adsabs/AstroCLIMB huggingface dataset:
Paper: The AstroSat UV Deep Field North: The Far- and Near-ultraviolet Photometric Catalog
Authors: Chayan Mondal, Kanak Saha, Souradeep Bhattacharya, Anshuman Borgohain, Shyam N. Tandon, Marc Rafelski, Rolf A. Jansen, Rogier A. Windhorst, Harry I. Teplitz, Brent M. Smith
Figure caption: The stars selected for constructing the empirical PSF in NUV are marked with red ellipses. The size of each ellipse is scaled up by multiplying a constant factor with the FWHM of that star. The ellipticity shows the actual measured value. The region marked with the black rectangle shows the HST coverage.
Paper: The AstroSat UV Deep Field North: The Far- and Near-ultraviolet Photometric Catalog
Authors: Chayan Mondal, Kanak Saha, Souradeep Bhattacharya, Anshuman Borgohain, Shyam N. Tandon, Marc Rafelski, Rolf A. Jansen, Rogier A. Windhorst, Harry I. Teplitz, Brent M. Smith
Figure caption: The recovery fraction of injected simulated sources as a function of input magnitude for the FUV F154W (left) and NUV N242W (right) filter images. The 90% and 50% completeness limits for each image are marked in black and blue respectively.
Paper: The AstroSat UV Deep Field North: The Far- and Near-ultraviolet Photometric Catalog
Authors: Chayan Mondal, Kanak Saha, Souradeep Bhattacharya, Anshuman Borgohain, Shyam N. Tandon, Marc Rafelski, Rolf A. Jansen, Rogier A. Windhorst, Harry I. Teplitz, Brent M. Smith
Figure caption: Radial intensity profiles of the three empirical PSFs shown in Figure 7 for F154W (top), N242W (middle), and N245M (bottom). The black dots are the isophotal flux measurements. The red dashed curves are Moffat profile fits to the inner portion of each PSF, and the blue dashed lines are exponential fits to the outer wings. The black dotted curve shows the combined model fit to the observed data.
Paper: The AstroSat UV Deep Field North: The Far- and Near-ultraviolet Photometric Catalog
Authors: Chayan Mondal, Kanak Saha, Souradeep Bhattacharya, Anshuman Borgohain, Shyam N. Tandon, Marc Rafelski, Rolf A. Jansen, Rogier A. Windhorst, Harry I. Teplitz, Brent M. Smith
Figure caption: The offset of measured magnitude from the input magnitude of injected simulated sources as a function of input magnitude for the FUV F154W (left), NUV N242W (middle), and NUV N245M (right) filter images. The 90% and 50% completeness limits for each image are marked in black and blue respectively.
Paper: The AstroSat UV Deep Field North: The Far- and Near-ultraviolet Photometric Catalog
Authors: Chayan Mondal, Kanak Saha, Souradeep Bhattacharya, Anshuman Borgohain, Shyam N. Tandon, Marc Rafelski, Rolf A. Jansen, Rogier A. Windhorst, Harry I. Teplitz, Brent M. Smith
Figure caption: The apparent magnitude (estimated with 1.″4 circular aperture with aperture correction) of UVIT identified objects that do not have source confusion within a 1.″4 radius as per the HST CANDELS/GOODS-N source catalog. These histograms are plotted for objects detected within the HST covered region of the UVIT field. F154W, N242W, and N245M filters are represented in blue, red, and green, respectively.
Paper: The AstroSat UV Deep Field North: The Far- and Near-ultraviolet Photometric Catalog
Authors: Chayan Mondal, Kanak Saha, Souradeep Bhattacharya, Anshuman Borgohain, Shyam N. Tandon, Marc Rafelski, Rolf A. Jansen, Rogier A. Windhorst, Harry I. Teplitz, Brent M. Smith
Figure caption: Curve of growth profiles of the PSFs in the F154W (shown in blue), N242W (green), and N245M (red) bands. The figure shows the percentage of the total flux (normalized) contained within apertures with increasing radii.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Contributions to the uncertainty of the distance modulus σ  μ  as a function of the redshift. Three components are represented: the color component (β σ  c ), the stretch component (﹩\alpha {\sigma }_{{x}_{1}}﹩), and the amplitude component (﹩{\sigma }_{{m}_{B}}﹩). The color component starts to contribute significantly to the distance uncertainty above β σ  c   ∼ 0.12 (or equivalently σ  c   ∼ 0.04). This threshold corresponds to the redshift limit value (here 0.71) for the observation of well-measured SNe Ia. These results are obtained from a full simulation of SN Ia light curves (regular cadence of 1 day). The SN Ia parameters are estimated from a SALT2 fit. The r-band measurements are not taken into account for z  ≳  0.6 (sharp slopes) because of large model uncertainties (see Section 4 for more details).
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Color uncertainty as a function of S/N b . Requiring σ  c  ≤ 0.04 is equivalent to applying the following selections: S/N i   ≥ 59, S/N z   ≥ 39, and S/N y   ≥ 20. These results are obtained from a full simulation of SN Ia light curves for an intrinsically faint SN Ia (i.e., with (x 1, c) = (−2.0, 0.2)) and a regular cadence of 1 day. The SN Ia parameters are estimated from a SALT2 fit. The r-band measurements are not taken into account for z  ≳  0.6 (sharp slopes in S/N z  and S/N y  around 67 and 27, respectively) because of large model uncertainties (see Section 4 for more details).
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Top: Number of visits per observing night as a function of the redshift completeness. A total of 131 visits with the filter allocation (N                   ﹩{}_{\mathrm{visits}}^{g}﹩, N                   ﹩{}_{\mathrm{visits}}^{r}﹩, N                   ﹩{}_{\mathrm{visits}}^{i}﹩, N                   ﹩{}_{\mathrm{visits}}^{z}﹩, N                   ﹩{}_{\mathrm{visits}}^{y}﹩) = (2, 9, 45, 64, 11) are required per observing night to reach z complete ∼ 0.8 for a cadence of 1 day. Bottom: Time budget per field and per season of observation as a function of z complete. A 1 day cadence and a season length of 180 days are assumed. Observing a field every night for 180 days with a redshift completeness of 0.80 corresponds to a time budget of 1.1%. For a DD survey with five fields observed for 10 yr (the current proposed strategy; see Section 5), this would correspond to a DD time budget of 40%.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption:  N SN (red dashed lines), DD time budget (black solid lines), and σ  w  (blue dotted lines) for three types of surveys: DU (top), deep rolling (DR) 10 yr (middle), and intensive DR (IDR) (bottom). Subscripts correspond to the number of seasons of observation (6 month season length) and superscripts to the redshift completeness. The number of y-band visits is less than 20 (per observing night). Only statistical uncertainties are included.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Cumulative sum of the number of nights (per field and per season) as a function of the time since the survey start (assumed to be late 2023). The following sequence of observations is considered: COSMOS, Euclid/Roman (×2), XMM-LSS, COSMOS, Euclid/Roman (×2), XMM-LSS, ELAIS-S1 (×2), and CDF-S (×2), with a maximum season length of 180 days for the deep fields, a cadence of 1 day, and only one field observed per night. The fields are required to be observable (airmass ≤ 1.5 and 20° ≤ altitude ≤ 86.°5) for at least 1 hr and 5 minutes (131 visits) for the ultradeep fields and for at least 34 minutes (68 visits) for the deep fields. The overlap, defined as the fraction of nights with more than one field observed, is ∼4%.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Ratio of the number of SNe ﹩{N}_{\mathrm{SN}}/{N}_{\mathrm{SN}}^{\mathrm{nodither}}﹩ (top) and z complete difference (bottom) as a function of tdo. The simulations labeled “Fakes” (dashed lines) correspond to regular cadences (1, 2, 3, and 5 days) with median observing conditions (5σ depths in single exposures of 24.13/23.84/23.45/22.74/22.10 for the g/r/i/y/z bands, respectively, and a season length equal to 180 days). We choose a random dithering from one visit to another. The results presented in this figure may depend on the dithering pattern.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Maximal season length as a function of the nightly observable time (in hours) (lower x-axis) or the number of visits (upper x-axis) per observing night. Fields are observable if the following requirements are met: 20° ≤ altitude ≤ 86.°5, and airmass ≤ 1.5. This plot is made using scripts and tools of the LSST scheduler. The black line corresponds to a season length of 180 days.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: DD time budget contours in the plane (N visits, season length) for a configuration of five fields with 2 (black) or 10 (red) observing seasons per field and a cadence of 1 day. The number of visits dramatically decreases with the number of seasons of observation when the time budget is limited. For a typical season length of 180 days and a time budget extending from 3% (minimal) to 15% (maximal), the number of visits ranges from 7 to 42 for 10 seasons (dashed red lines). Decreasing the number of seasons to 2 leads to an increase in the possible number of visits, from 36 to 208 (solid black lines).
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Median number of well-measured SNe N                   ﹩{}_{\mathrm{SN}}^{z\leqslant {z}_{\mathrm{complete}}}﹩ (per pixel of 0.21 deg2) as a function of the cadence of observation (top) and median cadence as a function of maximal internight gap (bottom) for a set of LSST simulated strategies studied in this paper. Yellow areas correspond to observing strategy parameters (cadence and maximal internight gaps) leading to a high-quality SN Ia sample.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Fraction of SNe Ia (≥z) as a function of the redshift (z) for two scenarios: IDR﹩{}_{0.80}^{0.60}﹩ (full red line) and DU0.65 (dashed blue line). The fraction of SNe Ia with z  ≥ 0.8 is 45% and 22% for the IDR﹩{}_{0.80}^{0.60}﹩ and DU0.65 surveys, respectively.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption:  N                   ﹩{}_{\mathrm{SN}}^{z\leqslant {z}_{\mathrm{complete}}}﹩ vs. z complete for the LSST simulated observing strategies considered in this paper.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Left: σ  w  (top) and Dark Energy Task Force (DETF) figure of merit (FoM) (bottom) as a function of time (years of survey) for scenarios with a DD time budget lower than 10%. The black circles (squares) correspond to a time budget of 5% (8%). Right: σ  w  (top) and DETF FoM (bottom) after 10 yr for DD strategies considered in this paper for DD time budgets of 5% (solid line) and 8% (dashed line).
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Left: Observing efficiencies (color curves) and number of exploding SNe Ia per square degree (purple dashed curve) as a function of the redshift. Right: Normalized cumulative distribution of the number of well-measured faint SNe as a function of redshift. The 95th percentile limit defines the z complete value. These plots correspond to one HEALPix pixel of the CDF-S field (with the number 144428) of the daily strategy.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Gap tracker metric as a function of MJD. The full black lines correspond to S/N z  and the blue points to observations (m 5). Four cases are represented: 3 day cadence surveys with no gaps (a), with gaps (b), with gaps and a recovery method based on the number of visits (c), and with gaps and a recovery method based on the gap tracker metric (d).
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Left: Mean values of σ  μ  as a function of the redshift for a set of surveys labeled by the redshift completeness. Right: Number of SNe Ia as a function of the redshift for a set of surveys labeled by the redshift completeness for the CDF-S field (survey area of 9.6 deg2 and season length of 180 days).
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Probability of having gaps (full line, left y-axis) and mean number of gaps (dashed line, right y-axis) as a function of the night gap estimated with a sliding window of 180 days using the LSST baseline simulation dubbed baseline_nexp1_v1.7_10yrs.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption:  z complete difference between estimations of the SALT3 and SALT2 models as a function of z complete estimated using the SALT2 model.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: DD time budget as a function of the total number of well-measured SNe Ia (left) and of the time budget (right) for a set of IDR, DR, and DU surveys. Subscripts (superscripts) correspond to the redshift completeness of ultradeep (deep) fields. Black dotted lines correspond to DD time budgets of 5% and 8%.
Paper: Designing an Optimal LSST Deep Drilling Program for Cosmology with Type Ia Supernovae
Authors: Philippe Gris, Nicolas Regnault, Humna Awan, Isobel Hook, Saurabh W. Jha, Michelle Lochner, Bruno Sanchez, Dan Scolnic, Mark Sullivan, Peter Yoachim
Figure caption: Number of well-measured SNe Ia observed in the DD survey as a function of time (years of survey) for deep fields (CDF-S, ELAIS-S1, and Euclid/Roman) (bottom) and ultradeep fields (COSMOS and XMM-LSS) (top). The black circles (squares) correspond to a time budget of 5% (8%).
Paper: Signature of Massive Neutrinos from the Clustering of Critical Points. I. Density-threshold-based Analysis in Configuration Space
Authors: Jeongin Moon, Graziano Rossi, Hogyun Yu
Figure caption: Autocorrelations of critical points in configuration space at z = 0, for three different rarity thresholds: ﹩{ \mathcal R }=5﹩ (top panels), ﹩{ \mathcal R }=10﹩ (middle panels), ﹩{ \mathcal R }=20﹩ (bottom panels). The two-point clustering statistics of minima, wall-type saddles, filament-type saddles, and maxima are shown from left to right. Three different cosmological models are considered, as indicated in the figure with contrasting colors: M  ν  = 0.0 eV (red), M  ν  = 0.1 eV (blue), and M  ν  = 0.4 eV (cyan). Error bars are estimated from averages of 100 independent QUIJOTE realizations for a given cosmology. BAO peaks are clearly visible in all of the panels, with the vertical dashed gray lines marking their exact scale (102.5 h −1 Mpc)—which is independent of critical point type, neutrino mass, and rarity.
Paper: Signature of Massive Neutrinos from the Clustering of Critical Points. I. Density-threshold-based Analysis in Configuration Space
Authors: Jeongin Moon, Graziano Rossi, Hogyun Yu
Figure caption: Inflection scales of the critical point autocorrelation functions at large separation r presented in Figure 3, indicating a clear sensitivity to massive neutrinos. In the various panels, measurements of ξ(r) at z = 0 are performed in the 110 h −1 Mpc < r < 140 h −1 Mpc interval, using a refined bin size of 1 h −1 Mpc. Top panels are for the baseline massless neutrino model, middle panels refer to the scenario with M  ν  = 0.1 eV, and bottom panels show the case of M  ν  = 0.4 eV. Results are averaged over 100 independent QUIJOTE realizations per given cosmology. The three rarity cuts are represented by different colors as indicated in the panels, and minima, wall-type saddles, filament-type saddles, and maxima are reported from left to right. Shaded horizontal error bars indicate ±0.1% variations in ξ. Remarkably, the inflection scales (highlighted by the vertical gray dashed lines), independently of rarity, are altered by a nonzero neutrino mass.
Paper: Signature of Massive Neutrinos from the Clustering of Critical Points. I. Density-threshold-based Analysis in Configuration Space
Authors: Jeongin Moon, Graziano Rossi, Hogyun Yu
Figure caption: BAO amplitudes (r 2 ξ) vs. their correspondent spatial position (i.e., 102.5 h −1 Mpc, independent of critical point type, neutrino mass, and rarity) at z = 0, derived from the autocorrelation measurements of minima, saddles 1, saddles 2, and maxima (top to bottom panels) reported in Figure 3. The three cosmological scenarios considered are represented with different colors (red: M  ν  = 0.0 eV; blue: M  ν  = 0.1 eV; green: M  ν  = 0.4 eV), and the rarity thresholds are indicated via contrasting symbols (pentagons: ﹩{ \mathcal R }=5﹩; triangles: ﹩{ \mathcal R }=10﹩; squares: ﹩{ \mathcal R }=20﹩).
Paper: Signature of Massive Neutrinos from the Clustering of Critical Points. I. Density-threshold-based Analysis in Configuration Space
Authors: Jeongin Moon, Graziano Rossi, Hogyun Yu
Figure caption: BAO amplitudes in massive neutrino cosmologies, normalized by their correspondent values in a massless neutrino scenario and expressed in percentages as a function of rarity threshold at z = 0. Colors refer to the same cosmological models examined in Figure 4. Departures from analogous measurements carried out in the baseline M  ν  = 0.0 eV framework can reach up to ∼7% at z = 0, even for a relatively small neutrino mass of M  ν  = 0.1 eV.
Paper: Signature of Massive Neutrinos from the Clustering of Critical Points. I. Density-threshold-based Analysis in Configuration Space
Authors: Jeongin Moon, Graziano Rossi, Hogyun Yu
Figure caption: Visualization of the spatial distribution of critical points (color-coded by their corresponding type) in massless and massive neutrino cosmologies at z = 0, from small QUIJOTE-simulated cubic density patches of 100 h −1 Mpc side and 50 h −1 Mpc depth, as a function of rarity—with ﹩{ \mathcal R }﹩ increasing from left to right, as indicated in the various panels. Red, green, cyan, and blue are used to display maxima, filament saddles (“saddle 2”), wall saddles (“saddle 1”), and minima, respectively. A “density-threshold-based” approach is adopted for the identification and classification of critical points, as reported in Section 3. See the main text for more details.
Paper: Signature of Massive Neutrinos from the Clustering of Critical Points. I. Density-threshold-based Analysis in Configuration Space
Authors: Jeongin Moon, Graziano Rossi, Hogyun Yu
Figure caption: Spatial projection of the DM overdensity field from three selected QUIJOTE simulations at z = 0. Top panels display ﹩100\times 100\,{[{h}^{-1}\,\mathrm{Mpc}]}^{2}﹩ slices of the simulation cubes; bottom panels are enlargements of a small ﹩10\times 10\,{[{h}^{-1}\,\mathrm{Mpc}]}^{2}﹩ inset with identical spatial location. The fiducial massless neutrino cosmology (left), a massive neutrino model with M  ν  = 0.1 eV (middle), and a massive neutrino scenario with M  ν  = 0.4 eV (right) are shown. Although tiny, morphological and topological differences induced by a nonzero neutrino mass on the cosmic web network—as traced by the large-scale density field—are visually perceptible.
Paper: Signature of Massive Neutrinos from the Clustering of Critical Points. I. Density-threshold-based Analysis in Configuration Space
Authors: Jeongin Moon, Graziano Rossi, Hogyun Yu
Figure caption: Configuration-space cross-correlations of critical points characterized by an identical overdensity sign, as a function of rarity threshold, at z = 0. Colors, line styles, rarity thresholds, and cosmologies are the same as those adopted in Figure 8. Left panels display cross-correlations of overdense critical point pairs (﹩{ \mathcal P }{ \mathcal F }﹩), while right panels are for underdense critical point cross-correlations (﹩{ \mathcal V }{ \mathcal W }﹩). Note that the shapes of the two-point cross-correlation clustering are akin to those of autocorrelation measurements (i.e., Figure 3), since here we consider cross-correlations of similarly biased tracers. BAO features appear as peaks at r = 102.5 h −1 Mpc, with the vertical dashed gray lines marking their exact scale.
Paper: Signature of Massive Neutrinos from the Clustering of Critical Points. I. Density-threshold-based Analysis in Configuration Space
Authors: Jeongin Moon, Graziano Rossi, Hogyun Yu
Figure caption: Configuration-space cross-correlations of critical points having opposite overdensity sign, as a function of rarity threshold, at z = 0. Colors and line styles are the same as in Figure 3. From left to right, ﹩{ \mathcal P }{ \mathcal V },{ \mathcal P }{ \mathcal W },{ \mathcal V }{ \mathcal F }﹩, and ﹩{ \mathcal W }{ \mathcal F }﹩ are displayed when ﹩{ \mathcal R }=5﹩ (top panels), ﹩{ \mathcal R }=10﹩ (middle panels), and ﹩{ \mathcal R }=20﹩ (bottom panels). BAO features now appear as dips at r = 102.5 h −1 Mpc, with the vertical dashed gray lines marking their exact scale.