adsabs/AstroCLIMB huggingface dataset:
Paper: Bayesian Black Hole Photogrammetry
Authors: Dominic O. Chang, Michael D. Johnson, Paul Tiede, Daniel C. M. Palumbo
Figure caption: Partial corner plot for a dual-cone fit to the 2017 April 6 EHT observations of M87*. The shown parameters are the mass-to-distance ratio (θ  g ), the black hole spin (a), the observer inclination (θ o), the jet opening angle (θ  s ), the projected position angle of the spin axis on the observer’s screen (p.a.), and the characteristic radius of the emissivity function (R). Vertical lines show other mass measurements of M87* in red (Gebhardt et al. 2011), blue (Walsh et al. 2013), black dotted M87* (VI), yellow (Liepold et al. 2023), and green (Simon et al. 2023). We also show the measured position angle and inclination of the large-scale jet in M87* (Walker et al. 2018). The inset images show the consensus April 6 image of M87* reported by the EHTC and a random sample drawn from the posterior, blurred with a 20 μas FWHM Gaussian beam. Both images have their peak brightness in the southeastern quadrant.
Paper: Bayesian Black Hole Photogrammetry
Authors: Dominic O. Chang, Michael D. Johnson, Paul Tiede, Daniel C. M. Palumbo
Figure caption: Random sample of images from the dual-cone model seen by a distant observer. All images shown are comprised of n = 0 and n = 1 subimages. In all images, the mass-to-distance ratio is the same, the observer inclination is 30°, the spin is −1, and the projected spin axis is oriented vertically on the observer’s screen. The remaining parameters are sampled from a prior similar to Table 1, with the exception that the range of the cone opening angle has been extended to θ  s  ∈ (0, π/2). We have classified the images into two categories, “inside the cone” (blue) and “outside the cone” (red). These are defined, respectively, by images generated with ﹩| \cos {\theta }_{s}| \lt | \cos {\theta }_{o}| ﹩ in the case of “inside the cone” images or ﹩| \cos {\theta }_{s}| \gt | \cos {\theta }_{o}| ﹩ in the case of “outside the cone” images.
Paper: Bayesian Black Hole Photogrammetry
Authors: Dominic O. Chang, Michael D. Johnson, Paul Tiede, Daniel C. M. Palumbo
Figure caption: Partial corner plot showing the mass-to-distance ratio (θ  g ), the black hole spin (a), the observer inclination (θ o), the jet opening angle (θ  s ), the projected position angle of the spin axis on the observer’s screen (p.a.), and the characteristic radius of the emissivity function (R). The true values of the GRMHD simulation are shown with black lines; the values inferred from by the model in our image-domain NxCORR fit of Section 3 are shown in red. Figure 17 shows the full corner plot for this fit. The posterior features two distinct clusters, depicted in blue and yellow, which we isolate using the k-means clustering algorithm.
Paper: Bayesian Black Hole Photogrammetry
Authors: Dominic O. Chang, Michael D. Johnson, Paul Tiede, Daniel C. M. Palumbo
Figure caption: Marginalized posteriors for snapshot fits to a R high = 160 SANE simulation (left) and R high = 20 MAD simulation (right). The distributions of two snapshot fits to each simulation are shown in blue and yellow. The parameters shown are the mass-to-distance ratio (θ  g ), spin (a), spin axis inclination with respect to the observer (θ  o ), and the spin axis position angle (p.a.). For each parameter, the true values and the best NxCORR values that were taken from fits to the time-averaged model in Section 3 are shown with red and black vertical lines, respectively.
Paper: Bayesian Black Hole Photogrammetry
Authors: Dominic O. Chang, Michael D. Johnson, Paul Tiede, Daniel C. M. Palumbo
Figure caption: Comparison of a time-averaged GRMHD image with the best-fit dual-cone model, both at native resolution (top) and after being blurred by a 10 μas FWHM Gaussian beam (bottom). The simulation corresponds to a retrograde, R high = 160, MAD simulation of Sgr A*. Columns show the GRMHD image (left), the best-fit model (left center), horizontal and vertical intensity cross sections (right center), and residual images as a fraction of peak intensity (right).
Paper: Bayesian Black Hole Photogrammetry
Authors: Dominic O. Chang, Michael D. Johnson, Paul Tiede, Daniel C. M. Palumbo
Figure caption: Assessing the influence of model parameters on various image features. The leftmost panel shows an image of one of the representative dual-cone models of Section 3. The direction of the spin is projected onto the image with a white arrow. The grid of panels to the right shows the magnitude of the partial derivatives of each image pixel with respect to the parameters of the dual-cone model. The color range in each of the derivative panels is rescaled to its minimum and maximum pixel value.
Paper: Bayesian Black Hole Photogrammetry
Authors: Dominic O. Chang, Michael D. Johnson, Paul Tiede, Daniel C. M. Palumbo
Figure caption: True image of the GRMHD simulation (left) with samples from the GRMHD posterior from each mode of the posterior (center and right). The top row shows images at the native resolution used to generate the data; the bottom row shows the same images after being blurred to the nominal EHT resolution of 20 μas. All images feature a disjoint double ring structure that is not discernible when seen at the EHT nominal resolution.
Paper: Bayesian Black Hole Photogrammetry
Authors: Dominic O. Chang, Michael D. Johnson, Paul Tiede, Daniel C. M. Palumbo
Figure caption: Snapshots of an R high = 20 simulation of a MAD accretion flow (top) and an R high = 160 simulation of a SANE accretion flow (bottom). These snapshots were used to produce data for the model fits shown in Figure 10.
Paper: Formation of GW230529 from Isolated Binary Evolution
Authors: Jin-Ping Zhu, Rui-Chong Hu, Yacheng Kang, Bing Zhang, Hui Tong, Lijing Shao, Ying Qin
Figure caption: NS and BH masses of our population synthesis simulations. The gray dotted lines represent the credible interval regions of the simulation results, spanning from 10% to 90%, estimated using kernel density estimation. The source-frame medians (star points) along with their corresponding 90% credible intervals (solid lines) are illustrated for the posterior distributions of GW200105 (green), GW200115 (blue), and GW230529 (orange). The right panel shows the probability density functions of the population distribution of the BH mass inferred by the GW observations (orange histogram; A. G. Abac et al. 2024), contrasted with that derived from our population synthesis simulations (gray histogram). For an EoS of AP4 (DD2), nonspinning BHNS mergers with component masses located at the bottom-left parameter space of the pink (light pink) dashed line can allow tidal disruption.
Paper: Formation of GW230529 from Isolated Binary Evolution
Authors: Jin-Ping Zhu, Rui-Chong Hu, Yacheng Kang, Bing Zhang, Hui Tong, Lijing Shao, Ying Qin
Figure caption: Source-frame mass parameter space for BHNS merger systems to allow NS tidal disruption. We mark the mass ratio from Q = 2 to 8 as dashed lines in each panel. The solid lines represent the primary BH-aligned spin χ BH,z  from −0.75 to 0.90. For a specific χ BH,z , BHNS mergers with component masses located at the bottom-left parameter space (denoted by the direction of the arrows) can undergo tidal disruption. For GW200105 (green), GW200115 (blue), and GW230529 (orange), the 90% credible posterior distributions (colored solid lines) and the medians (colored stars) are displayed, while the corresponding median values of χ BH,z  for these three sources are marked as dashed–dotted lines.
Paper: Formation of GW230529 from Isolated Binary Evolution
Authors: Jin-Ping Zhu, Rui-Chong Hu, Yacheng Kang, Bing Zhang, Hui Tong, Lijing Shao, Ying Qin
Figure caption: Probability density distributions of BHNS merger GRB γ-ray flux for GW230529. Two different core opening angles are considered: θ c = 3.°5 (left panels) and 7°, as well as two EoSs, namely AP4 (top panels) and DD2 (bottom panels). The red, orange, blue, and green histograms represent GRB with latitudinal viewing angle ranges of θ view < 7°, 7° < θ view < 14°, 14° < θ view < 21°, and >21°, respectively. The gray dashed lines indicate the effective sensitivity limit for γ-ray detectors. We note that the y-axis scalings differ between the top and bottom panels.
Paper: Formation of GW230529 from Isolated Binary Evolution
Authors: Jin-Ping Zhu, Rui-Chong Hu, Yacheng Kang, Bing Zhang, Hui Tong, Lijing Shao, Ying Qin
Figure caption: Probability density distributions of BHNS merger kilonova g-band (left panel) and r-band (right panel) peak magnitude for GW200115 and GW230529. The orange histograms represent the probability density for GW200115 with the consideration of the AP4 EoS, while the blue and green histograms show the probability densities for GW230529, employing the AP4 and DD2 EoSs, respectively. The gray solid, dashed, dotted, and dashed–dotted lines show the threshold depths of ZTF, Mephisto, WFST, and LSST for an exposure time of 300 s under the ideal observing conditions (J.-P. Zhu et al. 2023). Here, the bin width of the histograms is set as Δ = 0.2 mag.
Paper: Formation of GW230529 from Isolated Binary Evolution
Authors: Jin-Ping Zhu, Rui-Chong Hu, Yacheng Kang, Bing Zhang, Hui Tong, Lijing Shao, Ying Qin
Figure caption: Simulated local rate density of mgBHNS and BHNS mergers. The red, orange, green, and blue points correspond to different envelope efficiencies of α CE = 1, 2, 5, and 10, respectively. The solid and hollow points represent two NS natal kicks of σ NS = 100 and 265 km s−1. The top and right panels display the probability density functions of posteriors on the merger rate densities of mgBHNS and BHNS mergers obtained from A. G. Abac et al. (2024). The 10% and 50% credible interval regions of the posteriors are marked as solid gray lines. The shadow region in the bottom right represents the prohibited parameter space where ﹩{{ \mathcal R }}_{\mathrm{mgBHNS}}\gt {{ \mathcal R }}_{\mathrm{BHNS}}﹩.
Paper: Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
Authors: P. D. Aleo, A. W. Engel, G. Narayan, C. R. Angus, K. Malanchev, K. Auchettl, V. F. Baldassare, A. Berres, T. J. L. de Boer, B. M. Boyd, K. C. Chambers, K. W. Davis, N. Esquivel, D. Farias, R. J. Foley, A. Gagliano, C. Gall, H. Gao, S. Gomez, M. Grayling, D. O. Jones, C.-C. Lin, E. A. Magnier, K. S. Mandel, T. Matheson, S. I. Raimundo, V. G. Shah, M. D. Soraisam, K. M. de Soto, S. Vicencio, V. A. Villar, R. J. Wainscoat
Figure caption: The LAISS Pipeline. We ingest ZTF Alert Stream data, and extract statistical light-curve features with light-curve (Malanchev 2021) and contextual host galaxy features with GHOST (Gagliano et al. 2021). We apply strict preprocessing cuts to remove contaminants and obtain a reference data set of transient candidates. We apply a 70/30 train/test split, subsequently upsampling our training set with SMOTE (Chawla et al. 2002) such that each class is balanced. We train a random forest classifier to perform a binary classification task between “Normal” and an “Anomaly” class. We consider spectroscopic anomalies, contextual anomalies, and behavioral anomalies via expert vetting. In parallel, we apply principal component analysis (PCA) followed by approximate nearest neighbors (ANNs) search in feature-space via ANNOY (Bernhardsson 2018; Section 5) to find transient analogs with similar light-curve evolution and host galaxy environment. Finally, we deploy our anomaly detection model onto the ANTARES broker (Matheson et al. 2021) as a Filter. For transients with P(anom) ≥ 50% at any point in the light curve, we manually vet them and request for follow-up resources. We report to TNS any new or updated classifications from either our anomaly detection or approximate nearest neighbors search.
Paper: Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
Authors: P. D. Aleo, A. W. Engel, G. Narayan, C. R. Angus, K. Malanchev, K. Auchettl, V. F. Baldassare, A. Berres, T. J. L. de Boer, B. M. Boyd, K. C. Chambers, K. W. Davis, N. Esquivel, D. Farias, R. J. Foley, A. Gagliano, C. Gall, H. Gao, S. Gomez, M. Grayling, D. O. Jones, C.-C. Lin, E. A. Magnier, K. S. Mandel, T. Matheson, S. I. Raimundo, V. G. Shah, M. D. Soraisam, K. M. de Soto, S. Vicencio, V. A. Villar, R. J. Wainscoat
Figure caption: The observed completeness (left panel) and observed purity (right panel) of our model as a function of anomaly classification confidence, P(anom), calculated at full light-curve phase. Individual object confidence scores are represented as circles. Each curve is a different initialization from 1 of 25 different random seeds, where the bold line is the chosen model’s performance. At our chosen thresholds of P(anom) = 50% for this work, we achieve an overall purity score of 52% and a completeness of 29% for spectroscopic anomalies only before vetting (blue) and an overall purity score of 90% and a completeness of 38% for spectroscopic, contextual, and behavioral anomalies after vetting (green), reflected in the confusion matrices of Figures 2, 3, respectively.
Paper: Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
Authors: P. D. Aleo, A. W. Engel, G. Narayan, C. R. Angus, K. Malanchev, K. Auchettl, V. F. Baldassare, A. Berres, T. J. L. de Boer, B. M. Boyd, K. C. Chambers, K. W. Davis, N. Esquivel, D. Farias, R. J. Foley, A. Gagliano, C. Gall, H. Gao, S. Gomez, M. Grayling, D. O. Jones, C.-C. Lin, E. A. Magnier, K. S. Mandel, T. Matheson, S. I. Raimundo, V. G. Shah, M. D. Soraisam, K. M. de Soto, S. Vicencio, V. A. Villar, R. J. Wainscoat
Figure caption: Left: receiver operator characteristic (ROC) curves for spectroscopic, contextual, and behavioral anomalies after vetting (green solid line), spectroscopic anomalies only before vetting (blue solid line), and by random selection (red dashed line). Each curve is a different initialization from 1 of 25 different random seeds, where the bold line is the chosen model’s performance. The area under the curve (AUC) for the “Anomaly” classification is listed along with its standard deviation across the 25 different random seeds. Right: precision–recall curves for spectroscopic, contextual, and behavioral anomalies after vetting (green solid line), spectroscopic anomalies only before vetting (blue solid line), and by random selection (red solid line), where any object with P(anom) < 50% is cast to 0, and P(anom) ≥ 50% is cast to 1. The average precision (AP) score is shown in parentheses, and a 50% purity is shown as a dashed black line. Our anomaly detection model significantly outperforms random selection, and can achieve high levels of purity (≥50%) at a range of recall thresholds (≤70% after vetting, ≤32% before vetting).
Paper: Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
Authors: P. D. Aleo, A. W. Engel, G. Narayan, C. R. Angus, K. Malanchev, K. Auchettl, V. F. Baldassare, A. Berres, T. J. L. de Boer, B. M. Boyd, K. C. Chambers, K. W. Davis, N. Esquivel, D. Farias, R. J. Foley, A. Gagliano, C. Gall, H. Gao, S. Gomez, M. Grayling, D. O. Jones, C.-C. Lin, E. A. Magnier, K. S. Mandel, T. Matheson, S. I. Raimundo, V. G. Shah, M. D. Soraisam, K. M. de Soto, S. Vicencio, V. A. Villar, R. J. Wainscoat
Figure caption: Same as Figure 2, but including additional spectroscopic, contextual, and behavioral anomalies after expert vetting of the classification spectra, host galaxy environment, and light-curve evolution, respectively. We achieve 90% effective purity of detecting spectroscopic, contextual, and behavioral anomalies combined. We find this effective purity score is consistent with the spectroscopic subset of transients identified as anomalous in our real-time deployment on the nightly ZTF Alert Stream via ANTARES (∼83%, see Table 5).
Paper: Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
Authors: P. D. Aleo, A. W. Engel, G. Narayan, C. R. Angus, K. Malanchev, K. Auchettl, V. F. Baldassare, A. Berres, T. J. L. de Boer, B. M. Boyd, K. C. Chambers, K. W. Davis, N. Esquivel, D. Farias, R. J. Foley, A. Gagliano, C. Gall, H. Gao, S. Gomez, M. Grayling, D. O. Jones, C.-C. Lin, E. A. Magnier, K. S. Mandel, T. Matheson, S. I. Raimundo, V. G. Shah, M. D. Soraisam, K. M. de Soto, S. Vicencio, V. A. Villar, R. J. Wainscoat
Figure caption: The top 15 light-curve and host galaxy features, ordered by the greatest normalized impurity-based feature importance in our binary “Normal” vs. “Anomaly” task as determined by our final random forest classifier model. The features with highest importance can be broadly categorized by light-curve slope fit and error, g-band brightness and its variance, transient radial separation, and host galaxy color. The high importance of such host galaxy features is consistent with findings from Gomez et al. (2020) and Gagliano et al. (2021) despite their different classification tasks (SLSNe vs. non-SLSNe, and SNe Ia vs. CC SNe, respectively).
Paper: Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
Authors: P. D. Aleo, A. W. Engel, G. Narayan, C. R. Angus, K. Malanchev, K. Auchettl, V. F. Baldassare, A. Berres, T. J. L. de Boer, B. M. Boyd, K. C. Chambers, K. W. Davis, N. Esquivel, D. Farias, R. J. Foley, A. Gagliano, C. Gall, H. Gao, S. Gomez, M. Grayling, D. O. Jones, C.-C. Lin, E. A. Magnier, K. S. Mandel, T. Matheson, S. I. Raimundo, V. G. Shah, M. D. Soraisam, K. M. de Soto, S. Vicencio, V. A. Villar, R. J. Wainscoat
Figure caption: The Type Ib SN 2021hrj/ZTF21aarnjyd was retrospectively tagged as anomalous (P(anom) ≥ 50%) on MJD 59316 (2023 April 12) by our LAISS AD algorithm (dashed blue line), 1 week earlier than the first classification spectra from YSE (2023 April 19; dotted–dashed purple line) and ZTF (2023 April 20) on TNS. The YSE team originally requested spectra when this object passed their magnitude-limited sample criteria. If our algorithm was in place when SN 2021hrj was active, we could in principle have obtained a spectrum 1 week earlier in the light-curve evolution, providing valuable information in regards to the early time supernova physics and local environment of this peculiar, long-rising event.
Paper: Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
Authors: P. D. Aleo, A. W. Engel, G. Narayan, C. R. Angus, K. Malanchev, K. Auchettl, V. F. Baldassare, A. Berres, T. J. L. de Boer, B. M. Boyd, K. C. Chambers, K. W. Davis, N. Esquivel, D. Farias, R. J. Foley, A. Gagliano, C. Gall, H. Gao, S. Gomez, M. Grayling, D. O. Jones, C.-C. Lin, E. A. Magnier, K. S. Mandel, T. Matheson, S. I. Raimundo, V. G. Shah, M. D. Soraisam, K. M. de Soto, S. Vicencio, V. A. Villar, R. J. Wainscoat
Figure caption: A boxplot of the top seven most important features from our training set with the highest permutation importance using the method from Breiman (2001). We use 10 permutations per feature, finding the features that most negatively impact accuracy. All features except zExtNSigma are shown as among the most important identified by the impurity-based feature importance metric (see Figure 6). The light-curve feature feature_linear_fit_slope_magn_g remains the singular most important feature for identifying anomalies for this work.
Paper: Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
Authors: P. D. Aleo, A. W. Engel, G. Narayan, C. R. Angus, K. Malanchev, K. Auchettl, V. F. Baldassare, A. Berres, T. J. L. de Boer, B. M. Boyd, K. C. Chambers, K. W. Davis, N. Esquivel, D. Farias, R. J. Foley, A. Gagliano, C. Gall, H. Gao, S. Gomez, M. Grayling, D. O. Jones, C.-C. Lin, E. A. Magnier, K. S. Mandel, T. Matheson, S. I. Raimundo, V. G. Shah, M. D. Soraisam, K. M. de Soto, S. Vicencio, V. A. Villar, R. J. Wainscoat
Figure caption: Completeness (left panel) and purity (right panel) confusion matrices of our spectroscopic test set for binary classification performance between “Normal” SNe (SN Ia-normal, SN Ia-91T-like, SN II-normal, SN IIP) and “Anomaly” SNe (TDE, SLSN-II, SN IIn, SN IIb, SN Ib, SN Ic, SN Ic-BL), based on the original TNS spectroscopic label only (i.e., spectroscopic anomalies before vetting). We achieve 52% purity of detecting spectroscopic anomalies alone.
Paper: On the Origin of the Ancient, Large-scale Cold Front in the Perseus Cluster of Galaxies
Authors: Elena Bellomi, John A. ZuHone, Rainer Weinberger, Stephen A. Walker, Irina Zhuravleva, Mateusz Ruszkowski, Maxim Markevitch
Figure caption: Different subcluster masses and gas content. GGM images for the merger simulations with subcluster incoming angle θ = 20°, β = 100 and v sub = 1380 km s−1. Top panels: varying the mass ratio with gasless subclusters. Bottom panels: varying the mass ratio with gaseous subclusters. The white dashed line in each panel marks a circle of radius 700 kpc (the distance from the center at which the ancient CF in Perseus is found). All images are shown at 7.5 Gyr after the first pericenter passage.
Paper: On the Origin of the Ancient, Large-scale Cold Front in the Perseus Cluster of Galaxies
Authors: Elena Bellomi, John A. ZuHone, Rainer Weinberger, Stephen A. Walker, Irina Zhuravleva, Mateusz Ruszkowski, Maxim Markevitch
Figure caption: Different subcluster masses and gas content. Slices of the density, temperature, radial and tangential velocities, and magnetic field strength for the simulations R = 1:20 gaseous, R = 1:5 gaseous, and R = 1:5 gasless, and initial parameters θ = 30°, v sub = 2000 km s−1, and β = 200. All slices are made through the center of the main cluster, where the center is defined as the minimum of the gravitational potential. All images are shown at 7.5 Gyr after the first pericenter passage. The arrow indicates the split in the CF discussed in Section 4.1.
Paper: On the Origin of the Ancient, Large-scale Cold Front in the Perseus Cluster of Galaxies
Authors: Elena Bellomi, John A. ZuHone, Rainer Weinberger, Stephen A. Walker, Irina Zhuravleva, Mateusz Ruszkowski, Maxim Markevitch
Figure caption: Different subcluster masses and gas content for a faster encounter. GGM for the merger simulations with an incident angle θ = 30°, initial velocity v sub = 2000 km s−1, β = 200, and mass ratio R = 1: 20, 1: 10, 1: 5. Top panels: varying the mass ratio with gasless subclusters. Bottom panels: varying the mass ratio with gaseous subclusters. The white dashed line in each panel marks a circle of radius 700 kpc (the distance from the center at which the ancient CF in Perseus is found). All images are shown at 7.5 Gyr after the first pericenter passage.
Paper: On the Origin of the Ancient, Large-scale Cold Front in the Perseus Cluster of Galaxies
Authors: Elena Bellomi, John A. ZuHone, Rainer Weinberger, Stephen A. Walker, Irina Zhuravleva, Mateusz Ruszkowski, Maxim Markevitch
Figure caption: Radial profiles of Perseus compared with observations of Perseus. The vertical blue dotted line indicates the virial radius R 200c  and the horizontal dotted line indicates the cosmological gas fraction 0.156.