adsabs/AstroCLIMB huggingface dataset:
Paper: A Catalog of 45,206 Hα Emission-line Stars from LAMOST MRS DR7
Authors: Lihuan Yu, Jiangdan Li, Jinliang Wang, Tongyu He, Zhanwen Han
Figure caption: Examples of six categories (1E: J110243.48+554448.2, 1EA: J063453.62+092617.3, 2E: J041912.84+282932.8, 2EA: J064208.72+225205.7, PC: J020131.75+384248.6, and IPC: J082231.49+202957.2) of three major types with Hα emission lines.The red vertical dashed–dotted lines indicate the central wavelength of emission lines, while the green vertical dashed lines indicate the central wavelength of absorption lines.
Paper: A Catalog of 45,206 Hα Emission-line Stars from LAMOST MRS DR7
Authors: Lihuan Yu, Jiangdan Li, Jinliang Wang, Tongyu He, Zhanwen Han
Figure caption: Distributions of wavelength separations between Hα and [N II] of spectra of H II regions. Panel (a) represents the distribution of Δλb, and panel (b) represents Δλr, respectively.
Paper: A Catalog of 45,206 Hα Emission-line Stars from LAMOST MRS DR7
Authors: Lihuan Yu, Jiangdan Li, Jinliang Wang, Tongyu He, Zhanwen Han
Figure caption: H-R diagrams for stars with Hα emission lines across six different labels (1E, 1EA, 2E, 2EA, PC, and IPC). Gray dots indicate background stars within 100 pc from the Gaia catalog. Colored points show the density distribution of emission-line stars selected from our sample.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: A flowchart describing the pipeline structure.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Comparison of templates generated with different software at different times. We matched (as in Equation (3)) every template in the coarse bank, generated using the response generator gbm_drm_gen at met = 654,393,605.0 s (2021 September 27 00:00:00), with templates having the same spectral parameters and sky positions but generated using GBM’s response generator at the specified dates, which were randomly chosen. One can see that the maximal mismatch is only 15%, and that for the most part, the time at which the response was generated does not affect the template significantly. On 2018 January 17 at 16:51:09 (brown line), Fermi was in an offset-pointing position due to a target-of-opportunity observation (Obs ID 100601-1-1), which resulted in a slightly higher mismatch. A full table describing the observing modes during these times is presented in Appendix J.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Template bank coverage check. Match of each template in the reference template bank and each template in the GBM-like bank with all the templates in the coarse bank, keeping the maximal value. The y-axis is the cumulative fraction of templates with a maximal match larger than the number specified by the x-axis. The reference bank is a larger bank, containing 5 × 104 templates. We see that only a small fraction, ∼0.02, have a match less than 0.95. Compared with the GBM-like templates, our ∼500 templates cover more than 90% of the parameter space covered by the GBM search bank containing three spectral shapes for each of the 104 sky positions required for ∼4 deg2 resolution.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Visualization of the constructed template bank. The small blue dots represent the dense bank template distribution over the sky and the larger points represent the coarse bank created by applying the random placement algorithm. Each point in this plot represents a single point in the five-dimensional parameter space (α, β, Epeak, R.A., decl.). The coarse bank points are colored by one of the spectral parameters—Epeak.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Maximum-likelihood sky distribution of triggers with pastro = 1. Left: all triggers with pastro = 1 and ﹩{{ \mathcal B }}_{\oplus },{{ \mathcal B }}_{\odot }\lt 1﹩. One can see the overdensity of triggers close to the Galactic center. These triggers are relatively soft, with Epeak < 40 keV. Right: similar to the left panel but now applying peak energy cuts, keeping only triggers with 40 keV < Epeak < 2000 keV.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: BGO contaminant templates overlaid on the sample used to create them using SVD. The x-axis in the figure is plotted only in the lower-energy channels as the higher channels do not contain a signal.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Contaminant removal tests. The histograms illustrate the performance of the specialized tests developed to filter out contaminants from the trigger distribution. In all figures, the blue curves represent the distribution of all triggers, while the orange curves correspond to the distribution of the test statistic for triggers that were also identified as GRBs in the GBM catalog. Where relevant, the green curve shows the corresponding normal distribution PDF. The selected thresholds are indicated by red lines, and the triggers filtered out by the tests fall within the red-shaded bands. In all tests, we choose the 4σ thresholds. The histograms show that the catalog GRBs roughly follow the distribution of triggers we keep.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Comparison of the dense bank SVD for GRB 170817. Left: mean-square error (normalized) for the reconstruction of the full test statistic in the dense bank (5 × 104 templates). One can see that ∼100 singular vectors are sufficient for a 10−4 error. Right: background corrections for GRB 170817 using a direct calculation vs. using 100 singular values.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: GRB 140402007 sky localization with and without drift correction. Left: no correction; right: amplitude is drift corrected, resulting in the confidence region slightly increasing.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: SNR ratio between our pipeline and GBM detections for the 54 out of 57 recovered bursts up to 3.606 s. The GBM SNR is the reported significance from the TRIGDAT file, given in units of σ standard deviations, and hence has the same meaning as our SNR.
Paper: Discovering Cataclysmic Variables from the Rubin Observatory LSST
Authors: D. A. H. Buckley, Y. Tampo, P. Szkody, M. Motsoaledi, S. Scaringi, M. Lochner, N. Rawat, J. P. Marais, B. van Soelen, S. Macfarlane, A. van Dyk
Figure caption: Examples of our simulated light curves of WZ Sge stars, polars, and micronovae from top to bottom. The gray line represents the input light curve in the r band, only adjusted to the distance modulus. The inverse triangle means the upper limits.
Paper: Discovering Cataclysmic Variables from the Rubin Observatory LSST
Authors: D. A. H. Buckley, Y. Tampo, P. Szkody, M. Motsoaledi, S. Scaringi, M. Lochner, N. Rawat, J. P. Marais, B. van Soelen, S. Macfarlane, A. van Dyk
Figure caption: Detection efficiency dependence on distance according to the uniform distribution model for WZ Sge–type DNe, polars, and micronovae from left to right. The colors have the same meaning as Figure 1. The vertical lines indicate the distances of 47 Tuc, LMC, and SMC.
Paper: Discovering Cataclysmic Variables from the Rubin Observatory LSST
Authors: D. A. H. Buckley, Y. Tampo, P. Szkody, M. Motsoaledi, S. Scaringi, M. Lochner, N. Rawat, J. P. Marais, B. van Soelen, S. Macfarlane, A. van Dyk
Figure caption: Light-curve comparison of WZ Sge (T. Kato et al. 2009), recurrent nova U Sco (K. Muraoka et al. 2024), classical nova V339 Del (from AAVSO LCGv2, https://www.aavso.org/LCGv2/), FBOT AT 2018cow (S. J. Prentice et al. 2018), and LRN V1309 Sco (from AAVSO LCGv2).
Paper: Discovering Cataclysmic Variables from the Rubin Observatory LSST
Authors: D. A. H. Buckley, Y. Tampo, P. Szkody, M. Motsoaledi, S. Scaringi, M. Lochner, N. Rawat, J. P. Marais, B. van Soelen, S. Macfarlane, A. van Dyk
Figure caption: Left: cumulative histogram of the time difference between the epochs of the first and second detection in an outburst of the simulated WZ Sge–type DNe in the thin-disk distribution model. Only samples with detections in an outburst are used for the plot. Right: the number of samples with the filters of the first (x-axis) and second (y-axis) detections in an outburst. Samples with two or more detections on the outburst discovery night are used in the plot.
Paper: Discovering Cataclysmic Variables from the Rubin Observatory LSST
Authors: D. A. H. Buckley, Y. Tampo, P. Szkody, M. Motsoaledi, S. Scaringi, M. Lochner, N. Rawat, J. P. Marais, B. van Soelen, S. Macfarlane, A. van Dyk
Figure caption: Left: histogram of number of detections during a micronova regardless of the filters in the thin-disk distribution model. Right: histogram of the simulated amplitudes of micronovae. The vertical lines represent the r-band amplitude of the three input micronovae.
Paper: Discovering Cataclysmic Variables from the Rubin Observatory LSST
Authors: D. A. H. Buckley, Y. Tampo, P. Szkody, M. Motsoaledi, S. Scaringi, M. Lochner, N. Rawat, J. P. Marais, B. van Soelen, S. Macfarlane, A. van Dyk
Figure caption: Histogram of the r-band median magnitude of our polar samples in the 10 yr simulation under the thin-disk distribution model. The gray histogram represents that of the input light curves, while blue, magenta, and black histograms represent those of the simulated samples with ≥10, ≥100, and ≥100 in r-band detections.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Marginalized joint posteriors of redshifted component masses (﹩{m}_{1}^{z}﹩ and ﹩{m}_{2}^{z}﹩), their corresponding tidal deformabilities (Λ1 and Λ2), and luminosity distance (dL) inferred from GW data of a BNS. The black vertical and horizontal solid lines represent the injected values of the corresponding parameters.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Effective priors for the EoS parameters after imposing the causality constraint. The black solid lines represent the injected NS EoS parameters.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Comparison of the constraints on the NS EoS parameters from five GW events, following the Gaussian and double Gaussian mass distributions. The black solid lines correspond to the true NS EoS parameters. The uncertainty of each parameter corresponding to the 90% credible interval is shown at the top of the 1D posterior for both mass distributions.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Comparison of the constraints on the NS EoS parameters between 5 and 50 GW events, following the Gaussian mass distribution. The black solid lines correspond to the true NS EoS parameters. The uncertainty of each parameter, corresponding to the 90% credible interval, is shown at the top of the respective marginalized 1D posterior.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Comparison of the 90% credible regions of mass–radius posteriors between the Gaussian and double Gaussian mass distributions for five (left) and 50 (right) events. The black solid line indicates the true mass–radius relation corresponding to the injected NS EoS parameters.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Same as Figure 7, but using 50 events.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Same as Figure 13, but BNSs follow the double Gaussian mass distribution.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Joint posteriors of all hyperparameters describing the population, cosmology, and NS EoS, inferred from 50 events following the Gaussian mass distribution. The black solid lines indicate the true injected values. The 90% credible interval for each parameter is shown above the corresponding 1D posterior.
Paper: Joint Inference of Population, Cosmology, and Neutron Star Equation of State from Gravitational Waves of Dark Binary Neutron Stars
Authors: Tathagata Ghosh, Bhaskar Biswas, Sukanta Bose, Shasvath J. Kapadia
Figure caption: Comparison of the uncertainty in measuring H0 and ﹩{m}_{{\rm{\max }}}﹩ from 5 and 50 GW events, following the Gaussian (G) and double Gaussian (DG) mass distributions. The x-axis is not to scale.
Paper: Species-dependent Variability in the Energy Spectra of Intense Solar Energetic Particle Events Observed by PSP/ISʘIS/EPI-Hi/LET
Authors: S. Pak, M. E. Cuesta, H. A. Farooki, L. Y. Khoo, Z. G. Xu, A. J. Davis, R. A. Leske, C. M. S. Cohen, D. J. McComas, B. L. Shrestha, J. G. Mitchell, A. W. Labrador, J. S. Rankin, S. Kasapis, J. R. Szalay, A. C. Cummings, G. D. Muro, M. E. Wiedenbeck
Figure caption: Scatterplots of the low-energy (β1) and high-energy (β2) spectral indices for (a) H and He, and (b) O and Fe, during the low-intensity (DT0) and high-intensity (DT1) phases for the selected SEP events. Each panel includes Pearson (r) and Spearman (ρ) correlation coefficients, associated p-values, and sample sizes (N). The dashed line denotes the identity line (x = y).
Paper: Species-dependent Variability in the Energy Spectra of Intense Solar Energetic Particle Events Observed by PSP/ISʘIS/EPI-Hi/LET
Authors: S. Pak, M. E. Cuesta, H. A. Farooki, L. Y. Khoo, Z. G. Xu, A. J. Davis, R. A. Leske, C. M. S. Cohen, D. J. McComas, B. L. Shrestha, J. G. Mitchell, A. W. Labrador, J. S. Rankin, S. Kasapis, J. R. Szalay, A. C. Cummings, G. D. Muro, M. E. Wiedenbeck
Figure caption: The fitted low-energy (β1) and high-energy (β2) spectral indices and the ratio of β1 to β2 for 16 ion species measured by EPI-Hi/LET1 during the low-intensity (DT0) and high-intensity (DT1) phases for the selected SEP events.
Paper: Species-dependent Variability in the Energy Spectra of Intense Solar Energetic Particle Events Observed by PSP/ISʘIS/EPI-Hi/LET
Authors: S. Pak, M. E. Cuesta, H. A. Farooki, L. Y. Khoo, Z. G. Xu, A. J. Davis, R. A. Leske, C. M. S. Cohen, D. J. McComas, B. L. Shrestha, J. G. Mitchell, A. W. Labrador, J. S. Rankin, S. Kasapis, J. R. Szalay, A. C. Cummings, G. D. Muro, M. E. Wiedenbeck
Figure caption: LET-A geometric factors for (a) H, (b) He, (c) O, and (d) Fe during the nominal operation mode (DT0; blue solid lines) and dynamic threshold mode 1 (DT1; red dashed lines), including corrections for the FOV obstructions.
Paper: Species-dependent Variability in the Energy Spectra of Intense Solar Energetic Particle Events Observed by PSP/ISʘIS/EPI-Hi/LET
Authors: S. Pak, M. E. Cuesta, H. A. Farooki, L. Y. Khoo, Z. G. Xu, A. J. Davis, R. A. Leske, C. M. S. Cohen, D. J. McComas, B. L. Shrestha, J. G. Mitchell, A. W. Labrador, J. S. Rankin, S. Kasapis, J. R. Szalay, A. C. Cummings, G. D. Muro, M. E. Wiedenbeck
Figure caption: The energy spectra of ions observed by EPI-Hi/LET1 for SEP Event #2023-136. The fluxes averaged for the whole (a) DT0 and (b) DT1 periods are shown individually. The fitted spectra using the Band function parameters listed in Table 2 are depicted as solid curves.
Paper: Species-dependent Variability in the Energy Spectra of Intense Solar Energetic Particle Events Observed by PSP/ISʘIS/EPI-Hi/LET
Authors: S. Pak, M. E. Cuesta, H. A. Farooki, L. Y. Khoo, Z. G. Xu, A. J. Davis, R. A. Leske, C. M. S. Cohen, D. J. McComas, B. L. Shrestha, J. G. Mitchell, A. W. Labrador, J. S. Rankin, S. Kasapis, J. R. Szalay, A. C. Cummings, G. D. Muro, M. E. Wiedenbeck
Figure caption: EPI-Hi/LET1 A side (LET-A) H geometric factors for event categories (R1A to R6A) under (a) nominal operation mode (DT0) and (b) dynamic threshold mode 1 (DT1). Scales vary in different panels.
Paper: Species-dependent Variability in the Energy Spectra of Intense Solar Energetic Particle Events Observed by PSP/ISʘIS/EPI-Hi/LET
Authors: S. Pak, M. E. Cuesta, H. A. Farooki, L. Y. Khoo, Z. G. Xu, A. J. Davis, R. A. Leske, C. M. S. Cohen, D. J. McComas, B. L. Shrestha, J. G. Mitchell, A. W. Labrador, J. S. Rankin, S. Kasapis, J. R. Szalay, A. C. Cummings, G. D. Muro, M. E. Wiedenbeck
Figure caption: (a) ISʘIS location on the PSP spacecraft, (b) illustration of EPI-Hi, and (c) FOV of EPI-Hi LET with obstructions. Adapted from D. J. McComas et al. (2016).
Paper: Species-dependent Variability in the Energy Spectra of Intense Solar Energetic Particle Events Observed by PSP/ISʘIS/EPI-Hi/LET
Authors: S. Pak, M. E. Cuesta, H. A. Farooki, L. Y. Khoo, Z. G. Xu, A. J. Davis, R. A. Leske, C. M. S. Cohen, D. J. McComas, B. L. Shrestha, J. G. Mitchell, A. W. Labrador, J. S. Rankin, S. Kasapis, J. R. Szalay, A. C. Cummings, G. D. Muro, M. E. Wiedenbeck
Figure caption: Fractional contributions of each ion species to the ion population over 2.378–16 MeV nuc−1 measured by EPI-Hi/LET1 during the low-intensity (DT0) and high-intensity (DT1) phases for the selected SEP events. Scales vary in different panels.
Paper: Species-dependent Variability in the Energy Spectra of Intense Solar Energetic Particle Events Observed by PSP/ISʘIS/EPI-Hi/LET
Authors: S. Pak, M. E. Cuesta, H. A. Farooki, L. Y. Khoo, Z. G. Xu, A. J. Davis, R. A. Leske, C. M. S. Cohen, D. J. McComas, B. L. Shrestha, J. G. Mitchell, A. W. Labrador, J. S. Rankin, S. Kasapis, J. R. Szalay, A. C. Cummings, G. D. Muro, M. E. Wiedenbeck
Figure caption: Illustration of EPI-Hi/LET detector layers and segments. LET1 under nominal operation mode (DT0) and dynamic threshold mode 1 (DT1) are shown in (a) and (b), respectively, while LET2 under DT0 and DT1 are depicted in (c) and (d). Active detector segments and guard rings under DT0 are illustrated in (e), while all L0 segments and the outer L1 segments have a raised threshold under DT1. Adapted from D. J. McComas et al. (2016) and C. M. S. Cohen et al. (2021).
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Statistic distribution of different burst durations. The figure shows some of the different distributions obtained when searching for different burst durations using a specific template. The distributions of the test statistic are obtained by Monte Carlo simulations. For the shorter durations, it is apparent that the distribution is deep in the Poisson regime and does not resemble a normal distribution. As we increase the duration, we accumulate more photons and converge to a normal distribution. To get a similar interpretation for the different distributions, we fit the tail and calibrate the p-value to be the one that would have been obtained from the normal distribution (see main text and Equation (16)).
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Peak energy–duration distribution for 2014 triggers with pastro = 1. This scatter plot displays the peak energy vs. duration for triggers with pastro = 1 corresponding to SNR2 ≥ 70. A small jitter is added to mitigate the overlap caused by the discrete template bank. Color indicates the Earth Bayes factor ﹩{{ \mathcal B }}_{\oplus }﹩, and star symbols denote triggers classified as GRBs in the GBM catalog. The dashed black rectangle outlines the region used to estimate the number of new GRBs. The duration and high peak energy thresholds help eliminate short, Earth-associated triggers, while a low-energy cut (Epeak > 40 keV) suppresses the excess near the Galactic center, as shown in Figure 14. It is important to note that genuine GRBs may still occur below this energy threshold, particularly long GRBs with short-duration variability spikes, which can fall into this lower-energy regime.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Raw trigger distribution for 2014 data. Upper panel: histogram of the triggers obtained from running the pipeline over 2014 data, after maximization over the burst duration. The red band indicates the detection threshold for pastro = 1. The actual data output is denoted as “triggers,” and we also show the time slides and simulation outputs. More high-SNR events are cut from the figures for clarity. Lower panel: pastro calculated using Equation (1) by using either the time slides or the simulation as the noise distribution. It can be seen that above SNR2 ∼ 55 we have pastro = 0.5 and above SNR2 ∼ 70, marked by the red band, the simulation and time slides do not trigger on random fluctuations, and we essentially have pastro = 1.
Paper: A New Search Pipeline for Short Gamma-Ray Bursts in Fermi/GBM Data—A 50% Increase in the Number of Detections
Authors: Ariel Perera, Barak Zackay, Tejaswi Venumadhav
Figure caption: Wrong background effect on the statistic distribution. Left: simulation of the background misestimation effect. We sampled data from a background estimation of real data and applied the test statistic. The blue curve represents the result when the true background was used in the statistic expression (Equation (5)), while the orange curve corresponds to a case where the background was misestimated with a 25% negative error. The use of an incorrect background introduces a tail in the trigger distribution, leading to a loss in sensitivity. This effect is significantly reduced when drift correction is applied. The green curve shows the result after applying drift correction to the misestimated background statistic. Right: the statistic was calculated on actual data, with and without applying the correction. It can be seen that the behavior is similar to the simulation but with a much smaller drift.