adsabs/AstroCLIMB huggingface dataset:
Paper: The PHANGS-HST Survey: Physics at High Angular Resolution in Nearby Galaxies with the Hubble Space Telescope
Authors: Janice C. Lee, Bradley C. Whitmore, David A. Thilker, Sinan Deger, Kirsten L. Larson, Leonardo Ubeda, Gagandeep S. Anand, Mederic Boquien, Rupali Chandar, Daniel A. Dale, Eric Emsellem, Adam K. Leroy, Erik Rosolowsky, Eva Schinnerer, Judy Schmidt, James Lilly, Jordan Turner, Schuyler Van Dyk, Richard L. White, Ashley T. Barnes, Francesco Belfiore, Frank Bigiel, Guillermo A. Blanc, Yixian Cao, Melanie Chevance, Enrico Congiu, Oleg V. Egorov, Simon C. O. Glover, Kathryn Grasha, Brent Groves, Jonathan D. Henshaw, Annie Hughes, Ralf S. Klessen, Eric Koch, Kathryn Kreckel, J. M. Diederik Kruijssen, Daizhong Liu, Laura A. Lopez, Ness Mayker, Sharon E. Meidt, Eric J. Murphy, Hsi-An Pan, Jérôme Pety, Miguel Querejeta, Alessandro Razza, Toshiki Saito, Patricia Sánchez-Blázquez, Francesco Santoro, Amy Sardone, Fabian Scheuermann, Andreas Schruba, Jiayi Sun, Antonio Usero, E. Watkins, and Thomas G. Williams
Figure caption: Star cluster candidate selection regions defined in the multiple concentration index plane (MCI; see Equation (1)) based on synthetic star clusters (large contours) for four galaxies at increasing distances from left to right (NGC 3627, NGC 1792, NGC 1559, and NGC 1365). Selection regions are also defined semi-empirically (polygon; see also Figure 7). Candidates are selected from detections that satisfy basic signal-to-noise ratio criteria (gray), and exclude those in the stellar (point-source dominated) region (small red contours). Candidates within the empirical selection region are slated for visual inspection to a V-band magnitude limit of ∼24 mag. The classification of the much larger samples of candidates identified with outermost model contours is automated using convolutional neural network models. In all panels, visually classified class 1 (blue) and class 2 (green) clusters, are shown. Some clusters appear outside the polygon—these result from ad hoc human inspection to confirm that the density of clusters rapidly declines outside this selection region, as well as from the inspection of sources brighter than the Humphreys–Davidson limit.
Paper: The PHANGS-HST Survey: Physics at High Angular Resolution in Nearby Galaxies with the Hubble Space Telescope
Authors: Janice C. Lee, Bradley C. Whitmore, David A. Thilker, Sinan Deger, Kirsten L. Larson, Leonardo Ubeda, Gagandeep S. Anand, Mederic Boquien, Rupali Chandar, Daniel A. Dale, Eric Emsellem, Adam K. Leroy, Erik Rosolowsky, Eva Schinnerer, Judy Schmidt, James Lilly, Jordan Turner, Schuyler Van Dyk, Richard L. White, Ashley T. Barnes, Francesco Belfiore, Frank Bigiel, Guillermo A. Blanc, Yixian Cao, Melanie Chevance, Enrico Congiu, Oleg V. Egorov, Simon C. O. Glover, Kathryn Grasha, Brent Groves, Jonathan D. Henshaw, Annie Hughes, Ralf S. Klessen, Eric Koch, Kathryn Kreckel, J. M. Diederik Kruijssen, Daizhong Liu, Laura A. Lopez, Ness Mayker, Sharon E. Meidt, Eric J. Murphy, Hsi-An Pan, Jérôme Pety, Miguel Querejeta, Alessandro Razza, Toshiki Saito, Patricia Sánchez-Blázquez, Francesco Santoro, Amy Sardone, Fabian Scheuermann, Andreas Schruba, Jiayi Sun, Antonio Usero, E. Watkins, and Thomas G. Williams
Figure caption: Semi-empirically defined selection regions for compact star cluster candidates in the multiple concentration index (MCI; see Equation (1)) plane. This selection is devised to produce candidate sample sizes amenable for human inspection, while maximizing the yield of true clusters. We verify that the selection region includes the majority of visually verified class 1 and 2 clusters from 30 galaxies studied by the LEGUS program (Calzetti et al. 2015a; Adamo et al. 2017, colored points; https://archive.stsci.edu/prepds/legus/dataproducts-public.html). The larger set of candidates from LEGUS are also shown (gray points). Clusters are color coded based on the distance of their host galaxy. The lower bound of MCIout increases as the distance of the host galaxy increases (see also Figure 6), since the physical resolution decreases and clusters appear more compact. We adopt different lower bounds for the three different distance ranges indicated in the legend.
Paper: The PHANGS-HST Survey: Physics at High Angular Resolution in Nearby Galaxies with the Hubble Space Telescope
Authors: Janice C. Lee, Bradley C. Whitmore, David A. Thilker, Sinan Deger, Kirsten L. Larson, Leonardo Ubeda, Gagandeep S. Anand, Mederic Boquien, Rupali Chandar, Daniel A. Dale, Eric Emsellem, Adam K. Leroy, Erik Rosolowsky, Eva Schinnerer, Judy Schmidt, James Lilly, Jordan Turner, Schuyler Van Dyk, Richard L. White, Ashley T. Barnes, Francesco Belfiore, Frank Bigiel, Guillermo A. Blanc, Yixian Cao, Melanie Chevance, Enrico Congiu, Oleg V. Egorov, Simon C. O. Glover, Kathryn Grasha, Brent Groves, Jonathan D. Henshaw, Annie Hughes, Ralf S. Klessen, Eric Koch, Kathryn Kreckel, J. M. Diederik Kruijssen, Daizhong Liu, Laura A. Lopez, Ness Mayker, Sharon E. Meidt, Eric J. Murphy, Hsi-An Pan, Jérôme Pety, Miguel Querejeta, Alessandro Razza, Toshiki Saito, Patricia Sánchez-Blázquez, Francesco Santoro, Amy Sardone, Fabian Scheuermann, Andreas Schruba, Jiayi Sun, Antonio Usero, E. Watkins, and Thomas G. Williams
Figure caption: The number of compact star cluster candidates found in the first 15 galaxies processed through the PHANGS-HST pipeline, shown as a function of the sSFR. Candidates identified using the semi-empirical selection (polygon region) are shown in red, while those resulting from the larger model-based selection contours are shown in gray. The cluster candidates undergo a process of inspection to further remove contaminants and sort the objects into three morphological categories as summarized in Section 4.5, and described in detail in Wei et al. (2020) and Whitmore et al. (2021). The smaller samples resulting from the semi-empirical (polygon) selection are designed for human inspection, while the larger samples from the model contours are fed to convolutional neural networks for automated classification.
Paper: The PHANGS-HST Survey: Physics at High Angular Resolution in Nearby Galaxies with the Hubble Space Telescope
Authors: Janice C. Lee, Bradley C. Whitmore, David A. Thilker, Sinan Deger, Kirsten L. Larson, Leonardo Ubeda, Gagandeep S. Anand, Mederic Boquien, Rupali Chandar, Daniel A. Dale, Eric Emsellem, Adam K. Leroy, Erik Rosolowsky, Eva Schinnerer, Judy Schmidt, James Lilly, Jordan Turner, Schuyler Van Dyk, Richard L. White, Ashley T. Barnes, Francesco Belfiore, Frank Bigiel, Guillermo A. Blanc, Yixian Cao, Melanie Chevance, Enrico Congiu, Oleg V. Egorov, Simon C. O. Glover, Kathryn Grasha, Brent Groves, Jonathan D. Henshaw, Annie Hughes, Ralf S. Klessen, Eric Koch, Kathryn Kreckel, J. M. Diederik Kruijssen, Daizhong Liu, Laura A. Lopez, Ness Mayker, Sharon E. Meidt, Eric J. Murphy, Hsi-An Pan, Jérôme Pety, Miguel Querejeta, Alessandro Razza, Toshiki Saito, Patricia Sánchez-Blázquez, Francesco Santoro, Amy Sardone, Fabian Scheuermann, Andreas Schruba, Jiayi Sun, Antonio Usero, E. Watkins, and Thomas G. Williams
Figure caption: Structures across the physical scales of the star formation hierarchy in NGC 3351, identified by the PHANGS-HST pipeline, from single-peaked compact star clusters, the densest structures, to larger scale multipeaked stellar associations. Bottom left: color composite of WFC3/UVIS F275W+F336W (blue), WFC3/UVIS F435W+F555W (green), and WFC3/UVIS F814W (red). Bottom right: young stellar associations (<10 Myr) traced by the watershed-based method of K. Larson et al. (2021, in preparation, blue contours), together with all compact clusters and associations with human visual classifications (Class 1: circles, Class 2: squares, Class 3: diamonds; color coded by age as indicated), overlaid on the PHANGS-ALMA CO(2–1) map. A 650 pc section of the outer ring (yellow box) is shown in more detail in the top left and middle panels. All three classes of compact clusters and associations are represented in the selected section, and the magnified view allows all four levels traced by the watershed method (64 pc, 32 pc, 16 pc, and 8 pc) to be clearly shown. Top left: magnified view using an Hα map constructed from the VLT/MUSE IFU data cube. Top middle: magnified view using a color-composite image where CO is now shown in red. Top right: further magnification of 180 pc areas centered on examples of the three classes of compact clusters and associations found in the selected 650 pc section of the outer ring in all PHANGS-HST filters.
Paper: The PHANGS-HST Survey: Physics at High Angular Resolution in Nearby Galaxies with the Hubble Space Telescope
Authors: Janice C. Lee, Bradley C. Whitmore, David A. Thilker, Sinan Deger, Kirsten L. Larson, Leonardo Ubeda, Gagandeep S. Anand, Mederic Boquien, Rupali Chandar, Daniel A. Dale, Eric Emsellem, Adam K. Leroy, Erik Rosolowsky, Eva Schinnerer, Judy Schmidt, James Lilly, Jordan Turner, Schuyler Van Dyk, Richard L. White, Ashley T. Barnes, Francesco Belfiore, Frank Bigiel, Guillermo A. Blanc, Yixian Cao, Melanie Chevance, Enrico Congiu, Oleg V. Egorov, Simon C. O. Glover, Kathryn Grasha, Brent Groves, Jonathan D. Henshaw, Annie Hughes, Ralf S. Klessen, Eric Koch, Kathryn Kreckel, J. M. Diederik Kruijssen, Daizhong Liu, Laura A. Lopez, Ness Mayker, Sharon E. Meidt, Eric J. Murphy, Hsi-An Pan, Jérôme Pety, Miguel Querejeta, Alessandro Razza, Toshiki Saito, Patricia Sánchez-Blázquez, Francesco Santoro, Amy Sardone, Fabian Scheuermann, Andreas Schruba, Jiayi Sun, Antonio Usero, E. Watkins, and Thomas G. Williams
Figure caption: Comparison of UBVI color–color diagrams for multipeak stellar associations identified with the PHANGS-HST watershed-based procedure (left panel), and compact star clusters and associations from the LEGUS program (right panel). The stellar associations are based on a V-band map of point-source positions smoothed with a 16 pc FWHM Gaussian kernel.
Paper: The PHANGS-HST Survey: Physics at High Angular Resolution in Nearby Galaxies with the Hubble Space Telescope
Authors: Janice C. Lee, Bradley C. Whitmore, David A. Thilker, Sinan Deger, Kirsten L. Larson, Leonardo Ubeda, Gagandeep S. Anand, Mederic Boquien, Rupali Chandar, Daniel A. Dale, Eric Emsellem, Adam K. Leroy, Erik Rosolowsky, Eva Schinnerer, Judy Schmidt, James Lilly, Jordan Turner, Schuyler Van Dyk, Richard L. White, Ashley T. Barnes, Francesco Belfiore, Frank Bigiel, Guillermo A. Blanc, Yixian Cao, Melanie Chevance, Enrico Congiu, Oleg V. Egorov, Simon C. O. Glover, Kathryn Grasha, Brent Groves, Jonathan D. Henshaw, Annie Hughes, Ralf S. Klessen, Eric Koch, Kathryn Kreckel, J. M. Diederik Kruijssen, Daizhong Liu, Laura A. Lopez, Ness Mayker, Sharon E. Meidt, Eric J. Murphy, Hsi-An Pan, Jérôme Pety, Miguel Querejeta, Alessandro Razza, Toshiki Saito, Patricia Sánchez-Blázquez, Francesco Santoro, Amy Sardone, Fabian Scheuermann, Andreas Schruba, Jiayi Sun, Antonio Usero, E. Watkins, and Thomas G. Williams
Figure caption: Stellar associations in NGC 1566 color coded by SED-fit age. Associations were identified based on a map of F555W point-source positions smoothed with a 32 pc FWHM Gaussian kernel, and SED fitting was performed with CIGALE assuming a single-aged stellar population. Left: the youngest associations (≤3 Myr: blue, 3–5 Myr: green) overlaid on PHANGS ground-based Hα narrowband imaging (A. Razza et al. 2021, in preparation). The HST data do not extend to the corners of the imaging, and associations have not been traced in those areas. Middle: same as the left panel, but expanded to show greater detail in a portion of the northern spiral arm. Right: the same region as the middle panel, but now overlaid on the F555W (V-band) imaging and including older associations (5–10 Myr: yellow, 10–60 Myr: orange, >60 Myr: red). Age ranges are exclusive of the lower bound and inclusive of the upper bound.
Paper: Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design
Authors: Federica B. Bianco, Željko Ivezić, R. Lynne Jones, Melissa L. Graham, Phil Marshall, Abhijit Saha, Michael A. Strauss, Peter Yoachim, Tiago Ribeiro, Timo Anguita, Franz E. Bauer, Eric C. Bellm, Robert D. Blum, William N. Brandt, Sarah Brough, Márcio Catelan, William I. Clarkson, Andrew J. Connolly, Eric Gawiser, John Gizis, Renee Hlozek, Sugata Kaviraj, Charles T. Liu, Michelle Lochner, Ashish A. Mahabal, Rachel Mandelbaum, Peregrine McGehee, Eric H. Neilsen Jr., Knut A. G. Olsen, Hiranya V. Peiris, Jason Rhodes, Gordon T. Richards, Stephen Ridgway, Megan E. Schwamb, Dan Scolnic, Ohad Shemmer, Colin T. Slater, Anže Slosar, Stephen J. Smartt, Jay Strader, Rachel Street, David E. Trilling, Aprajita Verma, A. K. Vivas, Risa H. Wechsler, and Beth Willman
Figure caption: LSST footprint: the color encodes the number of visits across all filters for each position on the sky. The general survey footprint must meet the SRD design specifications: the WFD covers ≳ 18,000 deg2 to a median of 825 visits. Other surveys, like the Deep Drilling Fields (DDFs, which appear in yellow due to the higher number of observations), North Ecliptic Spur (NES), Galactic Plane (GP, which contains a large region on the bulge side, but also a smaller footprint on the antibulge side), and South Celestial Pole (SCP) that are shown in this figure cover regions outside of the WFD footprint and have more flexibility in the observing strategy, including the number of visits and their distribution among filters. The initial survey footprint was envisioned as in the left panel (map generated from OpSim baseline_nexp2_v1.7.1_10yrs, see Section 2), but details of the visit distribution for optimal science return are still being determined, including if this general survey footprint should be modified. Expansion of coverage into additional low dust extinction area and higher coverage of a portion of the Galactic plane, for example, may result in a footprint conceptually similar to what is shown on the right (generated from OpSim footprint_6_1.7.1_10yrs). This figure is discussed in Section 1.
Paper: Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design
Authors: Federica B. Bianco, Željko Ivezić, R. Lynne Jones, Melissa L. Graham, Phil Marshall, Abhijit Saha, Michael A. Strauss, Peter Yoachim, Tiago Ribeiro, Timo Anguita, Franz E. Bauer, Eric C. Bellm, Robert D. Blum, William N. Brandt, Sarah Brough, Márcio Catelan, William I. Clarkson, Andrew J. Connolly, Eric Gawiser, John Gizis, Renee Hlozek, Sugata Kaviraj, Charles T. Liu, Michelle Lochner, Ashish A. Mahabal, Rachel Mandelbaum, Peregrine McGehee, Eric H. Neilsen Jr., Knut A. G. Olsen, Hiranya V. Peiris, Jason Rhodes, Gordon T. Richards, Stephen Ridgway, Megan E. Schwamb, Dan Scolnic, Ohad Shemmer, Colin T. Slater, Anže Slosar, Stephen J. Smartt, Jay Strader, Rachel Street, David E. Trilling, Aprajita Verma, A. K. Vivas, Risa H. Wechsler, and Beth Willman
Figure caption: Distribution of median internight visit gaps (the time elapsed between visits to the same field in different nights) at a given location in the sky for two simulated LSST OpSim strategies: baseline_nexp2_v1.7.1_10yrs (left) and rolling_nm_scale0.90_nslice2_fpw0.9_nrw1.0v1.7_10yrs (right; for more details on the simulations see Section 2). Each curve shows the median internight gaps across pointing positions distributed on a healpixel grid with Nsides = 64 (corresponding to ∼0.85 deg2). In the top row the distribution is shown for all observations, and in the following rows it is shown for observations in each of the six Rubin LSST filters (as labeled). The distributions are smoothed via kernel density estimation (Rosenblatt 1956; Parzen 1962); the location of the peak of the underlying distribution is indicated to the right of each curve. This figure shows that different OpSim runs, developed in compliance with the SRD requirements, can be very different even in core features. Similarly, within a night, the short 30 s single-visit exposure time (potentially split into 2 × 15 s) and 2 s readout time enable the exploration of intranight timescales that are relevant for some science cases. Here too, simulations lead to a variety of possible LSST OpSim strategies, as explored for example in works responding to the 2018 Cadence White Papers call (Section 4.1, e.g., Bianco et al. 2019) and 2021 Cadence Notes (Section 5, e.g., Bellm et al. 2021; Li et al. 2022). Even shorter timescales have been explored for special surveys, as in Section 10.3 of COSEP, and in several Cadence White Papers (e.g., https://docushare.lsstcorp.org/docushare/dsweb/Get/Document-30579/gizis_brightstar_minisurvey.pdf, https://docushare.lsstcorp.org/docushare/dsweb/Get/Document-30645/olsen_mc_mini.pdf, https://docushare.lsstcorp.org/docushare/dsweb/Get/Document-30601/thomas_startrails_mini.pdf). The figure is discussed in more detail in Section 1.
Paper: Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design
Authors: Federica B. Bianco, Željko Ivezić, R. Lynne Jones, Melissa L. Graham, Phil Marshall, Abhijit Saha, Michael A. Strauss, Peter Yoachim, Tiago Ribeiro, Timo Anguita, Franz E. Bauer, Eric C. Bellm, Robert D. Blum, William N. Brandt, Sarah Brough, Márcio Catelan, William I. Clarkson, Andrew J. Connolly, Eric Gawiser, John Gizis, Renee Hlozek, Sugata Kaviraj, Charles T. Liu, Michelle Lochner, Ashish A. Mahabal, Rachel Mandelbaum, Peregrine McGehee, Eric H. Neilsen Jr., Knut A. G. Olsen, Hiranya V. Peiris, Jason Rhodes, Gordon T. Richards, Stephen Ridgway, Megan E. Schwamb, Dan Scolnic, Ohad Shemmer, Colin T. Slater, Anže Slosar, Stephen J. Smartt, Jay Strader, Rachel Street, David E. Trilling, Aprajita Verma, A. K. Vivas, Risa H. Wechsler, and Beth Willman
Figure caption: A segment of an “hourglass plot” showing a sequence of observations in conjunction with astronomical constraints. A simulated calendar of observations for the month of April in year 1 of LSST operations is shown, color coded by the bandpass of each visit (as indicated in the legend). The x-axis shows time away from midnight; the y-axis separates visits by the day of the month. The gray edges of the plot indicate twilight time, with observations continuing to −12° twilight in redder filters. Astronomical (−18°) twilight is shown with light gray, −12° twilight is dark gray, and periods where the Sun is above the horizon are in black. The time of lunar transit is shown as a wide yellow stripe; lunar rise and set times are shown by dotted yellow lines. Redder filters are used when the Moon is up and bluer filters when the Moon is down and the sky is dark. Each 30 s visit is separated from adjacent visits by thin white vertical lines indicating the time between exposures, which depends on the time it takes the telescope to slew to a new pointing and it is typically short (and often not visible in practice here). Thicker lines indicate longer than typical slew and setup times; for example, the time to change filters is about 2 minutes. Weather and telescope maintenance are also simulated: observing time lost to weather or telescope maintenance appears as larger white gaps on days 19 and 26. This figure is discussed in Section 2.
Paper: Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design
Authors: Federica B. Bianco, Željko Ivezić, R. Lynne Jones, Melissa L. Graham, Phil Marshall, Abhijit Saha, Michael A. Strauss, Peter Yoachim, Tiago Ribeiro, Timo Anguita, Franz E. Bauer, Eric C. Bellm, Robert D. Blum, William N. Brandt, Sarah Brough, Márcio Catelan, William I. Clarkson, Andrew J. Connolly, Eric Gawiser, John Gizis, Renee Hlozek, Sugata Kaviraj, Charles T. Liu, Michelle Lochner, Ashish A. Mahabal, Rachel Mandelbaum, Peregrine McGehee, Eric H. Neilsen Jr., Knut A. G. Olsen, Hiranya V. Peiris, Jason Rhodes, Gordon T. Richards, Stephen Ridgway, Megan E. Schwamb, Dan Scolnic, Ohad Shemmer, Colin T. Slater, Anže Slosar, Stephen J. Smartt, Jay Strader, Rachel Street, David E. Trilling, Aprajita Verma, A. K. Vivas, Risa H. Wechsler, and Beth Willman
Figure caption: A synopsis of the 46 Cadence White Papers submitted in response to the call issued in 2018: on the left the papers are grouped by the Rubin LSST Science Collaboration (SC) of the lead author with fractions from each SC indicated in the figure. 15% of the papers were submitted by lead authors unaffiliated with a SC. The right panel shows a screenshot of an interactive plot (available at https://observablehq.com/embed/@f7f7156e50925896/rubin-lsst-science-collaborations-cadence-white-paper-by-s?cells=chart) describing the co-authors network through a chord diagram. Each circle represents a paper, the size of the circle reflecting the size of the authoring team (the largest is 72 co-authors for Target of Opportunity Observations of Gravitational Wave Events with LSST), color coded, as in the left panel, by the SC of the lead author. Papers are linked by their co-authors. This figure is discussed in Sections 4 and 6.
Paper: Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design
Authors: Federica B. Bianco, Željko Ivezić, R. Lynne Jones, Melissa L. Graham, Phil Marshall, Abhijit Saha, Michael A. Strauss, Peter Yoachim, Tiago Ribeiro, Timo Anguita, Franz E. Bauer, Eric C. Bellm, Robert D. Blum, William N. Brandt, Sarah Brough, Márcio Catelan, William I. Clarkson, Andrew J. Connolly, Eric Gawiser, John Gizis, Renee Hlozek, Sugata Kaviraj, Charles T. Liu, Michelle Lochner, Ashish A. Mahabal, Rachel Mandelbaum, Peregrine McGehee, Eric H. Neilsen Jr., Knut A. G. Olsen, Hiranya V. Peiris, Jason Rhodes, Gordon T. Richards, Stephen Ridgway, Megan E. Schwamb, Dan Scolnic, Ohad Shemmer, Colin T. Slater, Anže Slosar, Stephen J. Smartt, Jay Strader, Rachel Street, David E. Trilling, Aprajita Verma, A. K. Vivas, Risa H. Wechsler, and Beth Willman
Figure caption: Illustration of a “radar plot” comparing science metrics from a variety of core science areas for five OpSim realizations (as indicated in the legend). Each corner of the hexagon represents a different metric, whose value is mapped to the distance from the center of the hexagon. Clockwise from the top, the metrics measure: the median number of visits per pointing in the WFD (N visits/pointing), the fraction of near-Earth objects of magnitude H ≤ 22 detected (Fraction of NEO), the numbers of galaxies (N Galaxies) and stars (N stars) expected to be recovered, the expected number of supernovae type Ia (SNIa) observed sufficiently well to contribute to cosmological measurements (Lochner et al. 2018), and a collective metric for static probes of the extragalactic sky (3x2pt FoM, combining weak lensing and galaxy clustering, Lochner et al. 2018). This set of OpSim runs varies the footprint (which impacts the science throughput for cosmological supernovae as measured by SNIa) and introduces a rolling cadence (which improves SNIa performance as a denser cadence enables better characterizations at relevant, short timescales and the distribution of visits in a more concentrated sky area leads to small efficiency improvements). This figure is discussed in Section 4.1.