Oldroyd, Audrey Thirouin, Quanzhi Ye, 泉志 叶
Figure caption: Comparison of the number of visits as a function of SE (at the center of the FOV) with and without a low-SE solar system twilight microsurvey. The bin size is 2°. The baseline simulation is baseline_v2.2_10yrs, and the selected low-SE solar system twilight microsurvey simulation is twi_neo_repeat4_iz_np1_v2.2_10yrs. We note that part of the orange histogram for the simulation that includes the low-SE twilight microsurvey is plotted underneath the blue histogram for the baseline simulation.
Paper: Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science
Authors: Megan E. Schwamb, R. Lynne Jones, Peter Yoachim, Kathryn Volk, Rosemary C. Dorsey, Cyrielle Opitom, Sarah Greenstreet, Tim Lister, Colin Snodgrass, Bryce T. Bolin, Laura Inno, Michele T. Bannister, Siegfried Eggl, Michael Solontoi, Michael S. P. Kelley, Mario Jurić, Hsing Wen Lin, 省文 林, Darin Ragozzine, Pedro H. Bernardinelli, Steven R. Chesley, Tansu Daylan, Josef Ďurech, Wesley C. Fraser, Mikael Granvik, Matthew M. Knight, Carey M. Lisse, Renu Malhotra, William J. Oldroyd, Audrey Thirouin, Quanzhi Ye, 泉志 叶
Figure caption: Impact of various microsurvey scenarios. The baseline (reference) simulation with the default scheduler configuration for this cadence experiment is the first entry on the left. All values have been normalized by this simulation’s output. The gray shading outlines changes that are within ±5% of the baseline simulation. Top: discovery metrics. Bottom: light-curve inversion metrics. The y-axis is truncated in the bottom plot at 1.6 for readability. The light-curve inversion NEO H = 19.0 and light-curve inversion PHA H = 19.0 extend to nearly 2.5 for the multi_short_v2.0_10yrs run.
Paper: A Dust Detection Database for the Inner Heliosphere Using the Parker Solar Probe Spacecraft
Authors: David M. Malaspina, Alexandru Toma, Jamey R. Szalay, Marc Pulupa, Petr Pokorný, Stuart D. Bale, Keith Goetz
Figure caption: Two-dimensional histograms of TDSmax peak and rms values (a) for solar encounters 1–7, (b) for encounters 8–13, and (c) for encounters 1–13. Column total and row total histograms are shown to the bottom and right, respectively, of each two-dimensional histogram. See the text for descriptions of the regions R1, R2, and R3.
Paper: A Dust Detection Database for the Inner Heliosphere Using the Parker Solar Probe Spacecraft
Authors: David M. Malaspina, Alexandru Toma, Jamey R. Szalay, Marc Pulupa, Petr Pokorný, Stuart D. Bale, Keith Goetz
Figure caption: (a) Comparison of dust impact rates determined for Encounter 10, including directly measured (I raw; black solid line), corrected for wave dead time (I wc; red dashed line), and corrected for wave dead time and undercounting (I ucc; blue dotted line). (b) Differential voltage power spectral density as a function of time, showing plasma wave activity during Encounter 10. See the text for details.
Paper: A Dust Detection Database for the Inner Heliosphere Using the Parker Solar Probe Spacecraft
Authors: David M. Malaspina, Alexandru Toma, Jamey R. Szalay, Marc Pulupa, Petr Pokorný, Stuart D. Bale, Keith Goetz
Figure caption: (Top) Impact rates corrected for undercounting, shown using the orange curve, as a function of impact rates not corrected for undercounting. A 1:1 line, representing no correction, is shown for comparison using the blue curve. (Bottom) The percentage difference between the corrected and uncorrected impact rates as a function of the uncorrected impact rate.
Paper: A Dust Detection Database for the Inner Heliosphere Using the Parker Solar Probe Spacecraft
Authors: David M. Malaspina, Alexandru Toma, Jamey R. Szalay, Marc Pulupa, Petr Pokorný, Stuart D. Bale, Keith Goetz
Figure caption: Example of a time-series burst data capture containing multiple dust impacts in less than 7 s. (a) Time series of differential voltage signals for channels V 12 and V 34. (b) Spectrogram of the data in (a). (c)–(e) Details of the differential voltage signals from (a), showing time-series waveforms near each dust impact.
Paper: A Dust Detection Database for the Inner Heliosphere Using the Parker Solar Probe Spacecraft
Authors: David M. Malaspina, Alexandru Toma, Jamey R. Szalay, Marc Pulupa, Petr Pokorný, Stuart D. Bale, Keith Goetz
Figure caption: Mounting, orientation, and labels for the four FIELDS electric field antennas in the plane of the heat shield. This view does not include the tail boom where the FIELDS magnetic field sensors and the fifth electric field antenna are mounted. See Bale et al. (2016) for more detail on FIELDS.
Paper: A Dust Detection Database for the Inner Heliosphere Using the Parker Solar Probe Spacecraft
Authors: David M. Malaspina, Alexandru Toma, Jamey R. Szalay, Marc Pulupa, Petr Pokorný, Stuart D. Bale, Keith Goetz
Figure caption: Distribution of measured waiting times between dust impacts, using all dust impacts recorded for Encounters 1 through 13 where the 8 hr impact rate was between 0.75 impacts minute−1 and 1.00 impacts minute−1. The thin red lines shows a best-fit curve, using Equation (3) with μ corr = 0.91.
Paper: Statistical Recovery of 21 cm Visibilities and Their Power Spectra with Gaussian-constrained Realizations and Gibbs Sampling
Authors: Fraser Kennedy, Philip Bull, Michael J. Wilensky, Jacob Burba, Samir Choudhuri
Figure caption: Signal recovery using Scheme 2 (foreground template fitting, left panel) and Scheme 3 (joint foreground sampling, right panel). These runs match the parameters seen in Figure 4: 800 total iterations, S/N at high delay of 5:1. These schemes differentiate between the foreground and EoR signal components and aim to recover the EoR component separately (unlike Scheme 1). Top panel: mean of the EoR power spectrum estimated from the GCR samples of the EoR signal across 800 iterations, with 95% confidence region shown. The injected noise level is shown as a dashed–dotted line. Middle panel: fractional residual between the true (input) EoR power spectrum and the mean of the power spectra derived from the GCR samples from the top panel. Outside the central delays where there is degeneracy between the foregrounds and EoR, the EoR signal recovery is essentially unbiased (within ∼5% at high delay in both cases). Biases inside the degeneracy region are seen to take on slightly different shapes for Scheme 2 and Scheme 3; 68% confidence regions are also shown. Bottom panel: fractional residual between the true delay spectrum and the inverse-Gamma sampled delay spectrum from each iteration, at the covariance sampling step of the sampling schemes. The zero residuals at the central delays are due to the prior that is imposed at low delays on this sampling step. Recovery of the delay spectrum is approximately unbiased when using both Gibbs sampling schemes under this test setup.
Paper: Statistical Recovery of 21 cm Visibilities and Their Power Spectra with Gaussian-constrained Realizations and Gibbs Sampling
Authors: Fraser Kennedy, Philip Bull, Michael J. Wilensky, Jacob Burba, Samir Choudhuri
Figure caption: Signal recovery using Scheme 1 (total signal sampler) after 800 iterations. Top panel: true input power spectrum (sum of EoR and foreground components, dashed line) compared with the mean of the power spectrum of the total sky signal estimated from samples of the signal from the GCR step (blue line). Power spectra are calculated after mean-subtraction and tapering with a Blackman–Harris window, which produces a small decrease at τ = 0 ns. The dotted–dashed line shows the noise power for each visibility (i.e., a single time sample; S/N of 5 at high τ); 1200 time samples are combined to measure the total power spectrum. The 95% confidence interval (green shaded region) is too small to see. Bottom panel: fractional residual between the true power spectrum and the recovered power spectrum from the upper panel. The error bars show the 68% confidence region. Outside ∣τ∣ ≳ 1000 ns, where the S/N is lower, this sampling scheme recovers a distribution that is biased low by around 5%. This bias does not appear when the S/N at high delay is increased.
Paper: Statistical Recovery of 21 cm Visibilities and Their Power Spectra with Gaussian-constrained Realizations and Gibbs Sampling
Authors: Fraser Kennedy, Philip Bull, Michael J. Wilensky, Jacob Burba, Samir Choudhuri
Figure caption: Flagging fraction comparison using the Scheme 2 sampler (foreground templates) for 800 iterations, and with a S/N of 5:1 at high delay in each case. Left column: no flags applied. Centre column: continuous flag through 5% of the band. Right column: continuous flag through 10% of the band. The applied flagging is approximately in the center of the frequency range and is applied to the data at all LSTs. Top panels show the mean P empirical(τ) and 95% confidence regions compared with the true power spectrum, and the injected noise level. Middle and lower panels show the fractional residual on P empirical(τ) and P(τ) samples across all iterations. Both statistics are approximately unbiased outside of the region of signal/foreground degeneracy (∣τ∣ < 300 μs) in all flagging cases, though as the flagging fraction increases more fluctuations are noted in the residual means. Flagging can be seen to increase the width of the P empirical(τ) distribution, particularly at low delay. The width of the P(τ) residual does not appear to be altered significantly when increasing the flagging fraction, as this is dominated by sample variance, but the recovery of the “best-fit” delay spectrum (i.e., the central estimate shown with each error bar) becomes noisier.
Paper: Statistical Recovery of 21 cm Visibilities and Their Power Spectra with Gaussian-constrained Realizations and Gibbs Sampling
Authors: Fraser Kennedy, Philip Bull, Michael J. Wilensky, Jacob Burba, Samir Choudhuri
Figure caption: Point-source foreground covariance matrices estimated from averaging over 1200 simulation time samples at a cadence of 40 s in the 100–120 MHz band. Real and imaginary parts (in square janskys) are shown in the top and bottom rows, respectively, while the middle row shows the correlation matrix of the real part, ﹩{\rho }_{{ij}}={C}_{{ij}}/\sqrt{{C}_{{ii}}{C}_{{jj}}}﹩. The first three columns are for baselines with length 14.6 m, oriented at approximately 0°, 60°, and 120° degrees from the E–W direction, respectively (the shortest redundant baselines). A clear directional dependence is found in the structure and amplitude of the covariance matrix. The last two panels show covariances from baselines of length 29.2 and 43.8 m (both E–W aligned). The real part of each of the covariances shows strong correlations, with an imaginary part that has a smaller variance in general.
Paper: Statistical Recovery of 21 cm Visibilities and Their Power Spectra with Gaussian-constrained Realizations and Gibbs Sampling
Authors: Fraser Kennedy, Philip Bull, Michael J. Wilensky, Jacob Burba, Samir Choudhuri
Figure caption: Signal-to-noise ratio comparison using the Scheme 3 sampler (joint foreground sampling) for 800 iterations, and with no flags applied. Left column: S/N 30. Centre column: S/N 3. Right column: S/N 0.5. Top panels show the mean signal GCR power spectrum and 95% confidence regions compared with the true power spectrum, and the injected noise level per LST (the other quantities are averaged over LST). The middle and lower panels show the fractional residual on P empirical(τ) and P(τ) across all iterations. As the S/N is decreased, larger fluctuations appear in the residuals of both statistics. The largest fluctuations in the residual appear at high delay, where the signal model is noise-like (flat) and the S/N is lowest.
Paper: Statistical Recovery of 21 cm Visibilities and Their Power Spectra with Gaussian-constrained Realizations and Gibbs Sampling
Authors: Fraser Kennedy, Philip Bull, Michael J. Wilensky, Jacob Burba, Samir Choudhuri
Figure caption: Runs with Schemes 2 (left panel) and 3 (right panel) making use of foreground covariance matrix priors that have faint sources below 15 Jy cut from the catalog. These runs are carried out to test the robustness of the method to faint source loss. The flux cut is set very high in order to examine a reasonable worst-case scenario. Both runs return an unbiased residual, and have very similar error bars to runs where the “correct” covariance matrix is used, e.g., the right panel is directly comparable with the center panels of Figure 7.
Paper: HEROES: The Hawaii eROSITA Ecliptic Pole Survey Catalog
Authors: A. J. Taylor, A. J. Barger, L. L. Cowie, G. Hasinger, E. M. Hu, A. Songaila
Figure caption: Pointing centers and overlap for the HSC data in the NEP. Our HEROES pointings are shown in blue, the H20 pointings in red, and the AKARI pointings in green. Our pointing targeting the JWST TDF is shown in yellow. Each of the pointings used either the HEROES dither pattern or something similar (see Section 2). Color shading shows pointing overlaps but does not explicitly indicate stacked imaging depth.
Paper: Statistical Recovery of 21 cm Visibilities and Their Power Spectra with Gaussian-constrained Realizations and Gibbs Sampling
Authors: Fraser Kennedy, Philip Bull, Michael J. Wilensky, Jacob Burba, Samir Choudhuri
Figure caption: Projection of the first eight foreground eigenmodes for the “correct” foreground covariance matrix (vertical axis) onto the ones for the foreground model with a flux cut at 15 Jy (horizontal axis), calculated as ﹩{{\boldsymbol{g}}}_{\mathrm{full}}\cdot {{\boldsymbol{g}}}_{\mathrm{faint}}^{\dagger }﹩. The real part is shown on the left and the imaginary part on the right. The color scheme is chosen arbitrarily to highlight regions of similar value for the projection.
Paper: Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science
Authors: Megan E. Schwamb, R. Lynne Jones, Peter Yoachim, Kathryn Volk, Rosemary C. Dorsey, Cyrielle Opitom, Sarah Greenstreet, Tim Lister, Colin Snodgrass, Bryce T. Bolin, Laura Inno, Michele T. Bannister, Siegfried Eggl, Michael Solontoi, Michael S. P. Kelley, Mario Jurić, Hsing Wen Lin, 省文 林, Darin Ragozzine, Pedro H. Bernardinelli, Steven R. Chesley, Tansu Daylan, Josef Ďurech, Wesley C. Fraser, Mikael Granvik, Matthew M. Knight, Carey M. Lisse, Renu Malhotra, William J. Oldroyd, Audrey Thirouin, Quanzhi Ye, 泉志 叶
Figure caption: Metric values for the primary metrics under consideration for baseline_v2.1_10yrs from the latest version of rubin_sim. These values represent the fraction of the simulated population that would “pass” the metric requirements. “Completeness” refers to the discovery completeness for each sample population at the indicated H value, while “Fraction LC Inversion” refers to the fraction of each population that would have observations that meet the metric requirements, implying that that object would be a good subject for light-curve inversion. Likewise for “Fraction 4 filters,” showing the fraction of each population that would be likely to obtain colors in four filters. Full descriptions of the metrics are listed in Tables 2 and 3. Numerical values are provided in Appendix C.
Paper: Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science
Authors: Megan E. Schwamb, R. Lynne Jones, Peter Yoachim, Kathryn Volk, Rosemary C. Dorsey, Cyrielle Opitom, Sarah Greenstreet, Tim Lister, Colin Snodgrass, Bryce T. Bolin, Laura Inno, Michele T. Bannister, Siegfried Eggl, Michael Solontoi, Michael S. P. Kelley, Mario Jurić, Hsing Wen Lin, 省文 林, Darin Ragozzine, Pedro H. Bernardinelli, Steven R. Chesley, Tansu Daylan, Josef Ďurech, Wesley C. Fraser, Mikael Granvik, Matthew M. Knight, Carey M. Lisse, Renu Malhotra, William J. Oldroyd, Audrey Thirouin, Quanzhi Ye, 泉志 叶
Figure caption: The total number of visits in all filters after 10 yr for a representative sample of LSST simulations. In several of these scenarios the effective on-sky footprint of the WFD survey and other observing areas, including the NES, Galactic plane (GP), and south celestial pole (SCP) regions, change depending on how the observing time on-sky is divided. The DDFs are also visible as a collection of single fields receiving a higher number of observations than a WFD pointing, with each DDF receiving approximately 1% of the total LSST observing time. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°.
Paper: HEROES: The Hawaii eROSITA Ecliptic Pole Survey Catalog
Authors: A. J. Taylor, A. J. Barger, L. L. Cowie, G. Hasinger, E. M. Hu, A. Songaila
Figure caption: Color excess vs. magnitude diagram for z ∼ 6.6 LAEs in HEROES. The black points are 1% of the HEROES catalog that pass our initial source quality cuts. The blue curve is the narrowband excess significance cut (Sobral et al. 2013), and the dashed black line is the narrowband excess cut. The red crosses are the spectroscopically confirmed z ∼ 6.6 LAEs from Taylor et al. (2020) and Songaila et al. (2022).
Paper: HEROES: The Hawaii eROSITA Ecliptic Pole Survey Catalog
Authors: A. J. Taylor, A. J. Barger, L. L. Cowie, G. Hasinger, E. M. Hu, A. Songaila
Figure caption: Area density of sources in the g, r, i, and z bands as a function of magnitude from HEROES (blue, 2″ diameter aperture magnitudes), COSMOS D/UD (red, 2″ diameter aperture magnitudes, Aihara et al. 2022), and the DESY3 GOLD catalog (green, single-object-fitted magnitudes, Sevilla-Noarbe et al. 2021; Hartley et al. 2022). While the DESY3 catalog appears to be slightly overdense in the g and r bands, HEROES shows strong agreement with the area densities from COSMOS D/UD in all four bands and DESY3 in i and z.
Paper: Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science
Authors: Megan E. Schwamb, R. Lynne Jones, Peter Yoachim, Kathryn Volk, Rosemary C. Dorsey, Cyrielle Opitom, Sarah Greenstreet, Tim Lister, Colin Snodgrass, Bryce T. Bolin, Laura Inno, Michele T. Bannister, Siegfried Eggl, Michael Solontoi, Michael S. P. Kelley, Mario Jurić, Hsing Wen Lin, 省文 林, Darin Ragozzine, Pedro H. Bernardinelli, Steven R. Chesley, Tansu Daylan, Josef Ďurech, Wesley C. Fraser, Mikael Granvik, Matthew M. Knight, Carey M. Lisse, Renu Malhotra, William J. Oldroyd, Audrey Thirouin, Quanzhi Ye, 泉志 叶
Figure caption: The footprint of the NES in equatorial coordinates. The light-blue shading represents the pointings requested as part of the NES. The solid black line represents the ecliptic. The dashed black lines represent ±10° ecliptic latitude. The solid blue line plots the center of the Galactic plane. The dashed blue lines mark ±10° Galactic latitude. The plot is centered on α = 0 and δ = 0. R.A. is marked every 30°, and decl. lines are visible every 15° up to and including ±75°.
Paper: HEROES: The Hawaii eROSITA Ecliptic Pole Survey Catalog
Authors: A. J. Taylor, A. J. Barger, L. L. Cowie, G. Hasinger, E. M. Hu, A. Songaila
Figure caption: Left: 1% of the objects that passed the quality cuts but not the g-dropout color–color cuts (black points), and 1% of the objects that also passed the g-dropout color–color cuts (red points). Center: 1% of the objects that passed the quality cuts but not the r-dropout color–color cuts (black points), and 10% of the objects that also passed the r-dropout color–color cuts (red points). Right: 1% of the objects that passed the quality cuts but not the i-dropout color–color cuts (black points), and 100% of the objects that also passed the i-dropout color–color cuts (red points).
Paper: HEROES: The Hawaii eROSITA Ecliptic Pole Survey Catalog
Authors: A. J. Taylor, A. J. Barger, L. L. Cowie, G. Hasinger, E. M. Hu, A. Songaila
Figure caption: 5σ detection limits for each filter across the survey field from the Oi et al. (2021) method. Note the depth increases in the centers of the g, r, i, z, y imaging from the overlapping archival data and the significant depth increases in r, z, and NB921 near the JWST TDF (R.A. 17:22:47.896, decl. +65:49:21.54) from our additional targeted pointing.
Paper: HEROES: The Hawaii eROSITA Ecliptic Pole Survey Catalog
Authors: A. J. Taylor, A. J. Barger, L. L. Cowie, G. Hasinger, E. M. Hu, A. Songaila
Figure caption: Patch locations for the HSC data in the NEP. The bounding box of each patch is shown in black and labeled with its patch ID. The field-imaging coverage is shown in the background in light gray shading.
Paper: Tuning the Legacy Survey of Space and Time (LSST) Observing Strategy for Solar System Science
Authors: Megan E. Schwamb, R. Lynne Jones, Peter Yoachim, Kathryn Volk, Rosemary C. Dorsey, Cyrielle Opitom, Sarah Greenstreet, Tim Lister, Colin Snodgrass, Bryce T. Bolin, Laura Inno, Michele T. Bannister, Siegfried Eggl, Michael Solontoi, Michael S. P. Kelley, Mario Jurić, Hsing Wen Lin, 省文 林, Darin Ragozzine, Pedro H. Bernardinelli, Steven R. Chesley, Tansu Daylan, Josef Ďurech, Wesley C. Fraser, Mikael Granvik, Matthew M. Knight, Carey M. Lisse, Renu Malhotra, William J. Oldroyd, Audrey Thirouin, Quanzhi Ye, 泉志 叶
Figure caption: The simulated probability of Planet Nine (top) compared to the possible number of visits in the LSST footprint from the baseline_v2.1_10yrs simulation (bottom). The Planet Nine probability density is taken from Brown (2022), which is based on 100,000 synthetic orbits and physical properties of Planet Nine (including on-sky locations and V-band apparent magnitudes) drawn from the distributions developed in Brown & Batygin (2021), where we have removed the ones that are flagged as being ruled out by constraints from the ZTF (Brown & Batygin 2022) and the DES (Belyakov et al. 2022; Bernardinelli et al. 2022). The top panel has the LSST footprint shaded by the number of observations that reach 5σ limiting magnitude of 24 in any filter. The most probable locations of Planet Nine are within the NES region, but the full LSST footprint is required to search and probe the majority of the Brown & Batygin (2021) predicted Planet Nine parameter space. The plots are centered on α = 0 and δ = 0. R.A. and decl. lines are marked every 30°.