dataesr/scientific-paragraphs-categorization huggingface dataset:
95 provide an optimal residual noise after the transit fit while avoiding overfitting. After the model was trained for 50 epochs, the best network-i. e. , with the lowest residual noiseis saved along with the corresponding detrended light curve, obtained by dividing the raw light curve by the network's prediction. A final transit fit is performed using a Markov Chain Monte Carlo procedure embedded in the PyLightcurve package  (Tsiaras et al. 2016b ). Both during and after training, the fixed planetary parameters used are those listed in Table  1  or computed using the ExoTETHyS open package  (Morello et al. 2020) , while the mid-transit time and R p /R * were left free. The raw and detrended Spitzer light curves are shown in Figure  6  (top: IRAC channel 1; bottom: IRAC channel 2), and the corresponding transmission data is available in Table  6 , Appendix A.
2018 ). Our results suggest the presence of AlO and TiH on the order of 10 -8 in volume mixing ratio, unconstrained SiH, and haze nondetection. Furthermore, the retrievals indicate a strong stellar contamination on the spectrum of WASP-17 b in the WFC3 data set too, where we do not expect stellar activity to play a big part. In all three scenarios we retrieved a spot temperature of around 4300 K, as well as spot and faculae covering fractions of the star on the order of 10%-20% and 40%-50%, respectively. Our results report a faculae temperature in the Case 1 transmission spectrum between 33 and 165 K higher than what Equation (5) predicts. On the other hand, T fac on Case 2 and Case 3 spectra is between 217 and 322 K higher than expected (T fac = 6919. ). Although the study by  Rackham et al.
The theoretical estimate is in accordance with the retrieved AlO abundance in Case 2, where we find a lower bound equal to log 10 (AlO) = -11. 1. However, in all other cases for both AlO and TiH, the retrieved abundances are 3σ-4σ away from the expected values. On the contrary, the retrieved uncertainties on the SiH abundances are in agreement with the theoretical value computed at p = 1 bar and T = ∼1400 K, it being log 10 (SiH) = ∼-10. We retrieve conflicting results for the cloud deck. The Case 1 and Case 3 posteriors returned a mean cloud deck at a pressure of ∼1. 1 × 10 4 Pa corresponding to ∼0. 11 bars. In Case 2 the cloud top pressure is higher in the atmosphere, at a pressure of 0. 9 mbar. The cloud coverage found at higher altitudes could be caused by the retrieval framework trying to fit the downward slope created by the STIS visit 2 data in the optical range, which cannot be explained by any molecular absorber.
Discussion WASP-17 b has been observed, studied, and modeled intensively in the past (e. g. ,  Anderson et al. 2011; Mandell et al. 2013; Pinhas et al. 2018; Goyal et al. 2020 ) by employing observations taken with WFC3/G141 (staring mode), HST/ STIS, and Spitzer. Our study, complementary to the work by  Alderson et al. (2022) , offers a consistent analysis performed by combining and reducing in a reproducible manner all currently available WASP-17 b transmission data obtained with space-based observatories. We include all public STIS and IRAC data, plus WFC3/G141 and WFC3/G102 observations, both taken in spatial scanning mode. This large breadth of observations allows us to investigate the planet's atmospheric spectrum spanning the wavelengths from the optical to the near-infrared. The combination of several data sets has its pros and cons.
1211 0. 0013 0. 0014 -+ ), confirming that the two instrumental calibrations are in overall agreement. Finally, we must note that this problematic STIS/G750L data point is not statistically significant for the spectral modeling. From Figures  8  and 9  we can observe that the TauREx 3 atmospheric model ignores this data point when computing the best-fit spectrum. On the other hand, between 3 and 5 μm lie the Spitzer data points, whose transit depths cannot be compared to any additional data set. We shifted the two Spitzer data points 100 ppm up and 100 ppm down from their original position to see whether the results would be considerably impacted. In either case the retrieved mean molecular abundances and other atmospheric features, such as radius and temperature, fluctuate at the order of the fifth decimal figure from the initial values, implying an accurate data reduction process and stability of the results.
Due to the discrepancies in the data, we decided to model the contrasting STIS data sets separately: in Case 1 we considered a spectrum that includes STIS visit 1&3, WFC3, and IRAC; in Case 2 we modeled the spectrum with STIS visit 2&3, WFC3, and IRAC. Additionally, we employed the WFC3 observations alone (Case 3) to check how the results would change when we do not include the problematic STIS data sets. To explain the unusually strong downward slope in the optical range displayed by the Case 2 spectrum (Figure  8 , inset), an active star was included in the retrieval setup. For consistency, we applied the same stellar model to Case 1 and Case 3 spectra. Potentially, STIS visits 2&3, being taken just a few days apart, were impacted by a stellar event, causing the spectrum to be best fitted by a model containing high spot and faculae covering fractions.
Furthermore, independently of the data sets modeled, our results suggest the presence of the same trace gases (H 2 O, AlO, TiH) as when accounting for a heterogeneous star. At this stage we do not possess enough evidence to discard a particular STIS data set in favor of another one, but the information content derived by WFC3 data alone is enough to propose the existence of those species in the atmosphere of WASP-17 b. No previous study has detected any metal hydrates or oxides in the atmosphere of this particular hot Jupiter, except  Bento et al. (2014)  searching for TiO signatures in the upper atmosphere of WASP-17 b. Theoretical studies by  Fortney et al. (2008)  propose the presence of TiO and VO in the atmosphere of planets with large day-night temperature contrasts. We do not exclude the presence of these trace gases, but our analyses favor a model containing AlO, TiH, SiH, CH 4 , and CO rather than TiO, VO, and FeH by a 2.
64σ away from model (1), which includes the same chemical species plus stellar variability. Ultimately, we used the best-fit model for the Case 1 spectral scenario also in the Case 2 data set and on the WFC3 data alone, to make a valid comparison between the scenarios in terms of molecular abundances. The retrievals conducted exclusively on the WFC3 data also favor a model containing H 2 O, AlO, TiH, SiH, clouds, hazes, stellar spots, and faculae. Our results suggest that WASP-17 b possesses mostly a clear atmosphere, free from a thick layer of clouds and aerosols. Sing et al. (2016)  report the atmosphere of this hot Jupiter to be the clearest among the 10 exoplanets in their study, presenting distinct water features. Pinhas et al. (2018)  find similar results, their spectrum being fitted best by a model containing mostly a cloudless atmosphere with a cloud/haze fraction of 0.
2 and a cloud top pressure of 0. 1 mbar. On the other hand,  Barstow et al. (2016)  report the presence of scattering aerosols at relatively high altitudes, but not so strong as to reduce the size of the other molecular features. Mandell et al. (2013)  also claim that their data are best fitted by a model that accounts for hazes or clouds. We were unable to identify the alkali lines (Na, K) reported by  Sing et al. (2016). Their data point at ∼0. 6 μm has an uncertainty three times larger than the other ones, and it is not statistically significant to demonstrate the presence of sodium in the atmosphere of WASP-17 b. Furthermore, during our exploratory retrievals we did not find evidence of CO 2 , which, on the contrary, is detected by  Alderson et al. (2022). Due to the sparse nature of the Spitzer data points and the influence that additional molecules can impart in the spectrum of an exoplanet at the wavelengths probed by IRAC, it is challenging to place precise constraints on the abundance of carbon-bearing molecules.
Conclusion Remote-sensing studies are the only technique through which we can unveil the atmospheric characteristics of planets outside our solar system. In recent years HST has pioneered the spectroscopic investigation of exoplanets. In this study we presented the transmission spectrum of WASP-17 b observed with the STIS and the WFC3 mounted on HST and the Infrared Camera Array aboard the Spitzer Space Telescope. The Hubble spectroscopic data were reduced with Iraclis, a specialized STIS and WFC3 data reduction routine. Photometric data obtained by Spitzer were instead reduced and analyzed with the TLCD-LSTM method by means of a long short-term memory network. Given the discordant results obtained for two STIS observations that cover the same wavelength range, we decided to model the spectrum of WASP-17 b in three separate cases: (a) including STIS visit 1&3, WFC3, IRAC (Case 1); (b) including STIS visit 2&3, WFC3, IRAC (Case 2); (c) WFC3 only (Case 3).
The publicly available HST observations presented here were taken as part of proposals 12473 and 14918, led by David Sing and Hannah Wakeford, respectively. These were obtained from the Hubble Archive, which is part of the Mikulski Archive for Space Telescopes. This work is also based in part on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. The publicly available Spitzer data were taken as part of program 90092 led by Jean-Michel Desert. This paper also uses data collected by the TESS mission, which are publicly available from the Mikulski Archive for Space Telescopes (MAST) and produced by the Science Processing Operations Center (SPOC) at NASA Ames Research Center  (Jenkins et al.
to the near-infrared and infrared absorbers (H 2 O, Polyansky et al. 2018; CH 4 , Yurchenko & Tennyson 2014; CO, Li et al. 2015; CO 2 ; NH 3 , Coles et al. 2019), by employing the line lists from the ExoMol (Tennyson et al. 2016; Chubb et al. 2021), HITEMP (Rothman et al. 2010; Rothman & Gordon 2014), HITRAN (Rothman et al. 1987; Gordon et al. 2016), and NIST (Kramida 2013) databases.
(2) Since the ResNet was trained with the Imagenet dataset  [20] , the values of A and b come from empirical statistics on Imagenet. When the network input is a gray-scale image encoded in RGB, since we apply different corrections (mean and standard deviation) to each channel, the image is not gray-scale anymore after normalization. This pre-processing step is quite common for machine learning methods and allows to have training data with similar statistical properties. Furthermore, the exact same normalization process has been already applied when training the ResNet network so it is important to normalize data in the same way in order to get the most of the pretrained ResNet network. Following the same logic, the training images are reshaped to be square in order to be similar to the images used during the training of the ResNet.
The author of DeOldify observed that the model giving the most realistic results was found after training on only a very small portion of the whole dataset chosen randomly (between 2% and 5% ). Afterwards, results oscillate between results equivalent to those obtained with the best model and bad over-saturated results (with typically orange skin), however there is no evident indication of such optimal spot when looking the evolution of the loss. Training cycles. In the "artistic" and "stable" versions, the colorizer network is trained in cycle, alternating the two training phases described above. There are 5 cycles for the "artistic" version and 3 for the "stable" version. For the "video" version, the network is trained with only one occurrence of each phase.
22657 +0. 00005 -0. 00004 d, a radius of  c = 2. 65 ± 0. 04  ⊕ , and a 3 upper mass limit of 8. 5 M ⊕. From the high-precision photometry we obtain radius uncertainties of ∼1. 6%, allowing us to conduct internal structure and atmospheric escape modelling. TOI-1064 b is one of the densest, well-characterised sub-Neptunes, with a tenuous atmosphere that can be explained by the loss of a primordial envelope following migration through the protoplanetary disc. It is likely that TOI-1064 c has an extended atmosphere due to the tentative low density, however further RVs are needed to confirm this scenario and the similar radii, different masses nature of this system. The high-precision data and modelling of TOI-1064 b is important for planets in this region of mass-radius space, and it allows us to identify a trend in bulk density-stellar metallicity for massive sub-Neptunes that may hint at the formation of this population of planets.
A breakdown of the individual visit start times and durations is detailed in Table  1. For all visits, we used an exposure time of 60 s. As the CHEOPS spacecraft is in a low-Earth orbit, sections of observations are unobtainable due to the on-board rejection of images due the level of stray-light being higher than the accepted threshold, occultations of the target by the Earth, or passages through the South Atlantic Anomaly (SAA) during which no data is downlinked. These effects occur on orbital timescales (∼98. 77 min) and lead to a decrease in the observational efficiency. As can be seen in Fig. 2  (and Figs. B1-B5), these interruptions are apparent in the CHEOPS photometric data with the efficiencies for the six visits listed in Table  1. Data for all visits were automatically processed using the latest version of the CHEOPS data reduction pipeline  (DRP v13; Hoyer et al.
2018)  as an input source list for objects' locations and brightnesses. By conducting aperture photometry on the simulated FoV with the target removed, light curves of the contamination from nearby sources are produced, as detailed in Section 6. 1 of  Hoyer et al. (2020). As can be seen in Fig. 3 , in the CHEOPS FoV there is a nearby object (Gaia EDR3 6683371813007224960, Δ = +3. 9 mag) 24. 6 away from the target that may affect the photometry of the target, and thus the contamination estimates were subtracted from the light curves of TOI-1064. The Gaia parallax and proper motion data indicate that this object and the target do not form a larger bound system. The right panel of Fig. 3  shows the inferred FoV of the target with the contamination removed. Fig. 3  also reveals additional multiple nearby sources with stars that have  < 17 mag numbered.
It should be noted that the sources numbered 1-5 are the same as those detected in the TESS FoV (Fig. 6 ), whereas 6-9 are too faint to be seen in the TESS target pixel file. The remaining objects in the FoV have Δ > +7 mag compared to TOI-1064 and thus, they will not contribute considerably to the photometry. For this study, we selected datasets that minimised the root mean square (RMS) of the light curves, which for all visits were obtained with the RINF aperture. This radius minimised the contribution of nearby sources whilst ensuring that the majority of the target's Point Spread Function (PSF) was within the aperture. Due to the nature of the CHEOPS orbit and the rotating FoV, nonastrophysical, short-term photometric trends caused by a varying background, nearby contaminants, or other sources, can be seen in the data.
Several studies (e. g. Lendl et al. 2020; Bonfanti et al. 2021; Delrez et al. 2021; Leleu et al. 2021 ) have found success in removing these systematics by conducting a linear decorrelation using several basis vectors, such as background, contamination, orbital roll angle, and  and  centroid positions. Upon inspection of the CHEOPS observations of TOI-1064, we found significant flux variations on orbital timescales. The selection of basis vectors of concern typically involves assessing the Bayesian Information Criteria (BIC) upon the detrending of the data using a combination of DRP-provided vectors. However, in this study a more data-driven approach was taken to identify which basis vectors were to be used in the detrending, as is detailed below in Appendix A.
Gemini-South Due to the 21 size of TESS pixels the TESS light curves may be contaminated by nearby sources. As mentioned above, for CHEOPS datasets contamination from stars in the Gaia DR2 catalogue is simulated and removed. However, should there be sources very near TOI-1064 that were not detected with Gaia, contamination may still occur. If an exoplanet host star has a spatially close companion, that companion (bound or line of sight) can create a false-positive transit signal if it is, for example, an eclipsing binary. "Third-light" flux from the companion star can lead to an underestimated planetary radius if not accounted for in the transit model  (Ciardi et al. 2015)  and cause non-detections of small planets residing with the same exoplanetary system  (Lester et al. 2021). Additionally, the discovery of close, bound companion stars, which exist in nearly one-half of FGK-type stars  (Matson et al.
ASAS-SN To further assess the host star, we obtained publicly available ASAS-SN V-band photometry  (Shappee et al. 2014; Kochanek et al. 2017)  of TOI-1064 taken over five consecutive seasons between 2014-May-04 and 2018-September-24. Upon inspection of the data, a dimming trend of roughly 0. 3 mag was seen over the four years of observations, with an abrupt increase (roughly 0. 2 mag) in flux occurring during the final season. Therefore, as the goal of using this dataset was to study shorter-period variation, we rejected data after BJD 2458300 in order to avoid photometry taken during the brightening event, and we removed the long-term trend by modelling the dataset with a broad Savitzky-Golay smoothing filter and dividing the fluxes by this model. Lastly, we removed outliers by conducting a 5-sigma clip, which resulted in 401 data points taken on 328 epochs which can be seen in Fig.
WASP Additionally, TOI-1064 was observed during the SuperWASP project  (Pollacco et al. 2006) , with data taken between 2008-March-26 and 2014-November-10. We retrieved photometry that had been extracted following standard procedures  (Pollacco et al. 2006) , and detrended for systematic effects using SysRem (see Collier  Cameron et al. 2006; Mazeh et al. 2007) , that preserves stellar variability and transit-like features in the dataset. Flux and flux uncertainty outliers were rejected via a 5-sigma clipping, with the subsequent light curve shown in Fig. 4b ). This yielded a total of 66 832 data points on 387 epochs covering four seasons of observations. Similarly to the ASAS-SN dataset, the median flux error for the WASP observations (28 ppt) means that the photometry is not precise enough to be used to detect the transits of the planetary candidates.
C1 ), no significant peaks potentially related to the stellar rotation period are apparent. Therefore, we assess the long-baseline ASAS-SN and WASP light curves to search for photometric variability. Firstly, for both datasets we performed a nightly binning that results in RMS scatters of 28 and 16 ppt for ASAS-SN and WASP, respectively. Subsequently, we produced Lomb-Scargle periodograms of both binned light curves and find significant peaks at 25. 9 and 26. 6 d as shown in Figs. 4a)  and b ). To independently confirm this variability period we applied a Gaussian Process (GP) regression with a quasi-periodic kernel to both ASAS-SN and WASP light curves separately. This kernel is chosen as multiple previous studies have found that it accurately represents flux modulation from stellar activity  (Haywood et al.
2014; Dubber et al. 2019; Mortier et al. 2020). Using the juliet Python package  (Espinoza et al. 2019 ) with unconstrained priors we obtain median and 1 uncertainties for the rotation period of the kernel to be 27. 0±4. 3 and 26. 6±1. 0 d, for ASAS-SN and WASP, respectively. Due to the higher S/N of the WASP observations, we adopt the value obtained from that dataset and interpret this signal to be the stellar rotation period. These values agree well with the rotation period of 26. 9±1. 6 d derived from the mean HARPS value of log  HK following the empirical relations in  Mamajek & Hillenbrand (2008). It is worth noting that the derived rotation periods are distinct from the average lunar synodic period of approximately 29. 53 d.
(  2021 ) for separating shape-driven radial-velocity offsets from genuine stellar Doppler shifts, and is detailed in Appendix A. In this study, we applied our novel method to the CHEOPS photometry and the CCF-based  (Collier Cameron et al. 2021)  to the HARPS data in order to model flux modulation due to PSF shape changes and RV variation due to stellar activity. For the CHEOPS light curves, we used the aforementioned method on the DRP produced fluxes (Section 2. 2) for the six visits separately, with the number of principal components, , chosen by the LOOCV method to be; 24, 17, 23, 10, 26, and 42, respectively, corresponding to: 5%, 6%, 8%, 4%, 10%, and 5%, of the vectors produced by the principal component analysis. Subsequently, we decorrelated the CHEOPS datasets against the selected vectors using the linear regression method, masking the in-transit fluxes.
The linear models produced by the regression, and used to decorrelate the light curves, are shown in the bottom panels of Fig. 2  and Figs. B1-B5. In order to be conservative in the subsequent global analysis detailed below and preserve the uncertainty of the decorrelation fits, the errors on the linear models were estimated from one thousand samples drawn from the posterior distributions of the regression coefficients and added in quadrature with the flux errors of the detrended photometry for each dataset. These decorrelated light curves yield 3 h noise estimates of: 58. 4 ppm, 51. 9 ppm, 49. 5 ppm, 53. 9 ppm, 45. 8 ppm, and 53. 0 ppm, respectively, and were used in our joint analysis below.
CCF Following the stellar variability analysis (see Section 3. 3), it was noted that the TESS-derived 12. 2 d orbital period for TOI-1064 c is close to the 13. 3 d first harmonic of the stellar rotation period. As stellar activity signals can impede the detection of exoplanets in RV data  (Haywood et al. 2014; Rajpaul et al. 2015; Mortier et al. 2016; Dumusque et al. 2017) , we employed the CCF-based  (Collier Cameron et al. 2021 ) to separate any stellar activity signals from planet-induced RV variations via the re-extraction of RVs from the 26 HARPS observations (see Section 2. 6). For this dataset, the LOOCV approach selected three principal components (12%) that well model the CCF shape variations and hence stellar activity. As reported in Collier Cameron et al. (  2021 ), by conducting a joint fit of the stellar activity and planetary RVs, orbital signals injected into Solar RV data can be more reliably retrieved using.
accepted). Normal priors were set for all limb-darkening coefficients centred on the determined values with a 1- uncertainty of 0. 1 taken. In addition to the modelling of planetary signals in transit photometry and RV data, and basis vectors to simulate the stellar activity in the RVs, we included further parameters to model instrumental and astrophysical noise in the form of GPs. For all transit photometry datasets, we used GPs with a Matérn-3/2 kernel against time in order to model long-term correlated noise, utilising the celerite package (Foreman-Mackey et al. 2017) within juliet. An example of a fitted GP to the TESS fluxes can be seen in Fig. 1. Moreover, for both photometry and RV data we fitted jitter terms in order to reflect any extra noise not previously accounted for, that is subsequently summed in quadrature with the uncertainties on the data.