adsabs/AstroCLIMB huggingface dataset:
Paper: Negative Lags on the Viscous Timescale in Quasar Photometry and Prospects for Detecting More with LSST
Authors: Amy Secunda, Jenny E. Greene, Yan-Fei Jiang, 燕飞 姜, Philippe Z. Yao, Abderahmen Zoghbi
Figure caption: The distribution of the short lags detected by JAVELIN for 100 mock long season cadence LSST light curves with different amplitude ratios (shown in the legend). We zoom in on the portion of the distribution between 0 and 20 days to better show the accuracy of the short lag when detected. The input short lags, shown as the vertical dashed line, are 7, 9, and 12 days for the left, middle, and right panels, respectively.
Paper: Negative Lags on the Viscous Timescale in Quasar Photometry and Prospects for Detecting More with LSST
Authors: Amy Secunda, Jenny E. Greene, Yan-Fei Jiang, 燕飞 姜, Philippe Z. Yao, Abderahmen Zoghbi
Figure caption: Distribution of the short lag recovered by the maximum-likelihood method for 100 mock light curves with input lags of −50 and 7 days (left panel) and −130 and 9 days (right panel). The dashed vertical line shows the input short lag. The green and light blue distributions are for mock light curves with the baseline v2.0 LSST cadence for the u and y band and g and z band, respectively. In addition to an LSST cadence, the distributions shown in pink have an additional LCO observation taken roughly every 10 days in each LSST observing gap.
Paper: Negative Lags on the Viscous Timescale in Quasar Photometry and Prospects for Detecting More with LSST
Authors: Amy Secunda, Jenny E. Greene, Yan-Fei Jiang, 燕飞 姜, Philippe Z. Yao, Abderahmen Zoghbi
Figure caption: Distribution of the long lag recovered by the maximum-likelihood method for 100 mock light curves with input lags of −50 and 7 days (left panel) and −130 and 9 days (right panel). The dashed vertical line shows the input long lag. The green and light blue distributions are for mock light curves with the baseline v2.0 LSST cadence for the u and y band and g and z band, respectively. In addition to an LSST cadence, the distributions shown in pink have an additional LCO observation taken roughly every 10 days in each LSST observing gap.
Paper: Negative Lags on the Viscous Timescale in Quasar Photometry and Prospects for Detecting More with LSST
Authors: Amy Secunda, Jenny E. Greene, Yan-Fei Jiang, 燕飞 姜, Philippe Z. Yao, Abderahmen Zoghbi
Figure caption: Distribution of the long lag recovered by the Von-Neumann method for 100 mock light curves with input lags of −50 and 7 days (left panel), −130 and 9 days (middle panel), and −400 and 12 days (right panel). The dashed vertical line shows the input long lag. The orange (green) distributions are for mock light curves with the long season (baseline) u and y band cadence.
Paper: Negative Lags on the Viscous Timescale in Quasar Photometry and Prospects for Detecting More with LSST
Authors: Amy Secunda, Jenny E. Greene, Yan-Fei Jiang, 燕飞 姜, Philippe Z. Yao, Abderahmen Zoghbi
Figure caption: The distributions of the long lags recovered by JAVELIN for 100 light curves with input lags of −50 and 7 days (left panel), −130 and 9 days (middle panel), and −400 and 12 days (right panel). The orange and green distributions are for mock light curves with the long season and baseline v2.0 LSST cadences, respectively, for the u and y band. The dashed vertical line shows the input long lag.
Paper: Negative Lags on the Viscous Timescale in Quasar Photometry and Prospects for Detecting More with LSST
Authors: Amy Secunda, Jenny E. Greene, Yan-Fei Jiang, 燕飞 姜, Philippe Z. Yao, Abderahmen Zoghbi
Figure caption: Distribution of the short lags recovered by the maximum-likelihood method for 100 mock long season cadence LSST light curves with different amplitude ratios (shown in the legend). The input short lags, shown as the vertical dashed line, are 7 days in the left panel and 9 days in the right panel.
Paper: First Results for Solar Soft X-Ray Irradiance Measurements from the Third-generation Miniature X-Ray Solar Spectrometer
Authors: Thomas N. Woods, Bennet Schwab, Robert Sewell, Anant Kumar Telikicherla Kandala, James Paul Mason, Amir Caspi, Thomas Eden, Amal Chandran, Phillip C. Chamberlin, Andrew R. Jones, Richard Kohnert, Christopher S. Moore, Stanley C. Solomon, Harry Warren
Figure caption: Analysis of EM, temperature (T), and AF evolution spanning the multiple flares on 2022 April 24. Two EMs are shown in the top plot and their respective two Ts are shown in the middle plot. For all three plots, a scaled GOES-16 XRS-B irradiance (black line) is shown to indicate the evolution of the flare. The cooler temperature component, T1, remains relatively stable across the duration of the flares, whereas the warmer temperature component, T2, varies along with the scaled XRS-B irradiance. These temperatures are compared with the single-temperature result found using the A/B ratio method from GOES-16 (T_XRS). The XRS single-temperature estimate is slightly higher than the warmer temperature in our 2T model. The AFs (FIP bias) of Mg, Si, S, and Fe are shown in the bottom plot to suddenly decrease from their preflare values at the beginning of the flare and slightly decrease during the rise of each sequential flare impulse, and then relax slowly toward preflare values between impulses.
Paper: Redshift Evolution of Electron Density in the Interstellar Medium at z ∼ 0–9 Uncovered with JWST/NIRSpec Spectra and Line-spread Function Determinations
Authors: Yuki Isobe, Masami Ouchi, Kimihiko Nakajima, Yuichi Harikane, Yoshiaki Ono, Yi Xu, Yechi Zhang, Hiroya Umeda
Figure caption:  M *–SFR relation of the JWST galaxies with the NIRCam photometry at 4 < z < 9 represented by the red circles. We also plot the median n e values of SFMS galaxies with M * ∼ 108–1010 M ⊙ at z ∼ 0–1 (blue squares, [O II]- or [S II]-based; Berg et al. 2012; Swinbank et al. 2019; Davies et al. 2021) and with M * ∼ 1010 M ⊙ at z ∼ 1–3 (green hexagons, [O II]- or [S II]-based; Steidel et al. 2014; Sanders et al. 2016a; Kaasinen et al. 2017; Kashino et al. 2017; Davies et al. 2021). The red solid, green solid, blue solid, and blue dashed lines illustrate SFMS galaxies at z = 5–6 (Santini et al. 2017), z = 2 (Speagle et al. 2014), z = 1 (Speagle et al. 2014), and z = 0 (Chang et al. 2015), respectively. We also show the median n e values of high specific SFR (sSFR) galaxies with M * ∼ 108 M ⊙ at z ∼ 0 (cyan diamond, [S II]-based; Berg et al. 2022) and with M * ∼ 109 M ⊙ at z ∼ 2–3 (yellow pentagon, [O II]-based; Christensen et al. 2012a, 2012b; Sanders et al. 2016b; Gburek et al. 2019).
Paper: First Results for Solar Soft X-Ray Irradiance Measurements from the Third-generation Miniature X-Ray Solar Spectrometer
Authors: Thomas N. Woods, Bennet Schwab, Robert Sewell, Anant Kumar Telikicherla Kandala, James Paul Mason, Amir Caspi, Thomas Eden, Amal Chandran, Phillip C. Chamberlin, Andrew R. Jones, Richard Kohnert, Christopher S. Moore, Stanley C. Solomon, Harry Warren
Figure caption: Example flare spectra for the M1.7 flare (green) on 2022 March 29 (DOY 088) at 21:52 UT and for the C2 flare (red) on 2022 April 24 (DOY 114) at 11:00 UT. The top panel shows the ratio of flare peak spectrum to preflare spectrum, normalized for the M1/C1 flare ratio (XRS factor of 10). The bottom panel shows the flare irradiance that is defined as the spectrum at the flare peak time minus the preflare spectrum. The ratio above 5 keV is not shown because of the low signals for the preflare spectra at these higher energies.
Paper: First Results for Solar Soft X-Ray Irradiance Measurements from the Third-generation Miniature X-Ray Solar Spectrometer
Authors: Thomas N. Woods, Bennet Schwab, Robert Sewell, Anant Kumar Telikicherla Kandala, James Paul Mason, Amir Caspi, Thomas Eden, Amal Chandran, Phillip C. Chamberlin, Andrew R. Jones, Richard Kohnert, Christopher S. Moore, Stanley C. Solomon, Harry Warren
Figure caption: The features expected in the DAXSS spectra are illustrated as a function of plasma temperature and are labeled with the primary ion as listed in Tables A1–A6. The colors of the labels are related to the intensity ratio relative to the nearby continuum intensity; features with a ratio greater than 2.0 are red, with ratio between 1.3 and 2.0 are green, and with ratio less than 1.3 are blue. An asterisk by the feature energy label signifies that the primary ion changes with plasma temperature.
Paper: Redshift Evolution of Electron Density in the Interstellar Medium at z ∼ 0–9 Uncovered with JWST/NIRSpec Spectra and Line-spread Function Determinations
Authors: Yuki Isobe, Masami Ouchi, Kimihiko Nakajima, Yuichi Harikane, Yoshiaki Ono, Yi Xu, Yechi Zhang, Hiroya Umeda
Figure caption: Our fitting results of the LSF-convolved Gaussian functions to the [O II] doublet. The gray dots denote data points with spectral errors. The red solid curve represents the total profile of the [O II] doublet. The blue dashed curve and the blue dashed–dotted curve show the profiles of [O II] λ3726 and [O II] λ3729, respectively.
Paper: First Results for Solar Soft X-Ray Irradiance Measurements from the Third-generation Miniature X-Ray Solar Spectrometer
Authors: Thomas N. Woods, Bennet Schwab, Robert Sewell, Anant Kumar Telikicherla Kandala, James Paul Mason, Amir Caspi, Thomas Eden, Amal Chandran, Phillip C. Chamberlin, Andrew R. Jones, Richard Kohnert, Christopher S. Moore, Stanley C. Solomon, Harry Warren
Figure caption: The AFs of Mg, Si, S, and Fe, as plotted against the hotter-temperature component (T2) of the 2T model fit, show a semilinear relationship. The AFs of these low-FIP and mid-FIP elements decline as the flare temperature increases.
Paper: Redshift Evolution of Electron Density in the Interstellar Medium at z ∼ 0–9 Uncovered with JWST/NIRSpec Spectra and Line-spread Function Determinations
Authors: Yuki Isobe, Masami Ouchi, Kimihiko Nakajima, Yuichi Harikane, Yoshiaki Ono, Yi Xu, Yechi Zhang, Hiroya Umeda
Figure caption: LSF as a function of velocity. In each panel, the purple, blue, green, yellow, and red curves represent LSFs obtained at wavelengths whose values correspond to the numbers with the same color listed in the top right corner (units: μm).
Paper: Variability of Interplanetary Shock and Associated Energetic Particle Properties as a Function of the Time Window Around the Shock
Authors: K. Moreland, M. A. Dayeh, G. Li, A. Farahat, R. W. Ebert, M. I. Desai
Figure caption: An example of particle and solar wind signatures during a typical Energetic Storm Particle (ESP) event. The top panel shows particle data from the LEMS120 instrument on ACE (∼.056 to ∼3.02 MeV). The bottom three panels show the magnitude of the solar wind velocity, density, and temperature from the SWEPAM instrument. The bottom panel shows the magnetic field vector (red, blue, and green lines) and magnitude (black line) from the MAG instrument. The vertical dotted red line shows the arrival time of the IP shock detected by the ACE spacecraft at 06:20:55 UT on 1998 August 26. This shock is driven by an interplanetary coronal mass ejection (ICME), with the sheath (turbulent magnetic field) and magnetic (field rotation) following.
Paper: What Are the Radial Distributions of Density, Outflow Rates, and Cloud Structures in the M82 Wind?
Authors: Xinfeng Xu, Timothy Heckman, Michitoshi Yoshida, Alaina Henry, Youichi Ohyama
Figure caption: Same as Figure 8, but for the hot wind fluid thermal pressure (left) and the ram pressure on the outflowing clouds (right). In both panels, we also show the lower limits on wind pressure from the X-ray data in Lopez et al. (2020) as the green lines.
Paper: Variability of Interplanetary Shock and Associated Energetic Particle Properties as a Function of the Time Window Around the Shock
Authors: K. Moreland, M. A. Dayeh, G. Li, A. Farahat, R. W. Ebert, M. I. Desai
Figure caption: Evolution of the Pearson correlation coefficient (r) for three shock properties: ﹩\left|{\rm{\Delta }}V\right|﹩, R  B , and R  N . Each panel shows the evolution of the correlation over 2 hr period in 2 minute time window increments. Panel (a) uses a moving time window method across the shock (Case I); panel (b) uses a fixed 2 minute upstream time window and moving-downstream time window (Case II). The 0.5 and 0.7 thresholds are marked by horizontal lines. The time where the properties fall below the thresholds are noted by color-coded arrows.
Paper: Variability of Interplanetary Shock and Associated Energetic Particle Properties as a Function of the Time Window Around the Shock
Authors: K. Moreland, M. A. Dayeh, G. Li, A. Farahat, R. W. Ebert, M. I. Desai
Figure caption: Time-sampling windows for the derived shock properties are done using an inclusive sampling method. Panel (a) shows the first time window where the data are taken from the time of the shock to the end of the time window; panel (b) shows the following time window, where the data are taken from the time of the shock to the end of that time window, including the previous selection. (c) Calculation of the ESP spectral indices is done using an exclusive time-sampling window, as illustrated. Here, data from the previous window are not included in the subsequent time interval calculations.
Paper: Variability of Interplanetary Shock and Associated Energetic Particle Properties as a Function of the Time Window Around the Shock
Authors: K. Moreland, M. A. Dayeh, G. Li, A. Farahat, R. W. Ebert, M. I. Desai
Figure caption: We show the spectral index (γ) distributions for four different time windows. The red bar indicates spectral indices for the 2 minute time window; blue for the 20 minute window; orange for the 60 minute time window; and green for the largest time window of 540 minutes. Panel (a) shows the index values calculated using the exclusive method downstream of the shock. Panel (b) shows the distribution of spectral index value using the exclusive method upstream of the shock.
Paper: Variability of Interplanetary Shock and Associated Energetic Particle Properties as a Function of the Time Window Around the Shock
Authors: K. Moreland, M. A. Dayeh, G. Li, A. Farahat, R. W. Ebert, M. I. Desai
Figure caption: Box plots show the variation in each shock property. The time windows are show on the y-axis. The median value is shown above the box plot, the whiskers display outliers, and the MAD as a percent is displayed in bold for each time window. We note that there are several events in which the data around the shock time are missing or bad and cause erroneous values. These events have been removed from the data set (see Table A1 in Appendix), and a more detailed explanation of the erroneous data is given in Section 3.
Paper: Variability of Interplanetary Shock and Associated Energetic Particle Properties as a Function of the Time Window Around the Shock
Authors: K. Moreland, M. A. Dayeh, G. Li, A. Farahat, R. W. Ebert, M. I. Desai
Figure caption: Averaged solar wind parameters plotted 2 days before and after the shock for all 278 events. The middle red line represents the mean, with the upper and lower quantiles plotted in black. The zoomed-in plots show the 60 minutes before and after the shock in closer detail.
Paper: Variability of Interplanetary Shock and Associated Energetic Particle Properties as a Function of the Time Window Around the Shock
Authors: K. Moreland, M. A. Dayeh, G. Li, A. Farahat, R. W. Ebert, M. I. Desai
Figure caption: Box plots for the upstream and downstream spectral indices along with the median values above each box plot and the MAD as a percentage in bold to the right. The individual time window colors match the previous distribution plots in Figure 6. For all time windows (gray-colored box plots) we find that the index values vary more from their median in the upstream regime of the shock.
Paper: Variability of Interplanetary Shock and Associated Energetic Particle Properties as a Function of the Time Window Around the Shock
Authors: K. Moreland, M. A. Dayeh, G. Li, A. Farahat, R. W. Ebert, M. I. Desai
Figure caption: A sample power-law fit of the form ﹩J\left(E\right)={J}_{o}{E}^{-\gamma }﹩ for a selected time interval. To fit the spectra in all events, we use the lowest four energy channels, P1’–P4’ (centered at .056 MeV, .088 MeV, .150 MeV, and .250 MeV), from the LEMS120 telescope of EPAM.
Paper: Variability of Interplanetary Shock and Associated Energetic Particle Properties as a Function of the Time Window Around the Shock
Authors: K. Moreland, M. A. Dayeh, G. Li, A. Farahat, R. W. Ebert, M. I. Desai
Figure caption: Distributions of all derived shock properties shown in stacked histograms for 2 minute (red) and 20 minute (blue) intervals for all 278 events.
Paper: Abundances and Transients from Neutron Star–White Dwarf Mergers
Authors: M. Alexander R. Kaltenborn, Chris L. Fryer, Ryan T. Wollaeger, Krzysztof Belczynski, Wesley Even, Chryssa Kouveliotou
Figure caption: Left panel: the wind-ejecta nuclear abundances for NS mass 1.25 M ⊙ and WD mass 1.00 M ⊙ for model CEEW07. Compared with the simpler network, the diversity of isotopes moving faster than 2 × 109 cm s−1 increased distinctly. The fastest-moving material is largely comprised of heavy iron (e.g., 60Fe) and heavy nickel (e.g., 66Ni). Right panel: a reduced subset of the nuclear abundances is compared to the simpler 21-isotope network for model CEEW07. The 44Ti became fuel for producing other isotopes, being reduced by nearly 2 orders of magnitude.
Paper: Abundances and Transients from Neutron Star–White Dwarf Mergers
Authors: M. Alexander R. Kaltenborn, Chris L. Fryer, Ryan T. Wollaeger, Krzysztof Belczynski, Wesley Even, Chryssa Kouveliotou
Figure caption: Luminosity as a function of time in the UV, U, I, and K bands for our three different disk models: HENW (left panel), CENW (middle panel), and CEEW (right panel). The line styles correspond to different mass pairings of the NS and WD: 1.8 M ⊙ NS, 1.25 M ⊙ WD (solid line), 1.1 M ⊙ NS, 1.25 M ⊙ WD (dashed line), and 1.1 M ⊙ NS, 0.5 M ⊙ WD (dashed–dotted line). All models produce similar trends with a strong UV component on the first day and a peak in the I band at 5–8 days. The models with more ejecta mass (high-entropy case) produce the most luminous light curves.
Paper: Abundances and Transients from Neutron Star–White Dwarf Mergers
Authors: M. Alexander R. Kaltenborn, Chris L. Fryer, Ryan T. Wollaeger, Krzysztof Belczynski, Wesley Even, Chryssa Kouveliotou
Figure caption: Spectra for a range of models varying the WD and NS mass and the disk model for our low ejecta-velocity models: HENW, CENW, and CEEW. We follow these models at three early timescales at 1.3, 2.8, and 4.3 days. The scales on these plots are different from the spectra in Figure 14 because the features are redder at early times and brighter at late times.
Paper: Abundances and Transients from Neutron Star–White Dwarf Mergers
Authors: M. Alexander R. Kaltenborn, Chris L. Fryer, Ryan T. Wollaeger, Krzysztof Belczynski, Wesley Even, Chryssa Kouveliotou
Figure caption: Nuclear yield of the wind ejecta versus the WD initial mass for the post-processed 495-isotope Torch network. Power-law fits to the data are shown for a select number of isotopes. 60Fe shows non-power-law behavior in the yields. A further study applying the yields to galactic NS–WD population models and comparison with observations is ongoing.
Paper: Abundances and Transients from Neutron Star–White Dwarf Mergers
Authors: M. Alexander R. Kaltenborn, Chris L. Fryer, Ryan T. Wollaeger, Krzysztof Belczynski, Wesley Even, Chryssa Kouveliotou
Figure caption: Spectra for a range of models varying the WD and NS mass and the disk model: HENW, CENW, and CEEW. We follow these models at three early timescales at 1.3, 2.8, and 4.3 days. These models show the range of spectra from our standard ejecta-velocity models.
Paper: Abundances and Transients from Neutron Star–White Dwarf Mergers
Authors: M. Alexander R. Kaltenborn, Chris L. Fryer, Ryan T. Wollaeger, Krzysztof Belczynski, Wesley Even, Chryssa Kouveliotou
Figure caption: Spectra for a fast (blue) and slow (orange) model, along with spectrum-scaled emissivity (green-dashed line) of iron (left panels) and calcium (right panels) at early (top panels) and late time (bottom panels).
Paper: Abundances and Transients from Neutron Star–White Dwarf Mergers
Authors: M. Alexander R. Kaltenborn, Chris L. Fryer, Ryan T. Wollaeger, Krzysztof Belczynski, Wesley Even, Chryssa Kouveliotou
Figure caption: Emission vs. wavelength for the HENW (left panel) and CEEW (right panel) models with a 1.25 M ⊙ WD being disrupted by a 1.1 M ⊙ NS, i.e., models HENW10 and CEEW10.
Paper: Relative Intrinsic Scatter in Hierarchical Type Ia Supernova Sibling Analyses: Application to SNe 2021hpr, 1997bq, and 2008fv in NGC 3147
Authors: Sam M. Ward, Stephen Thorp, Kaisey S. Mandel, Suhail Dhawan, David O. Jones, Kirsty Taggart, Ryan J. Foley, Gautham Narayan, Kenneth C. Chambers, David A. Coulter, Kyle W. Davis, Thomas de Boer, Kaylee de Soto, Nicholas Earl, Alex Gagliano, Hua Gao, Jens Hjorth, Mark E. Huber, Luca Izzo, Danial Langeroodi, Eugene A. Magnier, Peter McGill, Armin Rest, César Rojas-Bravo, Radosław Wojtak
Figure caption: Analysis of individual siblings' distance estimates. (top) BAYESN distance estimates from individual fits to the sibling trio; error bars are the distance measurement errors [bold] and the total errors that include σ 0 = 0.094 mag [faint]. (middle) Posteriors of σ Rel computed using analytic formulae in Section 2.3. Posteriors are weakly constraining, so more sibling galaxies are required in order to tightly constrain σ Rel. The 0.06 mag standard deviation is thus a poor estimator of σ Rel. (bottom) The individual distance estimates are combined under the three intrinsic scatter modeling assumptions (Table 3). The distance uncertainty is minimized when σ Rel = σ 0 is assumed, while the best estimate is obtained via σ Rel marginalization.
Paper: Relative Intrinsic Scatter in Hierarchical Type Ia Supernova Sibling Analyses: Application to SNe 2021hpr, 1997bq, and 2008fv in NGC 3147
Authors: Sam M. Ward, Stephen Thorp, Kaisey S. Mandel, Suhail Dhawan, David O. Jones, Kirsty Taggart, Ryan J. Foley, Gautham Narayan, Kenneth C. Chambers, David A. Coulter, Kyle W. Davis, Thomas de Boer, Kaylee de Soto, Nicholas Earl, Alex Gagliano, Hua Gao, Jens Hjorth, Mark E. Huber, Luca Izzo, Danial Langeroodi, Eugene A. Magnier, Peter McGill, Armin Rest, César Rojas-Bravo, Radosław Wojtak
Figure caption: Contrast plot of SN 2008fv fit posteriors showcases the improvement in parameter estimates from the individual fit [pink] to the joint fit [black]. For example, the uncertainties on the individual dust parameters, ﹩({A}_{V}^{s},{R}_{V}^{s})﹩, shrink by ≈(52, 29)%. With only BVR data in the individual fit, R  s   V  constraints are weak; however, the joint fit rules out low unphysical values ﹩{R}_{V}^{s}\approx 1﹩.
Paper: Relative Intrinsic Scatter in Hierarchical Type Ia Supernova Sibling Analyses: Application to SNe 2021hpr, 1997bq, and 2008fv in NGC 3147
Authors: Sam M. Ward, Stephen Thorp, Kaisey S. Mandel, Suhail Dhawan, David O. Jones, Kirsty Taggart, Ryan J. Foley, Gautham Narayan, Kenneth C. Chambers, David A. Coulter, Kyle W. Davis, Thomas de Boer, Kaylee de Soto, Nicholas Earl, Alex Gagliano, Hua Gao, Jens Hjorth, Mark E. Huber, Luca Izzo, Danial Langeroodi, Eugene A. Magnier, Peter McGill, Armin Rest, César Rojas-Bravo, Radosław Wojtak
Figure caption: Posterior from the joint fit to NGC 3147's siblings’ light curves. Constraints on the distance hyperparameter [black] and each sibling’s individual parameters [colors] improve compared to individual fits. This fit marginalizes over σ Rel with an informative prior, σ Rel ∼ U(0, σ 0), and σ 0 = 0.094 mag, to yield robust estimates of the distance and chromatic parameters.