Optically targeted search for gravitational waves emitted by core-collapse supernovae during the third observing run of Advanced LIGO and Advanced Virgo (2024)

Authorization Required

×

×

• 

  Figure 1

  Sky locations of CCSNe analyzed in this paper. All were recorded within 30Mpc during the third observing run of LIGO, Virgo, and KAGRA.

  Reuse & Permissions

• 

  See Also

  Call Centre Aps Jobs in All Adelaide SA - Sep 2024 | SEEK

  Figure 2

  Visual representation of the on-source windows (see Sec.2b), the data coverage for each detector, and the detector duty factors (percentage of available data inside the on-source window). These windows are plotted with respect to the discovery time $t_{\mathrm{disc}}$, and the brackets show CCSN discovery dates in UTC. The plotted interferometers (IFO) are LIGO H1 and LIGO L1.

  Reuse & Permissions

• 

  Figure 3

  The shock breakout estimation using quadratic (second degree) and quartic (fourth degree) polynomials. Because shock breakout was observed for KSN 2011a [68, 69, 70, 71], it allows one to test the usage of the polynomial interpolations in a case when a CCSN is discovered up to a few days after the shock breakout. While a quartic fit is reliable, the quadratic fit introduces biases.

  Reuse & Permissions

• 

  Figure 4

  The estimation of the shock breakout, $t_{\mathrm{SBO}}$, for SNe 2019ehk and 2020oi was performed with the quartic interpolation. The r-band magnitudes, including the preshock breakout values, are public and taken from Refs.[44, 72, 73, 74]. The quartic polynomial coefficients are determined with a chi-square minimization. The estimated time of the SBO (marked by the vertical lines) is determined by the intersection of the curves with the average pre-SBO values. The uncertainty in the time of the SBO is determined from the standard deviation of the estimated SBO times if we perform the interpolations for the data randomized within the telescope uncertainties provided for each data point.

  Reuse & Permissions

• 

  Figure 5

  SN 2020fqv loudest event with a $2.8\sigma$ detection significance. The data quality investigations show that this event is most likely of an instrumental origin. The pixel magnitudes are squared network signal-to-noise ratio.

  Reuse & Permissions

• 

  Figure 6

  The detection efficiency as a function of distance for SN 2019ejj. The numbers in the brackets are distances at 50% detection efficiencies. Horizontal dashed lines show 10%, 50%, and 90% detection efficiencies. Left panel shows the efficiencies for 12 CCSN models derived from multidimensional CCSN simulations. As references, the Galactic Center and Large Magellanic Cloud distances are plotted. Right panel provides the detection efficiencies for the extreme emission long-lasting bar mode model. Some models are reaching the distance of SN 2019ejj. Given a null detection, it allows one to exclude parameter spaces of this extreme emission model as discussed in Sec.5.

  Reuse & Permissions

• 

  Figure 7

  The upper limits on the GW energy ($E_{\mathrm{GW}}$) and luminosity (or power, $P_{\mathrm{GW}}$) emitted by a CCSN engine. The shaded region contains combined results from all analyzed CCSNe. The tightest results are obtained for SN 2019ejj. At 50Hz the stringent energy constraints are ${10}^{-4}{M}_{\odot }{c}^2$ for signals 1–100ms. The best upper limits for GW luminosity are $5\times {10}^{-4}{M}_{\odot }{c}^2/\mathrm{s}$ for signals at 50Hz and 1s long. Our results are around 2 times less stringent than those obtained with SN 2017eaw [20]. The $E_{\mathrm{GW}}$ upper limits are still much higher than those derived from numerical simulations [14].

  Reuse & Permissions

• 

  Figure 8

  The upper limits on the PNS ellipticity. Assuming a principal canonical moment of inertia for neutron stars, $I_{zz}={10}^{45}{\mathrm{g}\mathrm{cm}}^2$, the stringent upper limits on the ellipticities are down to around 3 at 2kHz.

  Reuse & Permissions

• 

  Figure 9

  Model exclusion probability $P_{\mathrm{excl}}$ for long-lasting bar mode instability model. The numbers are calculated by accumulating results from CCSNe in O1, O2, and O3. The GW emissions from bars with $\epsilon =10$ are excluded at almost 100%confidence above 900Hz for $\tau =1\mathrm{s}$ and $\tau =0.1\mathrm{s}$. The probabilities decrease with signal ellipticities and durations. The emissions with the ellipticity of 0.1 and $\tau =1\mathrm{s}$ are excluded up to around 50%.GW emission with $\tau =1\mathrm{ms}$ cannot yet be excluded.

  Reuse & Permissions

• 

  Figure 10

  The constraint of the PNS ellipticity for the long-lasting bar model using a population of the CCSNe analyzed in this and the previous [20] search. The best constraints are obtained for long signals and GW emission at 2kHz. These observational ellipticity constraints are around an order of magnitude less stringent than the ellipticities of the CCSN simulations [121, 122].

  Reuse & Permissions

• 

  Figure 11

  The physical frequency-dependent calibration errors for magnitude, panels (a) and (b), and phase, panels (c) and (d), for H1 and L1, respectively. These examples correspond to GPS times of the worst calibration errors during O3. The dashed lines in panels (a) and (b)show the amplitude calibration errors used in the previous all-sky search [144]. The dashed lines in panels (c) and (d)show the induced phase calibration errors when using a time jittering of 5ms and 10ms as indicated by the green and orange curves, respectively. When compared to the realistic calibration curves, these two methods yield estimates for the calibration errors that are nonrepresentative of the magnitude or frequency evolution of possible physical calibration errors. The realistic calibration errors are found to be negligible with respect to the previously used ones.

  Reuse & Permissions

×