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Paper: Updated Catalog of Kepler Planet Candidates: Focus on Accuracy and Orbital Periods
Authors: Jack J. Lissauer, Jason F. Rowe, Daniel Jontof-Hutter, Daniel C. Fabrycky, Eric B. Ford, Darin Ragozzine, Jason H. Steffen, Kadri M. Nizam
Figure caption: Comparison of the mean stellar density from our circular orbit transit models (ρ ⋆c) to the mean stellar density from our adopted stellar parameters (ρ ⋆). The red diagonal line is for ρ ⋆= ρ ⋆c, and the two parallel darker red lines show a bias of 10% in ρ ⋆. The colors of the plotted data points note the source of the stellar parameter: blue = DR25 (Thompson et al. 2018), green = Berger et al. (2020), and orange = CKS (Fulton & Petigura 2018). The left panel shows all planetary candidates from our sample with 0.1 g cm−3 < ρ ⋆< 10 g cm−3. The right panel is restricted to planetary candidates that have S/N > 10, b < 0.9, and uncertainty (average of σ +(ρ ⋆c) and σ −(ρ ⋆c)) of less than 20%. Uncertainties are half-widths of the 68.27% credible interval (±1σ).
Paper: Updated Catalog of Kepler Planet Candidates: Focus on Accuracy and Orbital Periods
Authors: Jack J. Lissauer, Jason F. Rowe, Daniel Jontof-Hutter, Daniel C. Fabrycky, Eric B. Ford, Darin Ragozzine, Jason H. Steffen, Kadri M. Nizam
Figure caption: The observed multiplicity of transiting planets from the Kepler sample as a function of period and radius, with individual squares representing a factor of 103/8 ≈ 2.371 in P and 12.51/6 ≈ 1.523 in R  p . Black dots represent individual Kepler planets, and black stars represent the terrestrial planets in our solar system. The colored squares give the multiplicity fraction for each area that contains at least four planets. A planet is considered to be part of a multiplanet system if more than one transiting planet candidate is seen in the photometric lightcurve. If a square were to contain four planets, three of which were from multiplanet systems, then the multiplanet fraction would be equal to 0.75. The multiplicity fraction shows a paucity in multiplanets observed for hot Jupiters and USP (P < 1 day) and long-period (P > 100 days) planets, with an observed increase in multiplicity as the planet radius decreases in the well-populated region with periods from 1 to 40 days.
Paper: Updated Catalog of Kepler Planet Candidates: Focus on Accuracy and Orbital Periods
Authors: Jack J. Lissauer, Jason F. Rowe, Daniel Jontof-Hutter, Daniel C. Fabrycky, Eric B. Ford, Darin Ragozzine, Jason H. Steffen, Kadri M. Nizam
Figure caption: The cumulative impact parameter distributions of various subsets of planet candidates, as well as KOIs rejected solely because they are too large (dispositioned as R in Table 1), that were observed by Kepler. Red curves represent PCs in multis, and blue curves represent single-planet candidates; small planets are shown by solid lines and large PCs by dashed lines. There are six small (R  p  < 5 R ⊕) and 54 large singles that have b > 1. In multis, there are two small and one large PCs that have b > 1. Planet candidates with S/N <12 are not counted for the cumulative distribution functions, but they are considered when determining the multiplicity of the systems in which the counted PCs reside.
Paper: Updated Catalog of Kepler Planet Candidates: Focus on Accuracy and Orbital Periods
Authors: Jack J. Lissauer, Jason F. Rowe, Daniel Jontof-Hutter, Daniel C. Fabrycky, Eric B. Ford, Darin Ragozzine, Jason H. Steffen, Kadri M. Nizam
Figure caption: Number of singles (blue) and multis (red) as functions of S/N. The dashed red curve is restricted to the lowest-S/N planet in each multiplanet system. All PCs that were observed by Kepler are represented. See Figure 3 for a plot of how the typical S/N varies with planetary radius and orbital period.
Paper: Updated Catalog of Kepler Planet Candidates: Focus on Accuracy and Orbital Periods
Authors: Jack J. Lissauer, Jason F. Rowe, Daniel Jontof-Hutter, Daniel C. Fabrycky, Eric B. Ford, Darin Ragozzine, Jason H. Steffen, Kadri M. Nizam
Figure caption: The Kepler planet population showing radius (R  p ) vs. period (P) on the left and radius vs. incident flux (S ⊕) on the right. The points have been colored by the radius of the host star (R ⋆). Solar system planets are marked with black stars, and the conservative habitable zone for an Earth-like planet around a Sun-like star (Kopparapu et al. 2014) is shown by the hatched green lines.
Paper: Updated Catalog of Kepler Planet Candidates: Focus on Accuracy and Orbital Periods
Authors: Jack J. Lissauer, Jason F. Rowe, Daniel Jontof-Hutter, Daniel C. Fabrycky, Eric B. Ford, Darin Ragozzine, Jason H. Steffen, Kadri M. Nizam
Figure caption: Histogram of Kepler planet-hosting star magnitudes, with hosts of a single PC in blue and stellar hosts of multis in red. All PCs that were observed by Kepler are represented in this plot.
Paper: Planetary Waves Drive Horizontal Variations in Trace Species in the Venus Deep Atmosphere
Authors: Maureen Cohen, James Holmes, Stephen Lewis, Manish Patel
Figure caption: Zonal mean zonal wind (left) and mean meridional mass stream function (right). The figures show long-term mean values over the 30 yr span of the Surface simulation. Westward wind (prograde in the context of Venus) is represented by negative values. The green dashed line shows the location of the tracer source in the Cloud deck experiment, and the magenta dashed line at 0 km shows the location of the tracer source in the Surface experiment.
Paper: Planetary Waves Drive Horizontal Variations in Trace Species in the Venus Deep Atmosphere
Authors: Maureen Cohen, James Holmes, Stephen Lewis, Manish Patel
Figure caption: Zonal mean age of air for the Surface (left) and Cloud deck (right) simulations after ∼30 and ∼5 Earth yr (94 and 15 Venus days), respectively. The overlaid contour lines represent the mean meridional mass stream function in 1010 kg s−1 as also shown in Figure 1(b)). The bottom panels depict detailed views of altitude ranges in each simulation with latitudinal gradients in the age of air. Note the difference in the y-axis range between the bottom two panels. Years in the color bar label refers to Earth years.
Paper: Planetary Waves Drive Horizontal Variations in Trace Species in the Venus Deep Atmosphere
Authors: Maureen Cohen, James Holmes, Stephen Lewis, Manish Patel
Figure caption: Time series of the zonal mean age of air at the equator in Earth years (left column), rate of change of the age of air time series (middle column), and smoothed time series fit to the curve a(1 − e −t/τ ), where a is the projected limit of the curve and τ is the e-folding time. The data are smoothed with a Savitzky–Golay filter to remove fluctuations caused by the daily solar cycle. Each row contains plots for a different model level: 4.5 km (first row), 35.2 km (second row), 46.9 km (third row) from the Surface simulation, and 101 km (fourth row)—the model top—from the Cloud deck simulation.
Paper: Planetary Waves Drive Horizontal Variations in Trace Species in the Venus Deep Atmosphere
Authors: Maureen Cohen, James Holmes, Stephen Lewis, Manish Patel
Figure caption: Six snapshots of the age of air in the Surface simulation at the south pole in an orthographic projection at 54 km. The outermost circle is 60°, and each radial grid line represents 5°. Days and years refer to Earth days and years. Day 0 is the first time output of the final Venus day (final 117 Earth days) of the Surface simulation. The series covers slightly more than 1/4 Venus day. Each snapshot is 6 Earth days or 0.05 Venus days apart.
Paper: Planetary Waves Drive Horizontal Variations in Trace Species in the Venus Deep Atmosphere
Authors: Maureen Cohen, James Holmes, Stephen Lewis, Manish Patel
Figure caption: Longitude–latitude maps of the age of air in the Surface simulation shown for two altitudes in the deep atmosphere and two times. The overlaid contour lines represent the geopotential height anomaly in meters. As in Figure 5, days and years refer to Earth days and years, and day 0 is the first time output of the final Venus day (final 117 Earth days) of the simulation. The snapshots are 12 Earth days or 0.1 Venus days apart.
Paper: Planetary Waves Drive Horizontal Variations in Trace Species in the Venus Deep Atmosphere
Authors: Maureen Cohen, James Holmes, Stephen Lewis, Manish Patel
Figure caption: Time series of the vertical wind at the approximate wave latitude of 71° and an arbitrary longitude of 0° for altitudes of 28, 47, and 64 km over the final 5 Venus days (final 1.6 Earth yr) of the Surface simulation.
Paper: Planetary Waves Drive Horizontal Variations in Trace Species in the Venus Deep Atmosphere
Authors: Maureen Cohen, James Holmes, Stephen Lewis, Manish Patel
Figure caption: Longitude–latitude maps of the vertical wind (colors) and horizontal wind (arrows) in the Surface simulation shown for two altitudes in the deep atmosphere and two times. Positive (red) values of the vertical wind signify upward flow. The altitudes and times are the same as those in Figure 6. As in Figure 5, days refer to Earth days, and day 0 is the first time output of the final Venus day of the simulation (final 117 Earth days). The snapshots are 12 Earth days or 0.1 Venus days apart.
Paper: Planetary Waves Drive Horizontal Variations in Trace Species in the Venus Deep Atmosphere
Authors: Maureen Cohen, James Holmes, Stephen Lewis, Manish Patel
Figure caption: Vertical profiles of the zonal mean age of air in the Surface (left) and Cloud deck (right) simulations after ∼30 and ∼5 Earth yr (94 and 15 Venus days). In each case, the blue, orange, and green lines represent the south pole, equator, and north pole, respectively. Years on the x-axis refers to Earth years.
Paper: Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact
Authors: Tim Lister, Cora Constantinescu, William Ryan, Eileen Ryan, Edward Gomez, Liz Phillips, Agata Rożek, Helen Usher, Brian P. Murphy, Joseph Chatelain, Sarah Greenstreet
Figure caption: The magnitude of (65803) Didymos over the full time span of available data on a linear scale. The main Sinistro 1 m data from the light-curve and tail monitoring blocks, supplemented by the educational user data sets, are shown as the red crosses. The FLI clear filter data from the time of impact are shown as blue plus signs. A linear fit to the declining brightness of the Didymos system following impact is shown as the orange dashed line, along with a JPL Horizons H(V), G model (black line) converted using the V − r color and the H, G 1, G 2 model (blue line) with the parameters of Hasselmann et al. (2023).
Paper: Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact
Authors: Tim Lister, Cora Constantinescu, William Ryan, Eileen Ryan, Edward Gomez, Liz Phillips, Agata Rożek, Helen Usher, Brian P. Murphy, Joseph Chatelain, Sarah Greenstreet
Figure caption: Median-combined stacked images of Didymos from 2023-02-17—2023-02-25 at MRO.
Paper: Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact
Authors: Tim Lister, Cora Constantinescu, William Ryan, Eileen Ryan, Edward Gomez, Liz Phillips, Agata Rożek, Helen Usher, Brian P. Murphy, Joseph Chatelain, Sarah Greenstreet
Figure caption: Plot of the projected Didymos tail length (in km) from manual measurements (blue points) along with lower limits (red upward-pointing arrows) as a function of time. The time within ±5 days of a full moon is shown by the gray vertical shaded area, as this will lead to brighter background and shorter measured tail lengths.
Paper: Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact
Authors: Tim Lister, Cora Constantinescu, William Ryan, Eileen Ryan, Edward Gomez, Liz Phillips, Agata Rożek, Helen Usher, Brian P. Murphy, Joseph Chatelain, Sarah Greenstreet
Figure caption: Median-combined stacked images of Didymos from 2023-01-24 to 2023-01-31 from the LCOGT network, along with annotated arrow pointing to Didymos. Note the larger scale bar of 10,000 km on these figures.
Paper: Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact
Authors: Tim Lister, Cora Constantinescu, William Ryan, Eileen Ryan, Edward Gomez, Liz Phillips, Agata Rożek, Helen Usher, Brian P. Murphy, Joseph Chatelain, Sarah Greenstreet
Figure caption: Median-combined stacked images of Didymos from 2022-10-21 to 2023-02-11 at MRO.
Paper: Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact
Authors: Tim Lister, Cora Constantinescu, William Ryan, Eileen Ryan, Edward Gomez, Liz Phillips, Agata Rożek, Helen Usher, Brian P. Murphy, Joseph Chatelain, Sarah Greenstreet
Figure caption: Median-combined stacked images of Didymos from 2022-11-30 to 2023-01-24 from the LCOGT network.
Paper: Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact
Authors: Tim Lister, Cora Constantinescu, William Ryan, Eileen Ryan, Edward Gomez, Liz Phillips, Agata Rożek, Helen Usher, Brian P. Murphy, Joseph Chatelain, Sarah Greenstreet
Figure caption: Median-combined stacked images of Didymos from 2022-10-30 to 2022-11-30 from the LCOGT network.
Paper: Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact
Authors: Tim Lister, Cora Constantinescu, William Ryan, Eileen Ryan, Edward Gomez, Liz Phillips, Agata Rożek, Helen Usher, Brian P. Murphy, Joseph Chatelain, Sarah Greenstreet
Figure caption: Median-combined stacked images of Didymos from 2022-10-02 to 2022-10-06 from the LCOGT network.
Paper: Long-term Monitoring of Didymos with the LCOGT Network and MRO after the DART Impact
Authors: Tim Lister, Cora Constantinescu, William Ryan, Eileen Ryan, Edward Gomez, Liz Phillips, Agata Rożek, Helen Usher, Brian P. Murphy, Joseph Chatelain, Sarah Greenstreet
Figure caption: Median-combined stacked images of Didymos from 2022-10-07 to 2022-10-28 from the LCOGT network.
Paper: A Spectral Method to Compute the Tides of Laterally Heterogeneous Bodies
Authors: Marc Rovira-Navarro, Isamu Matsuyama, Alexander Berne
Figure caption: Pattern of tidal heating for a spherically symmetric body (upper row) and for bodies with different types of lateral viscosity variations. For the spherically symmetric body, the total heating pattern is shown; for the rest, the difference between their corresponding heating pattern and that of a spherically symmetric body is shown. All plots are normalized by the average tidal heating of the spherically symmetric body. The reference Io model is employed (see Table 1) with peak-to-peak viscosity variations of 50%. The subplanet (SP) and antiplanet (AP) points as well as the centers of the trailing and leading hemispheres (TH and LH) are indicated. The longitudinally and longitudinally averaged tidal heating are also shown. In the first panel, Io’s volcanoes are indicated (blue dots), with contours corresponding to the density of Io’s volcanoes (red and blue contours indicating values higher and lower than the global average) (Steinke et al. 2020).
Paper: A Spectral Method to Compute the Tides of Laterally Heterogeneous Bodies
Authors: Marc Rovira-Navarro, Isamu Matsuyama, Alexander Berne
Figure caption: Tidal heating spectra (Equation (31)) corresponding to the cases of Figure 7. The energy spectra is given in terms of amplitude (upper panel) and the longitudinal eastward shift in the fraction of longitudinal wavelength with respect to ﹩{\mathfrak{R}}({Y}_{n}^{m})﹩ (lower panel).
Paper: A Spectral Method to Compute the Tides of Laterally Heterogeneous Bodies
Authors: Marc Rovira-Navarro, Isamu Matsuyama, Alexander Berne
Figure caption: Love number spectra for degree 2 and orders 0 (upper panel), 1 (center panel), and 2 (lower panel) tidal forcings for lateral variations of different degrees and orders (n LV, m LV). An elastic Io is assumed (Table 1), and the solution is cut off at perturbation order 4. Color intensity indicates the strength of the mode, and the terms with highest amplitude are indicated with a cross. For all cases, we assume peak-to-peak shear modulus variations of 10% of the mean shear modulus.
Paper: A Spectral Method to Compute the Tides of Laterally Heterogeneous Bodies
Authors: Marc Rovira-Navarro, Isamu Matsuyama, Alexander Berne
Figure caption: Gravitational Love numbers, ﹩{k}_{2,0}^{n,m}﹩, for lateral shear modulus variations of different wavelengths (n LV, m LV) for Io (solid lines), the Moon (dashed lines), and Mercury (dotted lines).
Paper: A Spectral Method to Compute the Tides of Laterally Heterogeneous Bodies
Authors: Marc Rovira-Navarro, Isamu Matsuyama, Alexander Berne
Figure caption: Same as Figure 2, but for the Enceladus model (Table 1). Differences are obtained as ﹩100\left|{\left({\rm{\Delta }}{k}_{\mathrm{2,0}}^{n,m}/{k}_{2}^{u}\right)}_{R}-{\left({\rm{\Delta }}{k}_{\mathrm{2,0}}^{n,m}/{k}_{2}^{u}\right)}_{X}\right|/{\left|{\rm{\Delta }}{k}_{\mathrm{2,0}}^{n,m}/{k}_{2}^{u}\right|}_{R}﹩, where X can either be the results obtained using the FEM model or the spectral-perturbation method. Solutions obtained using the FEM of Berne et al. (2023a) and the perturbation method of Qin et al. (2014) are indicated with filled circles and dashed lines, respectively.
Paper: A Spectral Method to Compute the Tides of Laterally Heterogeneous Bodies
Authors: Marc Rovira-Navarro, Isamu Matsuyama, Alexander Berne
Figure caption: Effect of lateral heterogeneities of different wavelengths for Europa (first row), Ganymede (second row), and Enceladus (third row). The highest-amplitude components of the tidal responses arising from zonal lateral variations of degrees 1 (first column), 2 (second column), and 3 (third column) are depicted. For reference, the degree 2 Love number of a spherically symmetric body, ﹩{k}_{2}^{u}﹩, is indicated (dashed black lines).
Paper: A Spectral Method to Compute the Tides of Laterally Heterogeneous Bodies
Authors: Marc Rovira-Navarro, Isamu Matsuyama, Alexander Berne
Figure caption: Difference in tidal heating computed using the stress and strain tensors (Equation (29), ﹩{\dot{E}}_{1}﹩) and the work done by the tidal force (Equation (35), ﹩{\dot{E}}_{2}﹩). We assume the viscoelastic Io model of Table 1 and zonal degree 2 lateral viscosity variations with peak-to-peak amplitudes of 50% of the mean viscosity.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: DHM (top row) and FLFM (bottom row) microscopes simultaneously collect 							images at 15 frames s−1 permitting detailed analysis of 							particle motility- and fluorescence-based biosignatures. These example 							images show the same motile and fluorescent microorganism in 4 s of raw 							data for both instruments. The microorganism (circled in red) makes a 							short rightward movement from T = 1 s to 								T = 2 s and a much longer burst or 							movement from T = 3 s to T = 4 s. All other particles slowly drift to 							the right with the background water flow. The observed Moire pattern in 							the top row is an artifact resulting from the raw holographic encoding, 							while the gridded appearance of the organism in the bottom row is an 							artifact of the light field encoding. Note that both microscopes were 							still in development when this data was taken, resulting in an alignment 							offset between the two image series.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: Examples of the same microorganism as it appears in the DHM (top) and 							FLFM (bottom) microscopes. The images here are particle crops from 								T = 2 s in Figure 2. Particles in the raw DHM images appear 							as an Airy pattern that increase in size as the particles become more 							distant from the central focal plane. Particles in the FLFM appear as a 							grid of bright spots corresponding to the lenselet array placed in front 							of the detector. Similarly, the pattern increases in size as the 							particle is farther from the focal plane. While particles may appear 							distorted to the naked eye, these images contain volumetric information 							permitting post hoc refocusing to any focal plane of interest.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: OSIA collects, analyzes, summarizes, and prioritizes scientific 							observations collaboratively with science team guidance and 							reconfiguration. This simplified illustration depicts a general OSIA 							ConOps strategy. Step 1: Ocean world lander collects surface samples and 							acquires instrument observations. Step 2: OSIA assesses and summarizes 							instrument data according to its current configuration and manual ground 							requests. Step 3: OSIA prioritizes available data products by estimated 							science value and, optionally, with respect to what has already been 							returned. Step 4: The highest-priority science data products are 							transmitted to Earth, maximizing downlink capacity. Lower-priority data 							is retained onboard for potential downlink at a later point in time. 							Step 5: Ground science teams review new observations vs. outstanding 							science questions and determine whether the OSIA's behavior requires 							redirection. Step 6: If so, scientists and operators generate an updated 							configuration that is most responsive to the current science intent. 							Step 7: Any manual data requests and new configurations are 							transmitted.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: The two OSIA algorithms described in this work (HELM and FAME) were 							designed as a set of modular components to facilitate iterative 							development. Each module is configurable to support different or 							changing use cases.
Paper: Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Authors: Mark Wronkiewicz, Jake Lee, Lukas Mandrake, Jack Lightholder, Gary Doran, Steffen Mauceri, Taewoo Kim, Nathan Oborny, Thomas Schibler, Jay Nadeau, James K. Wallace, Eshaan Moorjani, Chris Lindensmith
Figure caption: Two motile organisms were captured in MHIs during Mono Lake testing. The 							left MHI shows a clear zig-zagging movement pattern starting around 								t = 200. The right MHI shows a cell 							swimming in a consistently different direction than the passively 							drifting background particles starting around t = 100.