## Abstract

We present a preliminary analysis of the sensitivity of Anglo-Australian Planet Search data to the orbital parameters of extrasolar planets. To do so, we have developed new tools for the automatic analysis of large-scale simulations of Doppler velocity planet search data. One of these tools is the two-dimensional Keplerian Lomb–Scargle (LS) periodogram that enables the straightforward detection of exoplanets with high eccentricities (something the standard LS periodogram routinely fails to do). We used this technique to redetermine the orbital parameters of HD 20782b, with one of the highest known exoplanet eccentricities (*e*= 0.97 ± 0.01). We also derive a set of detection criteria that do not depend on the distribution functions of fitted Keplerian orbital parameters (which we show are non-Gaussian with pronounced, extended wings). Using these tools, we examine the selection functions in orbital period, eccentricity and planet mass of Anglo-Australian Planet Search data for three planets with large-scale Monte Carlo like simulations. We find that the detectability of exoplanets declines at high eccentricities. However, we also find that exoplanet detectability is a strong function of epoch-to-epoch data quality, number of observations and period sampling. This strongly suggests that simple parametrizations of the detectability of exoplanets based on ‘whole-of-survey’ metrics may not be accurate. We have derived empirical relationships between the uncertainty estimates for orbital parameters that are derived from least-squares Keplerian fits to our simulations and the *true* 99 per cent limits for the errors in those parameters, which are larger than equivalent Gaussian limits by the factors of 5–10. We quantify the rate at which false positives are made by our detection criteria, and find that they do not significantly affect our final conclusions. And finally, we find that there is a bias against measuring near-zero eccentricities, which becomes more significant in small, or low signal-to-noise ratio, data sets.

## 1 INTRODUCTION

Extrasolar planet detection using the Doppler method has played a dominant role in placing this field at the heart of astronomical research. The advances made in this field have been primarily due to the significantly increased stability of high-resolution spectrographs, and improved techniques for calibrating residual spectrograph variations. An excellent example of this can be seen in the Anglo-Australian Planet Search (AAPS), where a long-term velocity precision of 3 m s^{−1} over the initial ∼8 yr of observation (Tinney et al. 2005) has been improved in recent years to better than 2 m s^{−1} (e.g. O'Toole et al. 2007).

With the time baselines of Doppler surveys now approaching (or exceeding) a decade, and routine velocity precisions approaching 1 m s^{−1}, we are now in a position to ask, and answer, critical questions about the underlying distributions of exoplanet parameters. Questions such as how common are gas-giant planets in Jupiter-like orbits (i.e. ∼5 au near circular orbits)? How common are gas-giant planets in Earth-like orbits, likely to host habitable terrestrial satellites (i.e. ∼1 au near circular orbits)? How common are low-mass planets in close orbits (i.e. ∼ < 0.3 au with *M* sin *i*< 10*M*_{Earth})?

To answer these questions, we must first characterize and quantify the selection effects that are present in our observations. It is clear that the Doppler velocity planet searches *have* inherent observational biases. For example, Gaudi, Seager & Mallen-Ornelas (2005) have noted that there is a sharp cut-off at the periods of ∼3 d for planets detected in Radial Velocity (RV) surveys, while transit surveys have found the majority of their planets at periods below this. We need to know, in detail, what orbital parameter space Doppler surveys probe, and how their sensitivity varies over that orbital parameter space.

Analysis of Doppler survey selection effects have, to date, been forced to make a variety of simplifying assumptions. Narayan, Cumming & Lin (2005), for example, examined the detectability of short period, close-in planets, and so were able to ignore the complicating effects of eccentricity. They derived an empirical relation for detectability as a function of the number of observations and data quality. Wittenmyer et al. (2006) investigated the detectability of exoplanets in their data using simulations at *e*= 0 and 0.6, enabling them to determine that there *are* selection effects at high eccentricity, but not to quantify them across the full range of eccentricities which exoplanets have been found to display. The effects of eccentricity were considered by Cumming (2004), who derived empirical relationships for velocity thresholds relying on an *F* statistic for two different cases: when the orbital period is shorter than the time-span of the observations and when it is longer. More recently (in an expansion of work begun in Cumming, Marcy & Butler 1999), Cumming et al. (2008) used this technique to derive detection thresholds, determine selection effects and measure the incompleteness of Keck Planet Search data in order to investigate the exoplanetary minimum mass and orbital period distributions present in that data. However, the analytical method used in these studies makes a number of simplifying assumptions: that individual velocity uncertainties can be represented with a Gaussian distribution; that observations are evenly spaced and that the number of independent periods probed by a data set can be quantified in a meaningful way (Marcy et al. 2005). Unfortunately, real observations violate all three of these assumptions.

In this paper, therefore, we lay the groundwork for an investigation of the full range of physically interesting exoplanet parameters that Doppler data can probe (period *P*, eccentricity *e* and minimum planet mass *M* sin *i*) using star-by-star, epoch-by-epoch Monte Carlo simulations, in an effort to understand what our Doppler data are telling us about the orbital parameters of exoplanets, while making as few assumptions as possible. We introduce a set of automated planet detection criteria and combine it with large-scale simulations of Keplerian orbits for each star observed to determine the sensitivity of our ‘as observed’ data.

### 1.1 Observations, sampling and data quality

Objects in the AAPS catalogue are listed in Jones et al. (2002) and Tinney et al. (2003). The details of our observing programme are described in more detail elsewhere (Butler et al. 2001). Briefly, the data are taken using the University College London Echelle Spectrograph mounted at the coudé focus of the Anglo-Australian Telescope (AAT). An iodine absorption cell is placed in the beam, imprinting a forest of molecular iodine absorption lines on to the stellar spectrum. These lines are used as a wavelength reference to derive high-precision velocities as described in Butler et al. (1996).

The target stars of the AAPS (in common with most Doppler search targets) are observed in a non-uniform way. First, observing runs are scheduled in blocks spread unevenly throughout a semester, which necessarily needs to non-ideal (i.e. non-logarithmic) period sampling. Secondly, the weather during each block of observations affects the time sampling of data as well, with velocity precisions generally being poorer in poor weather conditions, and with bright objects tending to be generally observed more often when conditions are poor. (Note that in this paper we use the median measurement uncertainty of a given set of observations as an indicator of the data quality.)

Finally, large amounts of data tend to be acquired for objects where a planet is thought to exist and smaller amounts of data are acquired for stars where planets are thought unlikely (or where a possible planet has period longer than the current data string). As a result ‘high-priority’ targets get observed more densely. As a result of all these effects, time sampling can deviate markedly from the *ideal* of uniform logarithmic sampling in period space.

After each observing block is completed, the data are processed to update a data base of velocities. This is analysed periodically to look for objects showing significant variability or periodicity. These get promoted to the ‘higher priority’ status described above. This prioritization analysis has been done to date using a Lomb–Scargle (LS) periodogram, Keplerian fitting based on the most significant periods, the determination of False Alarm Probabilities (Marcy et al. 2005) and the application of simple tests asking ‘Have we seen at least one period?’ and ‘Do subsequent data obtained from a high-priority object match the prediction from initial fits?’ This prioritization maximizes the rate at which exoplanets can be extracted from our data, but means that our survey data base (like those of most Doppler planet searches) is quite non-uniform. Simulation of the as-observed data sets for all stars in our data base is therefore the only way to quantify the non-uniform selection effects inherent in Doppler velocity planet searches.

## 2 THE TWO-DIMENTIONAL KEPLERIAN LOMB–SCARGLE PERIODOGRAM

A tool commonly used for detecting variability in light and radial velocity curves is the LS periodogram (Lomb 1976; Scargle 1982). It involves determining, as a function of frequency, the difference between the χ^{2} of a sinusoid fit to data and the χ^{2} of a constant fit (with the resulting ‘power’ being normalized in some way). There are well-developed statistics surrounding LS power, allowing significant values to be attributed to possible detections (see e.g. Cumming 2004, for a discussion). In most cases where the LS periodogram is applied (e.g. in most areas of stellar pulsation and close binarity), the signal under study is approximately sinusoidal, and so the LS periodogram is applied appropriately.

The LS periodogram is now increasingly being used in Doppler velocity planet searches where, however, circular orbits (giving rise to sinusoidal velocity curves) are not common (Butler et al. 2006). It therefore makes more sense to fit Keplerians to data instead of sinusoids as discussed by Cumming (2004). As orbital eccentricity is an important parameter in a Keplerian function, we have expanded the traditional LS periodogram to include two dimensions – i.e. to examine power as a function of both period and eccentricity. We call this the two-dimensional Keplerian LS (2DKLS) periodogram. The method we use to calculate the 2DKLS periodogram was briefly discussed in O'Toole et al. (2007) and is described in more detail below.

### 2.1 Method

The 2DKLS is an extension of the traditional LS periodogram, where we vary period *and* eccentricity in the calculation of power. (While the argument of pericentre, ω, is also important in determining the *shape* of the velocity curve, it does *not* impact the orbital period measurement in the same way as eccentricity.) This is also an extension of the one-dimensional KLS periodogram introduced by Cumming (2004). We find, however, that not fixing eccentricity, while more efficient computationally, allows for possible non-physical values (i.e. outside the range 0–1). The ‘smoothness’ of the periodogram also depends a lot more on the initial guesses for the free parameters. We use a grid of fixed periods and eccentricities to calculate the 2DKLS, with *e*= 0–0.98 in steps of 0.01, and periods on a logarithmic scale from 1 d up to the maximum possible period of interest for that data sequence (in most cases 4500 d for current AAPS data), on a spacing of 10^{−3} in log_{10}*P*. A Keplerian described by equation (1) is then fitted to the data using a non-linear least-squares fitting routine with Levenberg–Marquardt minimization from Press, Flannery & Teukolsky (1986).

Here, *K* is the semi-amplitude, ν(*t*) is the true anomaly involving implicit dependence on the orbital period *P* and the time of periastron passage *T*_{p} and *V*_{0} is the velocity zero-point. The true anomaly is derived by solving Kepler's equation *M*(*t*) =*E*(*t*) −*e* sin *E*(*t*), where *E*(*t*) is the eccentric anomaly and *M*(*t*) = 2π*t*/*P* is the mean anomaly. The power, *z*(*P*, *e*), is determined using *z*(*P*, *e*) =Δχ^{2}/4 = (χ^{2}_{mean}−χ^{2}_{Kep})/4, where χ^{2}_{Kep} is the goodness-of-fit for the best-fitting Keplerian model and χ^{2}_{mean} is the equivalent for a constant fit to the data. For each value of *P* and *e*, we find the values of the remaining parameters that minimize χ^{2}_{Kep} and therefore maximize *z*(*P*, *e*). As discussed by Cumming (2004), when the noise level is not known in advance (i.e. for observations), *z*(*P*, *e*) must be normalized in the 2DKLS case by χ^{2}_{Kep}. This form of the 2DKLS was implemented by O'Toole et al. (2007) and is used in the next section. For the purposes of the simulations in this paper (described in Section 3), the power is not normalized because the noise level is an input parameter and therefore known in advance.

The 2DKLS has several advantages over the traditional LS periodogram. First, it is sensitive to high-eccentricity planets, which the traditional LS periodogram is not (as we show with an example in Section 2.2). Secondly, because the Keplerian functional form fitted by the 2DKLS is a better representation of real orbits, the peak in the 2DKLS power is higher than the traditional LS power. Thirdly, the width of the 2DKLS power peak more accurately indicates the level at which the eccentricity is determined by a given Doppler data set than the cross-terms in a single non-linear least-squares ‘best’ Keplerian fit. (Real eccentricity uncertainties are invariably much larger than the least-squares cross-term uncertainties.) In addition, the 2DKLS can be used in a similar manner to that of the clean algorithm (Högbom 1974), if a simultaneous fit of multiple Keplerians is also done. But perhaps most importantly for our purposes, the 2DKLS allows for a simpler automation of the planet detection process, as it much more rapidly narrows a Keplerian trial fit on the ‘correct’ best estimate of period and eccentricity.

### 2.2 Application to HD 20782

The 2DKLS periodogram aids significantly in the detection of high-eccentricity planets. As noted by Jones et al. (2006) in their paper on the extremely eccentric planet, HD 20782b, detecting high-eccentricity planets using traditional periodogram methods is extremely difficult. With the 2DKLS, however, the detection of a planet like HD 20782b becomes straightforward. The 2DKLS periodogram for all AAPS data on HD 20782 up to 2007 October 1 is shown in Fig. 1(a), along with slices through the 2DKLS at *e*= 0.97 (the best-fitting Keplerian eccentricity), 0.8 and 0.0 in Fig. 1. The planetary orbital signal is obvious at *e*= 0.97, but the power peak becomes progressively smaller at lower eccentricities; it is already difficult to discern at *e*= 0.8 and (most critically) completely undetectable at *e*= 0.0. In other words, this is a planet which an automated traditional LS ‘first-pass’ analysis would never detect.

We update the Jones et al. parameters for this planet using the most recent data in Table 1 and show the revised fit in Fig. 2. The planet is in a highly eccentric orbit; however, further refinement of the orbit with confirmation of additional observations near periastron (i.e. near the large velocity excursion) is important. The last excursion for the *e*= 0.97 solution occurred in the window 2008 June 18–21, when (unfortunately) HD 20782 was inaccessibly behind the Sun, so further refinement of the orbit will have to wait until the beginning of 2010. Measured velocities are given in Table 2. To highlight the importance of constraining eccentric planets in the narrow windows when their velocities change most rapidly, we also show in Table 1 the results of the fit excluding the most extreme velocity point. The significant change in the best-fitting orbital eccentricity (0.97 to 0.57) that occurs as a result of removing just one data point highlights the difficulties encountered in detecting and characterizing eccentric planets.

Parameter | All obs. | Without extreme pt. |

P (d) | 591.9 ± 2.8 | 577.9 ± 2.6 |

K (m s^{−1}) | 185.3 ± 49.7 | 21.7 ± 1.2 |

e | 0.97 ± 0.01 | 0.57 ± 0.04 |

ω | 147.7 ± 6.5° | 98.6 ± 5.7° |

T_{0} (JD-245 1000) | 83.8 ± 8.2 | 175.9 ± 8.4 |

M sin i (M_{Jup}) | 1.9 ± 0.5 | 0.73 ± 0.05 |

a (au) | 1.381 ± 0.005 | 1.359 ± 0.005 |

N_{obs} | 36 | 35 |

rms (m s^{−1}) | 5.6 | 4.8 |

χ^{2}_{ν} | 2.34 | 1.90 |

Jitter* (m s^{−1}) | 2.21 | 2.21 |

Parameter | All obs. | Without extreme pt. |

P (d) | 591.9 ± 2.8 | 577.9 ± 2.6 |

K (m s^{−1}) | 185.3 ± 49.7 | 21.7 ± 1.2 |

e | 0.97 ± 0.01 | 0.57 ± 0.04 |

ω | 147.7 ± 6.5° | 98.6 ± 5.7° |

T_{0} (JD-245 1000) | 83.8 ± 8.2 | 175.9 ± 8.4 |

M sin i (M_{Jup}) | 1.9 ± 0.5 | 0.73 ± 0.05 |

a (au) | 1.381 ± 0.005 | 1.359 ± 0.005 |

N_{obs} | 36 | 35 |

rms (m s^{−1}) | 5.6 | 4.8 |

χ^{2}_{ν} | 2.34 | 1.90 |

Jitter* (m s^{−1}) | 2.21 | 2.21 |

*Stellar jitter is calculated using the updated prescription of Wright (private communication).

JD (−245 1000) | RV (m s^{−1}) |

35.319456 | 17.7 ± 2.3 |

236.930648 | −10.7 ± 3.3 |

527.017315 | 3.2 ± 3.4 |

630.882407 | 25.5 ± 2.7 |

768.308854 | −10.8 ± 2.6 |

828.110660 | −11.8 ± 3.0 |

829.274491 | −10.8 ± 3.8 |

829.996250 | −30.6 ± 8.7 |

856.135301 | −14.5 ± 3.6 |

919.006597 | −7.8 ± 2.9 |

919.996296 | −5.8 ± 2.9 |

983.890093 | 0.000 ± 3.3 |

1092.304375 | 13.7 ± 2.3 |

1127.268137 | 13.5 ± 2.8 |

1152.163079 | 19.0 ± 2.5 |

1187.159653 | 20.7 ± 2.5 |

1511.206498 | −6.5 ± 2.3 |

1592.048162 | 13.1 ± 2.3 |

1654.960313 | 11.4 ± 2.2 |

1859.305274 | −206.2 ± 1.9 |

1946.138453 | −20.6 ± 2.0 |

1947.122481 | −17.3 ± 1.6 |

2004.001472 | −4.2 ± 1.8 |

2044.023669 | −3.3 ± 2.2 |

2045.960788 | −5.8 ± 1.9 |

2217.288060 | 4.7 ± 1.6 |

2282.220295 | 18.1 ± 1.9 |

2398.969109 | 17.5 ± 1.3 |

2403.960670 | 25.5 ± 2.5 |

2576.306902 | −12.7 ± 1.5 |

2632.281289 | −11.7 ± 1.6 |

2665.186505 | 1.7 ± 1.7 |

3013.216410 | 28.3 ± 1.5 |

3040.131498 | 20.8 ± 1.9 |

3153.970057 | −14.5 ± 2.1 |

3375.246543 | 10.2 ± 1.6 |

JD (−245 1000) | RV (m s^{−1}) |

35.319456 | 17.7 ± 2.3 |

236.930648 | −10.7 ± 3.3 |

527.017315 | 3.2 ± 3.4 |

630.882407 | 25.5 ± 2.7 |

768.308854 | −10.8 ± 2.6 |

828.110660 | −11.8 ± 3.0 |

829.274491 | −10.8 ± 3.8 |

829.996250 | −30.6 ± 8.7 |

856.135301 | −14.5 ± 3.6 |

919.006597 | −7.8 ± 2.9 |

919.996296 | −5.8 ± 2.9 |

983.890093 | 0.000 ± 3.3 |

1092.304375 | 13.7 ± 2.3 |

1127.268137 | 13.5 ± 2.8 |

1152.163079 | 19.0 ± 2.5 |

1187.159653 | 20.7 ± 2.5 |

1511.206498 | −6.5 ± 2.3 |

1592.048162 | 13.1 ± 2.3 |

1654.960313 | 11.4 ± 2.2 |

1859.305274 | −206.2 ± 1.9 |

1946.138453 | −20.6 ± 2.0 |

1947.122481 | −17.3 ± 1.6 |

2004.001472 | −4.2 ± 1.8 |

2044.023669 | −3.3 ± 2.2 |

2045.960788 | −5.8 ± 1.9 |

2217.288060 | 4.7 ± 1.6 |

2282.220295 | 18.1 ± 1.9 |

2398.969109 | 17.5 ± 1.3 |

2403.960670 | 25.5 ± 2.5 |

2576.306902 | −12.7 ± 1.5 |

2632.281289 | −11.7 ± 1.6 |

2665.186505 | 1.7 ± 1.7 |

3013.216410 | 28.3 ± 1.5 |

3040.131498 | 20.8 ± 1.9 |

3153.970057 | −14.5 ± 2.1 |

3375.246543 | 10.2 ± 1.6 |

## 3 SIMULATIONS

The goal of this work is to derive the underlying distributions of the orbital parameters (period, eccentricity and minimum mass) as revealed by our AAPS observations, so as to allow meaningful comparison with planet formation and evolution models. Each object in our catalogue has a different brightness, different intrinsic velocity variability and has been observed at different epochs with varying seeing and transparency conditions. The only way, therefore, to understand the selection functions inherent to our data set is to simulate it on a star-by-star and epoch-by-epoch basis. We have therefore begun a major programme of Monte Carlo simulations.

The time-stamps for the AAPS observations of our target stars were used to create artificial data sets for single planets (modelled as a single Keplerian using equation 1 with an input period, eccentricity and planet mass). The input periods on a logarithmic grid from log_{10}*P*_{i}= 0.0 to 3.6 in steps of 0.3 (or 1 to 3981 d); input eccentricities are on a grid from 0.0 to 0.9 in steps of 0.1 and planet masses are on a grid with *M*= (0.02, 0.05, 0.1, 0.2, 0.5, 1.0, 1.6, 2.3, 3.0, 4.0, 6.0, 9.0, 13.0, 20.0) in units of *M*_{J}. The semi-amplitudes for each artificial data set are derived using the following:

*M*

_{*}is the mass of the host star,

*M*

_{i}is the planet's mass, ‘

*i*’ is the (unknown) inclination of the system and

*G*is the gravitational constant. The subscript ‘

*i*’ denotes the input parameter. Measured parameters will be denoted with a subscript ‘m’. Stellar isochrone masses from Valenti & Fischer (2005) are used to estimate

*M*

_{*}for each host star.

At each epoch, the ideal Keplerian has noise added – which we model at present as being Gaussian, with a width given by the internal measurement uncertainty produced by that epoch's Doppler analysis. It is known that the measurement uncertainties themselves do not follow a Gaussian distribution, for a variety of reasons. A more realistic model for stellar noise in our simulations is currently planned. This will incorporate stellar noise sources such as magnetic activity (e.g. Wright 2005), stellar oscillations (O'Toole, Tinney & Jones 2008) and stellar convection and systematic measurement effects. The 2DKLS necessarily involves an increase in computation load compared to the LS, meaning that parallelization of code is vital. Each simulation takes anywhere between 20 s and 10 min of CPU time per processor depending on the number of data points. We have used the mpich implementation1 of the *Message Passing Interface* library to run our simulation analysis system in parallel. The analysis of our early simulations were run on a small Linux cluster comprising 10 processors at the Anglo-Australian Observatory, as well as some of the 224 processors available through the Miracle facility at University College, London.2 Subsequent analyses were moved to the Swinburne Centre for Astrophysics and Supercomputing3 in 2007 July utilizing around 160 processors per star.

One hundred simulations have been constructed for each set of parameters (*P*, *e*, *K*), leading to 182 000 simulations for each target object. In this paper, we focus on results for three stars: HD 20782, HD 179949 and HD 38382, whose relevant properties are shown in Table 3. The first two objects each have a known planet (Tinney et al. 2001; Jones et al. 2006). However, to also examine the effects of sampling and number of observations, we also consider further two subsets of the HD 179949 data – one using every second observation and the other every fourth – and simulated those as well.

Star | V (mag) | Spec type | N | ΔT (d) | Median unc. (m s^{−1}) |

HD 20782* | 7.36 | G3V | 35 | 3119 | 2.27 |

HD 38382 | 6.34 | F8V | 17 | 2452 | 3.80 |

HD 179949* | 6.25 | F8V | 56 | 2626 | 5.26 |

Star | V (mag) | Spec type | N | ΔT (d) | Median unc. (m s^{−1}) |

HD 20782* | 7.36 | G3V | 35 | 3119 | 2.27 |

HD 38382 | 6.34 | F8V | 17 | 2452 | 3.80 |

HD 179949* | 6.25 | F8V | 56 | 2626 | 5.26 |

*Indicates planet host star.

### 3.1 Distributions of fitted Keplerian parameters

In a study of exoplanet parameter uncertainties, Ford (2005) found that their distribution is typically non-Gaussian. In many ways this is not surprising, considering the correlations existing between parameters and as the description of Keplerian motion given by equation (1) is highly non-linear.

We can attempt to understand the uncertainty distributions of the Keplerian orbital parameters that are produced in a least-squares fit to an observed Doppler data set by using our simulations to look at the distributions of the orbital parameter *errors*4 (i.e. the differences between the input ‘i’ and measured ‘m’ simulation values). Fig. 3 shows the distribution of the *errors* in eccentricity for HD 179949, with log *P*_{i} fixed at 2.4 and *e*_{i} fixed at 0.4 (i.e. including eccentricity errors at all planet masses). The dashed line is a Gaussian fit to the histogram, while the solid line is a Lorentzian fit. The wings of the distribution diverge significantly from a Gaussian and are better matched by the Lorentzian. Note that we are not claiming that the distribution *is* Lorentzian, simply that the extended wings of this function is a better match to the extended wings of the observed distribution. Similarly, the distribution of measured periods and semi-amplitudes, shown in Figs 4 and 5, respectively, is non-Gaussian, with extended Lorentzian-like wings. The special case of *e*_{i}= 0 is not only non-Gaussian, but also non-symmetric. An obvious consequence of this and the analysis of Ford (2005) is that Gaussian statistics for the orbital parameter estimates (e.g. 3 or 5σ limits) cannot be used as criteria for exoplanet detection.

To demonstrate this, we show in Figs 3–5 both the 99 per cent limits of these error distributions *and* the data ranges that would correspond to such a limit were Gaussian statistics valid (i.e. 2.58σ). In every case, the *observed* 99 per cent confidence limits are much larger by the factors of ∼5–10. Thus, the *real* uncertainty on a Keplerian fit parameter is significantly larger than that one would predict based on Gaussian statistics alone, and Gaussian statistics must be either avoided or suitably modified, in any set of exoplanet detection criteria. We discuss a set of criteria that takes this effect into account in Section 4 and, in particular, we examine empirical relationships which can be used to calibrate and derive robust confidence limits for the orbital elements produced by least-squares Keplerian fits in Section 4.3.

## 4 DETECTION CRITERIA

One of the important practical considerations of our simulation system is that it must be automated. We therefore require a set of criteria to decide whether a planet has been detected, without any human intervention. These will be applied to the results of Keplerian fits to simulated data that should be both robust and not add significantly to the time budget of our analyses.

When determining adequate criteria, there are two important differences between the analysis of real and simulated data that must be considered. First, there is a difference between simply trying to determine whether one of 250 target stars has a planet, and whether one of millions of data sets has a real planet or not. In the former case, as much time can be spent as needed on trying to decide ‘by eye’ whether it is real or not. For the latter case, an automation is essential.

Secondly, the aim of these simulations is not the same as that for planet discovery. In the latter, the bias is towards seeing whether a planet has been found orbiting a target star, with subsequent observations being used to confirm or deny its status. For a simulated detection, there are no subsequent observations – one has to decide the status using *only* the simulated data. Moreover, there is a simulated planet present in *every* data set. What we need to know whether it has been detected with sufficient robustness that we can be sure (within a given confidence level) that it is a real detection. As such the simulated detection criteria will almost always be more stringent than the criteria used for the discovery of an exoplanet from actual planet search data.

Considered another way, these simulations are aiming to generalize the same process that is used in estimating the 1/*V*_{max} volume that is represented by each star going into the estimation of a luminosity function. In this case, we are estimating for each star *V*_{max} in the (*P*, *e*, *K*) phase space that is accessible to a given set of data. This means that a selection of a set of detection criteria that determines *V*_{max} is arbitrary – it determines the sensitivity, but not the results, of our survey. Have too loose a set of detection criteria, and you find lots of objects, and have a large *V*_{max}, but are subject to false positives. Have too tight a set of detection criteria, and you find few objects and have a small *V*_{max}, but are much less subject to false positives.

### 4.1 Previous sets of detection criteria

There are several methods that have previously been used to determine the reality of a planet detection. Marcy et al. (2005) presented an excellent discussion on two different approaches to the False Alarm Probability (FAP). The FAP is the probability that the best-fitting model Keplerian could have arisen simply as a result of noise fluctuations. The first involves the classical *F*-test (Bevington 1969) which has several weaknesses: it assumes that the uncertainties of the measurements follow a Gaussian distribution – even the smallest deviation from normality has been reported to be extremely non-robust (Lindman 1974); it cannot properly account for the actual uneven temporal sampling of the observations (e.g. Marcy et al. 2005) and it depends on the number of independent frequencies – a number which can only be approximated. Both Cumming (2004) and Cumming et al. (2008) used the *F*-test to investigate the detectability of planets in Doppler surveys based on the analytical FAPs.

The other method presented by Marcy et al. (2005) is empirical and involves creating 1000 or more quasi-artificial data sets by generating randomly scrambling the velocities, but keeping the times the same, and then analysing the new sets in the same way as the original data. The number of χ^{2}_{ν} values similar to the value for the candidate detection is then used to construct a FAP. This approach has the advantage that the distribution of uncertainties and temporal sampling of the observations are unimportant. If used alone, it cannot distinguish between peaks with similar significance in a power spectrum of the actual observations, as these are likely to have similar FAPs.

Because of the large number of simulations we plan to carry out, it is important that we have a simple set of criteria that can quickly test the reliability of a detection. This automatically rules out several approaches that are in themselves computationally intensive; in particular, the Marcy et al. scrambling method to determine a FAP described above would add considerably to the time budget of our simulation analysis. The *F*-test used by Cumming (2004) is also inappropriate for two reasons: first, AAPS data have uneven temporal sampling and secondly, because we have incorporated our velocity measurement uncertainties – which do not follow a Gaussian distribution – into our noise estimates, the simulated velocities show small departures from a pure Gaussian. We have therefore developed our own set of criteria, discussed below.

### 4.2 Our criteria

There are two final points to consider before we present our criteria. First, the criteria we use must be ‘blind’– i.e. they must only be based on measured quantities, and have no reliance on the input orbital parameters of the simulations. Secondly, the number of false positives should be as low as possible. The following are by no means the only criteria that we could use, however, as we show in Section 4.4, they produce an acceptable fraction of false positives. The set of criteria we have found to be useful is

*The rms of the simulated observations must be greater than or equal to the rms of the residuals of the best-fitting model.*That is, by subtracting a Keplerian model from the time-series, the overall scatter should decrease, rather than increase.

*The measured semi-amplitude must be greater than or equal to twice the semi-amplitude uncertainty, plus the rms of the residuals of the best-fitting model.*The uncertainties from the fit procedure we have used are correlated – the off-axis terms in the covariance matrix calculated in the non-linear least-squares fit are non-zero – which means that the fit uncertainties are lower limits. We approximate the semi-amplitude uncertainties here as twice the fit uncertainty plus the rms of the residuals of the best-fitting model. This criterion rejects poorly constrained semi-amplitudes (and therefore poorly constrained planet masses).

*The measured period must be greater than or equal to twice the period uncertainty*. As with the semi-amplitude uncertainties in criterion (iv), the period uncertainties are underestimated. We double the fit uncertainty to reject poorly constrained periods.

*The*χ

^{2}

_{ν}

*value must be less than or equal to 3.*This is a somewhat arbitrary cut, however it significantly reduces the number of false positives at high eccentricities, as discussed in Section 4.4.

As a simple test of our criteria we have used them to check whether each of the published planets in the AAPS target catalogue would be found. As a part of this test, we include a stellar jitter term in our fits, despite not including it in our simulations. This is because jitter is present in the real observations (and contributes to the individual measurement uncertainties of those real observations), while it is not present in our simulated observations, which only have had Gaussian noise added to them. By including the appropriate jitter values in our analyses (Wright, private communication), we find that 15 of the known planets satisfy our criteria. Excluding the χ^{2}_{ν} cut, all bar one of the planets pass the test. This bares no reflection of the reality of the planets, but rather a reflection of the strictness of our criteria. Other effects at play here are the presence of multiple companions, and accuracy of the jitter measurements used, which is only around ±50 per cent (Wright, private communication; note that the jitter contains unquantified time variability).

### 4.3 Orbital parameter confidence limits

We have seen in the discussion of distribution functions in Section 3.1 that Gaussian statistics cannot be used to model the distribution of errors in orbital parameters that arise from least-squares Keplerian fits to our simulated data (and obviously they similarly cannot be used to model the uncertainties in orbital parameters for detected Doppler planets from real data sets either).

The orbital parameter error distribution functions *do* contain information that is useful, however, in that they allow us to empirically calibrate the relationship between the uncertainties that arise from the covariance matrix in a least-squares Keplerian fit (i.e. the source of the traditional uncertainties in orbital parameters produced in analysing Doppler data sets) and our observed error distributions. To examine these relationships, we compare the uncertainties from the least-squares Keplerian fit and the 99 per cent confidence range from the simulations.

In Fig. 6, we show the 99 per cent confidence limit as a function of the median uncertainty of the fit δ*P*_{m} (top panel), δ*K*_{m} (middle panel) and δ*e*_{m} (bottom panel) for HD 20782 at fixed pairs of *P*_{i} and *e*_{i} or *P*_{i} and *K*_{i} (as in Section 3.1). There is clearly a correlation between these parameters, which we have characterized in the figure with a power law for each parameter. The power laws have the form: 99 per cent confidence =10^{α}*X*^{β}, where *X*=δ*P*_{m}, δ*e*_{m} and 99 per cent confidence =α+ β log δ*K*_{m} for semi-amplitude. The parameters α and β from these fits for HD 20782 are listed in Table 4 (along with the equivalent parameters for similar fits to the equivalent data for HD 38382 and HD 179949).

Parameter | HD 20782 | HD 38382 | HD 179949 |

α_{P} | 1.41 ± 0.03 | 1.67 ± 0.02 | 1.50 ± 0.02 |

β_{P} | 1.00 ± 0.01 | 1.07 ± 0.01 | 1.02 ± 0.01 |

α_{K} | 56.6 ± 1.3 | 90.0 ± 6.0 | 75.6 ± 4.3 |

β_{K} | 60.3 ± 4.9 | 134.5 ± 15.6 | 155.0 ± 10.2 |

α_{e} | 1.46 ± 0.13 | 1.82 ± 0.26 | 2.10 ± 0.26 |

β_{e} | 1.12 ± 0.06 | 1.33 ± 0.15 | 1.41 ± 0.13 |

Parameter | HD 20782 | HD 38382 | HD 179949 |

α_{P} | 1.41 ± 0.03 | 1.67 ± 0.02 | 1.50 ± 0.02 |

β_{P} | 1.00 ± 0.01 | 1.07 ± 0.01 | 1.02 ± 0.01 |

α_{K} | 56.6 ± 1.3 | 90.0 ± 6.0 | 75.6 ± 4.3 |

β_{K} | 60.3 ± 4.9 | 134.5 ± 15.6 | 155.0 ± 10.2 |

α_{e} | 1.46 ± 0.13 | 1.82 ± 0.26 | 2.10 ± 0.26 |

β_{e} | 1.12 ± 0.06 | 1.33 ± 0.15 | 1.41 ± 0.13 |

For the period data, it is clear that the power-law slope is consistent with an index of 1 – i.e. the 99 per cent confidence limits are linearly related to the 1σ fit uncertainties by the factors of 26–47, or equivalently Gaussian statistics overestimate the 99 per cent limits for a period determination from these simulated Doppler data by the factors of between 10 and 18. For eccentricity, the correlation is weaker, and the power-law fit indicated is slightly above 1. More importantly, the 99 per cent confidence limits are about 10–50 times larger than those which would be derived from simply trusting the Gaussian nature of the uncertainties coming from a least-squares Keplerian fit. This reflects the fact that eccentricity is, in general, very poorly constrained by Doppler data sets, as was seen in our analysis of the 2DKLS. The correlation between the observed 99 per cent confidence limits and the Keplerian fit uncertainties for semi-amplitude *K* is poor, and again the 99 per cent confidence limits in *K* are larger than those one would predict from the Keplerian fit uncertainties.

From our analysis of these three data sets, it would not appear that there are any general conclusions that can be reached, for every star and every data set, on how to relate *real* 99 per cent confidence limits to Keplerian fit uncertainties (other than that Keplerian fit uncertainties significantly underestimate – by the factors of greater than 10 – the real confidence limits). However, it is clear that for a *given* simulated data set, the meaningful correlations can be derived and applied. We have therefore used these relationships to convert fit uncertainties into meaningful confidence limits for our subsequent analysis of false positives.

### 4.4 False positives

It is important that the number of false positives (i.e. the number of incorrect detections) triggered by our detection criteria be (a) quantifiable and (b) as small as possible. We adopt as our ‘incorrectness’ criterion that the measured orbital parameter differs from the input orbital parameter by more than the 99 per cent confidence limit for that orbital parameter (as derived in Section 4.3).

For each simulation that results in a detection, we test for ‘correctness’ by asking:

is the error in period (i.e. the difference between measured and input periods) less than the 99 per cent confidence limit (as derived from calibrating the least-squares fit period uncertainty to a true confidence limit as described in Section 4.3)? If the error is larger than the 99 per cent limit, we call the period incorrect. We also ask:

is the error in the the semi-amplitude larger than the 99 per cent confidence limit? If it is then we call the semi-amplitude incorrect.

If both the period and the semi-amplitude are determined to be incorrect (at the 99 per cent level), we describe this as an incorrect detection, or false positive. The false positive rate is then the ratio of the number of incorrect detections to the total number of detections. Averaging over all the parameters, we find the false positive rate due to incorrect period and semi-amplitude to be 1.2 per cent for HD 179949, 2.2 per cent for HD 20782 and 9.0 per cent for HD 38382. In the sections that follow, we look in more detail at these numbers and their trends as a function of input orbital parameters.

*4.4.1 The*χ^{2}_{ν}≤ 3*detection criterion*

We are now in a position to demonstrate our reasons for including this particular criterion, which we do in Fig. 7, which shows the false positive fraction as a function of simulated input eccentricity both with this criterion applied (open squares) and not applied (filled squares). It immediately becomes apparent that without this particular criterion being applied, our data set is subject to an unacceptably large fraction of false positives at high eccentricity. Even with this criterion being applied, the number of false positives shows an increase over the ‘base’ level of 1–2 per cent seen at low eccentricity, up to 6 per cent at *e*= 0.9. (Similar patterns are seen for the other sets of simulations for the other stars.)

#### 4.4.2 Eccentricity

We show in the top panel of Fig. 8 the rate of false positives for each object as a function of input eccentricity, at all the values of *P*_{i} and *M*_{i}. For HD 179949 and HD 20782, the percentage of false positives remains at ∼1 per cent up to *e _{i}*≈ 0.6 and then increases to 3–6 per cent at

*e*

_{i}= 0.9. In the case of HD 38382, the false positive rate is around 7 per cent up to

*e*≈ 0.5, increasing to 18 per cent at

_{i}*e*

_{i}= 0.9. The higher false positive rate for this star is due to its having much fewer observations (just 17 epochs) than the other two stars – fewer observations make it harder to detect an exoplanet, and conversely makes that data set more subject to false positive detections. To demonstrate this, we show in the bottom panel of the same figure the false positive rate for the three HD 179949 subsets. The 28 epoch subset (crosses) has two to three times as many false positives as the full HD 179949 data set (with 56 epochs; diamonds) and the 14 epoch subset matches the HD 38382 false positive rate at low eccentricities and then becomes worse as eccentricity increases. The star-by-star approach in this case reproduces the expected behaviour – more observations give more confidence in an exoplanetary detection.

#### 4.4.3 Period

The false positive rates for our three stars are shown as a function of *P*_{i} (at all values of *e*_{i} and *M*_{i}) in Fig. 9. At periods longer than the time-span of the observations and below ∼4 d, the rate of false positives is 4–9 per cent for HD 179949 and HD 20782. As with eccentricity, the false positive rate is below ∼2 per cent for these stars. For HD 38382, the false positive rate steadily increases as a function of log *P* from around 6 per cent at short periods to ∼25 per cent at longer periods – again this is due to the sparser sampling of the HD 38382 data.

#### 4.4.4 Planet mass

Finally, the false positive rate as a function of *M*_{i} (at all values of *P*_{i} and *e*_{i}) is shown in Fig. 10. While the large numbers of false positives at low mass might at first appear troubling, it must be remembered that there is a significant selection effect *against* finding objects at very low masses. Therefore, the number of correct detections will decline steeply at very low masses (as low-mass planets are very hard to detect), while the incorrect detection rate should remain roughly constant. This will lead to a systematic increase in the false positive rate at very low masses.

We test this idea by showing the false positives as a function of the total number of simulations for each star in the top panel of Fig. 11. The *incorrect* detections seem to be approximately constant for HD 179949 and HD 20782, with values of ∼1–2 per cent for the former and ∼2–3 per cent for the latter. In the case of HD 38382, however, there is an increase from about 1.6 *M*_{Jup} up to around 20 per cent. Comparing HD 38382 to the three subsets of HD 179949 (bottom panel of Fig. 11), we see that the cause of the increase is observation density. The subset with *N*= 14– approximately the same as HD 38382 – suffers from the same effect. The *N*= 28 subset shows the effect to a lesser degree, and it is almost completely removed in the *N*= 56 subset. This shows one weakness in our detection criteria and we are currently investigating ways to minimize the problem.

## 5 RESULTS

Our automated detection criteria, and a means to analyse false positive rates, place us in a position to examine in detail the observational biases present in our data for the three stars under simulation. We define the detectability *D*′(*P _{i}*,

*e*,

_{i}*M*) as the detection rate as a function of

_{i}*P*

_{i},

*e*

_{i}and

*M*

_{i}– in the case of our simulations, we performed 100 realizations at each point in (

*P*

_{i},

*e*

_{i},

*M*

_{i}) space, so we divide the number of detections by 100. False positives have been removed from the

*D*′(

*P*,

_{i}*e*,

_{i}*M*) for each set of simulations. In Fig. 12, we show contour maps of detectability, for each star, as a function of input period and planet mass for three different eccentricities (

_{i}*e*= 0.1, 0.6 and 0.9). Note that the HD 179949 map is for the full set of 56 observations. The vertical dashed line indicates the time-span of the observations (Δ

*T*). These detectability surfaces display a number of common features.

First, it can be clearly seen that detectability is quite low at periods longer than the time-span of the observations (as seen by both Cumming 2004 and Wittenmyer et al. 2006), and is also low at short periods (below ∼2 d). Both these effects are precisely what one would naively expect based on our ability to sample the Doppler variability of our target stars. And both are reflected in the properties of the currently detected exoplanets. Few exoplanets are known at periods of 10 yr or longer as a result of Doppler searches, which are based on around a decade's worth of data. And, no Doppler exoplanet has been found at periods of less than ∼2 d without first being detected via a transit event.

Secondly, *D*′(*P _{i}*,

*e*,

_{i}*M*) as a function of planet mass, for a given eccentricity, reveals the same general pattern for each star, with detectability decreasing with increasing period. This is not surprising, since (to first order) for data sets with similar Doppler measurement precision over time, the ability to detect an exoplanet is determined by the size of the semi-amplitude

_{i}*K*

_{m}relative to that precision, which is in turn (from equation 2) a function of

*P*

_{m}of the form

The combination of these two effects means that the general form of the *D*′(*P _{i}*,

*e*,

_{i}*M*) surface is one of a transition region (with slope ) dividing highly detectable planets (or generally higher mass and shorter period) from undetectable ones (or lower mass and longer period), modified by a cut-on at short periods of ∼2 d (determined by the shortest data sampling obtained) and a cut-off at long periods (determined by the length of the observation string, Δ

_{i}*T*). These are the general behaviours that one would expect. Of more interest is the detailed behaviour these surfaces reveal for each target.

In particular, for example, we see that the steepness of the ‘transition region’ between highly detectable and mostly undetectable planets is a strong function of the number of observations obtained. The slope in the transition region is steep for HD 179949 and HD 20782, but shallow for the more poorly sampled HD 38382 data.

Moreover, the slope of the transition region is also a function of eccentricity, for all three stars, highly eccentric planets have a gently sloping transition region that mostly fills the entire available mass–period plane. Indeed, it is only at short periods that even the best-sampled data sets have high detectability.

To assist in the visualization of a more detailed analysis of these general trends, we define the integrated detectability, *D*′_{int}, such that *D*′_{int}(*P _{i}*) is simply the detectability at a given period

*P*

_{i}, over

*all*

*e*

_{i}and

*M*

_{i}[and by extension

*D*′

_{int}(

*M*) and

_{i}*D*′

_{int}(

*e*) are defined as the detectability over all the other relevant parameters in each case]. In particular, we pay special attention to the three subsets of the HD 179949 data set (as described in Section 3) in order to examine the impact of data sampling.

_{i}### 5.1 Eccentricity

Fig. 13 shows the integrated detectability for each of the HD 179949 subsets as a function of eccentricity, *D*′_{int}(*e _{i}*); recall that false positives have been removed. There is a clear difference at high eccentricity between each of the subsamples. At

*N*= 14 (squares),

*D*′

_{int}(

*e*) drops significantly at

_{i}*e*

_{i}= 0.5 and higher. At higher values of

*N*(28 – triangles; 56 – diamonds), the drop-off starts at higher eccentricity (

*e*

_{i}> 0.6 for

*N*= 28 and

*e*

_{i}> 0.7 for

*N*= 56). Below each of these values,

*D*′

_{int}(

*e*) is approximately constant. As demonstrated elsewhere (e.g. Cumming 2004), the implication here is that the higher observation density makes detection of high-eccentricity planets more likely.

_{i}The subsets of HD 179949 show significant variations in *D*′_{int}(*e _{i}*), but is there a difference between stars?Fig. 14 shows

*D*′

_{int}(

*e*) for each of the three objects HD 179949, HD 20782 and HD 38382. The shape of the curves, while in general decreasing at higher eccentricities, is different for each object. For example, when

_{i}*e*≤ 0.1, HD 179949 has the lowest fraction of planets redetected, however when

_{i}*e*≥ 0.8, it has the highest. Thus, data sampling and quality are fundamental to the selection effects present in the planet search observations and a simple parametrization of the detectability of exoplanet parameters using ‘whole-of-survey’ metrics – e.g. Cumming (2004)–

_{i}*cannot be done*. As an example, consider the case of HD 179949. Of the three sets of observations we discuss here, the HD 179949 data have the highest median measurement uncertainty (5.26 m s

^{−1}), and one might naively expect its detectabilities to be the lowest. However, the observation density (equal to the observation time-span/number of observations or Δ

*T*/

*N*) is highest at 47 d/epoch, which should counteract the first effect to some degree. It is not intuitively clear how to parametrize and compare the detectability of the HD 179949 observations, with, for example, that of the HD 38382 observations – which have lower observation density but also lower median measurement uncertainty – without the simulations we have carried out in this study. Therefore, carrying out simulations on a star-by-star basis is the

*only*way to understand the selection effects in Doppler velocity planet searches.

### 5.2 A Bias against zero eccentricity detections

In the previous section, we examined *D*′_{int}(*e _{i}*), the detectability at each

*e*

_{i}, which is at all

*P*

_{i}and

*M*

_{i}. This is fine for the case of these simulations, where we know the input parameter values a priori. However, as this is never the case for actual Doppler planet data, it is useful to consider our detectability at each

*e*

_{m}; i.e. the

*measured*eccentricities rather than the input eccentricities. We call this quantity

*D*′

_{int}(

*e*

_{m}). It is determined by counting the number of correct detections – i.e. false positives are excluded – in equally spaced bins of

*e*

_{m}, normalized by the number of simulations in each of bin.

We now compare the two quantities, *D*′_{int}(*e*_{m}) and *D*′_{int}(*e _{i}*), and show the difference between them for each of our three stars in Fig. 15. The striking feature is that when binned by measured eccentricity, the detectability is

*lower*by up to 15 per cent at

*e*

_{i}= 0 than when binned by input eccentricity. At other eccentricities

*e*

_{i}, there is an opposite effect, albeit smaller. What could be causing these apparent biases, especially against finding zero eccentricity orbits?

The answer can be seen in Fig. 16, which shows the measured semi-amplitude, *K*_{m}, as a function of the measured eccentricity, *e*_{m}, for HD 38382 where the input eccentricity is *e*_{i}= 0.0. Also plotted is the median value of *e*_{m} over various ranges of *K*_{m} (shown by the error bars in *K*_{m}) and the median value of the fit error for *e*_{m} over the same range of *K*_{m} (shown by the error bars in *e*_{m}). As semi-amplitude decreases, the measured eccentricity (*e*_{m}) *increases*, i.e. the Keplerian fit to noisy data with a perfectly circular orbit (*e*= 0.0) tends towards a higher eccentricity. This has the effect of decreasing the number of *e*_{m}= 0 orbits and increasing the number of non-zero eccentricity orbits, in particular for the *e*_{m}= 0.1 bin of orbits (Fig. 16). At low values of *K*_{m}, up to one-third of zero eccentricity orbits have best fits that move them out of the *e*= 0 bin. This occurs because the *shape* of the velocity curve is not well constrained in low signal-to-noise ratio data, even if the orbital period and semi-amplitude are. For *e _{i}*≥ 0.1 eccentricity orbits, the distribution of measured eccentricities is symmetric so the median of spread at low signal-to-noise ratio converges to the real value and the effect is not observed. The distribution of measured eccentricities is non-symmetric when

*e*→ 0.0, however, so the median is always non-zero. Similar plots for the other two stars show the same effect to varying degrees.

_{i}The conclusion from this analysis is that there is a small, but significant, bias *against* measuring an eccentricity of zero, especially at low signal-to-noise ratios. This bias has also been recently reported in an independent analysis by Shen & Turner (2008).

### 5.3 Period

We now turn our attention to *D*′_{int}(*P _{i}*), which is the integrated detectability at a given input period,

*P*

_{i}. Fig. 17 shows

*D*′

_{int}(

*P*) for each subset of the HD 179949 data as a function of

_{i}*P*

_{i}. Perhaps unsurprisingly, a higher density of observations leads to a higher detectability of planets at all the periods. For the 56 and 28 epoch data sets,

*D*′

_{int}(

*P*) is mostly a linear function of log

_{i}*P*

_{i}, with a drop at

*P*∼ 2 d and a large drop-off at

_{i}*P*≲2 d (as noted above). For the 14 epoch subset, however,

_{i}*D*′

_{int}(

*P*) drops sharply at ≲4 d, rather than at

_{i}*P*≲2 d, reflecting the fact that the reduced data set does not sample short periods well. At periods longer than Δ

_{i}*T*(=2626 d for HD 179949),

*D*′

_{int}(

*P*) drops off by almost a factor of 2.

_{i}We have seen the effects of sampling on the period detectability above, but what role does the data quality (indicated by the median measurement uncertainty in Table 3) play, if any? To investigate this, we show *D*′_{int}(*P _{i}*) for each of the three objects studied in Fig. 18. Once again, we see a drop in detectability at periods below ∼2 d and periods longer than Δ

*T*. While there is an offset between the

*D*′

_{int}(

*P*) for each of the HD 179949 subsets, caused by differing observation density, there is no clear offset between the three different stars, despite the significantly different observation densities (HD 179949 – 47 d epoch

_{i}^{−1}; HD 20782 – 89 d epoch

^{−1}and HD 38382 – 144 d epoch

^{−1}). If we examine the median measurement uncertainties as a proxy for data quality, we see, for example, that HD 20782, which has the lowest measurement uncertainties at 2.27 m s

^{−1}, has typically higher period detectabilities, despite having lower observation density than HD 179949. This suggests that once again a complicated combination of observation density and data quality is important in selection functions for Doppler planet search data.

We also consider the detectability at each *P*_{m}, the measured period, denoted *D*′_{int}(*P*_{m}), and compare it with the *D*′_{int}(*P _{i}*) discussed above. We calculate

*D*′

_{int}(

*P*

_{m}) by counting the number of correct detections in equally spaced bins of log

*P*

_{m}, normalized by the number of simulations in each of the bins. At periods up to 1000 d (log

*P*= 3.0), the difference in detectabilities is less than 0.5 per cent for all stars. There is a small offset for the two longest periods of up to ±5 per cent, which is caused by poorly constrained long periods (log

*P*= 3.6)‘leaking’ into the next shorter period bin (log

*P*= 3.3).

### 5.4 Planet mass

The integrated detectability as a function of planet mass, *D*′_{int}(*M _{i}*), is more complicated than the integrated detectability as a function of period or eccentricity, because planet mass is a function of both of these parameters (as well as semi-amplitude) through equation (2). Fig. 19 shows

*D*′

_{int}(

*M*) as a function of planet mass for the three HD 179949 subsets. As one might naively expect, we see that more data result in higher detectabilities for a given mass of planet. (Recall also that at low masses false positives begin to have a significant impact on the false positives for sparsely sampled data – they represent up to 20 per cent as a fraction of the total detections at

_{i}*M*< 0.2

*M*

_{Jup}, leading to an apparently higher detectability than data sets with more observations.)

Once again if we examine the *D*′_{int}(*M _{i}*) curves for simulations of each star, we find variations (see Fig. 20). The HD 20782 observations, which have the highest quality with a median measurement uncertainty of 2.27 m s

^{−1}, allow the detection of the lowest planet masses (after false positives are removed) as shown in Fig. 20. Even though there are more observations of HD 179949, the median uncertainty of HD 20782 is less than half of that star's value. In the case of HD 38382, the median uncertainty of the observations

*and*the number of epochs appear to be important, giving a slightly lower number of planets detected at intermediate masses (1

*M*

_{Jup}<

*M*

_{i}< 6

*M*

_{Jup}) compared with the HD 179949. It is this complexity that shows the importance of a star-by-star analysis.

Finally, we examine the detectability at *M*_{m}, the measured planet mass, which we denote as *D*′_{int}(*M*_{m}), and once again we compare it with the corresponding detectability for input planet mass. The detectabilities are binned in log *M*_{m}, with the centroid of each bin set to the corresponding value of log *M*_{i} to allow comparison. The width of the bins was set to half the difference between the adjacent bins. Recall that *D*′_{int}(*M _{i}*) is calculated by counting the number of detections at a given

*M*

_{i}, which assumes that the mass is known a priori. We find that the difference between

*D*′

_{int}(

*M*

_{m}) and

*D*′

_{int}(

*M*) is within ∼3 per cent for each star. We do not consider these differences to be significant.

_{i}## 6 SUMMARY

We have begun a project to investigate the observational biases inherent in Doppler velocity data, in particular in the AAPS. An essential part of this study is the development of a set of tools that will allow the automatic detection of exoplanets. We present the 2DKLS periodogram, a new algorithm based on an extension of the traditional LS periodogram, which includes eccentricity. This is very efficient at detecting high-eccentricity planets, which we highlight with a re-analysis of the extreme object HD 20782b.

We have constructed Monte Carlo like simulations of AAPS data that include the time-stamps of the observations and a simple noise model that incorporates into it the measurement uncertainties. The simulations cover the full range of physically important exoplanet orbital parameters: period, eccentricity and planet mass. As a part of the simulation system, we have developed a set of detection criteria, which can be applied to our simulated data sets in an automated fashion. We have investigated the false positives produced by these detection criteria and found them to be quantifiable and at an acceptably low level, which enables meaningful conclusions to be reached from our simulations. We also find that there is a bias against detecting zero-eccentricity orbits at low signal-to-noise ratios.

Finally, we present preliminary results from simulations of velocity observations of three AAPS objects: HD 179949, HD 38382 and HD 20782. Our investigation shows that the detectability of exoplanet orbital parameters differs significantly from object to object, meaning that the simple parametrizations invoked by previous studies are of limited validity.

*error*of a measurement (i.e. by how much it is wrong, which can only be known when one knows the ‘right answer’ as in these simulations) and its

*uncertainty*(i.e. an estimate of the quality of a measurement in the absence of knowing the ‘right answer’).

We thank Rob Wittenmyer for commenting on the manuscript. We gratefully acknowledge the superb technical support at the AAT, which has been critical to the success of this project. We acknowledge support by the PPARC grant PPC/C000552/1 (SJOT), NSF grant AST-9988087 and travel support from the Carnegie Institution of Washington (RPB), NASA grant NAG5-8299 and NSF grant AST95-20443 (GWM) and ARC grant DP0774000 (CGT). We thank the Australian and UK Telescope assignment committees for generous allocations of telescope time. This research has made use of NASA's Astrophysics Data System and the simbad data base, operated at CDS, Strasbourg, France. We would like to extend our effusive thanks to Professor Matthew Bailes and the staff at the Supercomputing Centre at the Swinburne University of Technology for allowing us the use of their facilities, and providing support and assistance when required. The authors acknowledge the use of UCL Research Computing facilities and services in the completion of this work.