2. The problem and motivations

This section provides a benchmark of the observational time required to spectroscopically survey all galaxies within a gravitational wave localisation area and below an apparent magnitude limit. To estimate the number of galaxies in a 1 deg $^2$ localisation area, we use the r-band luminosity function as measured by Loveday et al. (Reference Loveday2011). Figure 1 illustrates the estimated number of galaxies for a range of apparent magnitude limits, along with the corresponding exposure time required to spectroscopically observe each galaxy individually with a 4 m telescope like the Anglo-Australian Telescope (AAT; Wampler & Morton Reference Wampler and Morton1977). Childress et al. (Reference Childress2017) finds that the redshift success rate increases significantly at a signal-to-noise of 2 to 3 per 1-Å bin; we use this as the baseline SNR. Note that Figure 1 shows the total exposure time to observe each galaxy individually and thus does not take into account multiplexing, where multiple objects can be observed simultaneously during a single exposure. Additionally, these values represent only the exposure times and do not account for configuration times or field-of-view considerations.

Figure 1.

The fibre hours required on a telescope like the AAT to spectroscopically observe all galaxies individually within a one-square-degree localisation area are shown for five different apparent magnitude limits. These fibre hours represent only the exposure time and therefore exclude configuration time. The top panel presents exposure times for four combinations of signal-to-noise ratio and seeing conditions under dark time, while the bottom panel displays the same for bright time conditions. The numbers above each scatter point indicate the total number of galaxies within the localisation area that fall below the corresponding apparent magnitude limit. Note: the SNR 3, Seeing 2.0" are hidden behind the SNR 6, Seeing 1.0" points.

Using the AAT as an example, with a fibre density of 101 fibres per square degree, a 2-degree diameter field of view, and a configuration time of 40 min, observing all galaxies within a single square-degree localisation area to an apparent magnitude of $19.0$ under ideal conditions (dark time, 1.0" seeing, and SNR/Å of 3) requires $\sim6$ h. For an apparent magnitude limit of $21.0$ , corresponding to 6 671 galaxies, the total observational time increases to $\sim45$ h. In both such cases, the configuration time is the limiting factor in the number of redshifts that can be obtained.Footnote ^b

By contrast, modern instruments on 4-m-class telescopes like 4MOST (de Jong et al. Reference de Jong, McLean, Ramsay and Takami2012) and DESI (Collaboration et al. Reference Collaboration2022) are much more efficient due to their advanced multi-fibre systems and shorter configuration times. DESI, for example, has a fibre density of 621 fibres per square degree, a 3.2-diameter field of view and a configuration time of 2 min (Collaboration et al. Reference Collaboration2016). Unlike the AAT, where configuration and exposure are performed simultaneously, DESI configures and exposes sequentially. Nonetheless, its short configuration time renders this process highly effective, but still takes $\sim4.7$ h to observe all galaxies for $m\leq 21.0$ in a single square degree.Footnote ^c Thus, although DESI represents a major improvement over earlier-generation facilities and is no longer limited by configuration time, the total observing time remains substantial even for very small localisation areas. For more realistic localisation areas and deeper magnitude limits, the required observing time would increase significantly. Completing such observations would require many days of telescope time, making them both expensive and difficult to justify for time allocation. Moreover, DESI and 4MOST will, for the foreseeable future, operate exclusively as survey instruments, without the possibility of securing dedicated observing time for targeted follow-up.

This limitation in observational efficiency becomes particularly relevant in the context of host galaxy completeness for gravitational wave events. Rauf et al. (Reference Rauf, Howlett, Davis and Lagos2023) examines the binary black hole merger rate completeness as a function of distance for various r-band apparent magnitudes. For a maximum distance of 3 Gpc – encompassing all current detections with 50% localisation areas below 10 deg $^2$ – only 25–35% of binary black hole merger host galaxies would be observable with an apparent magnitude limit of 21.0.Footnote ^d Naturally, this completeness would improve for closer gravitational wave events or a higher apparent magnitude cutoff.

Even for small localisation areas and under ideal conditions, the total observation time grows rapidly as the apparent magnitude limit increases. This motivates an evaluation into whether the improved precision afforded by spectroscopic redshifts justifies the additional observing time, particularly given the potential limitations imposed by biases from photometric outliers and the effects of catalogue incompleteness on the accuracy of $H_0$ measurements.

3. Galaxy catalogues

Galaxy catalogues are essential for dark siren analyses; they provide the redshift information necessary to complement the gravitational wave’s luminosity distance, enabling the identification of potential host galaxies and a measurement of $H_0$ .

3.2 redMaGiC

To assess the reliability of photometric redshifts, we use data collected by the Dark Energy Survey, specifically the redMaGiC sample (Rozo et al. Reference Rozo2016). The redMaGiC algorithm was designed to minimise photometric redshift uncertainties in a sample of Luminous Red Galaxies. It was chosen for this analysis as a benchmark for the capabilities of current state-of-the-art and anticipated future surveys. The redMaGiC catalogue includes galaxies with both photometric and spectroscopic redshifts, primarily concentrated in the range $z\in[0.15, 0.7]$ . To ensure consistent comparisons, we filter MICEcat data to align with this interval.

After excluding outliers using sigma-clipping,Footnote ^e we establish an approximate relationship between the redMaGiC spectroscopic and photometric redshifts by binning the redshifts and computing the mean and standard deviation within each bin. The line of best fit through these standard deviations characterises the relationship, which can be seen in Figure 2. The oscillations present in Figure 2 about the best fit arise from structure present in the photometric redshift catalogues, likely due to the methods used to estimate redshifts from training data and input photometry. In particular, strong spectral features shift in and out of photometric filter bands with redshift, leading to discontinuities or artificial clustering in the estimated redshift distribution.

Figure 2.

The top panel shows the redMaGiC redshift sample of photometric redshifts minus the corresponding spectroscopic redshifts. Outliers are identified by calculating the p-value of the $\chi^2$ statistic between the photometric and spectroscopic redshift for each galaxy and removing the galaxy using the p-value as a sampling probability. The remaining redshifts are binned, each bin having a mean and standard deviation. The bottom panel shows the standard deviation of each bin, with a best fit line that relates the spectroscopic redshift to the standard deviation.

We find that the standard deviation follows roughly the relationship

(1) \begin{equation} \text{standard deviation} = \unicode{x03C3}_z(1+z_{\textrm{spec}})+A. \end{equation}

This relationship is applied in our first and third aims to generate observed redshifts from the true redshifts in MICEcat by setting $A=0$ and varying $\unicode{x03C3}_z$ . For the second aim, we use the best fit parameters to the redMaGiC data, as shown in Figure 2, with $A=-0.013$ and $\unicode{x03C3}_z=0.019$ . We then compare the results from this catalogue to those obtained when directly using the redMaGiC redshifts as the observed redshifts.

4. Statistical framework

Our analysis is based on the methodology and code provided in Gair et al. (Reference Gair2023) which we have modified for our purposes.Footnote ^f

Assuming we have a number of gravitational wave events with observed data $\{\hat{x}\}$ , by adopting a simplified approach where the observed data includes only the observed gravitational wave luminosity distances, $\{\hat{d}_L$ }, the posterior on $H_0$ can be expressed using Bayes’ theorem as

(2) \begin{equation} p(H_0 \mid\{\hat{d}_L\}) \propto \mathcal{L}(\{\hat{d}_L\} \mid H_0) \ p(H_0),\end{equation}

where $p(H_0)$ is the prior on $H_0$ , taken to be uniform over the range $H_0\in[40,100]$ km s⁻¹ Mpc⁻¹ and $\mathcal{L}(\{\hat{d}_L\} \mid H_0)$ is the likelihood of observing the data $\{\hat{d}_L\}$ given a value for $H_0$ . In this analysis, we distinguish observed from true parameters with ‘hats’ ( $\hat{\phantom{x}}$ ). To reduce ambiguity, a summary of all parameters referred to in the following framework is shown in Table 1. The likelihood of each individual luminosity distance measurement can be written as:

(3) \begin{align} \mathcal{L}(\hat{d}_L^i \mid H_0)=\frac{\iint d\Omega\ dz \ \mathcal{L}_{\textrm{GW}}(\hat{d}_L^{i} \mid d_L(z, H_0), \Omega) \ p_{\textrm{CBC}}(z,\Omega)}{\iint d\Omega\ dz \ p_{\textrm{det}}^{\textrm{GW}}(z,H_0, \Omega) \ p_{\textrm{CBC}}(z, \Omega)},\end{align}

where $\mathcal{L}_{\textrm{GW}}$ represents the likelihood of observing a luminosity distance $\hat{d}_L^i$ , given a galaxy with a true luminosity distance $d_L(z, H_0)$ . Here, $\Omega$ denotes the solid angle on the sky. Since we assume a uniform probability distribution within each localisation area – every galaxy within the cone is assigned equal weight – the angular dependence factors out, and the expression simplifies to an integral over redshift only. Future work will explore the impact of using more realistic probability maps, such as those with Gaussian profiles.

Table 1.

Summary of the parameters referred to in this framework. Their functional forms are defined throughout Section 4.

Assuming a Gaussian form for the gravitational wave likelihood, this can be expressed as:

(4) \begin{equation} \mathcal{L}_{\textrm{GW}}\left(\hat{d}_L^i \mid d_L\left(z, H_0\right)\right) = \mathcal{N}\left(d_L\left(z, H_0\right)\mid\unicode{x03C3}_{d_L}\right),\end{equation}

where $\unicode{x03C3}_{d_L}$ is the gravitational wave luminosity distance uncertainty.

As mentioned in Section 3.2, we apply a cut to the MICEcat catalogue to align with the redMaGiC sample, restricting the total possible redshift range to $z\in[0.15, 0.7]$ . This prior is likewise applied to the redshift interpolant, $p_\textrm{CBC}(z)$ .Footnote ^g Consequently, gravitational wave events in this analysis are simulated with true redshifts between $z_\textrm{lower}=0.15$ ( $d_L\approx 720$ Mpc) and $z_\textrm{upper}=0.7$ ( $d_L\approx 4\,360$ Mpc). These events are then considered detectable when their observed luminosity distance falls below a threshold of $\hat{d}_L^{\textrm{thr}} = 1\,550$ Mpc ( $z\approx0.29$ ), which aligns with the current detection capabilities of the LIGO-Virgo-KAGRA (hereafter LVK) Collaboration (The LIGO Scientific Collaboration and the Virgo Collaboration and the KAGRA Collaboration 2021). This choice of threshold is motivated by several reasons; firstly, while we anticipate a higher detection threshold in the future, we focus on the lower redshift regime as it is where we expect to get the most information on $H_0$ . Secondly, the detection threshold must remain sufficiently low to ensure that no portion of the gravitational wave likelihood (as shown in Figure 3) extends past the realm where we have redshift data. To ensure this, we restrict our gravitational wave events to a fractional luminosity distance uncertainty of 10 $\%$ , a value based on the error from the dark siren GW190814 (Abbott et al. Reference Abbott2020b) and representative of the projected error from the LVK network in upcoming years.

Figure 3.

The top panel shows the probability of detecting a gravitational wave at LIGO (given 2021 LVK capabilities) as a function of luminosity distance for two fractional luminosity distance uncertainties. In practice, the detection probability depends on the specific sensitivity of the gravitational wave detector. Unless otherwise stated, we simulate only gravitational wave events with a fractional luminosity distance uncertainty of $\unicode{x03C3}_{d_L}/d_L=10\%$ in this analysis. The dashed line at $\hat{d}_L^{\textrm{thr}}=1\,550$ Mpc represents the threshold assumed in our analysis, beyond which galaxies with higher observed luminosity distances are considered undetectable (essentially acting as a signal-to-noise cutoff). The bottom panel shows the gravitational wave likelihood for two fractional luminosity distance uncertainties. Gravitational waves can only be drawn from galaxies with true redshifts in the range $z_{\textrm{lower}}\leq z\leq z_\textrm{upper}$ .

The probability of detecting a gravitational wave can be written

(5) \begin{align} p_{\textrm{det}}^{\textrm{GW}}(z,H_0) &=\int_{-\infty}^{\infty} \Theta(\hat{d}_L ;\; \hat{d}_L^{\textrm{thr}}) \mathcal{L}_{\textrm{GW}}(\hat{d}_L \mid d_L(z, H_0)) d \hat{d}_L \end{align}

(6) \begin{align} &=\frac{1}{2}\left[1+\operatorname{erf}\left[\frac{\hat{d}_L^{\text {thr}}-d_L(z, H_0)}{\sqrt{2} \unicode{x03C3}_{d_L}}\right]\right], \end{align}

where $\Theta\left(\hat{d}_L ;\; \hat{d}_L^{\textrm{thr}}\right)$ is the Heaviside step function, which is unity when $\hat{d}_L \leq \hat{d}_L^{\textrm{thr}}$ and 0 otherwise. The detection probability and gravitational wave likelihood functions can be seen plotted as a function of luminosity distance and redshift respectively in Figure 3. From the top panel of Figure 3 as the fractional luminosity distance uncertainty approaches zero the detection probability reduces to a Heaviside step function (which can also be seen by looking at Equation 5).

Finally, the probability that a merger, that is, compact binary coalescence, CBC, will occur at redshift z is given by

(7) \begin{equation} p_{\textrm{CBC}}(z)=\frac{p_{\text {rate}}(z) p_{\text {cat}}(z)}{\int_0^{\infty} p_{\text {rate}}(z) p_{\text {cat}}(z) d z}.\end{equation}

Here, $p_{\text{cat}}$ represents the probability that a galaxy exists at a true redshift within the catalogue (however bear in mind we only have observed redshifts), and $p_{\text{rate}}(z)$ is the probability of a galaxy at that redshift hosting a compact binary coalescence. For this simplified mock analysis, we neglect the $p_{\text{rate}}(z)$ term. This choice is valid as long as the same assumption is applied consistently in both the data generation and analysis. Equation (7) thus simplifies to

(8) \begin{equation} p_{\textrm{CBC}}(z)\approx p_{\text{cat}}\big(z \mid\big\{\hat{z}^i_g\big\}\big) =\frac{1}{N_{\text{gal}}} \sum_i^{N_{\text{gal}}} p_{\text{red}}(z \mid \hat{z}_g^i), \end{equation}

where $p_{\textrm{red}}$ is the redshift posterior. If the galaxy redshifts are perfectly measured, Equation (8) reduces to a sum of Dirac delta distributions. However, we are interested in the effect of redshift uncertainties on the mean uncertainty in $H_0$ . So, $p_{\text{red}}(z \mid \hat{z}_g^i)$ represents the fact that we are not able to measure the true redshifts of galaxies but instead we have observed redshifts:

(9) \begin{equation} p_{\text{red}}\big(z \mid \hat{z}_g^i\big)=\frac{\mathcal{L}_{\text {red}}(\hat{z}_g^i \mid z) p_{\textrm{bg}}(z)}{\int dz \ \mathcal{L}_{\text{red}}(\hat{z}_g^i \mid z) p_{\textrm{bg}}(z)}. \end{equation}

Here, $p_\textrm{{bg}}(z)$ is the prior on the redshift distribution and $\mathcal{L}_{\text {red}}$ is the likelihood used to generate observed redshifts from the true redshifts of each galaxy within the catalogue,

(10) \begin{equation} \mathcal{L}_{\text {red }}\big(\hat{z}_g^i \mid z\big)=\mathcal{N}(z\mid \unicode{x03C3}_z(1+z)+A).\end{equation}

The standard deviation used in this likelihood takes the form of the relationship derived in Equation (1). A spectroscopic-like redshift is taken to have $\unicode{x03C3}_{z}=10^{-4}$ (Carr et al. Reference Carr2022), while a photometric-like redshift is assigned $\unicode{x03C3}_{z}=10^{-2}$ , which falls between the values reported by Cawthon et al. (Reference Cawthon2022) and those found in Figure 2.

In Equation (9), $p_{\textrm{bg}}(z)$ is a prior on the redshift distribution that reflects our understanding of the background distribution of galaxies. The simplest choice we can make for this prior is to be uniform in comoving volume,

(11) \begin{equation} p_{\textrm{bg}}(z)=\frac{\frac{d V_c}{d z}}{\int_0^{\infty} \frac{d V_c}{d z} d z},\end{equation}

where

(12) \begin{equation} \frac{d V_c}{d z}=\frac{4 \pi}{E(z)}\left[\frac{c}{H_0}\right]^3\left[\int_0^z \frac{d z^{\prime}}{E\left(z^{\prime}\right)}\right]^2.\end{equation}

Figure 4 shows the redshift posterior as described by Equation (9), alongside the uniform-in-comoving-volume prior and true redshift distribution. Figure 5 shows the schematic for the framework outlined in this section.

Figure 4.

Probability of a true host galaxy redshift given an ensemble of measured redshifts for a 50 deg $^2$ localisation area and a photometric-like redshift uncertainty ( $\unicode{x03C3}_{z}=10^{-2}$ ). The redshift bins show the distribution of true redshifts in the localisation area, and the dashed line shows the redshift prior (uniform-in-comoving-volume). The interpolant is given by $p_{\textrm{CBC}(z)}$ (Equation 8) for a single realisation.

Figure 5.

Schematic of the statistical framework used in this work.

5. Results: Gaussian case

We divide the results into three sections aligned with our three aims. As outlined in Section 1, our first aim is to determine whether spectroscopic redshifts with Gaussian errors provide a better constraint on $H_0$ compared to photometric redshifts with Gaussian errors and to identify the regime in which this effect is most pronounced. To achieve this, we generate observed redshifts from the true redshifts in MICEcat following Equation (1). In this simplified model, we set $A=0$ , leaving the redshift uncertainty ( $\unicode{x03C3}_z$ ) as the sole free parameter. We vary $\unicode{x03C3}_z$ from a spectroscopic- to photometric-like uncertainty ( $10^{-4}$ – $10^{-2}$ , respectively with an interim redshift uncertainty of $\unicode{x03C3}_z=10^{-3}$ ). For each combination of localisation area and redshift uncertainty, we simulate a single gravitational wave event in 200 directions, each yielding a posterior distribution for $H_0$ . The combined effect across all 200 directions is found by taking the product of the posteriors from each direction (or equivalently, the sum of the log-posteriors). The result provides a constraint on $H_0$ that reflects what could realistically be achieved in the near future, as 200 gravitational wave events become an increasingly plausible target with upcoming LVK observation runs. We then run this entire experiment for 200 realisations (i.e. across 200 different ‘universes’) to capture the variability in the posteriors as a result of Poisson noise. This approach differs from that of Gair et al. (Reference Gair2023), who generate 200 gravitational wave events along a single line-of-sight. This adjustment is motivated by practical considerations; any realistic sample will include gravitational waves from many lines-of-sight, and this has the advantage of avoiding any bias due to particular structures along one line-of-sight.

Each realisation is generated from the same sample of true redshifts (i.e. the same 200 lines-of-sight). Between each realisation, the following factors change:

1. 1. A new gravitational wave is generated (i.e. a new true luminosity distance is sampled).

2. 2. The observed parameters (specifically, $\hat{z}_g$ and $\hat{d}_L$ ) are resampled from the true parameters (redshift z and luminosity distance $d_L$ ).

Varying directions involves sampling different lines-of-sight within the MICEcat universe. Since the large-scale structure changes, it is impossible to vary directions and keep the realisation constant. Thus, the following elements vary across different directions:

1. 1. All changes from varying realisations.

2. 2. A different distribution of true redshifts (z) due to the change in large-scale structure.

Figure 6 shows how the redshift interpolant and true redshifts vary over different realisations and directions. By sampling different realisations we ensure that our results are independent of any particular noise realisation or gravitational wave event. By varying both realisations and directions, we can more accurately estimate the total uncertainty in $H_0$ by considering different patches of large-scale structure. This approach captures both the statistical uncertainty (from varying realisations) and the systematic uncertainty (due to sample variance) for any given noise realisation and direction.

Figure 6.

An illustration of how the redshift interpolant and the true redshift histogram vary across different realisations and directions. This interpolant corresponds to a localisation area of 1 deg $^2$ and $\unicode{x03C3}_z=10^{-2}$ . The purple shaded region indicates the possible values the interpolant can take, while the pink histogram shows the true redshift distribution. The left panel shows how the interpolant varies for different realisations and in a single line of sight. The right panels show how the true redshift histogram and redshift interpolant vary for three different directions.

In Figure 7, we show the resulting posteriors for each combination of localisation area and redshift uncertainty. The grey posteriors in each subplot show the the results from each of the 200 noise realisations, and the green Gaussian is generated with a mean and standard deviation given by the ensemble mean and mean uncertainty, respectively. In Figure 8, we present the ensemble mean and standard error on the mean (top panel), along with the mean uncertainty and error on the error (bottom panel) across all 200 realisations. We do find evidence of a slight offset from the true value, $H_{0,\text{true}}=70$ km s⁻¹ Mpc⁻¹, when considering the standard error on the mean. This offset, on the order of $\lesssim 0.3$ km s⁻¹ Mpc⁻¹, is attributed to the partial overlap in the lines-of-sight within the 10 and 50 deg $^2$ localisation areas, leading to shared large-scale structure. Consequently, the error on the mean is likely an underestimation in these cases. Given the small magnitude of this offset relative to the typical uncertainty from a single realisation of 200 gravitational wave events, we do not consider this bias significant. Although sampling non-overlapping cones would provide a straightforward way to mitigate this offset, the limited sky coverage of MICEcat prevents this for the two largest localisation areas, where covariance and thus error underestimation is most significant. Future work could instead address this by repeating the experiment with multiple independent simulation realisations or taking into account the covariance between overlapping lines-of-sight.

Table 2 provides a summary of the results from this analysis. We find that reducing the localisation area from 50 to 1 deg $^2$ yields a relative reduction in the mean uncertainty by a factor of ${\sim }1.8$ for spectroscopic-like redshifts and ${\sim }1.5$ for photometric-like redshifts. Spectroscopic-like redshifts consistently yield smaller mean uncertainties than photometric-like redshifts, with the greatest improvement – a factor of 1.2 reduction in uncertainty – observed in the 1 deg $^2$ localisation area. This highlights that spectroscopic redshifts are more impactful for smaller localisation areas, as there is a negligible reduction in mean uncertainty between photometric and spectroscopic redshifts for the 50 deg $^2$ localisation area.

Figure 7.

Each grey posterior represents a single noise realisation, generated from 200 distinct gravitational wave events each with their own line-of-sight direction. The black vertical line represents the true value of $H_0=70$ km s⁻¹ Mpc⁻¹. The green Gaussians show the ensemble mean and mean uncertainty across all grey posteriors. The ensemble mean and mean uncertainty are additionally annotated in the top right of each subplot (units are in km s⁻¹ Mpc⁻¹).

Figure 8.

Predicted constraints on $H_{0}$ as a function of redshift error and localisation area. In the top panel, each grey point represents the individual mean $H_0$ values from each of the 200 realisations (corresponding to the mean of each grey posterior in Figure 7). The dark green points represent the ensemble mean and standard error on the mean. In the bottom panel, the grey points represent the individual standard deviations for each realisation. The light green points show the mean uncertainty, which reflects the average width of the posterior distributions, and the errorbars represent the error on the error.

5.1 Additional tests

The results in Figure 8 show that the mean uncertainty in $H_0$ decreases with reductions in either the localisation area or the redshift uncertainty. However, these changes are relatively modest. To understand the regime in which we would expect a reduction in either localisation area or redshift uncertainty to have more a pronounced effect on the mean uncertainty in $H_0$ , we conduct additional stress-tests.

5.1.1 When do smaller localisation areas provide the most benefit?

The first test aims to determine the conditions under which a smaller localisation area has the greatest impact on the mean uncertainty in the Hubble constant, $\unicode{x03C3}_{H_0}$ . Specifically, we examine two scenarios: varying the number of gravitational wave events (each observed along a different line of sight) at a fixed fractional luminosity distance uncertainty of 10% and varying the fractional luminosity distance uncertainty while maintaining a fixed number of 200 events. Both scenarios are conducted using the methodology described earlier in this section. For the latter case, we note that although gravitational waves with larger distance uncertainties often also have poorer sky localisation, the purpose of this analysis is to isolate the source of any improvements. For this reason, we vary the luminosity distance uncertainty and the localisation area independently.

We find the former scenario always results in a consistent ${\sim }1.8\times$ improvement when going from 50 to 1 deg $^2$ , for event counts ranging from 50 to 200. In contrast, the second scenario – reducing the fractional luminosity distance uncertainty – demonstrates a much more pronounced enhancement in constraining power when the localisation area is decreased from 50 to 1 deg $^2$ . The results of this test are presented in Table 3.

Table 2.

Mean and statistical uncertainties of $H_0$ for each combination of localisation area and redshift uncertainty. The values outside the brackets are the mean and standard error on the mean, and inside the brackets are the mean uncertainty and error on the error. All values have units of km s⁻¹ Mpc⁻¹.

Table 3.

Mean uncertainty in $H_0$ (in units of km s⁻¹ Mpc⁻¹) for three values of fractional luminosity distance uncertainty, evaluated for localisation areas of 1 and 50 deg $^2$ (constant $\unicode{x03C3}_z=10^{-4}$ ). The bottom row indicates the relative improvement between the uncertainties from the two localisation areas.

Table 3 shows that reducing the localisation area from 50 to 1 deg $^2$ leads to a greater relative improvement in the mean $H_0$ uncertainty as the fractional luminosity distance uncertainty decreases. This suggests that the benefits of enhanced sky localisation diminish as radial uncertainty, rather than angular uncertainty (from the localisation area), becomes the primary source of error. While smaller localisation areas reduce the number of potential host galaxies, large fractional luminosity distance uncertainties exacerbate the degeneracy between redshift and luminosity distance, leading to broader constraints on $H_0$ .

5.1.2 When do spectroscopic redshifts provide the most benefit?

The second test aims to determine the conditions under which spectroscopic redshifts provide the most benefit. Similar to Section 5.1.1, we reduce the fractional luminosity distance uncertainty and compare the differences in $H_0$ uncertainties between spectroscopic- and photometric-like redshift catalogues for each of the three $\unicode{x03C3}_{d_L}$ values.

The results in Table 4 lead to conclusions similar to those in Section 5.1.1, emphasising that high fractional luminosity distance uncertainty significantly restricts the advantages of using spectroscopic redshifts. The issue is not that spectroscopic redshifts lack utility; rather, the high fractional luminosity distance uncertainty limits the extent to which their benefits can be realised.

Table 4.

The mean uncertainty in $H_0$ (in units of km s⁻¹ Mpc⁻¹) for three fractional luminosity distance uncertainties, considering both photometric and spectroscopic redshift uncertainties (fixed localisation area of 1 deg $^2$ ). The bottom row shows the relative improvement between spectroscopic- and photometric-like redshift catalogues for each $\unicode{x03C3}_{d_L}$ .

The uncertainty in luminosity distance primarily arises from its strong correlation with the binary system’s orbital inclination. This degeneracy can be mitigated through the detection of higher-order modes in the gravitational wave signal, enabling a more precise inference of the inclination and, consequently, a reduction in the uncertainty of the luminosity distance (Calderón Bustillo et al. Reference Calderón Bustillo, Leong, Dietrich and Lasky2021). Upcoming third-generation detectors like the Einstein Telescope (ET; Punturo et al. Reference Punturo2010) or the Cosmic Explorer (CE; Reitze et al. Reference Reitze2019) will offer significantly improved sensitivity, enabling the detection of these higher-order harmonics. For events below $z=0.1$ ( $\approx 462$ Mpc), incorporating these modes is expected to improve sky localisation by 5–25% and distance measurements by 20–45% (Borhanian et al. Reference Borhanian, Dhani, Gupta, Arun and Sathyaprakash2020). Leveraging the advanced capabilities of these future detectors, spectroscopic redshifts are anticipated to impose much tighter constraints on $H_0$ compared to photometric redshifts.

This section focused on addressing our first aim; to determine whether the increased precision of spectroscopic redshifts leads to tighter constraints on $H_0$ and whether these improvements depend on the localisation area. We confirm that the high precision spectroscopic redshifts ( $\unicode{x03C3}_z=10^{-4}$ ) consistently reduce the mean uncertainty in $H_0$ compared to lower precision photometric redshifts ( $\unicode{x03C3}_z=10^{-2}$ ). Additionally, this improvement was found to depend on the size of the localisation area, with spectroscopic redshifts providing the greatest benefit in smaller regions. For a 1 deg $^2$ localisation area, spectroscopic-like redshifts were found to reduce the mean uncertainty by a factor of 1.2, whereas for a 50 deg $^2$ localisation area, the reduction is negligible. Moreover, this reduction is also highly sensitive to the fractional luminosity distance uncertainty, with larger uncertainties reducing the benefits of spectroscopic redshifts. Based on these findings, spectroscopic follow-up of current gravitational wave events should be prioritised for those with the smallest fractional luminosity distance uncertainties, as this factor has the greatest influence on the benefits of higher-precision redshifts.

The discrepancy between our results and those of Borghi et al. (Reference Borghi2024) – who report a degradation in $H_0$ precision by a factor of ${\sim }3$ when using photometric redshifts, compared to our factor of ${\sim }1.2$ – can be attributed to several methodological differences. The most significant difference is that their assumed photometric redshift uncertainties are five times larger than those used here, naturally leading to a reduction in precision and a corresponding larger difference between spectroscopic and photometric $H_0$ constraints.Footnote ^h We have verified using our pipeline that increasing our photometric redshift to match that of Borghi et al. (Reference Borghi2024) raises the relative difference from approximately 1.2 to 2.1. The remaining discrepancy is likely because Borghi et al. (Reference Borghi2024) jointly perform a galaxy catalogue and spectral siren analysis to obtain their results. Doing so will likely impact constraints on $H_0$ using a photometric catalogue more severely than a spectroscopic one, as redshift uncertainties propagate not only through the redshift–distance relation but also through the transformation from source-frame to detector-frame masses.Footnote ⁱ

6. Results: realistic case

In addressing our first aim, we assumed Gaussian errors for both spectroscopic and photometric redshifts, thereby excluding the impact of outlier redshifts – cases where the observed and true redshift significantly differ. In reality, photometric redshift catalogues are highly prone to outliers due to the inherent limitations of the methods used to collect them. Photometric redshifts are estimated using a limited set of broadband filters, which can introduce ambiguities when different redshifts produce similar observed colours Massarotti, Iovino, & Buzzoni (Reference Massarotti, Iovino and Buzzoni2001). This degeneracy can lead to significant errors in redshift estimation. Additionally, the accuracy of photometric redshifts relies heavily on the spectral energy density (SED) templates used; if these templates fail to accurately represent the observed galaxies, the resulting redshift estimates can be incorrect. This motivates our second aim, which is to explore how outliers in photometric catalogues may bias the measurement of $H_0$ . To address this, we adopt a methodology similar to that outlined in Section 5, but now compare two methods for generating observed redshifts. Previously, we generated observed redshifts from the true MICEcat redshifts using Equation (1) with $A=0$ and a varying redshift uncertainty, $\unicode{x03C3}_z$ . For this section, our two observed redshifts are generated as follows:

1. 1. Observed redshifts are generated from true redshifts using Equation (10) with $\unicode{x03C3}_z=0.019$ and $A=-0.013$ , which models the general relationship between redMaGiC photometric and spectroscopic redshifts (as shown in Figure 2). As with Section 5, there will be no outliers in this sample. We call this method ‘Gaussian $z_{\textrm{obs}}$ ’.

2. 2. Observed redshifts are generated by cross-matching the MICEcat and redMaGiC redshifts, selecting the redMaGiC galaxies whose spectroscopic redshifts are closest to the MICEcat redshifts. The observed redshifts are taken to be the corresponding redMaGiC photometric redshifts (and photometric redshift uncertainties). We refer to this method as ‘redMaGiC $z_{\textrm{obs}}$ ’. This method will introduce a number of outliers consistent with a realistic photometric redshift catalogue. For redMaGiC, the outlier fraction – defined as $|z_\textrm{spec}-z_\textrm{photo}|\gt3\unicode{x03C3}_z$ – is approximately constant across the three localisation areas, with a mean value of $\sim6\%$ .

Since we only use the redMaGiC catalogue to derive observed redshifts from the sample of spectroscopic redshifts, there is no need to account for either the incompleteness of the redMaGiC sample, or the selection bias associated with the Luminous Red Galaxy sample. Given that our Gaussian $z_{\textrm{obs}}$ method is shown to be a good fit to the errors in Figure 2, the only difference between redMaGiC and Gaussian observed redshifts will be higher order redshift dependencies or non-Gaussianities (outliers) in the redMaGiC data.

With these two sets of observed redshifts, we generate posteriors as in Section 5; a single gravitational wave event is generated in 200 directions, over 200 noise realisations. The resulting posteriors for the two sets of observed redshifts and three localisation areas is shown in Figure 9.

Despite the outliers present in the redMaGiC sample, we consistently retrieve an unbiased measurement of $H_0$ for each localisation area, except for the slight bias in the standard error on the mean (Table 5) caused by overlapping cones in the 50 deg $^2$ localisation area. While we find the mean uncertainty across all three localisation areas to be slightly lower for the redMaGiC observed redshifts than for the Gaussian observed redshifts, the error on the error shown in Table 5 points towards this discrepancy being statistically insignificant. Furthermore, consistent with the findings in Section 5, the mean uncertainty for both types of observed redshifts increases with the size of the localisation area.

Table 5.

Table of means and statistical uncertainties for the Gaussian and redMaGiC observed redshifts. The ‘mean’ column represents the ensemble mean and the standard error on the mean, whereas the ‘statistical uncertainty’ column shows the mean uncertainty and error on the error. The slight bias in the standard error on the mean for the 50 deg $^2$ localisation area is attributed to overlapping large-scale structure, causing the error to be underestimated (as discussed in Section 5).

Figure 9.

Combined posteriors for a single gravitational wave event over 200 directions, with 200 noise realisations. In the left column, the observed redshifts are generated from Equation (10) with standard deviation given by Equation (1) with $A=-0.013$ and $\unicode{x03C3}_z=0.019$ . In the right column, the redMaGiC photometric redshifts are used as the observed redshifts. Each coloured Gaussian represents the ensemble mean and mean uncertainty calculated over all 200 realisations, with the colour reflecting the magnitude of the mean uncertainty. Scatter points are overlaid to visually reinforce the ensemble mean and mean uncertainty, which are also annotated in the top-right corner of each subplot for clarity (units in km s⁻¹ Mpc⁻¹).

For our second aim, we conclude that photometric redshift catalogues that contain redshift outliers do not bias the measurement of $H_0$ . While the inclusion of outliers leads to a slight reduction in the mean uncertainty, the difference in $H_0$ uncertainty between redMaGiC and MICEcat is not statistically significant; as shown in Table 2, the difference remains below $1\unicode{x03C3}$ across the 200 realisations. Consequently, photometric catalogues can serve as a reliable foundation for conducting dark siren analyses, with no risk of exacerbating the Hubble tension.

7. Results: uniform sub-sampling

Our final aim is to determine the completeness level at which the advantage of using spectroscopic redshifts is outweighed by the loss of accuracy due to an incomplete galaxy sample. To achieve this, we generate observed redshifts with a methodology that aligns with Section 5. Given that spectroscopic redshift catalogues are more prone to high levels of incompleteness, we use only a spectroscopic-like redshift uncertainty ( $\unicode{x03C3}_z=10^{-4}$ ) in this scenario. The redshift interpolant (Equation 7) currently only accounts for an in-catalogue component and thus assumes that the host galaxy of the gravitational wave event is always present in the galaxy catalogue. To address incompleteness, we adjust the redshift interpolant to also account for an out-of-catalogue component, following a simplified approach to the method outlined in Chen et al. (Reference Chen, Fishbach and Holz2018), Finke et al. (Reference Finke, Foffa, Iacovelli, Maggiore and Mancarella2021). The complete redshift interpolant for both in- and out-of-catalogue parts is:

(13) \begin{align} p_{\textrm{CBC}}(z)=f_{\textrm{comp}}p_{\textrm{in}}+(1-f_{\textrm{comp}})p_{\textrm{out}}\qquad\qquad\qquad\quad \end{align}

(14) \begin{align} \qquad\qquad =\frac{f_\textrm{comp}}{N_{\text {gal}}}\left( \sum_i^{N_{\text{gal}}} p_{\text{red}}(z \mid \hat{z}_g^i)\right)+(1-f_{\textrm{comp}}) p_{\textrm{bg}}(z),\end{align}

where $f_{\textrm{comp}}$ is the completeness fraction of the catalogue. When the catalogue is 100% complete, Equation (13) reduces to the original interpolant, as only the in-catalogue galaxies contribute to the probability distribution. For incomplete catalogues ( $f_{\textrm{comp}}\lt1$ ), the information from the in-catalogue galaxies is increasingly dampened by the contribution from the out-of-catalogue background distribution from the redshift prior, $p_{bg}(z)$ .

To simulate an incomplete catalogue, we generate a gravitational wave using the full redshift catalogue and remove a percentage of the galaxies that corresponds to the completeness fraction. While in reality incomplete catalogues will not be uniformly sub-sampled, but instead follow a selection function, the methodology required to take into account such selection functions is substantially more complex (see Gray et al. Reference Gray2023), and so we do not consider this in this work. Since we expect that in removing a fraction of the galaxies the shape of the redshift interpolant (as shown in Figure 4) will remain mostly unchanged, the dominant effect should be an increase in the mean uncertainty as the completeness fraction decreases, as information in the form of galaxies is being removed. Figure 10 shows the results for 3 localisation areas and 3 completeness fractions: 100% (which are the same results as in Section 5), 90% and 50%. As aforementioned, we use a spectroscopic redshift uncertainty of $\unicode{x03C3}_z=10^{-4}$ and generate observed redshifts with the same methodology as Section 5 (with 200 directions, each with a single event and over 200 noise realisations). The slight bias present in the standard error on the mean (errorbars in the top panel of Figure 10) can once again be attributed to the correlation arising from overlapping cones. Overall, we find that a lower completeness fraction leads to a higher mean uncertainty, with this effect being more pronounced for smaller localisation areas. For a 1 deg $^2$ localisation area, reducing the completeness from 100% to 50% increases the mean uncertainty by a factor of 1.3. In contrast, the same reduction in completeness for a 50 deg $^2$ localisation area results in only a 1.1-fold increase, highlighting the greater sensitivity of smaller localisation regions to catalogue completeness.

Figure 10.

Predicted constraints on $H_0$ as a function of localisation area and catalogue completeness fraction (constant spectroscopic-like redshift uncertainty of $\unicode{x03C3}_z=10^{-4}$ ). The top panel shows the mean and standard error on the mean for each combination of localisation area and completeness fraction. The bottom panel shows the mean uncertainty and error on the error.

A spectroscopic catalogue with a completeness fraction of 50% seems to yield a slightly larger mean uncertainty than having a fully complete photometric catalogue without outliers (using the results from Section 5 and Figure 8). Additionally, for small localisation areas ( $\lt10$ deg $^2$ ), having a 90% complete catalogue of spectroscopic redshifts yields similar results to having a fully complete photometric catalogue (including outliers). This highlights that while spectroscopic redshifts can improve the constraints on $H_0$ , their effectiveness heavily depends on maintaining a high completeness fraction, which, as noted in Section 2, is resource-intensive to achieve. Therefore, the most effective approach to obtaining an unbiased and precise measurement of $H_0$ will involve using a combination of both spectroscopic and photometric redshifts, at least until more complete spectroscopic catalogues become available. This approach, however, will introduce additional complexity in the selection function.