The Brown dwarf Atmosphere Monitoring (BAM) Project I: The largest near-IR monitoring survey of L- & T-dwarfs
AAstronomy & Astrophysics manuscript no. main c (cid:13)
ESO 2018August 7, 2018
The Brown dwarf Atmosphere Monitoring (BAM) Project I:The largest near-IR monitoring survey of L & T dwarfs
P. A. Wilson , A. Rajan , and J. Patience , Astrophysics Group, School of Physics, University of Exeter, Stocker Road, Exeter EX4 4QL, UKe-mail: [email protected] School of Earth & Space Exploration, Arizona State University, Tempe, AZ USA 8528Received November 6, 2013; accepted April 17, 2014
ABSTRACT
Using the SofI instrument on the 3.5 m New Technology Telescope, we have conducted an extensive near-infrared monitoring surveyof an unbiased sample of 69 brown dwarfs spanning the L0 to T8 spectral range, with at least one example of each spectral type.Each target was observed for a 2 – 4 hour period in the J s -band, and the median photometric precision of the data is ∼ p -value ≤
5% based on comparison with a median referencestar light curve. Approximately half of the variables show pure sinusoidal amplitude variations similar to 2MASSJ2139+0220, andthe remainder show multi-component variability in their light curves similar to SIMPJ0136+0933. It has been suggested that the L/Ttransition should be a region of a higher degree of variability if patchy clouds are present, and this survey was designed to test thepatchy cloud model with photometric monitoring of both the L/T transition and non-transition brown dwarfs. The measured frequencyof variables is 13 + − % across the L7 – T4 spectral range, indistinguishable from the frequency of variables of the earlier spectral types(30 + − %), the later spectral types (13 + − %), or the combination of all non-transition region brown dwarfs (22 + − %). The variables arenot concentrated in the transition, in a specific colour, or in binary systems. Of the brown dwarfs previously monitored for variability,only ∼
60% maintained the state of variability (variable or constant), with the remaining switching states. The 14 variables includenine newly identified variables that will provide important systems for follow-up multi-wavelength monitoring to further investigatebrown dwarf atmosphere physics.
Key words. stars: brown dwarfs – atmospheres – variables: general, techniques: photometric
1. Introduction
The L, T, and Y-type brown dwarfs represent a link betweenthe coolest stars and giant planets. Many brown dwarfs are evencooler than currently observable exoplanetary atmospheres (e.g.HR8799b, HD189733b; Barman et al. 2011, Sing et al. 2009,2011). The recently discovered Y dwarfs (Cushing et al. 2011)approach the temperature of Jupiter. Since brown dwarfs neverachieve a stable nuclear burning phase, they cool throughouttheir lifetimes, and temperature, rather than mass, is the domi-nant factor in defining the spectral sequence. As they cool, theiratmospheres undergo changes in the chemistry and physical pro-cesses that sculpt their emergent spectra. While spectroscopy canbe used to investigate atmospheric constituents and chemistry,photometric monitoring is an effective means to search for ev-idence of surface brightness inhomogeneities caused by cloudfeatures, storms, or activity.The transition region from late-L to early-T encompasses aparticularly interesting change in physical properties, as the at-mospheres transform from dusty to clear over a narrow effec-tive temperature range, and the observed infrared colours reversefrom red to blue. This is predicted to be an effect of the for-mation and eventual dissipation of dusty clouds in brown dwarfatmospheres (Chabrier & Baraffe 2000; Marley et al. 2002; Bur-
Based on observations made with ESO Telescopes at La Silla Obser-vatory under programme ID 188.C-0493. rows et al. 2006). Broadly, as brown dwarfs cool through thespectral sequence, the lower temperatures allow more complexmolecules to form, resulting in condensate clouds. When thetemperature is cool enough, large condensate grains cannot re-main suspended high in the atmosphere and sink below the ob-servable photosphere, allowing methane and molecular hydro-gen to become the dominant absorbers. Although there are sev-eral existing models for condensate cloud evolution, most cannoteasily explain the rapid colour change from red to blue over theL-to-T transition. A systematic survey of variability in browndwarfs including both L/T transition objects and comparisonhotter/cooler objects is required to search for differences in thestructure of condensate clouds in this important regime.Existing photometric monitoring campaigns of brown dwarfshave been conducted at different wavelengths: optical bands (e.g.Tinney & Tolley 1999 and Koen 2013), near-IR bands (e.g. Ar-tigau et al. 2003, Khandrika et al. 2013 and Buenzli et al. 2014),mid-IR (e.g. Morales-Calderón et al. 2006), and radio frequen-cies (e.g. Berger 2006). From small ( <
20 objects) initial sam-ples of ultracool field dwarfs, frequencies of variables rangedfrom 0% to 100% (e.g. summary in Bailer-Jones 2005), and re-sults from larger studies ( ∼
25 objects) have measured the fre-quency of variables to be in the range of 20% to 30% (e.g.Khandrika et al. 2013; Buenzli et al. 2014). Examples of ob-jects that vary in multiple wavebands have been identified (e.g.2MASS J22282889-4310262 Clarke et al. 2008; Buenzli et al.
Article number, page 1 of 15 a r X i v : . [ a s t r o - ph . S R ] J un &A proofs: manuscript no. main ∼ v sin i values (10 – 60 km/s for Ldwarfs and 15 – 40 km/s for T dwarfs – Zapatero Osorio et al.2006) and the ∼ M (cid:12) radius of these objects fromevolutionary models at the age of the field (Baraffe et al. 2003).Periodogram analysis of some variables has shown clear peaksassociated with periods in the range of ∼ J s -bandphotometric monitoring campaign of 69 field brown dwarfs withthe SofI instrument on the 3.5 m New Technology Telescope(NTT). This survey is a part of the BAM (Brown dwarf Atmo-sphere Monitoring) project. In Section 2, the properties of thesample, including magnitudes, spectral types, and companionsare summarised. Details of the observations are reported in Sec-tion 3, followed by the data reduction procedure, and method-ology used to characterise each target as variable or constant inSection 4. Section 5 presents the results of the program and acomparison to previous variability studies. Finally, we discussthe sensitivity of the BAM survey and investigate possible corre-lations between variability and various observables such as spec-tral type, colour and binarity in Section 6. The results are sum-marized in Section 7.
2. The BAM sample
The 69 objects in the BAM sample were drawn from the browndwarf archive ( dwarfarchives.org ) and were selected to spanthe full sequence of L- and T-spectral types from L0 to T8. Anequal proportion of targets with spectral types above, across andbelow the L/T transition region were included. In this paper,we consider the L-T transition to range from L7 – T4, follow-ing Golimowski et al. (2004). Spectral types including a frac-tional subtype have been rounded down - for example, an L6.5is considered L6 for the statistics. For the 48 targets with parallaxmeasurements (e.g. Dupuy & Liu 2012; Faherty et al. 2012), acolour-magnitude diagram was constructed and is shown in Fig-ure 1. The histogram of target spectral types and a plot of thecolour as a function of spectral type are shown in Figure 2. Thespectral types are based on IR spectroscopy for 54 targets andon optical spectroscopy for the remaining 15 targets that lackedan IR spectral classification. The spectral types, parallaxes, and -1 0 1 2 (J-K) (mag) M J , M A SS ( m a g ) L0-L6L7-T4T5-T8
Fig. 1.
Colour-magnitude diagram of the M-L-T spectrum (small greycircles). All brown dwarfs with known parallax in the BAM sample areoverplotted, with red representing the L dwarfs, yellow the L/T transi-tion dwarfs, and blue the T dwarfs (see Table 1 and 2). Half spectraltypes have been rounded down in the study. The photometry and paral-laxes for the field M-L-T objects are from Dupuy & Liu (2012). apparent 2MASS magnitudes of the targets are listed in Table 1for L dwarfs and Table 2 for T dwarfs.Additional factors that influenced the target selection werethe magnitudes and coordinates. To obtain high signal-to-noiseindividual measurements, the targets were limited to objects withmagnitudes brighter than J ∼ Article number, page 2 of 15ilson, Rajan & Patience: The Brown dwarf Atmosphere Monitoring (BAM) Project I
L0 L2 L4 L6 L8 T0 T2 T4 T6 T8
Spectral Type B D N u m b e r
23 Targets 23 Targets 23 Targets
L0 L2 L4 L6 L8 T0 T2 T4 T6 T8
Spectral Type ( J - K ) M A SS ( m a g ) Fig. 2.
Diagram on the left shows a histogram of the sample across their respective spectral classes, whilst on the right is a colour-colour diagramshowing the J-K colours of the same (coloured circles) overplotted on the full brown dwarf L-T spectral sequence (small grey circles). The L/Ttransition is indicated by the dashed lines defined in Golimowski et al. (2004). ple – 34 targets – and cover optical (Gelino et al. 2002; Koen2013), near-IR (Enoch et al. 2003; Koen et al. 2004, 2005; Clarkeet al. 2008; Khandrika et al. 2013; Buenzli et al. 2014), and radio(Berger 2006) wavelengths. It is important to note that the differ-ent variability monitoring studies apply different criteria to cate-gorise a target as variable or constant, and a range of observationwavelengths have been employed. Most of the previous monitor-ing has been conducted over timescales of hours similar to thisprogram, though a few studies covered longer timescales withlower cadence measurements (e.g. Gelino et al. 2002, Enochet al. 2003).
3. Observations
The observations took place from 4 - 11 October 2011 and 3 - 9April 2012 with the SofI (Son of ISAAC) instrument (Moorwoodet al. 1998) mounted on the NTT (New Technology Telescope)at the ESO La Silla observatory. Observations were performed inthe large field imaging mode that has a pixel scale of 0 (cid:48)(cid:48) .
288 px − and a field-of-view of 4 (cid:48) . × (cid:48) .
92. During the first observing run,some of the targets were observed in both the J s -band and K s -band, but only the J s -band was used during the second run. Asa consequence, six of the targets from the first run have J s -banddata with lower cadence. The J s filter (1.16-1.32 µ m ) was usedto avoid contamination by the water band centred at 1 . µ m thatwould have otherwise affected the photometry. An increase inthe telluric water column would have caused an anti-correlationbetween the brightness of the brown dwarfs and the referencestars in the J -band, since an increase in the water column willdecrease the flux from the reference stars to a greater extent com-pared to the brown dwarfs that have deep intrinsic water bands.The J s data should not suffer from this effect.Three sets of two target fields were observed most nights,alternating between each target roughly every 15 min over a ∼ .
4. Data Reduction and Photometry
For each image, basic data reduction steps consisting of cor-recting for the dark current and division by a flat field and skysubtraction were applied. Developing flat field images for theNTT/SofI instrument involved generating two different flats, aspecial dome flat and an illumination correction flat as docu-mented by the observatory. The dome flat requires observationsof an evenly illuminated screen with the dome lamp turned onand off in a particular set sequence. To correct for low frequencysensitivity variations across the array that are not completely re-moved by the dome flat, an illumination correction was applied.By observing the flux from a standard star in a grid pattern acrossthe array, a low order polynomial was fitted to the flux measure-ments, allowing large scale variations across the array to be char-acterised and removed. Flat field images were produced usingthe IRAF scripts provided by the observatory . As the flat fieldsare documented to be extremely stable over several months, asingle set of flat fields were used for all the targets in a givenrun.For the SofI instrument, the dark frames are a poor estimateof the underlying bias pattern, which varies as a function ofthe incident flux. Consequently, the dark and bias are subtractedfrom the science frames through the computation of a sky frame,which also removes the sky background from the science data.Sky frames were generated by median combining the ditheredscience frames. The final calibration step involved measuring theoffsets between the individual images and aligning all the sci-ence frames. The aligned frames within each ∼
15 min intervalwere subsequently median combined. We compared the photo-metric uncertainties on the median combined images calculatedusing IRAF, to the standard deviation of the unbinned imageswithin each bin. For most objects, the two methods for calculat-ing uncertainties gave very similar results. The IRAF uncertain-ties on the median combined images were used for all the objects IRAF is distributed by the National Optical Astronomy Observato-ries, which are operated by the Association of Universities for Researchin Astronomy, Inc., under cooperative agreement with the National Sci-ence Foundation. Article number, page 3 of 15 &A proofs: manuscript no. main
Table 1.
L dwarf Sample.
Target Name Spectral Parallax J MASS
Binary/ Instrument ReferencesType (mas) (mag) Single2MASS J00165953-4056541 L3.5* 15.316 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± for consistency. Median combining the images before perform-ing photometry rather than measuring the individual frames hadthe advantage of improving the centring, measurements of thefull width at half maximum (FWHM), and the photometry of thefainter comparison stars in the field. Aperture photometry was carried out using the APPHOT pack-age in IRAF. A median value of the FWHM was measured perimage using all the stars in the field-of-view. A range of apertureradii were explored and the size of 1 . × FWHM was selected,as it minimised the root-mean-square (RMS) scatter of the ref-erence star light curves that were created by dividing each refer-ence star by the weighted mean of the remaining reference starlight curves. The aperture was kept constant for all the stars ina single image, but was allowed to vary between individual im-ages to account for variations in seeing. The variable aperturealso yielded higher signal-to-noise measurements compared toa constant aperture, which would otherwise cause a loss in theflux measured within the aperture during poorer seeing condi-tions. We checked each target field to ensure that the photometrywas not impacted by nearby astrophysical sources. The steps taken to generate the target light curves in the sur-vey are given in the following list: • For each target, a list of reference star candidates was gener-ated by considering all stars visible in the field of view, dis-carding stars with peak counts less than 20 ADUs or greaterthan 10 ,
000 ADUs. These limits were imposed to ensureenough signal was present to accurately centre the aperturearound the object and to ensure that none of the referencestars were in the non-linear regime of the detector. • Reference candidates were trimmed by selecting up to 15 ofthe reference stars with the most similar brightness to thetarget. • Candidate reference star light curves were calculated by di-viding each reference star by a weighted mean of the remain-ing reference stars. • Candidate reference stars with light curves exhibiting a stan-dard deviation greater or equal to the median standard devi-ation for all reference star candidates were removed. • A master reference light curve was subsequently created bymedian combining the normalised light curves of all thequalifying reference stars. • The final target light curve was produced by dividing the tar-get brown dwarf flux by the weighted mean of all the quali-fying reference stars. The light curve was normalised by di-
Article number, page 4 of 15ilson, Rajan & Patience: The Brown dwarf Atmosphere Monitoring (BAM) Project I
Table 2.
T dwarf Sample.
Target Name Spectral Parallax J MASS
Binary/ Instrument ReferencesType (mas) (mag) Single2MASS J00345157+0523050 T6.5 105.4 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
13 13.613 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± viding the light curve by the median flux value of the lightcurve. • The target and reference star light curves were all airmassde-trended by dividing the light curves by a second orderpolynomial fit to the relative flux of the master reference asa function of airmass.The number of reference stars used for each target is given in Ta-ble 3 and Table 4, with six to eight references being typical. Theautomatic selection process was applied uniformly throughoutthe entire sample of objects. The uncertainties were calculatedusing IRAF. The target photometric uncertainty ( Q ) is defined asthe median value of the target light curve uncertainties. A his-togram of the Q values for each object is shown in Figure 3, andthe value for all targets are listed in Table 3 and Table 4. Themedian Q value for the entire survey is 0 . The significance of the variations were assessed in comparison totwo criteria. For the first assessment, the final target light curve N u m b e r Fig. 3.
Target photometric uncertainty of the survey defined as the me-dian value of the final target light curve uncertainties. The median targetphotometric uncertainty is 0 . was compared against a flat line using the reduced robust medianstatistic ( ˜ η ) (Enoch et al. 2003). The definition of ˜ η is expressedas˜ η = d N ∑ i = (cid:12)(cid:12)(cid:12)(cid:12) ∆ F i − median ( ∆ F ) σ i (cid:12)(cid:12)(cid:12)(cid:12) (1) Article number, page 5 of 15 &A proofs: manuscript no. main
Table 3.
Variables identified in this study.
Object Spectral Type Obs. Dur. (hours) Refs. DOF χ ν ˜ η Q (%) p -value (%) Amplitude ∗ (%)Variables with p -value ≤
5% and ˜ η ≥ . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . . ± . < p -value ≤
10% and ˜ η ≥ . . ± . . ± . . ± . ∗ These peak-to-trough amplitudes are calculated as the difference between the minimum and maximum points in the light curve. In some cases, these mightrepresent the lower limit of the true amplitude, especially for brown dwarfs which exhibit variability on longer time scales. p -value (%) B D N u m b e r Fig. 4. p -value histogram of the full brown dwarf sample. The objectsin the first bin includes 16 targets with p -value ≤ p -value between 5 – 10%. Of the 16 targets with p -value ≤ η ≥ where d defines the number of free parameters and σ i , the uncer-tainty on each photometric measurement in the final target lightcurve.For the second assessment, the reduced chi squared ( χ ν )value for each target light curve was calculated relative to themaster reference light curve. The definition of χ ν is expressed as χ ν = ν N ∑ i = ( O i − E i ) σ i (2)where ν is the degrees of freedom, O i is the final target lightcurve, E i is the master reference light curve and σ i is the uncer-tainty on the final target light curve and master reference lightcurve added in quadrature.Astrophysical variability was better determined calculating χ ν relative to the master reference light curve instead of astraight line, which was more prone to classifying variable con-ditions over intrinsic variability. We make use of the χ ν to es-timate the cumulative distribution function and thus the p -valuefor each final target light curve. The p -value is the probabilitythat the final target light curve is the same as ( p -value > p -value ≤ p -valuesfor the full sample, and the large number of objects in the firstbin gives an indication of the variables in the survey. The firstbin contains 16 objects with p -value ≤ p -value is 3 to 4 ob-jects (5%) for a sample of 69 targets. For the identification ofvariables, both p -value and an ˜ η thresholds were applied. Thenumber of targets with p -value ≤
5% but ˜ η > p -value is the probability, under the assumption that wedetect no variability (our null hypothesis), of observing variabil-ity greater or equal to what was observed in the master refer-ence light curve. The survey has 39 targets satisfying the crite-rion of ˜ η ≥ p -value ≤ p -value ≤
5% and ˜ η ≥
1, and they are listed in Table 3. Candi-date variables with a less restrictive p -value ≤
10% and ˜ η ≥
5. Results of the BAM survey
The primary result from the BAM survey is the identification of aset of 14 variable brown dwarfs with p -value ≤
5% and a furtherthree candidate variables with 5% < p -value ≤
10% (see §5.1.1).
Article number, page 6 of 15ilson, Rajan & Patience: The Brown dwarf Atmosphere Monitoring (BAM) Project I R e l a t i v e F l u x L0 J0106-5933 0.940.960.981.001.021.041.061.08 L3 J1300+1912 0.960.970.980.991.001.011.021.031.04 L5 J0358-41160.980.991.001.011.02 R e l a t i v e F l u x L5 J0835-0819 0.940.960.981.001.021.041.061.08
L5.5
J2255-5713 0.960.970.980.991.001.011.021.031.04 L6 J1010-04060.980.991.001.011.02 R e l a t i v e F l u x L6.5
J0439-2352 0.980.991.001.011.021.03
L6.5
J1126-5003 0.960.970.980.991.001.011.021.031.04 T0 J1207+02440.970.980.991.001.011.021.031.04 R e l a t i v e F l u x T1.5
J2139+0220 0.9800.9850.9900.9951.0001.0051.0101.0151.0201.025
T2.5
J0136+0933 0 1 2 3Time (Hours)0.970.980.991.001.011.021.03
T6.5
J2228-43100 1 2 3Time (Hours)0.940.960.981.001.021.041.061.08 R e l a t i v e F l u x T7 J0050-3322 0 1 2 3Time (Hours)0.9800.9850.9900.9951.0001.0051.0101.015 T7 J0348-6022
Fig. 5.
Final target light curves of the 14 variable objects (blue points) with a p -value ≤
5% and ˜ η ≥ . &A proofs: manuscript no. main R e l a t i v e F l u x L5.5
J0205-1159 0 1 2 3Time (Hours)0.960.981.001.021.041.061.08
T7.5
J0931+0327 0 1 2 3Time (Hours)0.970.980.991.001.011.021.03
T7.5
J1217-0311
Fig. 6.
Final target light curves of the candidate variables (larger blue points) with 5% < p -value ≤
10% and ˜ η ≥ . R e l a t i v e F l u x Q =0 . J0559-1404 0.9900.9951.0001.0051.0101.015 Q =0 . J0407+1546 0.9900.9951.0001.0051.010 Q =0 . J2322-31330.9850.9900.9951.0001.0051.010 R e l a t i v e F l u x Q =0 . J2151-4853 0.9850.9900.9951.0001.0051.0101.015 Q =0 . J1534-2952 0.9850.9900.9951.0001.0051.0101.015 Q =0 . J0510-42080 1 2 3Time (Hours)0.9800.9850.9900.9951.0001.0051.0101.0151.0201.025 R e l a t i v e F l u x Q =0 . J1155+0559 0 1 2 3Time (Hours)0.9800.9850.9900.9951.0001.0051.0101.0151.020 Q =0 . J1404-3159 0 1 2 3Time (Hours)0.960.970.980.991.001.011.021.031.04 Q =1 . J1007-4555
Fig. 7.
Final target light curves of a subset of constant objects in this survey (larger blue points) with the master reference light curves (yellowsmaller points). The target photometric uncertainty decreases from top to bottom and includes the light curve with the best photometry (top left)and light curve with the worst photometry (bottom right).
For the remaining analysis, we only consider the p -value ≤ Article number, page 8 of 15ilson, Rajan & Patience: The Brown dwarf Atmosphere Monitoring (BAM) Project I features (Metchev et al. 2013). Objects with light curves sim-ilar to SIMP0136 object, such as 2M0106, 2M0439, 2M0835,2M1126, 2M1207, 2M1300 are interesting for future follow up,to confirm whether or not they also show rapidly evolving lightcurves. Finally, the light curve of 2M0358 may be a fast rotatingvariable, since 2M0358 appears to oscillate through more thanone cycle within the limited timespan of the BAM monitoring.The light curve of 2M2228 shows similar short time scale vari-ations as 2M0358 and was previously found to be variable witha period of P = . + . − . hours by Clarke et al. (2008). Finaltarget light curves for the 14 BAM variables and the associatedcomparison master reference light curves are shown in Figure 5.Similar plots for the candidate variables are given in Figure 6.The amplitudes and p -values are reported in Table 3.Representative light curves of nine constant targets with arange of photometric qualities are shown in Figure 7. These lightcurves show the full range of the data quality for brown dwarfsof similar brightness to the variables identified in the study. Theconstant light curves are not all flat, however their variations arenot statistically distinct from their associated master reference.The constant targets do not satisfy the two separate criteria usedto identify the variables (specified in section 4.3) which requirethe final target light curve to be distinct from the master referencelight curve ( p -value) and a flat line ( ˜ η ). The p -values for constantsources are given in Table 4.Since the p -value ≤
5% cutoff is a statistical measure, thereremains a likelihood of a contamination level of 3 – 4 false vari-ables that are statistical fluctuations, ∼
5% of the entire sample.Continued monitoring of the variables should help identify falsepositives.
This J s -band SofI program is the largest uniform monitoring sur-vey conducted in the near-IR. Several previous surveys have tar-geted smaller sample sets (e.g. Enoch et al. 2003; Koen et al.2004, 2005; Clarke et al. 2008; Khandrika et al. 2013; Girardinet al. 2013; Buenzli et al. 2014) or searched in different wave-lengths such as I -band (e.g. Gelino et al. 2002; Koen 2004,2013). Apart from results of the study by Koen (2013), previ-ous surveys have typically targeted fewer than ∼
25 objects anddetected variability frequencies of ∼
30% in their sample sets,with a significant amount of overlap in the target samples usedin different studies (Khandrika et al. 2013). The BAM samplewas designed to uniformly cover the L-T spectral range (see Fig-ure 2) and includes 35 brown dwarfs that have not been previ-ously monitored in different surveys. There are nine new BAMvariables, six of which have not been previously monitored forvariability – 2M0050, 2M0106, 2M0358, 2M1010, 2M1207 and2M2255. The survey has three variables that were previouslyfound to be constant, and found nine brown dwarfs previouslyclassified as variable to be constant. Finally, there are five vari-ables that were found to vary in the literature and in this BAMstudy. A synopsis of the variables in the BAM and previous sur-veys is presented in Table 5. These objects are used to investigatethe persistence of variability in section 6.5. Table 6 presents theconstant brown dwarf sample in the BAM study. These are tar-gets that were monitored in previous surveys and were foundto be constant in the literature and in this study. In the followingtwo subsections, we compare our results with literature measure-ments for the variables identified in this sample and in previouswork.
In Table 5, we present information for all the targets that wereconsidered variable, either in this BAM study or in the literature.Three of the BAM variables – 2M0348, 2M0439 and 2M1126– were identified as constant brown dwarfs in prior surveys butappear to be variable in this survey. A further five brown dwarfs– SIMP0136, 2M0835, 2M1300, 2M2139 and 2M2228 – wereconfirmed to be variable both in this study and in the literature.Of these five, SIMP0136, 2M2139 and 2M2228 were previouslyfound to vary in the near-IR, similar to this study. The othertwo variables 2M0835 and 2M1300 were originally measuredto vary in the I c band, and also display multi-component varia-tions at near-IR wavelengths. Amongst the known variables withmeasured periods (from previous studies), only 2M2139 and2M1300 have periods longer than the duration of the BAM mon-itoring data ( > There are nine targets from the BAM sample that have been pre-viously reported as variable, but were found to be constant in thissurvey. These sources are listed in Table 5, with a summary of theprevious results pertaining to variability, including the observa-tion wavelength and any notes on the amplitudes and timescalesof the variations in brightness. One of these nine variables fromliterature – 2M0228 – has only been monitored in the optical.The remaining eight variables exhibited modulations in the near-IR. 2M0559, 2M0624, DENIS0817 and 2M1624 were found tohave small amplitude variations in the Buenzli et al. (2014) sur-vey carried out using the
HST grism data. In the
HST survey,2M1624 showed variability in the water band (1.35-1.44 µ m ) butwas found to be constant at J -band wavelengths. Similar to otherground based surveys that found some of these targets constant,this BAM survey likely does not have the photometric sensitiv-ity necessary to confirm the HST variables, nor is it possibleto monitor the water bands from ground. Another four targets –SDSS0423, 2M0939, 2M1534 and 2M2331 – also appear con-stant in the data. The photometric uncertainties on SDSS0423and 2M2331 are too large to confirm their lower amplitude vari-ability of ∼ .
8% and ∼ . K ’ band with an am-plitude of 3.1% (Khandrika et al. 2013), we are unable to con-firm any variability in J s with the BAM observations. 2M1534was detected to vary in the JHK s bands initially in (Koen et al.2004), but was constant in a later epoch (Koen et al. 2005). Koen(2013) further discounts the likelihood of detecting short periodvariability in 2M1534, but maintains that the target likely varieson the timescale of a few days. The reported amplitudes in the H -band and K -band are below the detection threshold in the datafor this target. Article number, page 9 of 15 &A proofs: manuscript no. main
Table 6.
Summary of constant sources.
Target Name Band References Notes2MASS J02572581-3105523 I c K132MASS J04070752+1546457 JK (cid:48) Kh13 no results2MASS J04151954-0935066 8.46 GHz B06 < µ Jy2MASS J04455387-3048204 I c K13 I c K048.46 GHz B06 < µ Jy2MASS J05233822-1403022 I c K13 I c K058.46 GHz B06 231 ± µ Jy2MASS J12255432-2739466 J KTTK05 <
12 mmag H <
14 mmag K s < JHK s KMM042MASS J12281523-1547342 I c K138.46 GHz B06 < µ Jy2MASS J12545393-0122474
JHK s KMM04 J Gi13 < I c K13 I c K038.46 GHz B06 < µ Jy2MASS J1511145+060742 J Kh13 < . K ’ < . JHK s KMM042MASS J15530228+1532369
JHK s KMM042MASS J16322911+1904407 8.46 GHz B06 < µ JyHST G141 Grism Bu132MASS J19360187-5502322 I c K132MASS J23224684-3133231 I c K13References: Berger (2006) [B06], Buenzli et al. (2014) [Bu13], Girardin et al.(2013) [Gi13], Khandrika et al. (2013) [Kh13], Koen (2004) [K04], Koen(2003) [K03], Koen et al. (2004) [KMM04], Koen et al. (2005) [KTTK05],Koen (2005) [K05], Koen (2013) [K13].
6. Discussion
To obtain an estimate of the variability frequency for browndwarfs across spectral types, it is essential to quantify the sensi-tivity of the data to detecting different amplitudes of variability.We estimate the sensitivity to variables of a certain amplitudeas three times the target photometric uncertainty of each finaltarget light curve. This places a limit on the minimum ampli-tude required for a detection above a certain statistical signifi-cance threshold. The proportion of the sample that is sensitive toa given variability amplitude is shown as a function of amplitudein Figure 8. As shown in Figure 8, the BAM survey is capableof detecting any object in the sample showing a peak-to-troughamplitude ≥ .
3% during the duration of the observations. Thedetection probability continues to decrease with decreasing am-plitude with a sensitivity of 50% occurring for variables with a ∼ .
7% amplitude. Given that the full BAM sample is sensitiveto variables with amplitudes ≥ . p -value ≤ .
05; this level in-cludes all but one BAM p ≤ .
05 variable. Figure 9 shows howthe variability frequency (considering all spectral types) varies asa function of amplitude to account for the declining proportionof the sample that is sensitive to lower amplitude variables.To calculate the uncertainty on the variability frequency weuse the binomial distribution B ( n ; N , ε v ) = N ! n ! ( N − n ) ! ε nv ( − ε v ) N − n . (3)where n is the number of variables, N the sample size and ε v thevariability frequency. This approach is based on Bayes’ theorem D e t e c t i o n p r o b a b ili t y f o r t h e s a m p l e ( % ) Fig. 8.
Proportion of the survey sensitive to variability as a functionof peak-to-trough amplitudes for different detection thresholds. Thedashed line represents the fraction of objects with a photometric accu-racy good enough to have allowed for the detection of variability. Theshaded area represents the region of sensitivity with the upper binomialerrors and amplitude uncertainties added to the variability fraction. V a r i a b ili t y F r e q u e n c y ( % ) Fig. 9.
Variability frequency as a function of amplitude (dashed line)with the binomial errors and amplitude uncertainties added to the vari-ability fraction (shaded area).
Period (hours) V a r i a b l e D e t e c t i o n P r o b a b ili t y ( % ) Amp = 1.5%Amp = 2.5%Amp = 5.0%
Fig. 10.
Percentage of simulated sinusoidal light curves detected as vari-able, as a function of period from 1 hour to 12 hours, for three differentamplitudes. We used the measured survey median noise of 0.7%, andeach sine curve was sampled at intervals of 15 minutes to imitate thebinned data of the survey. Additionally, we stepped through each sinecurve at 5 degree phase intervals, to ensure that we sampled the fullphase of the variable light curve. under the assumption of a uniform prior based on no a prioriknowledge and is ideal for small samples such as is the case forthe BAM survey.The rotation period is another factor that can influence thedetectability of a variable signal. In Figure 10, we present theresults of simulating light curves to test the detection probabil-ity of the survey to brown dwarf variables with different periods.We simulated sinusoidal light curves with three different ampli-
Article number, page 10 of 15ilson, Rajan & Patience: The Brown dwarf Atmosphere Monitoring (BAM) Project I
Table 4.
Limits on constant targets in this survey.
Object Spectral Type Obs. Dur. [hours] Refs. DOF χ ν ˜ η Q (%) p -value (%)2MASS J00165953-4056541 L3.5 3.43 6 8 0.9 0.8 0.54 51.12MASS J00184613-6356122 L2 3.43 7 8 1.1 1.0 0.42 35.42MASS J00345157+0523050 T6.5 2.98 7 7 0.9 1.1 0.53 49.12MASS J01282664-5545343 L1 2.66 5 5 0.5 2.1 0.61 77.72MASS J02284355-6325052 L0 2.84 3 10 0.8 1.0 0.42 62.72MASS J02572581-3105523 L8 3.33 7 5 0.3 1.2 0.48 90.22MASS J03185403-3421292 L7 1.97 5 4 1.9 1.8 0.99 10.12MASS J03400942-6724051 L7 2.61 6 5 0.2 1.1 0.75 96.42MASS J04070752+1546457 L3.5 3.19 7 7 0.7 0.7 0.43 68.42MASS J04151954-0935066 T8 3.29 4 7 0.3 0.6 0.83 95.8SDSS J042348.56-041403.5 T0 2.62 8 5 0.4 0.7 0.51 86.22MASS J04455387-3048204 L2 4.55 7 9 0.7 0.7 0.36 68.72MASS J05103520-4208140 T5 2.74 6 6 0.5 0.6 0.66 84.22MASS J05160945-0445499 T5.5 3.11 4 7 1.1 1.1 0.88 38.52MASS J05233822-1403022 L5 4.56 6 9 0.7 1.0 0.99 66.82MASS J05591914-1404488 T4.5 3.11 6 7 0.3 0.5 0.37 96.32MASS J06244595-4521548 L5 3.26 5 6 1.2 1.0 0.49 28.52MASS J07290002-3954043 T8 3.37 8 10 0.6 0.6 0.9 84.3DENIS J081730.0-615520 T6 3.48 8 13 0.8 0.7 0.68 64.52MASS J09153413+0422045 L7 3.33 6 7 0.9 0.8 0.55 50.12MASS J09393548-2448279 T8 3.06 8 9 1.0 0.8 0.98 46.52MASS J09490860-1545485 T2 3.16 8 6 1.7 1.3 0.67 11.02MASS J10043929-3335189 L4 3.14 8 11 1.4 1.0 0.99 17.62MASS J10073369-4555147 T5 3.41 8 13 0.3 0.5 1.62 99.52MASS J10210969-0304197 T3 3.17 7 6 0.7 0.6 0.88 64.12MASS J11145133-2618235 T7.5 2.90 5 5 0.4 1.0 1.0 86.32MASS J11555389+0559577 L7.5 2.87 5 7 0.6 1.2 0.74 79.32MASS J12255432-2739466 T6 2.85 7 5 0.4 0.3 0.67 86.92MASS J12281523-1547342 L6 3.39 7 10 1.0 1.1 0.5 42.52MASS J12314753+0847331 T5.5 2.56 5 5 0.3 1.6 1.63 89.62MASS J12545393-0122474 T2 3.17 7 9 1.0 1.1 0.51 45.22MASS J13262981-0038314 L5.5 2.80 4 7 0.9 0.9 1.17 50.82MASS J14044941-3159329 T2.5 3.01 8 8.5 0.4 0.6 0.82 89.42MASS J15074769-1627386 L5.5 4.16 7 15 0.4 0.6 1.21 98.5SDSS J151114.66+060742.9 T.0 3.85 7 11 0.8 0.6 0.8 64.12MASS J15210327+0131426 T2 6.37 7 12 0.5 0.6 0.9 93.62MASS J15344984-2952274 T5.5 3.88 8 8 0.5 0.6 0.62 85.32MASS J15462718-3325111 T5.5 3.50 4 7 0.4 0.5 1.45 93.32MASS J15530228+1532369 T7 3.82 7 8 2.7 1.0 0.72 0.72MASS J16241436+0029158 T6 3.21 8 8 0.3 0.4 0.66 95.62MASS J16322911+1904407 L8 3.89 4 14 1.1 1.0 1.76 36.42MASS J18283572-4849046 T5.5 3.06 8 7 0.4 0.5 0.5 88.72MASS J19360187-5502322 L5 3.21 7 6 0.4 0.5 0.4 86.9SDSS J204317.69-155103.4 L9.0 3.03 8 6 1.4 1.0 1.14 22.5SDSS J204749.61-071818.3 T0.0 2.68 8 5 1.6 1.2 1.16 15.82MASS J20523515-1609308 T1 3.08 8 7 0.8 0.8 0.7 62.92MASS J21513839-4853542 T4 3.08 8 7 0.8 0.7 0.51 63.02MASS J22521073-1730134 L7.5 2.34 5 5 0.8 1.3 0.65 58.9ULAS J232123.79+135454.9 T7.5 2.94 8 7 0.3 0.6 0.9 94.92MASS J23224684-3133231 L0 2.69 6 4 1.1 0.9 0.49 37.82MASS J23312378-4718274 T5 2.63 5 6 1.7 1.1 1.13 12.02MASS J23565477-1553111 T6 2.98 5 7 3.5 0.6 0.64 0.1 tudes, of 1.5%, 2.5%, and 5.0%, and with periods ranging froma minimum of 1 hour to a maximum of 12 hours (Zapatero Oso-rio et al. 2006). Gaussian noise equal to the median photometricuncertainty of the survey of 0.7% was added to each light curve.To mimic the binned SofI data, the light curves were sampled atintervals of 15 minutes, and each simulated dataset was dividedinto groups of 3 hours, similar to the typical duration of the BAMdata. For light curves with period longer than 3 hours, we gen-erated multiple datasets, by stepping through the sine curve insteps of 5 degrees of phase and calculating the p -value at eachphase, ensuring full sampling of the phase. Figure 10 shows thepercentage of simulated light curves that are detected as vari-able with a p -value ≤ ∼ ∼ types The frequency of variables as a function of spectral type is animportant topic, since models of brown dwarf atmospheres havesuggested that breakup of clouds across the L/T transition mayresult in both a higher rate of occurrence and a higher ampli-tude of variability compared to earlier L and later T objects.Amongst the previously known variables, the two largest am-plitude variable objects discovered to-date are L/T transition ob-jects - SIMP0136 ( ∼
5% in J -band but with a significant nightto night evolution, Artigau et al. 2009) and 2M2139 (as high as26% in the J -band, Radigan et al. 2012).As indicated by the variability frequencies reported in Ta-ble 7, the BAM results show no evidence that the frequency ofvariables in the L7 to T4 transition region is distinct from theearlier spectral types, the later spectral types, or the combina- Article number, page 11 of 15 &A proofs: manuscript no. main
Table 5.
Summary of variable sources.
Target Name Band Variable/Constant References NotesNew variables from this study with no prior observations2MASS J00501994-3322402 J s V2MASS J01062285-5933185 J s V2MASS J03582255-4116060 J s V2MASS J09310955+0327331 J s V Candidate Variable ( p -val = . J s V2MASS J12074717+0244249 J s V2MASS J22551861-5713056 J s VNew variables previously categorised as constantDENIS J0205.4-1159 I c C K13Candidate Variable ( p -val = . K s C E03 < < µ Jy2MASS J03480772-6022270 J C C08 <
10 mmag, periodic2MASS J04390101-2353083 I c C K13 I c C K058.46 GHz C B06 < µ Jy2MASS J11263991-5003550 I c C K13 possibly periodicLiterature variables confirmed as variable in this studySIMP J013656.5+093347.3 JK V A09, Ap13 ∆ J = . ± I c V K04, K13 ∆ I c =10-16 mmag, P=3.1 hr8.46 GHz C B06 < µ Jy2MASS J12171110-0311131 J V A03 ∆ J = . ± .
013 magCandidate Variable (p-val = . < µ JyZ062MASS J13004255+1912354 I c C K13 I V G02 P=238 ± < µ Jy J + K ’ C Kh13 J < K ’ < I c C K05
JHK s C KMM042MASS J21392676+0220226
JHK s V R12, Ap13 ∆ ( J , H , K )=(0.3, 0.18, 0.17) mag, P = 7.721 ± J + K ’ V Kh13 ∆ J = . K ’ < J V C08, Bu12 ∆ J = . ± . ± < µ JyObjects with reported IR variability measured as constants in this studySDSS J042348.56-041403.5 I c C K13 K s likely V E03 0 . ± .
18 mag, P = .
39 – 1 .
62 hr J V C08 8 . ± . = ± . JHK s C KTTK05 J <
15 mmag, H <
11 mmag, K < < µ Jy2MASS J05591914-1404488 HST G141 grism V Bu13 I c C K13 K s C E03 < J C08 C < I c C K048.46 GHz B06 < µ Jy2MASS J06244595-4521548 HST G141 grism V Bu13DENIS J081730.0-615520 HST G141 grism V Bu132MASS J09393548-2448279 K ’ V Kh13 0.31 mag J C < .
141 mag2MASS J15344984-2952274 I c C K13 I c C K05
JHK s C KTTK05 J <
10 mmag, H <
11 mmag, K <
18 mmag
JHK s V KMM04 H K = .
96 hr8.46 GHz C B06 < µ Jy2MASS J16241436+0029158 HST G141 grism V Bu13 Variability detected in water band (1.35-1.44 µ m ) JHK s C KMM048.46 GHz C B06 < µ Jy2MASS J23312378-4718274 J V C08 ∆ J = . ± . ± I c V K13References: Artigau et al. (2003) [A03], Artigau et al. (2009) [A09], Apai et al. (2013) [Ap13], Berger (2006) [B06], Buenzli et al. (2012) [Bu12], Buenzli et al.(2014) [Bu13], Clarke et al. (2008) [C08], Enoch et al. (2003) [E03], Gelino et al. (2002) [G02], Khandrika et al. (2013) [Kh13], Koen (2004) [K04], Koen et al.(2004) [KMM04], Koen et al. (2005) [KTTK05], Koen (2005) [K05], Koen (2013) [K13], Radigan et al. (2012) [R12], Zapatero Osorio et al. (2006) [Z06] tion of all non-transition region brown dwarfs. The variabilityfrequencies in Table 7 are calculated using the entire sampleof targets and an amplitude threshold of ≥ .
3% and p ≤ . Article number, page 12 of 15ilson, Rajan & Patience: The Brown dwarf Atmosphere Monitoring (BAM) Project I
Table 7.
Variability frequency.
Sample Sp. Type No. Targets No. Variables† Freq. (%)Early-L L0-L6 23 7 30 + − Late-T T5-T8 23 3 13 + − L/T Transition L7-T4 23 3 13 + − Outside L/T transition L0-L6 & T5-T8 46 10 22 + − Notes: † These are the variables with a p -value ≤ ≥ . L0 L2 L4 L6 L8 T0 T2 T4 T6 T8
Spectral Type σ ( mm a g ) σ ( % ) Temperature Range (K)
L0 L2 L4 L6 L8 T0 T2 T4 T6 T8
Spectral Type ( J - K ) M A SS ( m a g ) Fig. 11.
Diagram on the left shows the amplitude of the variables ( p -value ≤
5% – closed circles and 5% < p -value ≤
10% – closed circleswith cross) as well as the target photometric uncertainty of the non-varying objects (coloured triangles) across the entire spectral range of thesample. The diagram on the right shows the colour-colour diagram of the entire L through T spectral range with the full sample plotted with opencircles, showing the colour spread of the targets. The variables from the BAM sample are overplotted ( p -value ≤
5% – closed circles and 5% < p -value ≤
10% – closed circles with cross). The L/T transition is indicated by the dashed lines
The BAM variability frequency is very comparable to esti-mates for M stars. A variability frequency between ∼ J -band data, and the amplitudeof variations is expected to decline for longer wavelengths (e.g.Reiners et al. 2010).In a recent compilation of variability surveys, Khandrikaet al. (2013) reported a variability frequency of 30 ±
5% basedon a collection of different surveys with observations obtained inthe optical and near-IR passbands, covering 78 objects in total.Comparison between surveys is difficult as the variability fre-quency may depend on a variety of different factors, includingthe target selection criterion and the criteria used to define vari-ability in the targets which usually differs from one survey to thenext. Additionally, the observed wavelength may also alter thevariability frequency with different wavelength probing differ-ent depths in the atmosphere. Koen (2013) finds a poor overlapbetween the variables identified with optical and near-IR filters(of the 13 variables already observed in near-IR surveys, 7 werefound as constant and 6 as variable in the optical). Because of theuniform sensitivity of this survey, we did not incorporate the re-sults of previous studies into the statistics, presented in Table 7.The presence of highly variable objects outside the transition re-gion, may suggest the possibility of both early onset of cloudcondensation in the atmospheres of mid-L dwarfs and the emer-gence of sulfide clouds in mid-T dwarfs (Morley et al. 2012). Other physical processes that have been suggested to possiblyinduce variability in the atmospheres of brown dwarfs includecoupling clouds with global atmosphere circulation (Showman& Kaspi 2013; Zhang & Showman 2014), and variability causedby thermal perturbations emitted from deeper layers within thebrown dwarf atmosphere (Robinson & Marley 2014).
The J − K colour of the sample as a function of the spectral typeis shown in Figure 11 (right). The targets span nearly the fullcolour spread in early-L, transition and late-T sub sample. The14 BAM variables and the three candidates are not clustered to-ward either the red or the blue within any particular spectral type.Previous studies (e.g. Khandrika et al. 2013) have suggested thatbrown dwarfs with unusual colours (highly red or blue) com-pared to the median of the spectral type might be indicativeof variable cloud cover. We performed a two sample K-S testto determine whether or not the detrended colors of the BAMvariables were distinct from the rest of the sample. The maxi-mum difference between the cumulative distributions was 0.18with a corresponding p -value of ∼ Article number, page 13 of 15 &A proofs: manuscript no. main
Table 8.
Summary of Persistence Results
Total targets with 2 epochs 34Variable at 2 epochs 6Constant at 2 epochs 15Switch between variable and constant 13
The BAM sample includes 12 confirmed binaries out of 47 tar-gets studied for binarity with another four SpeX spectra bi-nary candidates. Including the binary candidates, 10 out of the16 binaries in the BAM sample fall in the L/T transition. Thisis consistent with previous detections of an increase in the bi-nary frequency across the L-T transition Burgasser et al. (2006).Amongst the BAM variables, only 2M2255 is a confirmed bi-nary, while 2M1207 and 2M2139 are binary candidates. Five ofthe variables are confirmed to be single, and six have not beenstudied for binarity. The limited data provides no evidence tosupport a correlation between variability and binarity amongstthe objects in the BAM survey.
A recent multi-epoch ( ∼ ∼ J -band, only to appear constant a fewmonths later. Similar night-to-night variations have also beenseen in SDSS J105213.51+442255.7 (Girardin et al. 2013). Theevolution indicates a lack of persistence in the source of vari-ability over timescales longer than a few weeks and it suggeststhat the brown dwarfs identified as constant in this study mightsimilarly exhibit periods of quiescence and enhanced activity.The BAM survey only examines variability on the timescale of asingle rotation period or less as compared to some surveys (e.g.Gelino et al. 2002; Enoch et al. 2003) that study the flux varia-tions of brown dwarfs over longer timescales.The BAM data, in combination with previous results, can beused to address the question of persistence of variability. Table 8summarizes the observations related to persistence of variabil-ity, using information presented in Table 5 and 6. For greatestconsistency with the BAM study, we consider other epochs ofnear-IR data rather than optical. A total of of 34 BAM targetshave an earlier epoch of observation. 2M0228 is the only sourcemeasured to be variable in the optical ( I c ) that switched fromvariable to constant. Table 8 indicates that brown dwarf variabil-ity does not necessarily persist on longer timescales, with onlyhalf the BAM variables showing variation in both epochs. Thesurvey finds four previously constant objects to be variable andnine targets previously reported as variable in the literature tobe constant, making these ideal candidates for multiple epochmonitoring programs.
7. Summary
We present the results of the largest near-IR brown dwarf vari-ability survey conducted in the J s -band using the NTT 3.5 m tele-scope. The BAM survey has an unbiased sample of 69 early-L through late-T brown dwarfs. A total of 14 variable objects weredetected: six new variables not previously studied for variability,three objects previously reported as constant, and five previouslyknown variables. The nine newly identified variables constitutea significant increase in the total number of known brown dwarfvariables characterisable using ground-based facilities. In a re-cent study of 57 L4-T9 brown dwarfs (Radigan et al. 2014), aset of 35 targets were observed by both studies, enabling a di-rect comparison of results. Of the 35 targets in both samples,both studies classify a common 26 targets as not variable and acommon four targets as variable. Of the five remaining variablesnoted in a single study (two in BAM, three in Radigan et al.2014), four can be explained by differences in sensitivity for thespecific light curves.Rather than quoting a single number for the variability fre-quency, we discuss how the frequency of variable brown dwarfsdepends on different factors such as the observed wavelengthand the variability amplitude. The BAM study, representing thelargest and most uniform ground-based search for variability,was designed to address the important question of the physicalproperties of brown dwarf atmospheres including the L-T transi-tion. One class of models has suggested that this colour change,that defines the transition, may be a manifestation of the breakupof clouds resulting in a patchy coverage across the surface (Ack-erman & Marley 2001), which would have the observable con-sequence of enhanced variability at the L-T transition. Consid-ering the results of this study, covering both transition and non-transition objects and statistical significance of the variability,there is no distinction between the variability frequency betweenthe brown dwarfs in the transition region or outside the transi-tion region. This suggests that the patchy cloud scenario maynot provide the full explanation for the L-T transition or thatthe induced level of variability is substantially below the detec-tion thresholds of the current study. The 14 variables, includingthe nine newly identified variables, will provide valuable sys-tems with which to pursue additional questions of the physics ofbrown dwarf atmospheres, including the longitudinal and verti-cal variations of clouds and active regions which can be inferredfrom multi-wavelength follow-up monitoring. Acknowledgements.
Based on observations made with ESO Telescopes at theLa Silla Paranal Observatory under programme ID 188.C-0493. We would liketo thank the anonymous referee for valuable suggestions thats helped improvethis paper. PAW acknowledges support from STFC. JP was supported by a Lev-erhulme research project grant (F/00144/BJ), and funding from an STFC stan-dard grant. This research has made use of the SIMBAD database and VizieRcatalogue access tool, CDS, Strasbourg, France. The original description of theVizieR service was published in A&AS 143, 23. This research has benefittedfrom the M, L, T, and Y dwarf compendium housed at DwarfArchives.org. Thisresearch made use of Astropy, a community-developed core Python package forAstronomy (Astropy Collaboration et al. 2013). We thank F. Pont, R. De Rosaand D. K. Sing for valuable feedback and discussion.
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Article number, page 14 of 15ilson, Rajan & Patience: The Brown dwarf Atmosphere Monitoring (BAM) Project I