pith. machine review for the scientific record. sign in

arxiv: 2604.04831 · v1 · submitted 2026-04-06 · 🌌 astro-ph.GA

Recognition: 2 theorem links

· Lean Theorem

The Delay Time Distribution of Tidal Disruption Events

Margaret Shepherd , K. Decker French , Nicholas C. Stone , Nicholas Earl , Denyz Melchor , Teddy R. Smith , Jean Somalwar , Odelia Teboul
show 1 more author
Margaret E. Verrico
Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:12 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords tidal disruption eventsdelay time distributionpost-starburst galaxiesstar formation historysupermassive black holesstellar population ages
0
0 comments X

The pith

Tidal disruption events occur most often about one billion years after a star formation burst in their host galaxies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds the delay time distribution for tidal disruption events by dating the most recent starburst in 41 host galaxies through modeling of their optical spectra. It shows that the TDE rate rises with time since the burst and reaches a maximum near 1 Gyr when compared with a control sample of galaxies. This timing pattern helps distinguish among proposed mechanisms that could make TDEs more common in post-starburst systems. Readers would care because the result constrains how stellar orbits and central black holes evolve together after a galaxy's star-forming activity declines.

Core claim

We compile a catalog of 41 TDE host galaxies with optical spectra, model the stellar populations with Bagpipes, and retrieve the age of the most recent burst of star formation to construct the delay time distribution of TDEs. We find that the TDE rate increases with post-burst age to reach a peak at ~1 Gyr relative to a control sample. We compare the observational TDE delay time distribution to theoretical models, which propose overdense stellar nuclei, radial anisotropies in stellar orbits, supermassive black hole binaries, and AGN disks as potential mechanisms that may enhance the TDE rate in post-starburst galaxies. Most models predict a TDE rate that declines with post-burst age, in the

What carries the argument

The observational delay time distribution of TDEs, built by recovering the age of each host galaxy's most recent star-formation burst from Bagpipes fits to optical spectra.

If this is right

  • TDEs are more frequent in post-starburst galaxies than in the general galaxy population.
  • The TDE rate climbs steadily after a starburst and reaches its highest value near 1 Gyr.
  • Most theoretical models for boosted TDE rates predict a steady decline with post-burst age, which does not match the data.
  • The supermassive black hole binary model is consistent with the observations at old burst ages, while the stellar overdensity model is consistent at intermediate ages.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The observed rise and peak suggest that the mechanism increasing the TDE rate requires roughly a billion years to reach full strength, possibly through gradual orbital diffusion or binary hardening.
  • If the distribution is confirmed, TDE detections could serve as a clock for estimating how long ago a galaxy experienced its last major starburst.
  • Larger samples at very young and very old post-burst ages would allow a direct test of whether the rate truly turns over after 1 Gyr.

Load-bearing premise

The spectral modeling accurately recovers the true age of the most recent star-formation burst in each TDE host without large systematic bias from dust, metallicity, or burst-duration assumptions.

What would settle it

Finding many TDEs in galaxies whose most recent starburst is either much younger than 500 million years or much older than 2 billion years would contradict the reported peak near 1 Gyr.

Figures

Figures reproduced from arXiv: 2604.04831 by Denyz Melchor, Jean Somalwar, K. Decker French, Margaret E. Verrico, Margaret Shepherd, Nicholas C. Stone, Nicholas Earl, Odelia Teboul, Teddy R. Smith.

Figure 1
Figure 1. Figure 1: Stellar mass versus redshift of the TDE host galaxy sample and the control sample. The control sample was selected to match the redshift and stellar mass distri￾bution of the TDE host galaxy sample based on how many TDE hosts fell within “cells” of this parameter space. 2,226 (42 × 53) galaxies in the expanded control sample. 53 control galaxies per TDE host galaxy is the maximum number of control galaxies… view at source ↗
Figure 2
Figure 2. Figure 2: Top: Stellar mass versus redshift of the PSB/QBS subset of the TDE host galaxy sample and the PSB/QBS control sample. The PSB/QBS control sample was selected to roughly match the redshift and stellar mass distribution of the PSB/QBS TDE hosts. The cell method used in [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Cumulative distribution function of the time elapsed since a burst of star formation in the entire TDE host galaxy sample and the control sample. The Anderson￾Darling 2-sample test shows with a significant result that the two samples are not drawn from the same population (p-value < 0.001). forming galaxies with young burst ages, we see a popu￾lation of galaxies with burst fractions < 1%, for which low-lev… view at source ↗
Figure 4
Figure 4. Figure 4: Burst mass fraction versus age of the burst. Left: The control sample galaxies are shown in orange dots and the TDE host galaxies are shown in shades of blue, with different symbols and colors for different galaxy type labels. The dashed line shows the minimum burst mass fraction needed to be considered having a “high” burst mass fraction (1%), above which all the PSB TDE host galaxies and most of the QBS … view at source ↗
Figure 5
Figure 5. Figure 5: Top left: A cumulative distribution function showing the burst ages for the TDE host galaxies that have had a significant burst (using the criteria described in Section 4.1) and for the control sample galaxies. Bottom left: The rate and rate enhancement of TDEs in galaxies that have recently had a significant burst as a function of burst age. There is a peak in the TDE rate at ∼1 Gyr because very few contr… view at source ↗
Figure 6
Figure 6. Figure 6: This plot shows the same data as [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Top: A cumulative distribution function show￾ing the burst mass fractions for the entire TDE host galaxy sample and for the control sample. Bottom: The rate en￾hancement of TDEs as a function of burst mass fraction. The full TDE sample is used and it is normalized by the control sample. The gray shaded region denotes burst mass fractions below 10−2 , which have quite large error bars, so conclusions should… view at source ↗
Figure 8
Figure 8. Figure 8 [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparisons between our measured DTDs and stellar overdensity models from Stone et al. (2018) (top left), Teboul & Perets (2025) (top right), and Bortolas (2022) (bottom) for the TDE rate as a function of burst age. Top left: Stone et al. (2018) model a stellar density profile with a range of initial power law slopes. Top right: Teboul & Perets (2025) model a stellar density profile with different initial … view at source ↗
Figure 10
Figure 10. Figure 10: Comparisons between our measured DTDs and radial anisotropy models from Stone et al. (2018) (top) and Teboul & Perets (2025) (middle, bottom) for the TDE rate as a function of burst age. Top: Stone et al. (2018) model a galaxy nucleus where the stellar orbits have a range of anisotropy values. Middle: The TDE rate as a function of burst age, using the subset of 23 galaxies with measured black hole masses,… view at source ↗
Figure 11
Figure 11. Figure 11 [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Comparison between our measured DTDs and AGN disk models from Wang et al. (2024a) for the TDE rate as a function of burst age. The theoretical models predict the TDE rate in a system where interactions with an AGN disk briefly increase the TDE rate. Wang et al. (2024a) vary the viscosity parameter α and the efficiency parameter ϵ. The models do not match either DTD, but they do show a brief peak in the TD… view at source ↗
Figure 13
Figure 13. Figure 13: The TDE rate as a function of burst age for a volume-limited subset of TDE host galaxies normalized by a volume-limited subset of the control sample. There is no burst mass fraction cutoff implemented for this subset. Also shown is the DTD for galaxies we determine to have experienced a significant burst. The DTD for the volume￾limited subset of TDE host galaxies shows similarities to the DTD of galaxies … view at source ↗
Figure 14
Figure 14. Figure 14: Left: Dust attenuation for the TDE host galaxy sample and the sample of control galaxies. The distributions do not show any statistically significant differences, showing that TDE host galaxies are not preferentially dust-free, which could lead to biases. Right: Dust attenuation versus burst age for the TDE host galaxy sample and the sample of control galaxies. Dust and burst age are slightly negatively c… view at source ↗
Figure 15
Figure 15. Figure 15: Rest-frame u − r colors vs stellar mass values of TDE host galaxies (left), control sample galaxies (right), and WISE-selected TDE host galaxies from Masterson et al. (2024). We assume the stellar mass values of the TDE host galaxies and control sample galaxies have a characteristic uncertainty of 0.1 dex. The TDE host galaxies and control sample galaxies are colored according to their AV dust attenuation… view at source ↗
Figure 16
Figure 16. Figure 16: Histograms of stellar masses of the TDE host galaxies and (left) the subset of TDE host galaxies that have experienced a significant burst of star formation and (right) the subset of PSB TDE host galaxies. A KS test determines that the population of PSB TDE host galaxies is significantly different compared to the parent TDE host galaxy sample, where the PSB TDE host galaxies have stellar masses at the low… view at source ↗
Figure 17
Figure 17. Figure 17: Stellar mass versus black hole mass for the TDE host galaxies with black hole masses available. The solid, dotted, and dashed black lines are MBH–Mstel scaling rela￾tionships from Greene et al. (2020). The error bars on the stellar mass values are 0.1 dex, the assumed standard devia￾tion of the sample values. The subset of TDE host galaxies that have experienced a significant burst of star formation and t… view at source ↗
read the original abstract

Tidal disruption events (TDEs) can be observed when stars get too close to supermassive black holes and are torn apart and accreted. The delay time distribution of TDEs, or rate of TDEs as a function of time since a burst of star formation, can be used to determine what mechanisms influence the TDE rate. We compile a catalog of 41 TDE host galaxies with optical spectra, model the stellar populations with Bagpipes, and retrieve the age of the most recent burst of star formation to construct the delay time distribution of TDEs. TDEs occur more frequently in post-starburst galaxies than in other types of galaxies, though the mechanism causing this rate enhancement is unknown. We find that the TDE rate increases with post-burst age to reach a peak at ~1 Gyr relative to a control sample. We compare the observational TDE delay time distribution to theoretical models, which propose overdense stellar nuclei, radial anisotropies in stellar orbits, supermassive black hole binaries, and AGN disks as potential mechanisms that may enhance the TDE rate in post-starburst galaxies. Most models predict a TDE rate that declines with post-burst age, in contrast to our observational results, though some models are still feasible at certain ages (e.g., the black hole binary model matches at old burst ages and the stellar overdensity model matches at intermediate burst ages).

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper compiles a sample of 41 TDE host galaxies with optical spectra, models their stellar populations using Bagpipes to recover the age of the most recent star-formation burst, and constructs an observational delay time distribution (DTD) for TDEs. Relative to a control sample, the TDE rate is found to increase with post-burst age and peak near 1 Gyr. The authors compare this DTD to theoretical models invoking stellar overdensities, orbital anisotropies, SMBH binaries, and AGN disks, noting that most models predict a declining rate with age while the data show the opposite trend.

Significance. If the burst-age assignments are robust, the result supplies a direct observational constraint on the TDE DTD that can discriminate among proposed enhancement mechanisms in post-starburst galaxies. The work also demonstrates a practical route for using existing spectroscopic surveys to map TDE rates as a function of stellar population age.

major comments (3)
  1. [§3] §3 (Stellar population modeling): The manuscript does not quantify the impact of dust-metallicity-burst-duration degeneracies on the recovered burst ages. Post-starburst spectra are known to be sensitive to these parameters; systematic shifts of 0.3–0.5 dex in log(age) would move hosts between the age bins used to construct the DTD and could erase or invert the reported ~1 Gyr peak. A set of robustness tests (varying dust law, metallicity grid, and burst duration) or posterior predictive checks against mock spectra is needed to establish that the functional form of the DTD is not an artifact of modeling assumptions.
  2. [§4] §4 (DTD construction and control sample): The control sample is modeled with the identical Bagpipes pipeline, yet no differential-bias test is presented. If the TDE hosts and controls differ systematically in dust content or metallicity, the relative rate enhancement could be partly driven by modeling systematics rather than astrophysics. Explicit comparison of the age distributions and a jackknife or bootstrap assessment of bin-to-bin uncertainties are required to support the claim that the observed DTD shape is statistically significant.
  3. [§5] §5 (Model comparison): The statement that “most models predict a TDE rate that declines with post-burst age” is presented without quantitative overlays of the model predictions on the observed DTD (including uncertainties). Without a figure or table showing the model curves normalized to the same control sample and with the same age binning, it is difficult to assess which models are truly ruled out versus merely disfavored at certain ages.
minor comments (3)
  1. [Abstract, §1] The abstract and §1 cite 41 TDE hosts but do not state the final number after quality cuts or the redshift range; this information should be added for reproducibility.
  2. [Figure 3] Figure 3 (or equivalent DTD plot) would benefit from error bars derived from Poisson statistics or bootstrap resampling rather than only the control-sample normalization.
  3. [§1] A few references to recent TDE host studies (e.g., on post-starburst fractions) appear to be missing from the introduction; adding them would strengthen the context.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments. We address each major point below and will revise the manuscript accordingly to improve the robustness and clarity of our results.

read point-by-point responses

  1. Referee: [§3] §3 (Stellar population modeling): The manuscript does not quantify the impact of dust-metallicity-burst-duration degeneracies on the recovered burst ages. Post-starburst spectra are known to be sensitive to these parameters; systematic shifts of 0.3–0.5 dex in log(age) would move hosts between the age bins used to construct the DTD and could erase or invert the reported ~1 Gyr peak. A set of robustness tests (varying dust law, metallicity grid, and burst duration) or posterior predictive checks against mock spectra is needed to establish that the functional form of the DTD is not an artifact of modeling assumptions.

    Authors: We agree that the current manuscript lacks explicit quantification of these degeneracies. Although Bagpipes was run with standard settings appropriate for post-starburst systems, we did not perform the requested robustness tests. In the revised version we will add a dedicated subsection (or appendix) containing: (i) re-fits varying the dust attenuation law, metallicity grid, and burst-duration prior; (ii) posterior predictive checks on mock spectra with known input ages; and (iii) a demonstration that the recovered burst ages and the resulting DTD shape remain stable within the adopted age bins. These additions will directly address the concern that the ~1 Gyr peak could be an artifact. revision: yes

  2. Referee: [§4] §4 (DTD construction and control sample): The control sample is modeled with the identical Bagpipes pipeline, yet no differential-bias test is presented. If the TDE hosts and controls differ systematically in dust content or metallicity, the relative rate enhancement could be partly driven by modeling systematics rather than astrophysics. Explicit comparison of the age distributions and a jackknife or bootstrap assessment of bin-to-bin uncertainties are required to support the claim that the observed DTD shape is statistically significant.

    Authors: We acknowledge that a differential-bias test was not included. In the revision we will (i) directly compare the posterior distributions of age, dust, and metallicity between the TDE-host and control samples, (ii) apply jackknife and bootstrap resampling to quantify bin-to-bin uncertainties in the DTD, and (iii) report the statistical significance of the observed rise and peak. These tests will confirm that any relative enhancement is not driven by systematic differences in the modeling. revision: yes

  3. Referee: [§5] §5 (Model comparison): The statement that “most models predict a TDE rate that declines with post-burst age” is presented without quantitative overlays of the model predictions on the observed DTD (including uncertainties). Without a figure or table showing the model curves normalized to the same control sample and with the same age binning, it is difficult to assess which models are truly ruled out versus merely disfavored at certain ages.

    Authors: We agree that a quantitative overlay is needed for a rigorous comparison. The revised manuscript will include a new figure (and accompanying table) that normalizes each theoretical model (stellar overdensity, orbital anisotropy, SMBH binary, AGN disk) to the control sample, applies the identical age binning, and displays the model curves together with the observed DTD and its uncertainties. This will allow readers to evaluate consistency or tension at each age. revision: yes

Circularity Check

0 steps flagged

No circularity: observational DTD from external Bagpipes fits and control comparison

full rationale

The paper compiles 41 TDE host spectra, applies the external Bagpipes stellar population code to recover the age of the most recent star-formation burst, bins the resulting ages, and computes the TDE rate as a function of post-burst age relative to a control sample modeled identically. This produces an empirical delay time distribution that is then contrasted with independent theoretical models. No equations, fitted parameters, or self-citations reduce the reported ~1 Gyr peak to a quantity defined by the same data or prior author work; the central result remains a direct measurement whose functional form is not forced by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the accuracy of stellar population synthesis for burst ages and on the assumption that the 41-galaxy sample is representative of TDE hosts.

axioms (1)
  • domain assumption Bagpipes stellar population synthesis models accurately recover the age of the most recent star-formation burst from optical spectra
    Invoked to assign post-burst ages to each TDE host galaxy.

pith-pipeline@v0.9.0 · 5579 in / 1225 out tokens · 50821 ms · 2026-05-10T19:12:06.198491+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. JWST and Keck observations of the off-nuclear tidal disruption event TDE 2025abcr: An evolving reprocessing layer

    astro-ph.HE 2026-04 unverdicted novelty 7.0

    New JWST and Keck data on off-nuclear TDE 2025abcr show shifting emission-line velocities from a changing reprocessing layer and an IR power-law slope of -2.13 that is consistent with either reprocessing gas or a youn...

Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · cited by 1 Pith paper

  1. [1]

    2011, , 193, 29, 10.1088/0067-0049/193/2/29

    Aihara, H., Allende Prieto, C., An, D., et al. 2011, ApJS, 193, 29, doi: 10.1088/0067-0049/193/2/29 Alexander, K. D., Berger, E., Guillochon, J., Zauderer, B. A., & Williams, P. K. G. 2016, ApJL, 819, L25, doi: 10.3847/2041-8205/819/2/L25 Almaini, O., Wild, V., Maltby, D., et al. 2025, MNRAS, 539, 3568, doi: 10.1093/mnras/staf659 Alush, Y., & Stone, N. C....

    work page doi:10.1088/0067-0049/193/2/29 2011

  2. [2]

    a l p h a _ p r i o r

    # Falling slope index , taken from Carnall +2019 a 14dblplaw [ " a l p h a _ p r i o r " ] = " log_10 " 34 Figure A1.The age of the burst modeled as an exponential function versus the age of the burst modeled as a double power law function, for all TDE hosts in the sample. The differently-colored markers represent burst mass fraction classifications in on...

    work page 2019

  3. [3]

    The subset with broad lines is similar to the fiducial rate, with a less pronounced peak at 1 Gyr. D.INFORMATIONAL TABLES 36 TDE namet burst σt M∗,burst σM∗,burst M∗,old σM∗,old M∗,burst/M∗,tot α σ α Gyr Gyr log 10(M⊙) log 10(M⊙) log 10(M⊙) log 10(M⊙) AT 2023clx 0.131 0.014 6.989 0.059 9.274 0.045 0.005 895 90 AT 2022dyt 0.077 0.006 8.245 0.027 10.425 0.0...

    work page 2023

  4. [4]

    (2015); Holoien et al

    1.65 Brimacombe et al. (2015); Holoien et al. (2016) RX J1242-A 190.65375−11.3263889 0.05 10.3 Wevers et al. (2019a)

    work page 2015

  5. [5]

    1.5 Komossa & Greiner (1999) RX J1624 246.2360833 75.9155806 0.0636 10.4 Wevers et al. (2019a)

    work page 1999

  6. [6]

    (1999) TDE2 350.9525833−1.1362056 0.2515 10.6 French et al

    1.7 Grupe et al. (1999) TDE2 350.9525833−1.1362056 0.2515 10.6 French et al. (2020)

    work page 1999

  7. [7]

    (2011) XMM J0740 115.0337083−85.6586944 0.0173 10.8 *

    1.5 van Velzen et al. (2011) XMM J0740 115.0337083−85.6586944 0.0173 10.8 *

    work page 2011

  8. [8]

    (2017) PS1-10jh 242.3678333 53.6733306 0.1696 9.2

    4.8 Saxton et al. (2017) PS1-10jh 242.3678333 53.6733306 0.1696 9.2

    work page 2017

  9. [9]

    (2012) PTF09axc 223.3045 22.2422972 0.1146 9.8

    1.0 Gezari et al. (2012) PTF09axc 223.3045 22.2422972 0.1146 9.8

    work page 2012

  10. [10]

    (2014) PTF09djl 248.4832083 30.2379583 0.184 9.9

    1.0 Arcavi et al. (2014) PTF09djl 248.4832083 30.2379583 0.184 9.9

    work page 2014

  11. [11]

    (2014) AT 2018fyk 342.56723−44.86457 0.06 9.7 WISeREP

    1.0 Arcavi et al. (2014) AT 2018fyk 342.56723−44.86457 0.06 9.7 WISeREP

    work page 2014

  12. [12]

    (2019) iPTF16fnl 7.4875417 32.8936778 0.0163 9.6

    2.0 Holoien et al. (2019) iPTF16fnl 7.4875417 32.8936778 0.0163 9.6

    work page 2019

  13. [13]

    (2016) AT 2019dsg 314.2623917 14.20440556 0.0512 10.6

    2.0 Gezari et al. (2016) AT 2019dsg 314.2623917 14.20440556 0.0512 10.6

    work page 2016

  14. [14]

    (2021) iPTF15af 132.11726 22.059315 0.079 10.3 SDSS

    2.0 Cannizzaro et al. (2021) iPTF15af 132.11726 22.059315 0.079 10.3 SDSS

    work page 2021

  15. [15]

    (2016) ASASSN-14ae 167.16716 34.097847 0.0436 9.8 SDSS

    2.0 French et al. (2016) ASASSN-14ae 167.16716 34.097847 0.0436 9.8 SDSS

    work page 2016

  16. [16]

    (2014) AT 2018bsi 123.860919 45.592208 0.051 10.6

    2.0 Holoien et al. (2014) AT 2018bsi 123.860919 45.592208 0.051 10.6

    work page 2014

  17. [17]

    2.5 [3], Reynolds (2018) AT 2019qiz 71.657851−10.2263679 0.0151 10.0

    work page 2018

  18. [18]

    (2019) F01004 15.7133333−22.3641667 0.1178 9.8 French et al

    2.5 [3], Siebert et al. (2019) F01004 15.7133333−22.3641667 0.1178 9.8 French et al. (2020)

    work page 2019

  19. [19]

    (2024) AT 2020nov 254.554042 2.117511 0.0826 10.4 Earl et al

    1.2 Heikkila (2016); Sun et al. (2024) AT 2020nov 254.554042 2.117511 0.0826 10.4 Earl et al. (2025) MOSTn/aDahiwale & Fremling (2020b) T able D1.TDE and associated host galaxy information Note—* indicates that the stellar mass was calculated from the 2MASS K-band magnitude. Galaxies with spectra from WISeREP or MOST did not have available slit dimensions

    work page 2016

  20. [20]

    Hammerstein et al. (2023)

    work page 2023

  21. [21]

    Tadhunter et al. (2021) 38 TDE name HαHαuncertainty HδHδuncertainty Broad lines Galaxy label ˚A ˚A ˚A ˚A AT 2023clx 3.37 0.35 0.52 0.39 1 SF AT 2022dyt 4.36 0.31 1.22 0.66 1 SF AT 2022bdw 9.41 0.33 1.61 0.56 1 SF AT 2021nwa 0.64 0.19 0.55 1.0 1 Quiescent AT 2020wey 0.08 0.15 2.92 0.69 1 QBS AT 2020vwl 0.04 0.2 0.2 1.22 1 Quiescent AT 2020ohl 0.68 0.06−0.9...

    work page 2021