adsabs/AstroCLIMB huggingface dataset:
Paper: Probing the Low-mass End of Core-collapse Supernovae Using a Sample of Strongly-stripped Calcium-rich Type IIb Supernovae from the Zwicky Transient Facility
Authors: Kaustav K. Das, Mansi M. Kasliwal, Christoffer Fremling, Sheng Yang, Steve Schulze, Jesper Sollerman, Tawny Sit, Kishalay De, Anastasios Tzanidakis, Daniel A. Perley, Shreya Anand, Igor Andreoni, C. Barbarino, K. Brudge, Andrew Drake, Avishay Gal-Yam, Russ R. Laher, Viraj Karambelkar, S. R. Kulkarni, Frank J. Masci, Michael S. Medford, Abigail Polin, Harrison Reedy, Reed Riddle, Yashvi Sharma, Roger Smith, Lin Yan, Yi Yang, Yuhan Yao
Figure caption: The expansion velocity of the He I line for each SN of our sample represented by the filled symbols. The blue shaded region shows the 1σ region of the ejecta velocity calculated for a sample of usual Type IIb SNe in Fremling et al. (2018).
Paper: Probing the Low-mass End of Core-collapse Supernovae Using a Sample of Strongly-stripped Calcium-rich Type IIb Supernovae from the Zwicky Transient Facility
Authors: Kaustav K. Das, Mansi M. Kasliwal, Christoffer Fremling, Sheng Yang, Steve Schulze, Jesper Sollerman, Tawny Sit, Kishalay De, Anastasios Tzanidakis, Daniel A. Perley, Shreya Anand, Igor Andreoni, C. Barbarino, K. Brudge, Andrew Drake, Avishay Gal-Yam, Russ R. Laher, Viraj Karambelkar, S. R. Kulkarni, Frank J. Masci, Michael S. Medford, Abigail Polin, Harrison Reedy, Reed Riddle, Yashvi Sharma, Roger Smith, Lin Yan, Yi Yang, Yuhan Yao
Figure caption:  56Ni mass distribution for our sample of SNe IIb compared to the SN IIb population presented in Taddia et al. (2018).
Paper: Probing the Low-mass End of Core-collapse Supernovae Using a Sample of Strongly-stripped Calcium-rich Type IIb Supernovae from the Zwicky Transient Facility
Authors: Kaustav K. Das, Mansi M. Kasliwal, Christoffer Fremling, Sheng Yang, Steve Schulze, Jesper Sollerman, Tawny Sit, Kishalay De, Anastasios Tzanidakis, Daniel A. Perley, Shreya Anand, Igor Andreoni, C. Barbarino, K. Brudge, Andrew Drake, Avishay Gal-Yam, Russ R. Laher, Viraj Karambelkar, S. R. Kulkarni, Frank J. Masci, Michael S. Medford, Abigail Polin, Harrison Reedy, Reed Riddle, Yashvi Sharma, Roger Smith, Lin Yan, Yi Yang, Yuhan Yao
Figure caption: Ejecta mass distribution for our sample of SNe IIb compared to the SN IIb population presented in Taddia et al. (2018).
Paper: Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws
Authors: Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
Figure caption: Expression generation sketch. The process starts at the top left RNN block. For each token, the RNN is given the contextual information regarding the surroundings of the next token to generate, namely, the parent, sibling, and previously sampled token along with their units, the required units for the token to be generated, and the dangling number (i.e., the minimum number of tokens needed to obtain a valid expression). Based on this information, the RNN produces a categorical distribution over the library of available tokens (top histograms) as well as a state that is transmitted to the RNN on its next call. The generated distribution is then masked based on local units constraints (bottom histograms), forbidding tokens that would lead to nonsensical expressions. The resulting token is sampled from this distribution, leading to the token “+” in this example. Repeating this process, from left to right, allows one to generate a complete physical expression, here [+, v 0,/, x, t], which translates into v 0 + x/t in the infix notation we are more familiar with.
Paper: Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws
Authors: Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
Figure caption: Exact symbolic recovery rates, rates of accurate expressions (having R 2 > 0.999), and rates of accurate expressions normalized by the number of free parameters appearing in expressions for PhySO and other baseline methods on the Feynman benchmark. PhySO vastly outperforms all other methods in symbolic recovery in the presence of even minimal levels of noise (>0.1%). In addition, the effectiveness of the dimensional analysis schemes of our Φ-SO approach are clearly visible when comparing DSR (a purely RL method) with our implementation: PhySO (combining RL with dimensional analysis). Error bars indicate a 95% confidence interval, ♦ denotes performances of DSR (Landajuela et al. 2021b) reported in Landajuela et al. (2022) on noiseless data with free constants allowed and * denotes that benchmarking conditions may vary and scores are polluted by approximately 5% of results from another benchmark.
Paper: Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws
Authors: Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
Figure caption: Computational time optimizing free constants {a, b} in ﹩y=a\sin (b.x)+{e}^{-x}﹩ over 20 iterations using 103 data points when running this task 10,000 times in parallel with our PhySO algorithm on an Apple M1 laptop (a machine with four fast CPU cores) and an Intel Xeon W-2155 CPU (a machine with a high number of cores).
Paper: Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws
Authors: Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
Figure caption:  R 2 value on training data and percentage of expressions natively proposed by the neural network that have balanced physical units averaged across the Feynman benchmark with error regions indicating a 95% confidence interval. Φ-SO’s neural network learns to produce not only good-fitting expressions but also physically meaningful ones.
Paper: Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws
Authors: Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
Figure caption: Illustration of the symbolic expression search space reduction enabled by our in situ physical units prior. We represent paths (in prefix notation) leading to expressions with physically possible units (in red), a sample of the paths that lead to expressions with unphysical units (in black) with other unphysical paths redacted for readability summarized with dotted lines and their total number. Here we consider the recovery of a velocity v using a library of symbols ﹩\{+,/,\cos ,\,{v}_{0},x,t\}﹩, where v 0 is a velocity, x is a length, and t is a time (limiting ourselves to five symbol long expressions for readability). This reduces the search space from 268 expressions to only six.
Paper: Variation of the High-mass Slope of the Stellar Initial Mass Function: Theory Meets Observations
Authors: Sami Dib
Figure caption: The Γ vs. σ * relation based on combining all models that have A cl = 5500 and η = 0.01 and for the three values of β (2.5, 2.75, and 3), and four values of the core-to-star efficiency parameter ϵ *(0.35, 0.4, 0.45, and 0.5). The models are displayed as a 2D histogram and compared to the fit to the observations (Equation (1)). The shaded area represents the 1σ uncertainties of the observations. The color contours, going from dark colors to lighter colors correspond to 1, 4, 7, 10, 15, 20, and 25 occurrences, respectively.
Paper: Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws
Authors: Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
Figure caption: Pareto front encoding accuracy complexity trade-off of recovered physical formulae typically recovered using our Φ-SO method when applied to data for the relativistic energy of a particle. We recover the relativistic expression as well as the classical approximation. Note that although the exact classical expression ﹩\tfrac{1}{2}{{mv}}^{2}﹩ is encountered by Φ-SO it is Pareto dominated by the simpler mv 2 expression.
Paper: Deep Symbolic Regression for Physics Guided by Units Constraints: Toward the Automated Discovery of Physical Laws
Authors: Wassim Tenachi, Rodrigo Ibata, Foivos I. Diakogiannis
Figure caption: Complexity vs. rate of expressions having R 2 > 0.999 at a 10% noise level for PhySO and other SR methods from the literature on the Feynman benchmark (La Cava et al. 2021). Lines and colors denote the 1st, 2nd, 3rd, 4th, 5th, and 6th Pareto fronts following the SRBench algorithm comparison framework. PhySO is a Pareto optimum producing simple yet effective expressions.
Paper: The Detection of Higher-order Millimeter Hydrogen Recombination Lines in the Large Magellanic Cloud
Authors: Marta Sewiło, Kazuki Tokuda, Stan E. Kurtz, Steven B. Charnley, Thomas Möller, Jennifer Wiseman, C.-H. Rosie Chen, Remy Indebetouw, Álvaro Sánchez-Monge, Kei E. I. Tanaka, Peter Schilke, Toshikazu Onishi, Naoto Harada
Figure caption: Multiwavelength images of N 105–1 (from left to right, top to bottom): the Hα image from the MCELS2 survey (PI: You-Hua Chu), VMC Y and J band (Cioni et al. 2011), IRSF H band (Kato et al. 2007), VMC K  s  band, Spitzer/SAGE IRAC 3.6–8.0 μm and MIPS 24 and 70 μm (Meixner et al. 2006; SAGE 2006), Herschel/HERITAGE PACS 100 and 160 μm and SPIRE 250–500 μm (HERITAGE Team 2013; Meixner et al. 2013), ALMA 1.2 mm (Sewiło et al. 2022), ALMA 3 mm (this paper), and ATCA 3 and 6 cm (Indebetouw et al. 2004). The 1.2 mm continuum contours with contour levels of (3, 30) ×  the image rms of 6.9 × 10−5 mJy beam−1 are overlaid on selected images for reference. Red circles in the 4.5 and 100 μm images indicate the positions of Spitzer YSOs associated with 1 A and 1 B (Whitney et al. 2008; Gruendl & Chu 2009; Seale et al. 2009), while the blue circle shows the position of the Herschel YSO candidate (Seale et al. 2014).
Paper: The Detection of Higher-order Millimeter Hydrogen Recombination Lines in the Large Magellanic Cloud
Authors: Marta Sewiło, Kazuki Tokuda, Stan E. Kurtz, Steven B. Charnley, Thomas Möller, Jennifer Wiseman, C.-H. Rosie Chen, Remy Indebetouw, Álvaro Sánchez-Monge, Kei E. I. Tanaka, Peter Schilke, Toshikazu Onishi, Naoto Harada
Figure caption: Upper panel: The HCO+ (4–3) integrated intensity images of N 105–1 A incorporating the emission with velocities in the 233–235 km s−1 (left) and 241–243 km s−1 (right) ranges. These velocity ranges correspond to the blueshifted and redshifted CO velocity components, respectively, shown in Figure 12. Lower left panel: Three-color mosaic combining the HCO+ integrated intensity images shown in the upper panel (for the velocity range 241–243 km s−1 in red and for 233–235 km s−1 in blue), and the H36β recombination line (green) images. Solid magenta/cyan/yellow contours correspond to the CS/SO/SO2 emission with contour levels of (20, 50, 90)%/(20, 50, 90)%/50% of the CS/SO/SO2 emission peak (Sewiło et al. 2022). Dashed white contours correspond to the H36β emission with contour levels of (20, 90)% of the H36β emission peak. Lower right panel: The same image as in the lower left panel, zoomed in on the central region. Blue/red contours correspond to the HCO+ blueshifted/redshifted emission with contour levels of (20, 90)% times the corresponding emission peak. Dashed white contours are the same as in the lower left panel. The black cross symbol in both images in the lower panel indicates the position of the 1.2 mm continuum peak.
Paper: The Detection of Higher-order Millimeter Hydrogen Recombination Lines in the Large Magellanic Cloud
Authors: Marta Sewiło, Kazuki Tokuda, Stan E. Kurtz, Steven B. Charnley, Thomas Möller, Jennifer Wiseman, C.-H. Rosie Chen, Remy Indebetouw, Álvaro Sánchez-Monge, Kei E. I. Tanaka, Peter Schilke, Toshikazu Onishi, Naoto Harada
Figure caption: Spectra of N 105–1 A (shown in blue) centered on rest frequencies of the mm-RL transitions (up to θ-transitions, Δn = 8) covered by our Band 3 (top two panels) and Band 6 (bottom two panels) observations, ordered by increasing frequency (from left to right, top to bottom). The red solid line shows the XCLASS synthetic spectrum fitted to Band 6 mm-RL transitions; it represents the pure mm-RL contribution assuming LTE with a single component. The XCLASS spectrum is extended to lower frequencies (shown as the red dashed line) to compare line intensities predicted by the LTE model to our observed Band 3 spectra, revealing the beam dilution effects in Band 3. The XCLASS spectrum taking into account both mm-RLs and all molecular species detected toward 1 A (discussed in Sewiło et al. 2022; see also Appendix A) is shown as the gray solid line. The gray vertical dashed line indicates the source velocity.
Paper: The Detection of Higher-order Millimeter Hydrogen Recombination Lines in the Large Magellanic Cloud
Authors: Marta Sewiło, Kazuki Tokuda, Stan E. Kurtz, Steven B. Charnley, Thomas Möller, Jennifer Wiseman, C.-H. Rosie Chen, Remy Indebetouw, Álvaro Sánchez-Monge, Kei E. I. Tanaka, Peter Schilke, Toshikazu Onishi, Naoto Harada
Figure caption: Upper panel: The HCO+ (4–3) integrated intensity (left) and velocity (right) images of N 105–1 A, incorporating the emission with velocities in the 235.5–238 km s−1 range. Pixels corresponding to regions with the integrated intensity below 3σ are masked in both images. Lower panel: Single channel HCO+ (4–3) intensity images for the emission with the velocity of 236.5 km s−1 (left) and 237.5 km s−1 (right). In the lower left panel, dashed lines outline the filamentary structure seen in HCO+ and other tracers north of the millimeter/radio continuum peak. In the lower right panel, dashed lines indicate possible accretion streamers. The 1.2 mm continuum contours are overlaid on all images for reference, with contour levels of (3, 30)σ. The ALMA beam size is indicated in red in the bottom left corners of images in the upper panel.
Paper: The Detection of Higher-order Millimeter Hydrogen Recombination Lines in the Large Magellanic Cloud
Authors: Marta Sewiło, Kazuki Tokuda, Stan E. Kurtz, Steven B. Charnley, Thomas Möller, Jennifer Wiseman, C.-H. Rosie Chen, Remy Indebetouw, Álvaro Sánchez-Monge, Kei E. I. Tanaka, Peter Schilke, Toshikazu Onishi, Naoto Harada
Figure caption: The 13CO (1–0) integrated intensity (top panel), velocity (lower left), and line-width (lower right) images of N 105–1. The emission was integrated between 233.4 and 246.4 km s−1. The dashed and dotted circles in the velocity map indicate the Band 6 and Band 7 fields of view, respectively. The 1.2 and 3 mm continuum contours are overlaid on all the images in black and red, respectively, with contour levels the same as in Figure 1.
Paper: The Detection of Higher-order Millimeter Hydrogen Recombination Lines in the Large Magellanic Cloud
Authors: Marta Sewiło, Kazuki Tokuda, Stan E. Kurtz, Steven B. Charnley, Thomas Möller, Jennifer Wiseman, C.-H. Rosie Chen, Remy Indebetouw, Álvaro Sánchez-Monge, Kei E. I. Tanaka, Peter Schilke, Toshikazu Onishi, Naoto Harada
Figure caption: Three-color mosaic of the star-forming region N 105 combining the Hα (red), [S II] (green), and [O III] (blue) images from the MCELS survey (Smith & MCELS Team 1998; Paredes et al. 2015; an angular resolution of ≲5″) is shown in the left panel. The white box indicates the area shown in the middle panel: a zoomed-in view on the region hosting source N 105–1 A with the detection of higher-order mm-RLs. The [S II] image of the same area is presented in the right panel. White contours in the middle and right panels correspond to the 3 mm continuum emission with contour levels the same as in Figure 1. Green contours in the right panel correspond to (50, 70, 95)% of the [S II] emission peak.
Paper: The Detection of Higher-order Millimeter Hydrogen Recombination Lines in the Large Magellanic Cloud
Authors: Marta Sewiło, Kazuki Tokuda, Stan E. Kurtz, Steven B. Charnley, Thomas Möller, Jennifer Wiseman, C.-H. Rosie Chen, Remy Indebetouw, Álvaro Sánchez-Monge, Kei E. I. Tanaka, Peter Schilke, Toshikazu Onishi, Naoto Harada
Figure caption: Corner plot of the MCMC error estimate for the XCLASS fit of the LTE model with one component shown in Figure B1. The 16% and 84% quantiles are plotted as black dashed lines; the 50% quantile (median) is shown in blue, while the lowest χ 2 value is shown in orange. The plot demonstrates that we cannot reliably determine either the electron temperature (T e) or emission measure (EM); it is likely due to the signal-to-noise ratio being too low for most of the Band 6 transitions.
Paper: The Detection of Higher-order Millimeter Hydrogen Recombination Lines in the Large Magellanic Cloud
Authors: Marta Sewiło, Kazuki Tokuda, Stan E. Kurtz, Steven B. Charnley, Thomas Möller, Jennifer Wiseman, C.-H. Rosie Chen, Remy Indebetouw, Álvaro Sánchez-Monge, Kei E. I. Tanaka, Peter Schilke, Toshikazu Onishi, Naoto Harada
Figure caption: ALMA Band 6 spectra of N 105–1 A. The detected (black) and tentatively detected (gray) spectral lines are labeled. The results of the molecular line analysis are presented in Sewiło et al. (2022).
Paper: The Detection of Higher-order Millimeter Hydrogen Recombination Lines in the Large Magellanic Cloud
Authors: Marta Sewiło, Kazuki Tokuda, Stan E. Kurtz, Steven B. Charnley, Thomas Möller, Jennifer Wiseman, C.-H. Rosie Chen, Remy Indebetouw, Álvaro Sánchez-Monge, Kei E. I. Tanaka, Peter Schilke, Toshikazu Onishi, Naoto Harada
Figure caption: ALMA Band 3 spectrum of N 105–1 A: a full spectral window covering hydrogen recombination lines. The detected spectral lines are labeled.
Paper: X-PSI Parameter Recovery for Temperature Map Configurations Inspired by PSR J0030+0451
Authors: Serena Vinciguerra, Tuomo Salmi, Anna L. Watts, Devarshi Choudhury, Yves Kini, Thomas E. Riley
Figure caption: Schematic representation of naming convention adopted within X-PSI. Note that the protruding P configurations include the eccentric E ones, which, in turn, include the concentric ones C. In the case of antipodal symmetry (-S in the table), the lightest hot spot indicates that it is located on the hemisphere opposite to the observer. The dots in the last row of the table suggest how additional models could be built, allowing for different geometries for the two hot spots.
Paper: X-PSI Parameter Recovery for Temperature Map Configurations Inspired by PSR J0030+0451
Authors: Serena Vinciguerra, Tuomo Salmi, Anna L. Watts, Devarshi Choudhury, Yves Kini, Thomas E. Riley
Figure caption: Schematic representation of the ST+PST model and the parameters describing it.
Paper: Kinetic Simulations of Nonrelativistic High-mach-number Perpendicular Shocks Propagating in a Turbulent Medium
Authors: Karol Fulat, Artem Bohdan, Gabriel Torralba Paz, Martin Pohl
Figure caption: Variations of the obliquity angle in the region extending from the upstream to the overshoot. The upper and middle panels show maps of the difference between the local obliquity angle and the inclination angle of the external magnetic field for the runs H and T2, respectively, and the time ﹩t\approx 8.8{{\rm{\Omega }}}_{i}^{-1}﹩. The maps are presented in logarithmic and sign-preserving scale: ﹩\mathrm{sgn}({\rm{\Delta }}\theta )\cdot [2.5+\mathrm{log}\{\max ({10}^{-2.5},| {\rm{\Delta }}\theta | /{\theta }_{0})\}]﹩. The lower panel shows the rms of the difference between the local obliquity angle and the inclination angle of the external magnetic field for each run. The profiles are obtained from Δθ maps by calculating the rms over the y-direction for each time step in the latest reformation cycle. Then the quantity is time averaged and normalized by the factor θ 0.
Paper: Kinetic Simulations of Nonrelativistic High-mach-number Perpendicular Shocks Propagating in a Turbulent Medium
Authors: Karol Fulat, Artem Bohdan, Gabriel Torralba Paz, Martin Pohl
Figure caption: Level of the speed and density fluctuations for all simulations in the region extending from upstream to downstream of the shock. The upper panel shows the rms of the ion bulk speed fluctuations normalized to the shock speed, and the lower panel shows the rms of the ion density fluctuations normalized to the initial upstream density. The profiles are obtained from the velocity and the density maps by calculating the rms over the y-direction and combining two neighboring cells in the x-direction. The profiles are time-averaged over the latest reformation cycle.
Paper: Kinetic Simulations of Nonrelativistic High-mach-number Perpendicular Shocks Propagating in a Turbulent Medium
Authors: Karol Fulat, Artem Bohdan, Gabriel Torralba Paz, Martin Pohl
Figure caption: Maps of the magnitude of the curl of the electron current density (top panel), the ion current density (middle panel), and the magnetic field (bottom panel), for run T2 in the middle of the third reformation cycle (﹩\sim 8.5{{\rm{\Omega }}}_{i}^{-1}﹩). The current densities are normalized by the factor J 0 = n 0 q  e  c.
Paper: Kinetic Simulations of Nonrelativistic High-mach-number Perpendicular Shocks Propagating in a Turbulent Medium
Authors: Karol Fulat, Artem Bohdan, Gabriel Torralba Paz, Martin Pohl
Figure caption: Left: normalized ion number density map showing the well-developed structure of the compressible turbulence, after ﹩1{{\rm{\Omega }}}_{i}^{-1}﹩. Right: time evolution of the mean kinetic energy, as well as the sum of the thermal energy and the electromagnetic field energy. The initial values are subtracted, and the energies are normalized to the ion rest energy.
Paper: Kinetic Simulations of Nonrelativistic High-mach-number Perpendicular Shocks Propagating in a Turbulent Medium
Authors: Karol Fulat, Artem Bohdan, Gabriel Torralba Paz, Martin Pohl
Figure caption: The evolution of the energy density of the Weibel-generated magnetic field (top panel) and number of the magnetic vortices (bottom panel), for all simulations. The energy density is normalized by the factor ﹩{U}_{{B}_{0}}=| {B}_{0}{| }^{2}/(2{\mu }_{0})﹩. The vortex number corresponds to the number of local maxima in the z-component of the magnetic vector potential, for visualization purposes the data was smoothed.
Paper: Kinetic Simulations of Nonrelativistic High-mach-number Perpendicular Shocks Propagating in a Turbulent Medium
Authors: Karol Fulat, Artem Bohdan, Gabriel Torralba Paz, Martin Pohl
Figure caption: The electron energy spectrum in the downstream region for runs H (the blue-dashed line) and T2 (the red-dotted line). The black solid line denotes a fitted kappa distribution.
Paper: Kinetic Simulations of Nonrelativistic High-mach-number Perpendicular Shocks Propagating in a Turbulent Medium
Authors: Karol Fulat, Artem Bohdan, Gabriel Torralba Paz, Martin Pohl
Figure caption: Left: the matching procedure presented for one grid cell. Right: maps of the magnetic field after around half an ion Larmor time after merging. The interpolation zone spans from x = 5.3–5.8λ si (middle of the panel).
Paper: Pitch-angle Anisotropy Imprinted by Relativistic Magnetic Reconnection
Authors: Luca Comisso, Brian Jiang
Figure caption: (a) Examples of time evolutions of γ/σ 0 and ﹩\cos \alpha ﹩ from simulations with σ 0 = 16 and guide-field strengths B  g  = B 0/8 (red), B  g  = B 0 (yellow), and B  g  = 8B 0 (blue). The x-axis is scaled by ﹩{\rm{\Delta }}t{(c/{L}_{x}){[1+({B}_{g}/{B}_{0})}^{2}]}^{-1/2}﹩, where Δt corresponds to the time interval from the particle injection from the thermal pool (γ ≃ γ th) to higher Lorentz factors (γ ≫ γ th). (b) Pitch-angle correlation function ﹩\langle \cos \alpha \cos {\alpha }_{0}\rangle ﹩ (normalized by ﹩\langle {\cos }^{2}{\alpha }_{0}\rangle ﹩), computed from the same simulations. t 0 indicates the initial time for the measurement. (c) Pitch-angle scattering timescale t scatt (normalized by L  x /c), determined by fitting the pitch-angle correlation function with the exponential ﹩{e}^{-t/{t}_{\mathrm{scatt}}}﹩, for simulations with σ 0 = 16 and various B  g  values. A dashed blue line indicating ﹩1+{({B}_{g}/{B}_{0})}^{2}﹩ is provided for reference.
Paper: Pitch-angle Anisotropy Imprinted by Relativistic Magnetic Reconnection
Authors: Luca Comisso, Brian Jiang
Figure caption: 2D histograms illustrating the peak anisotropy ﹩\min \langle {\sin }^{2}\alpha \rangle ﹩ (a), along with the power-law indices m < (b) and m > (c), describing the pitch-angle distribution produced by magnetic reconnection, as a function of the dimensionless guide-field strength B  g /B 0 and the magnetization σ 0.