adsabs/AstroCLIMB huggingface dataset:
Paper: The California Legacy Survey. V. Chromospheric Activity Cycles in Main-sequence Stars
Authors: Howard Isaacson, Andrew W. Howard, Benjamin Fulton, Erik A. Petigura, Lauren M. Weiss, Stephen R. Kane, Brad Carter, Corey Beard, Steven Giacalone, Judah Van Zandt, Joseph M. Akana Murphy, Fei Dai, Ashley Chontos, Alex S. Polanski, Malena Rice, Jack Lubin, Casey Brinkman, Ryan A. Rubenzahl, Sarah Blunt, Samuel W. Yee, Mason G. MacDougall, Paul A. Dalba, Dakotah Tyler, Aida Behmard, Isabel Angelo, Daria Pidhorodetska, Andrew W. Mayo, Rae Holcomb, Emma V. Turtelboom, Michelle L. Hill, Luke G. Bouma, Jingwen Zhang, Ian J. M. Crossfield, Nicholas Saunders
Figure caption: Stars with stellar activity cycles are plotted with their cycle amplitude as a function of fundamental stellar properties: T eff, log(g), [Fe/H], and ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ (panels (A), (B), (C), and (D), respectively). The derived parameters of M ⋆, R ⋆, chromospheric age, and activity-derived rotation period are plotted in panels (E), (F), (G), and (H), respectively. Age uncertainties are 60% (Mamajek & Hillenbrand 2008). Orange crosses highlight solar-type stars with 5600 K < T eff < 5900 K. Pink stars have T eff less than 4700 K. Note their distinct parameter space from the FGK stars. The ages and rotation periods for these cooler stars are not well calibrated with activity and are only shown for completeness. Cycle amplitudes are S-value peak amplitudes, not peak-to-peak. HD 38529 with a radius of 2.5 R ⊙ is not shown in panel (F).
Paper: The California Legacy Survey. V. Chromospheric Activity Cycles in Main-sequence Stars
Authors: Howard Isaacson, Andrew W. Howard, Benjamin Fulton, Erik A. Petigura, Lauren M. Weiss, Stephen R. Kane, Brad Carter, Corey Beard, Steven Giacalone, Judah Van Zandt, Joseph M. Akana Murphy, Fei Dai, Ashley Chontos, Alex S. Polanski, Malena Rice, Jack Lubin, Casey Brinkman, Ryan A. Rubenzahl, Sarah Blunt, Samuel W. Yee, Mason G. MacDougall, Paul A. Dalba, Dakotah Tyler, Aida Behmard, Isabel Angelo, Daria Pidhorodetska, Andrew W. Mayo, Rae Holcomb, Emma V. Turtelboom, Michelle L. Hill, Luke G. Bouma, Jingwen Zhang, Ian J. M. Crossfield, Nicholas Saunders
Figure caption: Stars with stellar activity cycles are plotted with their cycle period as a function of their fundamental stellar properties T eff, log(g), [Fe/H], and ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ (panels (A), (B), (C), and (D), respectively) and derived parameters M ⋆, R ⋆, chromospheric age, and chromospheric rotation period (panels (E), (F), (G), and (H)). Orange crosses highlight solar-type stars with 5600 K < T eff < 5900 K. Stars with T eff < 4700 K are plotted in pink. Note their distinct parameter space from the FGK stars. The ages and rotation periods for these cooler stars are not well calibrated and are only shown for completeness.
Paper: The California Legacy Survey. V. Chromospheric Activity Cycles in Main-sequence Stars
Authors: Howard Isaacson, Andrew W. Howard, Benjamin Fulton, Erik A. Petigura, Lauren M. Weiss, Stephen R. Kane, Brad Carter, Corey Beard, Steven Giacalone, Judah Van Zandt, Joseph M. Akana Murphy, Fei Dai, Ashley Chontos, Alex S. Polanski, Malena Rice, Jack Lubin, Casey Brinkman, Ryan A. Rubenzahl, Sarah Blunt, Samuel W. Yee, Mason G. MacDougall, Paul A. Dalba, Dakotah Tyler, Aida Behmard, Isabel Angelo, Daria Pidhorodetska, Andrew W. Mayo, Rae Holcomb, Emma V. Turtelboom, Michelle L. Hill, Luke G. Bouma, Jingwen Zhang, Ian J. M. Crossfield, Nicholas Saunders
Figure caption: Stellar activity cycle period is presented as a function of chromospheric activity ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ for different temperature ranges. The Sun is placed at ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ of −4.9 and an 11 yr cycle period. The average cycle period increases at all activity levels for every temperature bin. In the range of ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ between −4.7 and −4.9, 33/42 stars have cycles. In each specified range of T eff and activity, the cycle period is tightly grouped. The gray data points represent all stars between [4700, 5900].
Paper: The California Legacy Survey. V. Chromospheric Activity Cycles in Main-sequence Stars
Authors: Howard Isaacson, Andrew W. Howard, Benjamin Fulton, Erik A. Petigura, Lauren M. Weiss, Stephen R. Kane, Brad Carter, Corey Beard, Steven Giacalone, Judah Van Zandt, Joseph M. Akana Murphy, Fei Dai, Ashley Chontos, Alex S. Polanski, Malena Rice, Jack Lubin, Casey Brinkman, Ryan A. Rubenzahl, Sarah Blunt, Samuel W. Yee, Mason G. MacDougall, Paul A. Dalba, Dakotah Tyler, Aida Behmard, Isabel Angelo, Daria Pidhorodetska, Andrew W. Mayo, Rae Holcomb, Emma V. Turtelboom, Michelle L. Hill, Luke G. Bouma, Jingwen Zhang, Ian J. M. Crossfield, Nicholas Saunders
Figure caption: The S-value standard deviation is plotted as a function of T eff, log(g), and [Fe/H] for all 710 stars in our full sample. Stars with low log(g) are in blue, and stars with T eff < 4000 K are in maroon. The lowest-metallicity stars are shown in green. Eleven very-low-metallicity stars are taken off the plot. Cyan marks stars with detected cycles. The dichotomy of stars with low log(g) values is intriguing. We do not sample all of the parameter space equally, but the marked cycles still identify the area most likely to contain periodically cycling stars. The gray data points represent stars with T eff > 4000 K, logg > 4.0, and [Fe/H] > −0.5 and include stars above and below our threshold of 45 observations.
Paper: The California Legacy Survey. V. Chromospheric Activity Cycles in Main-sequence Stars
Authors: Howard Isaacson, Andrew W. Howard, Benjamin Fulton, Erik A. Petigura, Lauren M. Weiss, Stephen R. Kane, Brad Carter, Corey Beard, Steven Giacalone, Judah Van Zandt, Joseph M. Akana Murphy, Fei Dai, Ashley Chontos, Alex S. Polanski, Malena Rice, Jack Lubin, Casey Brinkman, Ryan A. Rubenzahl, Sarah Blunt, Samuel W. Yee, Mason G. MacDougall, Paul A. Dalba, Dakotah Tyler, Aida Behmard, Isabel Angelo, Daria Pidhorodetska, Andrew W. Mayo, Rae Holcomb, Emma V. Turtelboom, Michelle L. Hill, Luke G. Bouma, Jingwen Zhang, Ian J. M. Crossfield, Nicholas Saunders
Figure caption: The values of T eff (panels (A) and (B)), stellar surface gravity (panels (C) and (D)), and [Fe/H] (panels (E) and (F)) are plotted vs. the S-value and ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ for the 710 stars. Maroon data points represent cool stars, with T eff < 4000 K. Blue data points are stars with log(g) < 4.0, i.e., subgiants, and green data points represent stars with metallicity less than −0.5. Cyan symbols identify stars with cycles. The bottom two panels exclude 11 stars with [Fe/H] < −1. Gray data points represent stars not in the extremes of T eff, log(g), and [Fe/H] and without cycles.
Paper: The California Legacy Survey. V. Chromospheric Activity Cycles in Main-sequence Stars
Authors: Howard Isaacson, Andrew W. Howard, Benjamin Fulton, Erik A. Petigura, Lauren M. Weiss, Stephen R. Kane, Brad Carter, Corey Beard, Steven Giacalone, Judah Van Zandt, Joseph M. Akana Murphy, Fei Dai, Ashley Chontos, Alex S. Polanski, Malena Rice, Jack Lubin, Casey Brinkman, Ryan A. Rubenzahl, Sarah Blunt, Samuel W. Yee, Mason G. MacDougall, Paul A. Dalba, Dakotah Tyler, Aida Behmard, Isabel Angelo, Daria Pidhorodetska, Andrew W. Mayo, Rae Holcomb, Emma V. Turtelboom, Michelle L. Hill, Luke G. Bouma, Jingwen Zhang, Ian J. M. Crossfield, Nicholas Saunders
Figure caption: Distributions of T eff, log(g), [Fe/H], R ⋆, M ⋆, and ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ for the 710 stars in our sample. The yellow areas show the 256 stars that have more than 45 observations, and cyan identifies the 138 stars with a cycle from this work. The stellar parameters of the stars that are searched and those that have cycles are representative of the entire sample, and are not strictly confined to particular stellar properties. Twenty-eight stars have radii larger than 2.5.
Paper: The California Legacy Survey. V. Chromospheric Activity Cycles in Main-sequence Stars
Authors: Howard Isaacson, Andrew W. Howard, Benjamin Fulton, Erik A. Petigura, Lauren M. Weiss, Stephen R. Kane, Brad Carter, Corey Beard, Steven Giacalone, Judah Van Zandt, Joseph M. Akana Murphy, Fei Dai, Ashley Chontos, Alex S. Polanski, Malena Rice, Jack Lubin, Casey Brinkman, Ryan A. Rubenzahl, Sarah Blunt, Samuel W. Yee, Mason G. MacDougall, Paul A. Dalba, Dakotah Tyler, Aida Behmard, Isabel Angelo, Daria Pidhorodetska, Andrew W. Mayo, Rae Holcomb, Emma V. Turtelboom, Michelle L. Hill, Luke G. Bouma, Jingwen Zhang, Ian J. M. Crossfield, Nicholas Saunders
Figure caption: The average cycle period and the cycle period scatter increase for every temperature bin at ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ = −4.90. For more active stars, cycle period and T eff are tightly coupled. For less active stars, cycle period is not related to T eff. The bin size is 0.05 and error bars represent the standard deviation in each temperature/activity bin.
Paper: The California Legacy Survey. V. Chromospheric Activity Cycles in Main-sequence Stars
Authors: Howard Isaacson, Andrew W. Howard, Benjamin Fulton, Erik A. Petigura, Lauren M. Weiss, Stephen R. Kane, Brad Carter, Corey Beard, Steven Giacalone, Judah Van Zandt, Joseph M. Akana Murphy, Fei Dai, Ashley Chontos, Alex S. Polanski, Malena Rice, Jack Lubin, Casey Brinkman, Ryan A. Rubenzahl, Sarah Blunt, Samuel W. Yee, Mason G. MacDougall, Paul A. Dalba, Dakotah Tyler, Aida Behmard, Isabel Angelo, Daria Pidhorodetska, Andrew W. Mayo, Rae Holcomb, Emma V. Turtelboom, Michelle L. Hill, Luke G. Bouma, Jingwen Zhang, Ian J. M. Crossfield, Nicholas Saunders
Figure caption: Effective temperature, stellar surface gravity, and median ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩ value for the 710 stars in our sample. Diamonds show detections of 138 stellar activity cycles. Circles identify the 285 stars with more than 45 observations that are searched for cycles, and the color scale represents ﹩\mathrm{log}({R}_{\mathrm{HK}}^{{\prime} })﹩.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: The sources (blue dots) are shown in color–color space, along with model colors for hot compact stars (log g = 7 and 9, T eff = 100,000–15,000 K, purple lines), main-sequence stars and supergiants (red and yellow sequences, respectively), sample binaries consisting of a WD of T eff = 100,000 K, 30,000 K, and 20,000 K (purple, black, and purple, respectively), radius = 0.02R ⊙ and 0.1R ⊙, and a main-sequence or giant star companion of representative spectral types. The QSO locus is also shown (cyan; the large diamond marks redshift = 0). Low-redshift QSOs occupy a locus approximately in the center of this plot, which overlaps with some types of stellar binaries. Model colors are unreddened; thin arrows shown on some models indicate E  B−V  = 0.2 mag reddening.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: FUV−NUV vs. NUV-r. Data and model colors are the same as in the previous figures. The sample is restricted to FUV−NUV ≤ 0.1 mag, but a wider Y-axis range is also plotted to show types of single stars and binaries excluded by the color cut. NUV-r separates main-sequence and giant stars from the WDs (log g = 6, 7, and 9 shown) more than in previous plots. In FUV−NUV, standard-template QSO colors (cyan) differ significantly from Ly α -enhanced QSOs (dark blue; L. Bianchi et al. 2009). Known QSOs+AGN+Seyfert, marked with large cyan dots, mostly cluster around FUV–NUV ≳ 0, where the template colors predicts them to be. Reddening arrows for E  B−V  = 0.2, shown on some models, are nearly horizontal, because FUV−NUV is essentially reddening free. The spread of the observed QSOs around the unreddened template colors is due to photometric uncertainties and variations around an average template; the larger spread in the extinction direction is due to reddening. At FUV−NUV ≤ −0.3 mag, the contamination from QSOs and B supergiants is negligible. Single-star and binary candidates selected in Figure 5 are marked with green and orange dots, respectively.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: Broadband GALEX color FUV−NUV as a function of T eff from stellar models: Kurucz models with solar metallicity, with log g = 5.0 and 3.5, and Tlusty pure-H models with log g = 7.0. The effect of reddening is negligible for small E  B−V  values (for E  B−V  = 0 and = 0.25mag, shown in black and green, the lines are almost indistinguishable) and becomes appreciable at values higher than E  B−V  = 0.40 mag. Reddening is applied for a Milky Way–type extinction curve with R  V   = 3.1. The reddening effect is larger for UV-steep extinction curves such as those found in the Magellanic Clouds (see, e.g., Table 1 of L. Bianchi et al. 2017).
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: Same as in Figure 4; a green line shows the adopted separation between candidate single (green dots) and binary (orange dots) hot evolved stars. Sources with Simbad type = QSO or AGN or Seyfert are overplotted with cyan dots, of larger size than the other sources for visibility, but they are only 941 sources out of the 35,294 total analysis sample; they occupy exactly the locus of the QSO average-template colors for redshift ∼0.5−1.5 (cyan for the standard QSO template, dark blue for the Ly α -enhanced template of L. Bianchi et al. 2009). Triangles on the purple single WD sequences (log g = 7 and 9) mark T eff = 200 kK, 30 kK, 20 kK, and 15 kK.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: Histogram of FUV and NUV magnitudes of the total clean sample (SDSS errors ≤ 0.2 mag), and of the single (green) and binary (orange) hot-star candidates, and of the known sources classified as QSOs or AGN or Seyfert (cyan). The contamination by the known extragalactic objects is minimal (≤ 8%, but only about half of the sample consists of known objects with classification). At the depth of the GALEX AIS, from which this sample is extracted, QSOs do not stand out for increasing at faint magnitudes, as they do in the deeper MIS survey (L. Bianchi et al. 2011a), where the number of extragalactic sources with respect to Milky Way stellar sources increases rapidly at fainter magnitudes. Therefore, in this sample, the QSO contamination in the binary locus cannot be eliminated by their magnitude and colors.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: Left: distribution of combined errors in the culled analysis sample for the FUV−NUV color (purple), for FUV, NUV, and SDSS u, g, r, i (black), and for the five SDSS bands u, g, r, i, z combined (red). The dashed histograms are the errors combined and divided by the number of colors, i.e., one for FUV−NUV, five and four for the other two combinations. Right: histogram of photometric errors in individual filters. For the majority of sources, the SDSS photometric errors are smaller than the GALEX photometric errors; therefore, the SDSS magnitudes have more relative weight, for example, in driving the SED fitting results. However, a number of sources have SDSS photometric errors up to ∼102 mag.
Paper: Hot Stars in the GALEX Ultraviolet Sky Surveys (GUVcat_AISxSDSS_HS) and the Binary Fraction of Hot Evolved Stars
Authors: Luciana Bianchi
Figure caption: Left: distribution of the separation between FUV and NUV position of the extracted sources (“SEP” tag from the GALEX pipeline). Right: histogram of [F/N]UV_FWHM_IMAGE (in pixels, 1 pxl = 1.″5) measured by the GALEX pipeline. There is no correlation between these two extraction parameters over the sample.
Paper: X-Ray Winds in Nearby-to-distant Galaxies (X-WING). I. Legacy Surveys of Galaxies with Ultrafast Outflows and Warm Absorbers in z ∼ 0–4
Authors: Satoshi Yamada, Taiki Kawamuro, Misaki Mizumoto, Claudio Ricci, Shoji Ogawa, Hirofumi Noda, Yoshihiro Ueda, Teruaki Enoto, Mitsuru Kokubo, Takeo Minezaki, Hiroaki Sameshima, Takashi Horiuchi, Shoichiro Mizukoshi
Figure caption: Top: logarithmic V out vs. logarithmic outflow radius normalized by the Schwarzschild radius (r/r s). The dashed line shows the minimum outflow radius assuming the escape velocity, whereas the dotted line represents the threshold of the UFO and WA. Bottom left: logarithmic ξ vs. log(r/r s). Bottom right: logarithmic N H vs. log(r/r s). The symbols mark the minimum or maximum outflow radius (﹩r={r}_{\min }﹩ or ﹩{r}_{\max }﹩) of the UFO (light blue), low-ξ UFO (green), and WA (orange). The orange filled region indicates the sublimation radii of the X-WING AGNs.
Paper: X-Ray Winds in Nearby-to-distant Galaxies (X-WING). I. Legacy Surveys of Galaxies with Ultrafast Outflows and Warm Absorbers in z ∼ 0–4
Authors: Satoshi Yamada, Taiki Kawamuro, Misaki Mizumoto, Claudio Ricci, Shoji Ogawa, Hirofumi Noda, Yoshihiro Ueda, Teruaki Enoto, Mitsuru Kokubo, Takeo Minezaki, Hiroaki Sameshima, Takashi Horiuchi, Shoichiro Mizukoshi
Figure caption: Same as in Figure 3 and the top panels of Figure 4, but the symbols mark the instrument that observed the X-ray outflow. The dashed and solid curves indicate the simulated limitations of the 3σ detection of the X-ray winds with XMM-Newton/PN and XRISM/Resolve, respectively.
Paper: X-Ray Winds in Nearby-to-distant Galaxies (X-WING). I. Legacy Surveys of Galaxies with Ultrafast Outflows and Warm Absorbers in z ∼ 0–4
Authors: Satoshi Yamada, Taiki Kawamuro, Misaki Mizumoto, Claudio Ricci, Shoji Ogawa, Hirofumi Noda, Yoshihiro Ueda, Teruaki Enoto, Mitsuru Kokubo, Takeo Minezaki, Hiroaki Sameshima, Takashi Horiuchi, Shoichiro Mizukoshi
Figure caption: Same as in Figure 5, but the symbols mark the methods of the detection of X-ray winds, utilized by the blueshifted Fe K absorption lines Fe XXVI Lyα and Fe XXV Heβ above the rest-frame 6 keV band (red) or blueshifted absorption lines below the 6 keV band (light green).
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Details of the network structure’s implementation, as depicted in Figure 2. Similar modules in our model are coded with the same colors. The LC component comprises the input for N LCs (x(t)) and images with a shape of batch size, channel number, height, and width, as illustrated within the solid rounded green rectangle. ResNet-18 can generate feature maps both with and without spatial information. The RPN module is shown as an orange box, while the zoomed-in LCs are represented as x zoomed(t). The FC layer is divided into two sections: the lower portion represents the input of three basic LC features, and the upper portion signifies the input feature map. The output of the FC layer has a shape of (N, C), where C denotes the number of classes. By integrating all of the zoomed-in and initial features, the final FC layer produces class predictions. Dashed connectors and outputs are optimized during training and can be utilized as needed during inference. For the PS component, inputs include N PSs (x(f)) and images computed directly from LCs; this process mirrors that of the LC component. Lastly, the parameter component processes the preset parameters with an FC layer. N  p  is the number of parameters of each object.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Network schematic diagram of our model. The diagram shows three main components, which are enclosed by thick black lines. At the top of these components is the LC component, which has two inputs: raw LC data and image data of plotted LCs. A CNN model extracts feature maps from the images and zooms in on the input LC based on these feature maps. Then, the zoomed-in LCs enter into the same CNN model to extract features again. Finally, all the extracted features are combined to make a prediction for the class label. The PS component receives the LC from the LC component and calculates a Lomb–Scargle periodogram as input for the network. Similar to the LC component, the frequency spectra and their images are feature-extracted and zoomed in to smaller regions. Then, with the most significant frequencies in the zoomed-in regions, we can phase-fold the LCs, which are then used by a CNN model to extract features. The predictive labels are generated by combining all of the features produced by these CNN models. The parameter component utilizes all manually selected physical or systematic parameters to make higher-level predictions. The optional spectrum or SED component demonstrates the potential for extensibility to additional astronomical products.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Classification intermediate results of TIC 397330244, a δ Sct star. The left section of the figure shows (top panel) the raw LC with three zoomed-in regions highlighted in semitransparent colors and (middle and bottom panels) the zoomed-in LCs corresponding to the highlighted regions, with predictive labels indicated in their titles. The title of the top panel includes the predicted labels from the results combined with the PS component, as well as those derived solely from the entire raw LC (in brackets). On the right, the panels relate to the PS. The left panels depict the PS with zoomed-in regions and a dashed red line indicating the 1% false-alarm level. The right panels show the corresponding folded LC, based on the most significant peak from the left PS panels. Predictive labels are provided in the titles of the right PS panels, with the top-left PS panel title reflecting the label derived from the entire PS component.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Details of the RPN structure for the LC and PS components. From left to right, we present the input to output flow. The shape of the input is defined by batch size, number of channels, height, and width. The numbers in the convolutional blocks represent filter sizes (shapes), strides, and the number of output channels.
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Normalized confusion matrix of the best LC component (a) and PS component (b) models on the variable test set (Kepler, TESS). The numbers presented in the matrix represent the proportion of objects that are correctly identified as positive for a specific class (column) out of the total number of objects belonging to that class (row).
Paper: Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Authors: Kaiming Cui, D. J. Armstrong, Fabo Feng
Figure caption: Normalized confusion matrix of the best LC component (a) and PS component (b) models on the combined test set (Kepler, TESS, ZTF). Similar to Figure 5. The zeros displayed in the figures are rounded numbers. We keep them to ensure correct summation for each row and column.