adsabs/AstroCLIMB huggingface dataset:
Paper: Characterizing the Fast Radio Burst Host Galaxy Population and its Connection to Transients in the Local and Extragalactic Universe
Authors: Shivani Bhandari, Kasper E. Heintz, Kshitij Aggarwal, Lachlan Marnoch, Cherie K. Day, Jessica Sydnor, Sarah Burke-Spolaor, Casey J. Law, J. Xavier Prochaska, Nicolas Tejos, Keith W. Bannister, Bryan J. Butler, Adam T. Deller, R. D. Ekers, Chris Flynn, Wen-fai Fong, Clancy W. James, T. Joseph W. Lazio, Rui Luo, Elizabeth K. Mahony, Stuart D. Ryder, Elaine M. Sadler, Ryan M. Shannon, JinLin Han, Kejia Lee, Bing Zhang
Figure caption: BPT classification diagram for FRB hosts The gray-scale background shows the density distribution of SDSS galaxies with redshifts 0.02 < z < 0.4. The dashed and dotted black lines represent the demarcation line between SF galaxies and AGNs (Kauffmann et al. 2003), and AGN and LINERs (Cid Fernandes et al. 2010), respectively. The majority of FRB hosts are SF and LINER galaxies. Top and sideways inset present the histograms of the ratio of [N II]/Hα and [O III]/Hβ, respectively, for FRBs (black), AGNs (red), SF (light blue), and LINER (orange) galaxies. We note that a smaller sample of FRBs is shown in this plot, due to either lack of available spectral line data or the nondetection of required emission lines.
Paper: Characterizing the Fast Radio Burst Host Galaxy Population and its Connection to Transients in the Local and Extragalactic Universe
Authors: Shivani Bhandari, Kasper E. Heintz, Kshitij Aggarwal, Lachlan Marnoch, Cherie K. Day, Jessica Sydnor, Sarah Burke-Spolaor, Casey J. Law, J. Xavier Prochaska, Nicolas Tejos, Keith W. Bannister, Bryan J. Butler, Adam T. Deller, R. D. Ekers, Chris Flynn, Wen-fai Fong, Clancy W. James, T. Joseph W. Lazio, Rui Luo, Elizabeth K. Mahony, Stuart D. Ryder, Elaine M. Sadler, Ryan M. Shannon, JinLin Han, Kejia Lee, Bing Zhang
Figure caption: Approximation of the stellar density in blue and red galaxies as presented by blue and red contours, respectively, and derived from the PRIMUS galaxies in the color–magnitude diagram for the two redshift bins. The shading from white to respective color represent outer to inner contour levels. The contour levels are set the same for blue and red galaxies. The bright red region shows that most of the stars in the local universe are concentrated in massive red galaxies. Green and magenta data points are the sample of one-off and repeating FRB hosts, respectively. We observe that FRBs are distributed in normal blue galaxies.
Paper: Characterizing the Fast Radio Burst Host Galaxy Population and its Connection to Transients in the Local and Extragalactic Universe
Authors: Shivani Bhandari, Kasper E. Heintz, Kshitij Aggarwal, Lachlan Marnoch, Cherie K. Day, Jessica Sydnor, Sarah Burke-Spolaor, Casey J. Law, J. Xavier Prochaska, Nicolas Tejos, Keith W. Bannister, Bryan J. Butler, Adam T. Deller, R. D. Ekers, Chris Flynn, Wen-fai Fong, Clancy W. James, T. Joseph W. Lazio, Rui Luo, Elizabeth K. Mahony, Stuart D. Ryder, Elaine M. Sadler, Ryan M. Shannon, JinLin Han, Kejia Lee, Bing Zhang
Figure caption: Top: comparison of FRB cumulative mass distribution with mass-weighted stellar mass distribution for all field galaxies (brown), star-forming galaxies (blue) and quiescent galaxies (red). Middle: comparison of FRB host cumulative SFR distributions (black) with the star formation rate volume density distribution function for all field galaxies in the local universe (brown). Bottom: comparison of sSFRs for FRBs in the mass range ﹩9.5\lt \mathrm{log}(M)\lt 11.5﹩ with the log-normal (solid) and double exponential functions (dashed) for sSFRs of main-sequence star-forming galaxies.
Paper: Characterizing the Fast Radio Burst Host Galaxy Population and its Connection to Transients in the Local and Extragalactic Universe
Authors: Shivani Bhandari, Kasper E. Heintz, Kshitij Aggarwal, Lachlan Marnoch, Cherie K. Day, Jessica Sydnor, Sarah Burke-Spolaor, Casey J. Law, J. Xavier Prochaska, Nicolas Tejos, Keith W. Bannister, Bryan J. Butler, Adam T. Deller, R. D. Ekers, Chris Flynn, Wen-fai Fong, Clancy W. James, T. Joseph W. Lazio, Rui Luo, Elizabeth K. Mahony, Stuart D. Ryder, Elaine M. Sadler, Ryan M. Shannon, JinLin Han, Kejia Lee, Bing Zhang
Figure caption: SED models for the host galaxies of FRB20180301A, FRB20191228A, FRB20200906A, FRB20190714A. The best-fit SED models from CIGALE are shown as solid black lines, the observed magnitudes (corrected for Galactic extinction and converted into fluxes) as blue squares, and the model fluxes as red dots. In all models, the redshift has been fixed to the redshift of respective host galaxies.
Paper: Characterizing the Fast Radio Burst Host Galaxy Population and its Connection to Transients in the Local and Extragalactic Universe
Authors: Shivani Bhandari, Kasper E. Heintz, Kshitij Aggarwal, Lachlan Marnoch, Cherie K. Day, Jessica Sydnor, Sarah Burke-Spolaor, Casey J. Law, J. Xavier Prochaska, Nicolas Tejos, Keith W. Bannister, Bryan J. Butler, Adam T. Deller, R. D. Ekers, Chris Flynn, Wen-fai Fong, Clancy W. James, T. Joseph W. Lazio, Rui Luo, Elizabeth K. Mahony, Stuart D. Ryder, Elaine M. Sadler, Ryan M. Shannon, JinLin Han, Kejia Lee, Bing Zhang
Figure caption: A zoomed in version of the optical spectrum of the host of FRB20180301A, FRB20191228A, FRB20200906A and FRB20200430A.
Paper: Characterizing the Fast Radio Burst Host Galaxy Population and its Connection to Transients in the Local and Extragalactic Universe
Authors: Shivani Bhandari, Kasper E. Heintz, Kshitij Aggarwal, Lachlan Marnoch, Cherie K. Day, Jessica Sydnor, Sarah Burke-Spolaor, Casey J. Law, J. Xavier Prochaska, Nicolas Tejos, Keith W. Bannister, Bryan J. Butler, Adam T. Deller, R. D. Ekers, Chris Flynn, Wen-fai Fong, Clancy W. James, T. Joseph W. Lazio, Rui Luo, Elizabeth K. Mahony, Stuart D. Ryder, Elaine M. Sadler, Ryan M. Shannon, JinLin Han, Kejia Lee, Bing Zhang
Figure caption: SED models for the host galaxies of FRB20190611B, FRB20190523A, and FRB20200430A.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: Distribution of a selection of physical parameters from the SOM. Each node displays the median value for that parameter as determined by the baxels that were assigned to that node. Baxels were included in the median if the reduced χ 2 values of their pPXF fits were less than 2. Note the color scale of panel (b) was clipped at 3σ of the full range.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: Each node in the SOM has a corresponding prototype. The spectra shown have been generated by sending the low-dimensional prototypes from the SOM through the decoder. The sample spectra from Figure 1 have been assigned to the nodes as indicated by the label above the box.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: Top: the distribution of M */L  r  values from nodes (9, 12) and (2, 5), which are widely separated on the DESOM. Older populations have higher mass-to-light ratios. The median values are 1.26 ± 0.51 and –0.60 ± 0.29, respectively. Bottom: the distribution of M */L  r  in nodes are closer together on the map (10, 5) and (8, 6), with median values 0.86 ± 0.52 and 0.51 ± 0.41, respectively.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: By assigning all baxel spectra from the MaNGA galaxy 1-114956 to the SOM, we can summarize this galaxy. The number above each decoded prototype indicates how many spectra have been assigned to the node. These decoded prototypes have been colored by mean half-light radius.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: As an example, 341 spectra of a galaxy are reduced to a width of 256 by an autoencoder. The 341 spectra are distributed on the map as a unique pattern being the fingerprint for that galaxy. The unique patterns of all galaxies in the survey can be fed into another DESOM. The output of this DESOM is a distribution of galaxies clustered by similarity. This example galaxy is assigned to the top middle edge of DESOM-2, with an image shown.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: A selection of spectra from the MaNGA data set. These were selected to show the range of a few physical parameters as estimated by full spectrum fitting by pPXF.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: The architecture of our autoencoder component uses convolutional blocks. The encoder embeds data to the latent layer. The decoder takes the latent layer and attempts to reconstruct the original input.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: Three template spectra for the indicated nodes. Templates were created by co-adding spectra that were assigned to the indicated node. The spectra were first normalized following Section 2 before taking the mean value.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: Decoded prototype galaxies of DESOM-2. DESOM-2 assigns each galaxy to one of these nodes based on how similar the fingerprint is to the prototype. Elliptical galaxies have spectral characteristics that cause the fingerprint to be clustered into the bottom right corner (see Figure 11).
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: Each of the 4609 galaxies is assigned a node on DESOM-2. Here we show one randomly selected galaxy from each node to show the general characteristic of that node. The number of galaxies in each node can be found in Figure 9.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: Three nodes have been selected for additional scrutiny. The first three columns are from node (6, 1), the next three columns are from node (3, 1) and finally from (7, 9).
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: The number of galaxies assigned to each node of DESOM-2 show the map is well utilized.
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: We select a morphology classification and show the proportion of galaxies in each node that are assigned that classification. Morphology information is from Hernández-Toledo et al. (2010).
Paper: Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
Authors: Hossen Teimoorinia, Finn Archinuk, Joanna Woo, Sara Shishehchi, Asa F. L. Bluck
Figure caption: Sérsic and b/a values for each node of DESOM-2 based on the galaxies that were assigned there.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: The dynamic spectrum of the 6.67 GHz methanol maser emission in G358.93−0.03 for the period 2019 January–July. The black dotted lines indicate flares of the methanol and water masers. The red lines indicate the dates of the VLA epoch I and II observations (M2O data: the HartRAO 26 m telescope monitoring program).
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: The 6.67 GHz CH3OH maser emission detected in G358.93−0.03 with the VLA. Left panels: VLA images of the brightest 6.67 GHz CH3OH maser emission spots detected at (a) epoch I and (b) epoch II. Yellow plus signs mark the peaks of maser emission (maser spots). (c) Superimposed 6.67 GHz methanol maser spot maps and spectra (the markers on the spectra correspond to the maser spots on the map) from epochs I and II. Plots are color-coded by radial velocity (see color bar for color scale). The error bars indicate the position fitting errors from Table 5.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: The 6.18 GHz CH3OH maser emission detected in G358.93−0.03 with the VLA. (a) VLA image of the brightest 6.18 GHz CH3OH maser emission spot detected at epoch II. The yellow plus sign marks the fitted peak of maser emission. (b) 6.18 GHz methanol maser spot map and spectrum (the marker on the spectrum corresponds to the maser spot on the map). Plots are color-coded by radial velocity (see color bar for color scale). The gray error bar indicates the position fitting errors from Table 4.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Same as Figure 3, but for the 23.12 GHz CH3OH maser.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Continuum images of G358.93−0.03 at 20 GHz at the first (left panels) and second (right panels) VLA epochs. Contour levels are [4, 6, 8, 10] × 10 μJy beam−1. The yellow plus sign indicates the detected peak(s) of continuum emission at a certain frequency. The yellow star marks the position of MM1 from Brogan et al. (2019). The synthesized beam size of an image is shown with a black ellipse in the lower left corner of each panel.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Same as Figure 2, but for the 12.23 GHz CH3OH maser.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Same as Figure 2, but for the 20.97 GHz CH3OH maser.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Same as Figure 3, but for the 12.18 GHz CH3OH maser.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: The centimeter (this work) and millimeter (Brogan et al. 2019) continuum peak position for MM1. The K-band peak detected with the VLA at epoch I is shown in red. The gray error bars indicate the position fitting error from Table 3.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Overplot of the methanol maser features detected in G358.93−0.03 at VLA epochs I (blue markers) and II (red markers). The histograms show the number of the detected maser spots relative to R.A. and decl. at VLA epochs I (blue) and II (red). The histogram bin size is 0.″02. The dotted blue and dashed red lines correspond to the maximums of the maser spot density at VLA epochs I and II, correspondingly. The dotted circle marks the distance at which the maximum density of the methanol maser emission was found at epoch I, and the dashed circle marks the distance for epoch II.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Comparison of spatial distribution and spectra of the methanol maser emission detected in G358.93−0.03 at the (a) flare (VLA epoch I) and (b) post-flare (VLA epoch II) epochs. Top panels: combined maser spot maps. Plots are color-coded by radial velocity (see color bar for color scale). Bottom panels: spectra of the maser emission.
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Comparison of spatial distribution of the methanol maser emission detected in G358.93−0.03 with the VLA on 2019 March 24 (Chen et al. 2020b) and (a) the VLA on 2019 February 25 (this paper; epoch I) and (b) the VLA on 2019 June 4 (this paper; epoch II).
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Comparison of the spatial distribution of the methanol maser emission detected in G358.93−0.03 with the VLA on 2019 February 25 (this paper; epoch I) and LBA on 2019 February 28 (Burns et al. 2020).
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Comparison of spatial distribution of the methanol maser emission detected in G358.93−0.03 with (a) the VLA on 2019 February 25 (this paper; epoch I) and SMA on 2019 March 14 (Brogan et al. 2019) and (b) the VLA on 2019 June 4 (this paper; epoch II) and ALMA on 2019 April 16 (Brogan et al. 2019).
Paper: A Multitransition Methanol Maser Study of the Accretion Burst Source G358.93–0.03-MM1
Authors: O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen, T. R. Hunter, S. E. Kurtz, G. C. MacLeod, A. M. Sobolev, K. Sugiyama, I. E. Val'tts, Y. Yonekura
Figure caption: Comparison of the single-dish and cross-correlation VLA spectra of the 6.7 GHz methanol maser emission detected in G358.93−0.03 (a) on 2019 February 25 (epoch I) and (b) on 2019 June 4 (epoch II). The single-dish data are obtained in the iMet program.
Paper: An Algorithm to Calibrate and Correct the Response to Unpolarized Radiation of the X-Ray Polarimeter Onboard IXPE
Authors: John Rankin, Fabio Muleri, Allyn F. Tennant, Matteo Bachetti, Enrico Costa, Alessandro Di Marco, Sergio Fabiani, Fabio La Monaca, Paolo Soffitta, Antonino Tobia, Alessio Trois, Fei Xie, Luca Baldini, Niccolò Di Lalla, Alberto Manfreda, Stephen L. O'Dell, Matteo Perri, Simonetta Puccetti, Brian D. Ramsey, Carmelo Sgrò, Martin C. Weisskopf
Figure caption: Comparison of the first term (Observation) and second term (Spurious map) of the uncertainty expression (Equation (41)) as a function of the number of events of the observation. It can be seen that the second term is often negligible. Also plotted is the total quadratic sum of the two terms, which tends to an asymptotic value that is the residual due only to calibration. This example is at 2.7 keV with 7·106 events in the spurious modulation map.
Paper: An Algorithm to Calibrate and Correct the Response to Unpolarized Radiation of the X-Ray Polarimeter Onboard IXPE
Authors: John Rankin, Fabio Muleri, Allyn F. Tennant, Matteo Bachetti, Enrico Costa, Alessandro Di Marco, Sergio Fabiani, Fabio La Monaca, Paolo Soffitta, Antonino Tobia, Alessio Trois, Fei Xie, Luca Baldini, Niccolò Di Lalla, Alberto Manfreda, Stephen L. O'Dell, Matteo Perri, Simonetta Puccetti, Brian D. Ramsey, Carmelo Sgrò, Martin C. Weisskopf
Figure caption: Examples of spurious modulation maps for Stokes parameters q and u at 2.7 keV. The central region is smoother because it was calibrated with a larger statistics. It is also worth noting that values at the border are biased because photoelectric tracks are truncated by the physical size of the ASIC used for reading out the signal. The bias is such that the angle is biased to 90° for vertical edges and to 0° for horizontal edges. q is defined as the cosine of twice such angle (Equation (2)), and therefore, values are biased to ﹩\sim \cos (2\cdot 90^\circ )﹩ and ﹩\sim \cos (2\cdot 0^\circ )﹩. u (Equation (3)), instead, is ﹩\sim \sin (2\cdot 90^\circ )﹩ and ﹩\sim \sin (2\cdot 0^\circ )﹩, and therefore the bias is around 0.
Paper: An Algorithm to Calibrate and Correct the Response to Unpolarized Radiation of the X-Ray Polarimeter Onboard IXPE
Authors: John Rankin, Fabio Muleri, Allyn F. Tennant, Matteo Bachetti, Enrico Costa, Alessandro Di Marco, Sergio Fabiani, Fabio La Monaca, Paolo Soffitta, Antonino Tobia, Alessio Trois, Fei Xie, Luca Baldini, Niccolò Di Lalla, Alberto Manfreda, Stephen L. O'Dell, Matteo Perri, Simonetta Puccetti, Brian D. Ramsey, Carmelo Sgrò, Martin C. Weisskopf
Figure caption: Rotation of the source to decouple the contribution to polarization due to spurious modulation and due to the polarization of the source. A rotation of 90°, from the point of view of the detector, changes the sign of the Stokes parameters of the source but not of spurious modulation. For this reason, all calibration measurements are taken at two orthogonal rotation angles.
Paper: An Algorithm to Calibrate and Correct the Response to Unpolarized Radiation of the X-Ray Polarimeter Onboard IXPE
Authors: John Rankin, Fabio Muleri, Allyn F. Tennant, Matteo Bachetti, Enrico Costa, Alessandro Di Marco, Sergio Fabiani, Fabio La Monaca, Paolo Soffitta, Antonino Tobia, Alessio Trois, Fei Xie, Luca Baldini, Niccolò Di Lalla, Alberto Manfreda, Stephen L. O'Dell, Matteo Perri, Simonetta Puccetti, Brian D. Ramsey, Carmelo Sgrò, Martin C. Weisskopf
Figure caption: Modulation curves for polarized (left), unpolarized at 5.9 keV (upper right), and unpolarized at 2.3 keV (bottom right) radiation. For polarized radiation, a modulation is seen, which is absent for unpolarized radiation at high energies but reappears at low energies as spurious modulation.