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gaia data release 3 documentation

20.13 Variability tables

20.13.1 vari_summary

Summary table that provides the information on where a source_id can be found in the different Variability tables and statistical parameters of time series, using only transits not rejected.
Note that NULL is reported when the statistical parameter value is missing or cannot be calculated.

Columns description:

solution_id : Solution Identifier (long)

All Gaia data processed by the Data Processing and Analysis Consortium comes tagged with a solution identifier. This is a numeric field attached to each table row that can be used to unequivocally identify the version of all the subsystems that were used in the generation of the data as well as the input data used. It is mainly for internal DPAC use but is included in the published data releases to enable end users to examine the provenance of processed data products. To decode a given solution ID visit https://gaia.esac.esa.int/decoder/solnDecoder.jsp

source_id : Unique source identifier (long)

A unique single numerical identifier of the source obtained from gaia_source (for a detailed description see gaia_source.source_id).

num_selected_g_fov : Total number of G FOV transits selected for variability analysis (short)

The number of processed observations for variability analyses of this source, using only transits not rejected.

mean_obs_time_g_fov : Mean observation time for G FoV transits (double, Time[Barycentric JD in TCB - 2 455 197.5 (day)])

Name: The mean observation time

Control parameters: None

Output: Let yi be a time series of size N at times ti. The mean t¯ is defined as

t¯=1Ni=1Nti. (20.1)

time_duration_g_fov : Time duration of the time series for G FoV transits (float, Time[day])

Name: The time duration of the time series

Control parameters: None

Output: Let yi be a time series of size N at times ti, with i=1 to N. The time duration of the time series is equal to tN-t1.

min_mag_g_fov : Minimum G FoV magnitude (float, Magnitude[mag])

Name: The minimum value of the time series

Control parameters: None

Output: Let yi be a time series of size N at times ti, with i=1 to N. The minimum value of the time series is defined as:

min(yi)i(1,N) (20.2)

max_mag_g_fov : Maximum G FoV magnitude (float, Magnitude[mag])

Name: The maximum value of the time series

Control parameters: None

Output: Let yi be a time series of size N at times ti, with i=1 to N. The maximum value of the time series is defined as:

max(yi)i(1,N) (20.3)

mean_mag_g_fov : Mean G FoV magnitude (float, Magnitude[mag])

Name: The mean of the time series

Control parameters: None

Output: Let yi be a time series of size N. The mean y¯ is defined as

y¯=1Ni=1Nyi. (20.4)

median_mag_g_fov : Median G FoV magnitude (float, Magnitude[mag])

Name: The median of the time series

Control parameters: None

Output: The 50th percentile unweighted value.

Let yi be a time series of size N ordered such as y(1)y(2)y(N). The m-th (per cent) percentile Pm is defined for 0<m100 as follows:

Pm={y(1)if 0<m<100/N,y(i)if Nm/100-i=0,y(i+1)otherwise. (20.5)

range_mag_g_fov : Difference between the highest and lowest G FoV magnitudes (float, Magnitude[mag])

Name: The range of the time series

Control parameters: None

Output: Let yi be a time series, ymax its largest element, and ymin its smallest element, then the range is defined as

R=ymax-ymin (20.6)

trimmed_range_mag_g_fov : Trimmed difference between the highest and lowest G FoV magnitudes (float, Magnitude[mag])

Name: The trimmed range

Control parameters: trim level α]0,50[

Output: The trimmed unweighted range.

Let yi be a time series of size N, with error bars Δyi. Let Yα be the αth percentile, and Y100-α be the (100-α)th percentile. Let yi be the trimmed time series of size N’ which is derived by iteratively removing the smallest or the largest values of the yi time series until Yα<yi<Y100-α for all i{1,,N}. The trimmed range R’ is defined as

R=ymax-ymin (20.7)

For DR3, the trim level α was set to 5.0.

std_dev_mag_g_fov : Square root of the unweighted G FoV magnitude variance (float, Magnitude[mag])

Name: The square root of the unbiased unweighted variance.

Output: Let yi be a time series of size N. The unweighted standard deviation σ^ is defined as the square root of the sample-size unbiased unweighted variance:

σ^=1N-1i=1N(yi-y¯)2.

skewness_mag_g_fov : Standardized unweighted G FoV magnitude skewness (float)

Name: The standardised unbiased unweighted skewness.

Output: Let yi be a time series of size N>2. The sample-size unbiased unweighted skewness moment is defined as:

=N(N-1)(N-2)i=1N(yi-y¯)3.

The standardized unbiased skewness E is defined as:

E=σ^3

where σ^ is the square root of the unbiased unweighted variance around the unweighted mean. While is an unbiased estimate of the population value, E becomes unbiased in the limit of large N.

kurtosis_mag_g_fov : Standardized unweighted G FoV magnitude kurtosis (float)

Name: The standardised unbiased unweighted kurtosis.

Output: Let yi be a time series of size N>3. The sample-size unbiased unweighted kurtosis cumulant 𝒦 is defined as:

𝒦=N(N+1)(N-1)(N-2)(N-3)i=1N(yi-y¯)4-3(N-2)(N-3)[i=1N(yi-y¯)2]2.

The standardized unbiased kurtosis K is defined as:

K=𝒦σ^4

where σ^2 is the unbiased unweighted variance around the unweighted mean. While 𝒦 is an unbiased estimate of the population value, K becomes unbiased in the limit of large N.

mad_mag_g_fov : Median Absolute Deviation (MAD) for G FoV transits (float, Magnitude[mag])

Name: The Median Absolute Deviation (MAD)

Control parameters: None

Output: Let yi be a time series of size N. The MAD is defined as the median of the absolute deviations from the median of the data, scaled by a factor of 1/Φ-1(3/4)1.4826 (where Φ-1 is the inverse of the cumulative distribution function for the standard normal distribution), so that the expectation of the scaled MAD at large N equals the standard deviation of a normal distribution:

MAD=1.4826median{|yi-median{yj,j(1,N)}|,i(1,N)}. (20.8)

abbe_mag_g_fov : Abbe value for G FoV transits (float)

Name: The Abbe value

Control parameters: None

Output: Let {ti,yi} be a time-sorted time series of size N, such that ti<ti+1 for all i<N. The Abbe value 𝒜 is defined as

𝒜=i=1N-1(yi+1-yi)22i=1N(yi-y¯)2 (20.9)

where y¯ is the unweighted mean.

iqr_mag_g_fov : Interquartile range for G FoV transits (float, Magnitude[mag])

Name: The Interquartile Range (IQR)

Control parameters: None

Output: The difference between the (unweighted) 75th and 25th percentile values: IQR=P75-P25.

Let yi be a time series of size N ordered such as y(1)y(2)y(N). The m-th (per cent) percentile Pm is defined for 0<m100 as follows:

Pm={y(1)if 0<m<100/N,y(i)if Nm/100-i=0,y(i+1)otherwise. (20.10)

stetson_mag_g_fov : Stetson G FoV variability index (float)

Name: The single-band Stetson variability index

Control parameters: The pairing time interval

Output: Let yi be a time series of size N, with error bars Δyi. The Stetson value (Stetson 1996) is based on paired observations closely separated in time (within a chosen time interval). The time difference between the components of a pair should be small compared to the shortest time-scale of variation being sought. If this condition is not satisfied, a transformation is applied to the unpaired measurement, in order to include its information together with paired observations. The maximum number n of pairs may exceed N/2, since multiple pairs can be formed within a given time interval (as long as the latter is much smaller than the minimum variability time-scale sought). The Stetson J index is defined as

J=1nk=1nsgn(Pk)|Pk|,

where the index k refers to the k-th pair, composed of measurements i and j, such that

Pk={δi(k)δj(k), if i(k)j(k)δi(k)2-1, if i(k)=j(k)

with the normalized deviation of the i-th measurement from the time-series mean y¯ expressed by:

δi(k)=NN-1yi(k)-y¯Δyi(k).

std_dev_over_rms_err_mag_g_fov : Signal-to-Noise G FoV estimate (float)

Name: The signal-to-noise estimate

Control parameters: None

Output: Let yi be a time series of size N, with error bars Δyi. The signal-to-noise ratio (S/N) is estimated as

S/N=i=1N(yi-y¯)2i=1NΔyi2 (20.11)

where y¯ is the mean of the time-series values.

outlier_median_g_fov : Greatest absolute deviation from the G FoV median normalized by the error (float)

Name: The most outlying measurement with respect to the median

Control parameters: None

Output: Let yi be a time series of size N, with error bars Δyi. The most outlying measurement is defined as the greatest absolute deviation from the median normalized by the error:

outlierMedian=max{|yi-median{yj,j(1,N)}|Δyi,i(1,N)}. (20.12)

num_selected_bp : Total number of BP observations selected for variability analysis (short)

The number of processed observations for variability analyses of this source, using only transits not rejected.

mean_obs_time_bp : Mean observation time for BP observations (double, Time[Barycentric JD in TCB - 2 455 197.5 (day)])

Name: The mean observation time

Control parameters: None

Output: Let yi be a time series of size N at times ti. The mean t¯ is defined as

t¯=1Ni=1Nti. (20.13)

time_duration_bp : Time duration of the BP time series (float, Time[day])

Name: The time duration of the time series

Control parameters: None

Output: Let yi be a time series of size N at times ti, with i=1 to N. The time duration of the time series is equal to tN-t1.

min_mag_bp : Minimum BP magnitude (float, Magnitude[mag])

Name: The minimum value of the time series

Control parameters: None

Output: Let yi be a time series of size N at times ti, with i=1 to N. The minimum value of the time series is defined as:

min(yi)i(1,N) (20.14)

max_mag_bp : Maximum BP magnitude (float, Magnitude[mag])

Name: The maximum value of the time series

Control parameters: None

Output: Let yi be a time series of size N at times ti, with i=1 to N. The maximum value of the time series is defined as:

max(yi)i(1,N) (20.15)

mean_mag_bp : Mean BP magnitude (float, Magnitude[mag])

Name: The mean of the time series

Control parameters: None

Output: Let yi be a time series of size N. The mean y¯ is defined as

y¯=1Ni=1Nyi. (20.16)

median_mag_bp : Median BP magnitude (float, Magnitude[mag])

Name: The median of the time series

Control parameters: None

Output: The 50th unweighted percentile value.

Let yi be a time series of size N ordered such as y(1)y(2)y(N). The m-th (per cent) percentile Pm is defined for 0<m100 as follows:

Pm={y(1)if 0<m<100/N,y(i)if Nm/100-i=0,y(i+1)otherwise. (20.17)

range_mag_bp : Difference between the highest and lowest BP magnitudes (float, Magnitude[mag])

Name: The range of the time series

Control parameters: None

Output: Let yi be a time series, ymax its largest element, and ymin its smallest element, then the range is defined as

R=ymax-ymin (20.18)

trimmed_range_mag_bp : Trimmed difference between the highest and lowest BP magnitudes (float, Magnitude[mag])

Name: The trimmed range

Control parameters: trim level α]0,50[

Output: The trimmed unweighted range.

Let yi be a time series of size N, with error bars Δyi. Let Yα be the αth percentile, and Y100-α be the (100-α)th percentile. Let yi be the trimmed time series of size N’ which is derived by iteratively removing the smallest or the largest values of the yi time series until Yα<yi<Y100-α for all i{1,,N}. The trimmed range R’ is defined as

R=ymax-ymin (20.19)

For DR3, the trim level α was set to 5.0.

std_dev_mag_bp : Square root of the unweighted BP magnitude variance (float, Magnitude[mag])

Name: The square root of the unbiased unweighted variance.

Output: Let yi be a time series of size N. The unweighted standard deviation σ^ is defined as the square root of the sample-size unbiased unweighted variance:

σ^=1N-1i=1N(yi-y¯)2.

skewness_mag_bp : Standardized unweighted BP magnitude skewness (float)

Name: The standardised unbiased unweighted skewness.

Output: Let yi be a time series of size N>2. The sample-size unbiased unweighted skewness moment is defined as:

=N(N-1)(N-2)i=1N(yi-y¯)3.

The standardized unbiased skewness E is defined as:

E=σ^3

where σ^ is the square root of the unbiased unweighted variance around the unweighted mean. While is an unbiased estimate of the population value, E becomes unbiased in the limit of large N.

kurtosis_mag_bp : Standardized unweighted BP magnitude kurtosis (float)

Name: The standardised unbiased unweighted kurtosis.

Output: Let yi be a time series of size N>3. The sample-size unbiased unweighted kurtosis cumulant 𝒦 is defined as:

𝒦=N(N+1)(N-1)(N-2)(N-3)i=1N(yi-y¯)4-3(N-2)(N-3)[i=1N(yi-y¯)2]2.

The standardized unbiased kurtosis K is defined as:

K=𝒦σ^4

where σ^2 is the unbiased unweighted variance around the unweighted mean. While 𝒦 is an unbiased estimate of the population value, K becomes unbiased in the limit of large N.

mad_mag_bp : Median Absolute Deviation (MAD) for BP observations (float, Magnitude[mag])

Name: The Median Absolute Deviation (MAD)

Control parameters: None

Output: Let yi be a time series of size N. The MAD is defined as the median of the absolute deviations from the median of the data, scaled by a factor of 1/Φ-1(3/4)1.4826 (where Φ-1 is the inverse of the cumulative distribution function for the standard normal distribution), so that the expectation of the scaled MAD at large N equals the standard deviation of a normal distribution:

MAD=1.4826median{|yi-median{yj,j(1,N)}|,i(1,N)}. (20.20)

abbe_mag_bp : Abbe value for BP observations (float)

Name: The Abbe value

Control parameters: None

Output: Let {ti,yi} be a time-sorted time series of size N, such that ti<ti+1 for all i<N. The Abbe value 𝒜 is defined as

𝒜=i=1N-1(yi+1-yi)22i=1N(yi-y¯)2 (20.21)

where y¯ is the unweighted mean.

iqr_mag_bp : Interquartile BP magnitude range (float, Magnitude[mag])

Name: The Interquartile Range (IQR)

Control parameters: None

Output: The difference between the (unweighted) 75th and 25th percentile values: IQR=P75-P25.

Let yi be a time series of size N ordered such as y(1)y(2)y(N). The m-th (per cent) percentile Pm is defined for 0<m100 as follows:

Pm={y(1)if 0<m<100/N,y(i)if Nm/100-i=0,y(i+1)otherwise. (20.22)

stetson_mag_bp : Stetson BP variability index (float)

Name: The single-band Stetson variability index

Control parameters: The pairing time interval

Output: Let yi be a time series of size N, with error bars Δyi. The Stetson value (Stetson 1996) is based on paired observations closely separated in time (within a chosen time interval). The time difference between the components of a pair should be small compared to the shortest time-scale of variation being sought. If this condition is not satisfied, a transformation is applied to the unpaired measurement, in order to include its information together with paired observations. The maximum number n of pairs may exceed N/2, since multiple pairs can be formed within a given time interval (as long as the latter is much smaller than the minimum variability time-scale sought).

J=1nk=1nsgn(Pk)|Pk|,

where the index k refers to the k-th pair, composed of measurements i and j, such that

Pk={δi(k)δj(k), if i(k)j(k)δi(k)2-1, if i(k)=j(k)

with the normalized deviation of the i-th measurement from the time-series mean y¯ expressed by:

δi(k)=NN-1yi(k)-y¯Δyi(k).

std_dev_over_rms_err_mag_bp : Signal-to-Noise BP estimate (float)

Name: The signal-to-noise estimate

Control parameters: None

Output: Let yi be a time series of size N, with error bars Δyi. The signal-to-noise ratio (S/N) is estimated as

S/N=i=1N(yi-y¯)2i=1NΔyi2 (20.23)

where y¯ is the mean of the time-series values.

outlier_median_bp : Greatest absolute deviation from the BP median normalized by the error (float)

Name: The most outlying measurement with respect to the median

Control parameters: None

Output: Let yi be a time series of size N, with error bars Δyi. The most outlying measurement is defined as the greatest absolute deviation from the median normalized by the error:

outlierMedian=max{|yi-median{yj,j(1,N)}|Δyi,i(1,N)}. (20.24)

num_selected_rp : Total number of RP observations selected for variability analysis (short)

The number of processed observations for variability analyses of this source, using only transits not rejected.

mean_obs_time_rp : Mean observation time for RP observations (double, Time[Barycentric JD in TCB - 2 455 197.5 (day)])

Name: The mean observation time

Control parameters: None

Output: Let yi be a time series of size N at times ti. The mean t¯ is defined as

t¯=1Ni=1Nti. (20.25)

time_duration_rp : Time duration of the RP time series (float, Time[day])

Name: The time duration of the time series

Control parameters: None

Output: Let yi be a time series of size N at times ti, with i=1 to N. The time duration of the time series is equal to tN-t1.

min_mag_rp : Minimum RP magnitude (float, Magnitude[mag])

Name: The minimum value of the time series

Control parameters: None

Output: Let yi be a time series of size N at times ti, with i=1 to N. The minimum value of the time series is defined as:

min(yi)i(1,N) (20.26)

max_mag_rp : Maximum RP magnitude (float, Magnitude[mag])

Name: The maximum value of the time series

Control parameters: None

Output: Let yi be a time series of size N at times ti, with i=1 to N. The maximum value of the time series is defined as:

max(yi)i(1,N) (20.27)

mean_mag_rp : Mean RP magnitude (float, Magnitude[mag])

Name: The mean of the time series

Control parameters: None

Output: Let yi be a time series of size N. The mean y¯ is defined as

y¯=1Ni=1Nyi. (20.28)

median_mag_rp : Median RP magnitude (float, Magnitude[mag])

Name: The median of the time series

Control parameters: None

Output: The 50th unweighted percentile value.

Let yi be a time series of size N ordered such as y(1)y(2)y(N). The m-th (per cent) percentile Pm is defined for 0<m100 as follows:

Pm={y(1)if 0<m<100/N,y(i)if Nm/100-i=0,y(i+1)otherwise. (20.29)

range_mag_rp : Difference between the highest and lowest RP magnitudes (float, Magnitude[mag])

Name: The range of the time series

Control parameters: None

Output: Let yi be a time series, ymax its largest element, and ymin its smallest element, then the range is defined as

R=ymax-ymin (20.30)

trimmed_range_mag_rp : Trimmed difference between the highest and lowest RP magnitudes (float, Magnitude[mag])

Name: The trimmed range

Control parameters: trim level α]0,50[

Output: The trimmed unweighted range.

Let yi be a time series of size N, with error bars Δyi. Let Yα be the αth percentile, and Y100-α be the (100-α)th percentile. Let yi be the trimmed time series of size N’ which is derived by iteratively removing the smallest or the largest values of the yi time series until Yα<yi<Y100-α for all i{1,,N}. The trimmed range R’ is defined as

R=ymax-ymin (20.31)

For DR3, the trim level α was set to 5.0.

std_dev_mag_rp : Square root of the unweighted RP magnitude variance (float, Magnitude[mag])

Name: The square root of the unbiased unweighted variance.

Output: Let yi be a time series of size N. The unweighted standard deviation σ^ is defined as the square root of the sample-size unbiased unweighted variance:

σ^=1N-1i=1N(yi-y¯)2.

skewness_mag_rp : Standardized unweighted RP magnitude skewness (float)

Name: The standardised unbiased unweighted skewness.

Output: Let yi be a time series of size N>2. The sample-size unbiased unweighted skewness moment is defined as:

=N(N-1)(N-2)i=1N(yi-y¯)3.

The standardized unbiased skewness E is defined as:

E=σ^3

where σ^ is the square root of the unbiased unweighted variance around the unweighted mean. While is an unbiased estimate of the population value, E becomes unbiased in the limit of large N.

kurtosis_mag_rp : Standardized unweighted RP magnitude kurtosis (float)

Name: The standardised unbiased unweighted kurtosis.

Output: Let yi be a time series of size N>3. The sample-size unbiased unweighted kurtosis cumulant 𝒦 is defined as:

𝒦=N(N+1)(N-1)(N-2)(N-3)i=1N(yi-y¯)4-3(N-2)(N-3)[i=1N(yi-y¯)2]2.

The standardized unbiased kurtosis K is defined as:

K=𝒦σ^4

where σ^2 is the unbiased unweighted variance around the unweighted mean. While 𝒦 is an unbiased estimate of the population value, K becomes unbiased in the limit of large N.

mad_mag_rp : Median Absolute Deviation (MAD) for RP observations (float, Magnitude[mag])

Name: The Median Absolute Deviation (MAD)

Control parameters: None

Output: Let yi be a time series of size N. The MAD is defined as the median of the absolute deviations from the median of the data, scaled by a factor of 1/Φ-1(3/4)1.4826 (where Φ-1 is the inverse of the cumulative distribution function for the standard normal distribution), so that the expectation of the scaled MAD at large N equals the standard deviation of a normal distribution:

MAD=1.4826median{|yi-median{yj,j(1,N)}|,i(1,N)}. (20.32)

abbe_mag_rp : Abbe value for RP observations (float)

Name: The Abbe value

Control parameters: None

Output: Let {ti,yi} be a time-sorted time series of size N, such that ti<ti+1 for all i<N. The Abbe value 𝒜 is defined as

𝒜=i=1N-1(yi+1-yi)22i=1N(yi-y¯)2 (20.33)

where y¯ is the unweighted mean.

iqr_mag_rp : Interquartile RP magnitude range (float, Magnitude[mag])

Name: The Interquartile Range (IQR)

Control parameters: None

Output: The difference between the (unweighted) 75th and 25th percentile values: IQR=P75-P25.

Let yi be a time series of size N ordered such as y(1)y(2)y(N). The m-th (per cent) percentile Pm is defined for 0<m100 as follows:

Pm={y(1)if 0<m<100/N,y(i)if Nm/100-i=0,y(i+1)otherwise. (20.34)

stetson_mag_rp : Stetson RP variability index (float)

Name: The single-band Stetson variability index

Control parameters: The pairing time interval

Output: Let yi be a time series of size N, with error bars Δyi. The Stetson value (Stetson 1996) is based on paired observations closely separated in time (within a chosen time interval). The time difference between the components of a pair should be small compared to the shortest time-scale of variation being sought. If this condition is not satisfied, a transformation is applied to the unpaired measurement, in order to include its information together with paired observations. The maximum number n of pairs may exceed N/2, since multiple pairs can be formed within a given time interval (as long as the latter is much smaller than the minimum variability time-scale sought). The Stetson J index is defined as

J=1nk=1nsgn(Pk)|Pk|,

where the index k refers to the k-th pair, composed of measurements i and j, such that

Pk={δi(k)δj(k), if i(k)j(k)δi(k)2-1, if i(k)=j(k)

with the normalized deviation of the i-th measurement from the time-series mean y¯ expressed by:

δi(k)=NN-1yi(k)-y¯Δyi(k).

std_dev_over_rms_err_mag_rp : Signal-to-Noise RP estimate (float)

Name: The signal-to-noise estimate

Control parameters: None

Output: Let yi be a time series of size N, with error bars Δyi. The signal-to-noise ratio (S/N) is estimated as

S/N=i=1N(yi-y¯)2i=1NΔyi2 (20.35)

where y¯ is the mean of the time-series values.

outlier_median_rp : Greatest absolute deviation from the RP median normalized by the error (float)

Name: The most outlying measurement with respect to the median

Control parameters: None

Output: Let yi be a time series of size N, with error bars Δyi. The most outlying measurement is defined as the greatest absolute deviation from the median normalized by the error:

outlierMedian=max{|yi-median{yj,j(1,N)}|Δyi,i(1,N)}. (20.36)

in_vari_classification_result : Source is present in vari_classifier_result (boolean)

Source is present in vari_classifier_result (and not VariClassificationResult as the column name suggests).

in_vari_rrlyrae : Source is present in vari_rrlyrae (boolean)

Source is present in vari_rrlyrae

in_vari_cepheid : Source is present in vari_cepheid (boolean)

Source is present in vari_cepheid

in_vari_planetary_transit : Source is present in vari_planetary_transit (boolean)

Source is present in vari_planetary_transit

in_vari_short_timescale : Source is present in vari_short_timescale (boolean)

Source is present in vari_short_timescale

in_vari_long_period_variable : Source is present in vari_long_period_variable (boolean)

Source is present in vari_long_period_variable

in_vari_eclipsing_binary : Source is present in vari_eclipsing_binary (boolean)

Source is present in vari_eclipsing_binary

in_vari_rotation_modulation : Source is present in vari_rotation_modulation (boolean)

Source is present in vari_rotation_modulation

in_vari_ms_oscillator : Source is present in vari_ms_oscillator (boolean)

Source is present in vari_ms_oscillator

in_vari_agn : Source is present in vari_agn (boolean)

Source is present in vari_agn

in_vari_microlensing : Source is present in vari_microlensing (boolean)

Source is present in vari_microlensing

in_vari_compact_companion : Source is present in vari_compact_companion (boolean)

Source is present in vari_compact_companion